Generative AI is a rapidly developing field of artificial intelligence that is capable of creating new content, such as text, images, and music. This technology has the potential to revolutionize many industries and professions, but it is also likely to significantly impact the job market. 

The rise of Generative AI

While generative AI has been around for several decades, it has only recently become a reality thanks to the development of deep learning techniques. These techniques allow AI systems to learn from large amounts of data and generate new content that is indistinguishable from human-created content.

The testament of the AI revolution is the emergence of numerous foundation models including GPT-4 by Open AI, paLM by Google, and many more topped by the release of numerous tools harnessing LLM technology. Different tools are being created for specific industries.

Read -> LLM Use Cases – Top 10 industries that can benefit from using large language models 

Potential benefits of Generative AI

Generative AI has the potential to bring about many benefits, including:

• Increased efficiency: It can automate many tasks that are currently done by humans, such as content writing, data entry, and customer service. This can free up human workers to focus on more creative and strategic tasks.
• Reduced costs: It can help businesses to reduce costs by automating tasks and improving efficiency.
• Improved productivity: Support businesses to improve their productivity by generating new ideas and insights.
• New opportunities: Create new opportunities for businesses and workers in areas such as AI development, data analysis, and creative design.

Job disruption

While AI has the potential to bring about many benefits, it is also likely to disrupt many jobs. Some of the industries that are most likely to be affected by AI include:

• Education:

It is revolutionizing education by enabling the creation of customized learning materials tailored to individual students.

It also plays a crucial role in automating the grading process for standardized tests, alleviating administrative burdens for teachers. Furthermore, the rise of AI-driven online education platforms may change the landscape of traditional in-person instruction, potentially altering the demand for in-person educators.

Learn about -> Top 7 Generative AI courses

• Legal services:

The legal field is on the brink of transformation as Generative Artificial Intelligence takes center stage. Tasks that were once the domain of paralegals are dwindling, with AI rapidly and efficiently handling document analysis, legal research, and the generation of routine documents. Legal professionals must prepare for a landscape where their roles may become increasingly marginalized.

• Finance and insurance:

Finance and insurance are embracing the AI revolution, and human jobs are on the decline. Financial analysts are witnessing the gradual erosion of their roles as AI systems prove adept at data analysis, underwriting processes, and routine customer inquiries. The future of these industries undoubtedly features less reliance on human expertise.

• Accounting:

In the near future, AI is poised to revolutionize accounting by automating tasks such as data entry, reconciliation, financial report preparation, and auditing. As AI systems demonstrate their accuracy and efficiency, the role of human accountants is expected to diminish significantly.

Read  –> How is Generative AI revolutionizing Accounting

• Content creation:

Generative AI can be used to create content, such as articles, blog posts, and marketing materials. This could lead to job losses for writers, editors, and other content creators.

• Customer service:

Generative AI can be used to create chatbots that can answer customer questions and provide support. This could lead to job losses for customer service representatives.

• Data entry:

Generative AI can be used to automate data entry tasks. This could lead to job losses for data entry clerks.

Job creation

While generative AI is likely to displace some jobs, it is also likely to create new jobs in areas such as:

• AI development: Generative AI is a rapidly developing field, and there will be a need for AI developers to create and maintain these systems.
• AI project managers: As organizations integrate generative AI into their operations, project managers with a deep understanding of AI technologies will be essential to oversee AI projects, coordinate different teams, and ensure successful implementation. 
• AI consultants: Businesses across industries will seek guidance and expertise in adopting and leveraging generative AI. AI consultants will help organizations identify opportunities, develop AI strategies, and navigate the implementation process.
• Data analysis: Generative AI will generate large amounts of data, and there will be a need for data analysts to make sense of this data.
• Creative design: Generative AI can be used to create new and innovative designs. This could lead to job growth for designers in fields such as fashion, architecture, and product design.

The importance of upskilling

The rise of generative AI means that workers will need to upskill to remain relevant in the job market. This means learning new skills, such as data analysis, AI development, and creative design. There are many resources available to help workers improve, such as online courses, bootcamps, and government programs.

Ethical considerations

The rise of generative AI also raises some ethical concerns, such as:

• Bias: Generative AI systems can be biased, which could lead to discrimination against certain groups of people.
• Privacy: Generative AI systems can collect and analyze large amounts of data, which could raise privacy concerns.
• Misinformation: Generative AI systems could be used to create fake news and other forms of misinformation.

It is important to address these ethical concerns as generative AI technology continues to develop.

Government and industry responses

Governments and industries are starting to respond to the rise of generative AI. Some of the things that they are doing include:

• Developing regulations to govern the use of generative Artificial Intelligence.
• Investing in research and development of AI technologies.
• Providing workforce development programs to help workers upskill.

Leverage AI to increase your job efficiency

In summary, Artificial Intelligence is poised to revolutionize the job market. While offering increased efficiency, cost reduction, productivity gains, and fresh career prospects, it also raises ethical concerns like bias and privacy. Governments and industries are taking steps to regulate, invest, and support workforce development in response to this transformative technology.

As we move into the era of revolutionary AI, adaptation and continuous learning will be essential for both individuals and organizations. Embracing this future with a commitment to ethics and staying informed will be the key to thriving in this evolving employment landscape.

A study by the Equal Rights Commission found that AI is being used to discriminate against people in housing, employment, and lending. Thinking why? Well! Just like people, Algorithmic biases can occur sometimes.

Imagine this: You know how in some games you can customize your character’s appearance? Well, think of AI as making those characters. If the game designers only use pictures of their friends, the characters will all look like them. That’s what happens in AI. If it’s trained mostly on one type of data, it might get a bit prejudiced.

For example, picture a job application AI that learned from old resumes. If most of those were from men, it might think men are better for the job, even if women are just as good. That’s AI bias, and it’s a bit like having a favorite even when you shouldn’t.

Artificial intelligence (AI) is rapidly becoming a part of our everyday lives. AI algorithms are used to make decisions about everything from who gets a loan to what ads we see online. However, AI algorithms can be biased, which can have a negative impact on people’s lives.

What is AI bias?

AI bias is a phenomenon that occurs when an AI algorithm produces results that are systematically prejudiced due to erroneous assumptions in the machine learning process. This can happen for a variety of reasons, including:

• Data bias: The training data used to train the AI algorithm may be biased, reflecting the biases of the people who collected or created it. For example, a facial recognition algorithm that is trained on a dataset of mostly white faces may be more likely to misidentify people of color.
• Algorithmic bias: The way that the AI algorithm is designed or implemented may introduce bias. For example, an algorithm that is designed to predict whether a person is likely to be a criminal may be biased against people of color if it is trained on a dataset that disproportionately includes people of color who have been arrested or convicted of crimes.
• Human bias: The people who design, develop, and deploy AI algorithms may introduce bias into the system, either consciously or unconsciously. For example, a team of engineers who are all white men may create an AI algorithm that is biased against women or people of color.

Understanding fairness in AI

Fairness in AI is not a monolithic concept but a multifaceted and evolving principle that varies across different contexts and perspectives. At its core, fairness entails treating all individuals equally and without discrimination. In the context of AI, this means that AI systems should not exhibit bias or discrimination towards any specific group of people, be it based on race, gender, age, or any other protected characteristic.

However, achieving fairness in AI is far from straightforward. AI systems are trained on historical data, which may inherently contain biases. These biases can then propagate into the AI models, leading to discriminatory outcomes. Recognizing this challenge, the AI community has been striving to develop techniques for measuring and mitigating bias in AI systems.

These techniques range from pre-processing data to post-processing model outputs, with the overarching goal of ensuring that AI systems make fair and equitable decisions.

Read in detail about ‘Algorithm of Thoughts’ 

Companies that experienced biases in AI

Here are some examples and stats for bias in AI from the past and present:

• Amazon’s recruitment algorithm: In 2018, Amazon was forced to scrap a recruitment algorithm that was biased against women. The algorithm was trained on historical data of past hires, which disproportionately included men. As a result, the algorithm was more likely to recommend male candidates for open positions.
• Google’s image search: In 2015, Google was found to be biased in its image search results. When users searched for terms like “CEO” or “scientist,” the results were more likely to show images of men than women. Google has since taken steps to address this bias, but it is an ongoing problem.
• Microsoft’s Tay chatbot: In 2016, Microsoft launched a chatbot called Tay on Twitter. Tay was designed to learn from its interactions with users and become more human-like over time. However, within hours of being launched, Tay was flooded with racist and sexist language. As a result, Tay began to repeat this language, and Microsoft was forced to take it offline.
• Facial recognition algorithms: Facial recognition algorithms are often biased against people of color. A study by MIT found that one facial recognition algorithm was more likely to misidentify black people than white people. This is because the algorithm was trained on a dataset that was disproportionately white.

These are just a few examples of AI bias. As AI becomes more pervasive in our lives, it is important to be aware of the potential for bias and to take steps to mitigate it.

Here are some additional stats on AI bias:

A study by the AI Now Institute found that 70% of AI experts believe that AI is biased against certain groups of people.

The good news is that there is a growing awareness of AI bias and a number of efforts underway to address it. There are a number of fair algorithms that can be used to avoid bias, and there are also a number of techniques that can be used to monitor and mitigate bias in AI systems. By working together, we can help to ensure that AI is used for good and not for harm.

Here’s another interesting article about FraudGPT: The dark evolution of ChatGPT into an AI weapon for cybercriminals in 2023

The pitfalls of algorithmic biases

Bias in AI algorithms can manifest in various ways, and its consequences can be far-reaching. One of the most glaring examples is algorithmic bias in facial recognition technology.

Studies have shown that some facial recognition algorithms perform significantly better on lighter-skinned individuals compared to those with darker skin tones. This disparity can have severe real-world implications, including misidentification by law enforcement agencies and perpetuating racial biases.

Moreover, bias in AI can extend beyond just facial recognition. It can affect lending decisions, job applications, and even medical diagnoses. For instance, biased AI algorithms could lead to individuals from certain racial or gender groups being denied loans or job opportunities unfairly, perpetuating existing inequalities.

The role of data in bias

To comprehend the root causes of bias in AI, one must look no further than the data used to train these systems. AI models learn from historical data, and if this data is biased, the AI model will inherit those biases. This underscores the importance of clean, representative, and diverse training data. It also necessitates a critical examination of historical biases present in our society.

Consider, for instance, a machine learning model tasked with predicting future criminal behavior based on historical arrest records. If these records reflect biased policing practices, such as the over-policing of certain communities, the AI model will inevitably produce biased predictions, disproportionately impacting those communities.

Mitigating bias in AI

Mitigating bias in AI is a pressing concern for developers, regulators, and society as a whole. Several strategies have emerged to address this challenge:

1. Diverse Data Collection: Ensuring that training data is representative of the population and includes diverse groups is essential. This can help reduce biases rooted in historical data.
2. Bias Audits: Regularly auditing AI systems for bias is crucial. This involves evaluating model predictions for fairness across different demographic groups and taking corrective actions as needed.
3. Transparency and explainability: Making AI systems more transparent and understandable can help in identifying and rectifying biases. It allows stakeholders to scrutinize decisions made by AI models and holds developers accountable.
4. Ethical guidelines: Adopting ethical guidelines and principles for AI development can serve as a compass for developers to navigate the ethical minefield. These guidelines often prioritize fairness, accountability, and transparency.
5. Diverse development teams: Ensuring that AI development teams are diverse and inclusive can lead to more comprehensive perspectives and better-informed decisions regarding bias mitigation.
6. Using unbiased data: The training data used to train AI algorithms should be as unbiased as possible. This can be done by collecting data from a variety of sources and by ensuring that the data is representative of the population that the algorithm will be used to serve.
7. Using fair algorithms: There are a number of fair algorithms that can be used to avoid bias. These algorithms are designed to take into account the potential for bias and to mitigate it.
8. Monitoring for bias: Once an AI algorithm is deployed, it is important to monitor it for signs of bias. This can be done by collecting data on the algorithm’s outputs and by analyzing it for patterns of bias.
9. Ensuring transparency: It is important to ensure that AI algorithms are transparent, so that people can understand how they work and how they might be biased. This can be done by providing documentation on the algorithm’s design and by making the algorithm’s code available for public review.

Regulatory responses

In recognition of the gravity of bias in AI, governments and regulatory bodies have begun to take action. In the United States, for example, the Federal Trade Commission (FTC) has expressed concerns about bias in AI and has called for transparency and accountability in AI development.

Additionally, the European Union has introduced the Artificial Intelligence Act, which aims to establish clear regulations for AI, including provisions related to bias and fairness.

These regulatory responses are indicative of the growing awareness of the need to address bias in AI at a systemic level. They underscore the importance of holding AI developers and organizations accountable for the ethical implications of their technologies.

The road ahead

Navigating the complex terrain of fairness and bias in AI is an ongoing journey. It requires continuous vigilance, collaboration, and a commitment to ethical AI development. As AI becomes increasingly integrated into our daily lives, from autonomous vehicles to healthcare diagnostics, the stakes have never been higher.

To achieve true fairness in AI, we must confront the biases embedded in our data, technology, and society. We must also embrace diversity and inclusivity as fundamental principles in AI development. Only through these concerted efforts can we hope to create AI systems that are not only powerful but also just and equitable.

In conclusion, the pursuit of fairness in AI and the eradication of bias are pivotal for the future of technology and humanity. It is a mission that transcends algorithms and data, touching the very essence of our values and aspirations as a society. As we move forward, let us remain steadfast in our commitment to building AI systems that uplift all of humanity, leaving no room for bias or discrimination.

Conclusion

AI bias is a serious problem that can have a negative impact on people’s lives. It is important to be aware of AI bias and to take steps to avoid it. By using unbiased data, fair algorithms, and monitoring and transparency, we can help to ensure that AI is used in a fair and equitable way.

The intersection of art and technology has led us into a captivating realm where AI-generated art challenges conventional notions of creativity and authorship. A recent ruling by a US court in Washington, D.C. has ignited a debate: Can a work of art created solely by artificial intelligence be eligible for copyright protection under US law? Let’s delve into the details of this intriguing case and explore the implications it holds for the evolving landscape of intellectual property. 

The court’s decision 

In a decision that echoes through the corridors of the digital age, US District Judge Beryl Howell firmly established a precedent. The ruling states that a work of art generated entirely by AI, without any human input, is not eligible for copyright protection under current US law. This verdict stemmed from the rejection by the Copyright Office of an application filed by computer scientist Stephen Thaler, on behalf of his AI system known as DABUS. 

Human Authors and Copyrights 

The heart of the matter revolves around the essence of authorship. Judge Howell’s ruling underlines that only works produced by human authors are entitled to copyright protection. The decision, aligned with the Copyright Office’s stance, rejects the notion that AI systems can be considered authors in the legal sense. This judgment affirms the historical significance of human creativity as the cornerstone of copyright law. 

Read about — > LLM for Lawyers, enrich your precedents with the use of AI

The DABUS controversy 

Stephen Thaler, the innovator behind the DABUS AI system, sought to challenge this status quo. Thaler’s attempts to secure US patents for inventions attributed to DABUS were met with resistance, mirroring his quest for copyright protection. His persistence extended to patent applications filed in various countries, including the UK, South Africa, Australia, and Saudi Arabia, with mixed outcomes. 

A dissenting voice and the road ahead 

Thaler’s attorney, Ryan Abbott, expressed strong disagreement with the court’s ruling and vowed to appeal the decision. Despite this, the Copyright Office has stood its ground, asserting that the ruling aligns with their perspective. The fast-evolving domain of generative AI has introduced unprecedented questions about intellectual property, challenging the very foundation of copyright law. 

AI and the artistic toolbox 

As artists increasingly incorporate AI into their creative arsenals, the landscape of copyright law is set to encounter uncharted territories. Judge Howell noted that this evolving dynamic presents “challenging questions” for copyright law, indicating a shifting paradigm in the realm of creativity. While the intersection of AI and art is revolutionary, the court’s ruling underscores that this specific case is more straightforward than the broader issues AI will raise. 

The case in question 

At the center of this legal discourse is Thaler’s application for copyright protection for “A Recent Entrance to Paradise,” a piece of visual art attributed to his AI system, DABUS. The Copyright Office’s rejection of this application in the previous year sparked the legal battle. Thaler contested the rejection, asserting that AI-generated works should be entitled to copyright protection as they align with the constitution’s aim to “promote the progress of science and useful arts.” 

Authorship as a Bedrock requirement 

Judge Howell concurred with the Copyright Office, emphasizing the pivotal role of human authorship as a “bedrock requirement of copyright.” She reinforced this stance by drawing on centuries of established understanding, reiterating that creativity rooted in human ingenuity remains the linchpin of copyright protection. 

Navigating Generative AI: Mitigating Intellectual Property challenges in law and creativity

Generative Artificial Intelligence (AI) represents a groundbreaking paradigm in AI research, enabling the creation of novel content by leveraging existing data. This innovative approach involves the acquisition of knowledge from vast datasets, which the generative AI model then ingeniously utilizes to fabricate entirely new examples.  

For instance, an adept generative AI model, well-versed in legal jargon from a corpus of legal documents, exhibits the remarkable ability to craft entirely novel legal documents. 

Current applications of Generative AI in law 

There are a number of current applications of generative AI in law. These include: 

• Legal document automation and generation: Generative AI models can be used to automate the creation of legal documents. For example, a generative AI model could be used to generate contracts, wills, or other legal documents. 

• Natural language processing for contract analysis: Generative AI models can be used to analyze contracts. For example, a generative AI model could be used to identify the clauses in a contract, determine the meaning of those clauses, and identify any potential problems with the contract. 
• Predictive modeling for case outcomes: Generative AI models can be used to predict the outcome of legal cases. For example, a generative AI model could be used to predict the likelihood of a plaintiff winning a case, the amount of damages that a plaintiff might be awarded, or the length of time it might take for a case to be resolved. 
• Legal chatbots and virtual assistants: Generative AI models can be used to create legal chatbots and virtual assistants. These chatbots and assistants can be used to answer legal questions, provide legal advice, or help people with legal tasks. 
• Improving legal research and information retrieval: Generative AI models can be used to improve legal research and information retrieval. For example, a generative AI model could be used to generate summaries of legal documents, identify relevant legal cases, or create legal research reports. 

Generative AI and copyright law 

In 2022, a groundbreaking event occurred at the Colorado State Fair’s art competition when an AI-generated artwork claimed victory. The artist, Jason Allen, utilized a generative AI system called Midjourney, which had been trained on a vast collection of artworks from the internet. Despite the AI’s involvement, the creative process was far from automated; Allen spent approximately 80 hours and underwent nearly 900 iterations to craft and refine his submission. 

The triumph of AI in the art competition, however, sparked a heated online debate, with one Twitter user decrying the perceived demise of authentic artistry. 

AI’s revolutionary impact on creativity

Comparing the emergence of generative AI to the historical introduction of photography in the 1800s, we find that both faced challenges to be considered genuine art forms. Just as photography revolutionized artistic expression, AI’s impact on creativity is profound and transformative. 

A major concern in the debate revolves around copyright laws, which were designed to promote and protect artistic creativity. However, the advent of generative AI has blurred traditional notions of authorship and copyright infringement. The use of copyrighted artworks for training AI models raises ethical questions even before the AI generates new content. 

AI transforming prior artwork 

While AI systems cannot legally own copyrights, they possess unique capabilities that can mimic and transform prior artworks into new outputs, making the issue of ownership more intricate. As AI-generated outputs often resemble works from the training data, determining rightful ownership becomes a challenging legal task. The degree of meaningful creative input required to claim ownership in generative AI outputs remains uncertain. 

To address these concerns, some experts propose new regulations that protect and compensate artists whose work is used for AI training. These proposals include granting artists the option to opt out of their work being used for generative AI training or implementing automatic compensation mechanisms. 

Additionally, the distinction between outputs that closely resemble or significantly deviate from training data plays a crucial role in the copyright analysis. Outputs that resemble prior works raise questions of copyright infringement, while transformative outputs might claim a separate ownership. 

Ultimately, generative AI offers a new creative tool for artists and enthusiasts alike, akin to traditional artistic mediums like cameras or painting brushes. However, its reliance on training data complicates tracing creative contributions back to individual artists. The interpretation and potential reform of existing copyright laws will significantly impact the future of creative expression and the rightful ownership of AI-generated art. 

Why can Generative AI give rise to intellectual property issues? 

While generative AI is a recent addition to the technology landscape, existing laws have significant implications for its application. Courts are currently grappling with how to interpret and apply these laws to address various issues that have arisen with the use of generative AI. 

In a case called Andersen v. Stability AI et al., filed in late 2022, a class of three artists sued multiple generative AI platforms, alleging that these AI systems used their original works without proper licenses to train their models. This allowed users to generate works that were too similar to the artists’ existing protected works, potentially leading to unauthorized derivative works. If the court rules in favor of the artists, the AI platforms may face substantial infringement penalties. 

Similar cases in 2023 involve claims that companies trained AI tools using vast datasets of unlicensed works. Getty, a renowned image licensing service, filed a lawsuit against the creators of Stable Diffusion, claiming improper use of their watermarked photograph collection, thus violating copyright and trademark rights. 

These legal battles are centered around defining the boundaries of “derivative work” under intellectual property laws. Different federal circuit courts may interpret the concept differently, making the outcomes of these cases uncertain. The fair use doctrine, which permits the use of copyrighted material for transformative purposes, plays a crucial role in these legal proceedings. 

Technological advancements vs copyright law – Who won?

This clash between technology and copyright law is not unprecedented. Several non-technological cases, such as the one involving the Andy Warhol Foundation, could also influence how generative AI outputs are treated. The outcome of the case brought by photographer Lynn Goldsmith, who licensed an image of Prince, will shed light on whether a piece of art is considered sufficiently different from its source material to be deemed “transformative.” 

All this legal uncertainty poses challenges for companies using generative AI. Risks of infringement, both intentional and unintentional, exist in contracts that do not address generative AI usage by vendors and customers. Businesses must be cautious about using training data that might include unlicensed works or generate unauthorized derivative works not covered by fair use, as willful infringement can lead to substantial damages. Additionally, there is a risk of inadvertently sharing confidential trade secrets or business information when inputting data into generative AI tools. 

A way forward for AI-generated art

As the use of generative AI becomes more prevalent, companies, developers, and content creators must take proactive steps to mitigate risks and navigate the evolving legal landscape. For AI developers, ensuring compliance with intellectual property laws when acquiring training data is crucial. Customers of AI tools should inquire about the origins of the data and review terms of service to protect themselves from potential infringement issues. 

Developers must also work on maintaining the provenance of AI-generated content, providing transparency about the training data and the creative process. This information can protect business users from intellectual property claims and demonstrate that AI-generated outputs were not intentionally copied or stolen. 

Content creators should actively monitor their works in compiled datasets and social channels to detect any unauthorized derivative works. Brands with valuable trademarks should consider evolving trademark and trade dress monitoring to identify stylistic similarities that may suggest misuse of their brand. 

Businesses should include protections in contracts with generative AI platforms, demanding proper licensure of training data and broad indemnification for potential infringement issues. Adding AI-related language to confidentiality provisions can further safeguard intellectual property rights. 

Going forward, content creators may consider building their own datasets to train AI models, allowing them to produce content in their style with a clear audit trail. Co-creation with followers can also be an option for sourcing training data with permission. 

In today’s era of advanced artificial intelligence, language models like OpenAI’s GPT-3.5 have captured the world’s attention with their astonishing ability to generate human-like text. However, to harness the true potential of these models, it is crucial to master the art of prompt engineering.

How to curate a good prompt?

A well-crafted prompt holds the key to unlocking accurate, relevant, and insightful responses from language models. In this blog post, we will explore the top characteristics of a good prompt and discuss why everyone should learn prompt engineering. We will also delve into the question of whether prompt engineering might emerge as a dedicated role in the future.

Prompt engineering refers to the process of designing and refining input prompts for AI language models to produce desired outputs. It involves carefully crafting the words, phrases, symbols, and formats used as input to guide the model in generating accurate and relevant responses. The goal of prompt engineering is to improve the performance and output quality of the language model.

Here’s a simple example to illustrate prompt engineering:

Imagine you are using a chatbot AI model to provide information about the weather. Instead of a generic prompt like “What’s the weather like?”, prompt engineering involves crafting a more specific and detailed prompt like “What is the current temperature in New York City?” or “Will it rain in London tomorrow?”

Read about —> Which AI chatbot is right for you in 2023

By providing a clear and specific prompt, you guide the AI model to generate a response that directly answers your question. The choice of words, context, and additional details in the prompt can influence the output of the AI model and ensure it produces accurate and relevant information.

Quick exercise –> Choose the most suitable prompt

Prompt engineering is crucial because it helps optimize the performance of AI models by tailoring the input prompts to the desired outcomes. It requires creativity, understanding of the language model, and attention to detail to strike the right balance between specificity and relevance in the prompts.

Different resources provide guidance on best practices and techniques for prompt engineering, considering factors like prompt formats, context, length, style, and desired output. Some platforms, such as OpenAI API, offer specific recommendations and examples for effective prompt engineering.

Why everyone should learn prompt engineering:

1. Empowering communication: Effective communication is at the heart of every interaction. By mastering prompt engineering, individuals can enhance their ability to extract precise and informative responses from language models. Whether you are a student, professional, researcher, or simply someone seeking knowledge, prompt engineering equips you with a valuable tool to engage with AI systems more effectively.

2. Tailored and relevant information: A well-designed prompt allows you to guide the language model towards providing tailored and relevant information. By incorporating specific details and instructions, you can ensure that the generated responses align with your desired goals. Prompt engineering enables you to extract the exact information you seek, saving time and effort in sifting through irrelevant or inaccurate results.

3. Enhancing critical thinking: Crafting prompts demand careful consideration of context, clarity, and open-endedness. Engaging in prompt engineering exercises cultivates critical thinking skills by challenging individuals to think deeply about the subject matter, formulate precise questions, and explore different facets of a topic. It encourages creativity and fosters a deeper understanding of the underlying concepts.

4. Overcoming bias: Bias is a critical concern in AI systems. By learning prompt engineering, individuals can contribute to reducing bias in generated responses. Crafting neutral and unbiased prompts helps prevent the introduction of subjective or prejudiced language, resulting in more objective and balanced outcomes.

Top characteristics of a good prompt with examples

A good prompt possesses several key characteristics that can enhance the effectiveness and quality of the responses generated. Here are the top characteristics of a good prompt:

1. Clarity:

A good prompt should be clear and concise, ensuring that the desired question or topic is easily understood. Ambiguous or vague prompts can lead to confusion and produce irrelevant or inaccurate responses.

Example:

Good Prompt: “Explain the various ways in which climate change affects the environment.”

Poor Prompt: “Climate change and the environment.”

2. Specificity:

Providing specific details or instructions in a prompt help focus the generated response. By specifying the context, parameters, or desired outcome, you can guide the language model to produce more relevant and tailored answers.

Example:

Good Prompt: “Provide three examples of how rising temperatures due to climate change impact marine ecosystems.”
Poor Prompt: “Talk about climate change.”

3. Context:

Including relevant background information or context in the prompt helps the language model understand the specific domain or subject matter. Contextual cues can improve the accuracy and depth of the generated response.

Example: 

Good Prompt: “In the context of agricultural practices, discuss how climate change affects crop yields.”

Poor Prompt: “Climate change effects

4. Open-endedness:

While specificity is important, an excessively narrow prompt may limit the creativity and breadth of the generated response. Allowing room for interpretation and open-ended exploration can lead to more interesting and diverse answers.

Example:

Good Prompt: “Describe the short-term and long-term consequences of climate change on global biodiversity.”

Poor Prompt: “List the effects of climate change.”

5. Conciseness:

Keeping the prompt concise helps ensure that the language model understands the essential elements and avoids unnecessary distractions. Lengthy or convoluted prompts might confuse the model and result in less coherent or relevant responses.

Example:
Good Prompt: “Summarize the key impacts of climate change on coastal communities.”

Poor Prompt: “Please explain the negative effects of climate change on the environment and people living near the coast.”

6. Correct grammar and syntax:

A well-structured prompt with proper grammar and syntax is easier for the language model to interpret accurately. It reduces ambiguity and improves the chances of generating coherent and well-formed responses.

Example:

Good Prompt: “Write a paragraph explaining the relationship between climate change and species extinction.”
Poor Prompt: “How species extinction climate change.”

7. Balanced complexity:

The complexity of the prompt should be appropriate for the intended task or the model’s capabilities. Extremely complex prompts may overwhelm the model, while overly simplistic prompts may not challenge it enough to produce insightful or valuable responses.

Example:

Good Prompt: “Discuss the interplay between climate change, extreme weather events, and natural disasters.”

Poor Prompt: “Climate change and weather.”

8. Diversity in phrasing:

When exploring a topic or generating multiple responses, varying the phrasing or wording of the prompt can yield diverse perspectives and insights. This prevents the model from repeating similar answers and encourages creative thinking.

Example:

Good Prompt: “How does climate change influence freshwater availability?” vs. “Explain the connection between climate change and water scarcity.”

Poor Prompt: “Climate change and water.

9. Avoiding leading or biased language:

To promote neutrality and unbiased responses, it’s important to avoid leading or biased language in the prompt. Using neutral and objective wording allows the language model to generate more impartial and balanced answers.

Example:

Good Prompt: “What are the potential environmental consequences of climate change?”

Poor Prompt: “How does climate change devastate the environment?”

10. Iterative refinement:

Crafting a good prompt often involves an iterative process. Reviewing and refining the prompt based on the generated responses can help identify areas of improvement, clarify instructions, or address any shortcomings in the initial prompt.

Example:

Prompt iteration involves an ongoing process of improvement based on previous responses and refining the prompts accordingly. Therefore, there is no specific example to provide, as it is a continuous effort.

By considering these characteristics, you can create prompts that elicit meaningful, accurate, and relevant responses from the language model.

Read about —-> How LLMs (Large Language Models) technology is making chatbots smarter in 2023?

Two different approaches of prompting

Prompting by instruction and prompting by example are two different approaches to guide AI language models in generating desired outputs. Here’s a detailed comparison of both approaches, including reasons and situations where each approach is suitable:

1. Prompting by instruction:

• In this approach, the prompt includes explicit instructions or explicit questions that guide the AI model on how to generate the desired output.
• It is useful when you need specific control over the generated response or when you want the model to follow a specific format or structure.
• For example, if you want the AI model to summarize a piece of text, you can provide an explicit instruction like “Summarize the following article in three sentences.”
• Prompting by instruction is suitable when you need a precise and specific response that adheres to a particular requirement or when you want to enforce a specific behavior in the model.
• It provides clear guidance to the model and allows you to specify the desired outcome, length, format, style, and other specific requirements.

Examples of prompting by instruction:

1. In a classroom setting, a teacher gives explicit verbal instructions to students on how to approach a new task or situation, such as explaining the steps to solve a math problem.
2. In Applied Behavior Analysis (ABA), a therapist provides a partial physical prompt by using their hands to guide a student’s behavior in the right direction when teaching a new skill.
3. When using AI language models, an explicit instruction prompt can be given to guide the model’s behavior. For example, providing the instruction “Summarize the following article in three sentences” to prompt the model to generate a concise summary.

Tips for prompting by instruction:

• • Put the instructions at the beginning of the prompt and use clear markers like “A:” to separate instructions and context.
  • Be specific, descriptive, and detailed about the desired context, outcome, format, style, etc.
  • Articulate the desired output format through examples, providing clear guidelines for the model to follow.

2. Prompting by example:

• In this approach, the prompt includes examples of the desired output or similar responses that guide the AI model to generate responses based on those examples.
• It is useful when you want the model to learn from specific examples and mimic the desired behavior.
• For example, if you want the AI model to answer questions about a specific topic, you can provide example questions and their corresponding answers.
• Prompting by example is suitable when you want the model to generate responses similar to the provided examples or when you want to capture the style, tone, or specific patterns from the examples.
• It allows the model to learn from the given examples and generalize its behavior based on them.

Examples of prompting by example:

1. In a classroom, a teacher shows students a model essay as an example of how to structure and write their own essays, allowing them to learn from the demonstrated example.
2. In AI language models, providing example questions and their corresponding answers can guide the model in generating responses similar to the provided examples. This helps the model learn the desired behavior and generalize it to new questions.
3. In an online learning environment, an instructor provides instructional prompts in response to students’ discussion forum posts, guiding the discussion and encouraging deep understanding. These prompts serve as examples for the entire class to enhance the learning experience.

Tips for prompting by example:

• • Provide a variety of examples to capture different aspects of the desired behavior.
  • Include both positive and negative examples to guide the model on what to do and what not to do.
  • Gradually refine the examples based on the model’s responses, iteratively improving the desired behavior.

Which prompting approach is right for you?

Prompting by instruction provides explicit guidance and control over the model’s behavior, while prompting by example allows the model to learn from provided examples and mimic the desired behavior. The choice between the two approaches depends on the level of control and specificity required for the task at hand. It’s also possible to combine both approaches in a single prompt to leverage the benefits of each approach for different parts of the task or desired behavior.

To become proficient in prompt engineering, register now in our upcoming Large Language Models Bootcamp

Artificial intelligence (AI) is rapidly transforming the way we work. From automating repetitive tasks to generating creative content, AI tools are widely helping businesses of all sizes to be more productive and efficient. 

Here are some of the most exciting AI tools that can revolutionize your work environment: 

1. Bard is a knowledge assistant developed by Google that uses LLM-based technology to help you with tasks such as research, writing, and translation. [Free to use]  
2. ChatGPT is a versatile knowledge assistant that can be used for a variety of purposes, including customer service, marketing, and sales. [Free to use] 
3. ChatSpot is a content and research assistant from HubSpot that can help you with marketing, sales, and operational tasks. [Free to use] 
4. Docugami is an AI-driven business document management system that can help you to organize, store, and share documents more effectively. [Free trial available] 
5. Einstein GPT is a content, insights, and interaction assistant from Salesforce that can help you to improve your customer interactions. [Free trial available] 
6. Google Workspace AI Features are a suite of generative AI capabilities that are integrated into Google Workspace products, such as Docs, Sheets, and Slides. [Free to use] 
7. HyperWrite is a business writing assistant that can help you to create clear, concise, and persuasive content. [Free trial available] 
8. Jasper for Business is a smart writing creator that can help you to maintain brand consistency for external content. [Free trial available] 
9. Microsoft 365 Copilot/Business Chat are AI-enabled content creation and contextual user data-driven business chatbots. [Free trial available] 
10. Notably is an AI-assisted business research platform that can help you to find and understand relevant information more quickly. [Free trial available] 
11. Notion AI is a content and writing assistant that is tailored for business applications. [Free trial available] 
12. Olli is AI-generated analytics and business intelligence dashboard that are engineered for enterprise use. [Free trial available] 
13. Poe by Quora is a chatbot knowledge assistant that leverages Anthropic’s cutting-edge AI models. [Free trial available] 
14. Rationale is an AI-powered business decision-making tool that can help you to make more informed decisions. [Free trial available] 
15. Seenapse is an AI-supported ideation tool that is designed specifically for business purposes. [Free trial available] 
16. Tome is an AI-driven tool that empowers users to create dynamic PowerPoint presentations. [Free trial available] 
17. WordTune is a versatile writing assistant with broad applications. [Free trial available] 
18. Writer is an AI-based writing assistant that is designed to enhance writing proficiency and productivity. [Free trial available] 

These are just a few of the many AI tools that are available to businesses today. As AI continues to evolve, we can expect to see even more innovative tools that can help us to work more efficiently and effectively. 

Are AI tools a threat to the workforce? 

AI tools can be a threat to the workforce in some cases, but they can also create new jobs and opportunities. It is important to consider the following factors when assessing the impact of AI on the workforce:   

The type of work:

Some types of work are more susceptible to automation than others. For example, jobs that involve repetitive tasks or that require a high level of accuracy are more likely to be automated by AI. 

The skill level of the workforce:

Workers with low-level skills are more likely to be displaced by AI than workers with high-level skills. This is because AI tools are often able to perform tasks that require a high level of accuracy and precision, which are skills that are often possessed by workers with high-level education and training. 

The pace of technological change:

The pace of technological change is also a factor to consider. If AI tools are adopted rapidly, it could lead to a significant number of job losses in a short period of time. However, if AI tools are adopted more gradually, it will give workers more time to adapt to the changing landscape and acquire the skills they need to succeed in the new economy. 

Overall, the impact of AI on the workforce is complex and uncertain. There is no doubt that AI will displace some jobs, but it will also create new jobs and opportunities. It is important to be proactive and prepare for the changes that AI will bring.   

Mitigate the negative impact of AI tools

Here are some things that can be done to mitigate the negative impact of AI on the workforce: 

Upskill and reskill workers:

Workers need to be prepared for the changes that AI will bring. This means upskilling and reskilling workers so that they have the skills they need to succeed in the new economy. 

Create new jobs:

AI will also create new jobs. It is important to create new jobs that are specifically designed for the skills that AI will automate. 

Provide social safety nets:

If AI does lead to significant job losses, it is important to provide social safety nets to help those who are displaced. This could include things like unemployment benefits, retraining programs, and job placement services. 

By taking these steps, we can ensure that AI is used to benefit the workforce, not to displace it. 

Who can benefit from using AI tools? 

AI tools can benefit businesses of all sizes, from small businesses to large corporations. They can be used by a wide range of employees, including marketing professionals, sales representatives, customer service representatives, and even executives. 

What are the benefits of using AI tools? 

There are many benefits to using AI tools, including: 

• Increased productivity: AI tools can help you to automate repetitive tasks, freeing up your time to focus on more strategic work. 
• Improved accuracy: AI tools can help you to produce more accurate results, reducing the risk of errors. 
• Enhanced creativity: AI tools can help you to generate new ideas and insights, stimulating your creativity. 
• Improved customer service: AI tools can help you to provide better customer service, by answering questions more quickly and accurately. 
• Increased efficiency: AI tools can help you to streamline your operations, making your business more efficient. 

Conclusion 

AI tools are powerful tools that can help businesses to improve their productivity, accuracy, creativity, customer service, and efficiency. As AI continues to evolve, we can expect to see even more innovative tools that can help businesses to succeed in the digital age. Learn more about Generative AI here.

Machine learning courses are not just a buzzword anymore; they are reshaping the careers of many people who want their breakthrough in tech. From revolutionizing healthcare and finance to propelling us towards autonomous systems and intelligent robots, the transformative impact of machine learning knows no bounds.

Safe to say that the demand for skilled machine learning professionals is skyrocketing, and many are turning to online courses to upskill and stay competitive in the job market. Fortunately, there are many great resources available for those looking to dive into the world of machine learning.

If you are interested in learning more about machine learning courses, there are many free ones available online.

Top free machine learning courses

Here are 9 free machine learning courses from top universities that you can take online to upgrade your skills: 

1. Machine Learning with TensorFlow by Google AI

This is a beginner-level course that teaches you the basics of machine learning using TensorFlow, a popular machine-learning library. The course covers topics such as linear regression, logistic regression, and decision trees.

2. Machine Learning for Absolute Beginners by Kirill Eremenko and Hadelin de Ponteves

This is another beginner-level course that teaches you the basics of machine learning using Python. The course covers topics such as supervised learning, unsupervised learning, and reinforcement learning.

3. Machine Learning with Python by Andrew Ng

This is an intermediate-level course that teaches you more advanced machine-learning concepts using Python. The course covers topics such as deep learning and reinforcement learning.

4. Machine Learning for Data Science by Carlos Guestrin

This is an intermediate-level course that teaches you how to use machine learning for data science tasks. The course covers topics such as data wrangling, feature engineering, and model selection.

5. Machine Learning for Natural Language Processing by Christopher Manning, Jurafsky and Schütze

This is an advanced-level course that teaches you how to use machine learning for natural language processing tasks. The course covers topics such as text classification, sentiment analysis, and machine translation.

6. Machine Learning for Computer Vision by Andrew Zisserman

This is an advanced-level course that teaches you how to use machine learning for computer vision tasks. The course covers topics such as image classification, object detection, and image segmentation.

7. Machine Learning for Robotics by Ken Goldberg

This is an advanced-level course that teaches you how to use machine learning for robotics tasks. The course covers topics such as motion planning, control, and perception.

8. Machine Learning: A Probabilistic Perspective by Kevin P. Murphy

This is a graduate-level course that teaches you machine learning from a probabilistic perspective. The course covers topics such as Bayesian inference and Markov chain Monte Carlo methods.

9. Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville

This is a graduate-level course that teaches you deep learning. The course covers topics such as neural networks, convolutional neural networks, and recurrent neural networks.

Are you interested in machine learning, data science, and analytics? Take the first step by enrolling in our comprehensive data science course. 

Each course is carefully crafted and delivered by world-renowned experts, covering everything from the fundamentals to advanced techniques. Gain expertise in data analysis, deep learning, neural networks, and more. Step up your game and make accurate predictions based on vast datasets.

Decoding the popularity of ML among students and professional 

Among the wave of high-paying tech jobs, there are several reasons for the growing interest in machine learning, including: 

1. High Demand: As the world becomes more data-driven, the demand for professionals with expertise in machine learning has grown. Companies across all industries are looking for people who can leverage machine-learning techniques to solve complex problems and make data-driven decisions. 
2. Career Opportunities: With the high demand for machine learning professionals comes a plethora of career opportunities. Jobs in the field of machine learning are high-paying, challenging, and provide room for growth and development. 
3. Real-World Applications: Machine learning has numerous real-world applications, ranging from fraud detection and risk analysis to personalized advertising and natural language processing. As more people become aware of the practical applications of machine learning, their interest in learning more about the technology grows. 
4. Advancements in Technology: With the advances in technology, access to machine learning tools has become easier than ever. There are numerous open-source machine-learning tools and libraries available that make it easy for anyone to get started with machine learning. 
5. Intellectual Stimulation: Learning about machine learning can be an intellectually stimulating experience. Machine learning involves the study of complex algorithms and models that can make sense of large amounts of data. 

Enroll yourself in these courses now

In conclusion, if you’re looking to improve your skills, taking advantage of these free machine learning courses from top universities is a great way to get started. By investing the time and effort required to complete these courses, you’ll be well on your way to building a successful career in this exciting and rapidly evolving field.

This blog outlines a collection of 12 AI tools that can assist with day-to-day activities and make tasks more efficient and streamlined.  

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Looking for AI jobs? Well, here are our top 5 AI jobs along with all the skills needed to land them

Rapid technological advances and the promotion of machine learning have shifted manual processes to automated ones. This has not only made the lives of humans easier but has also generated error-free results. To only associate AI with IT is baseless. You can find AI integrated into our day-to-day lives. From self-driven trains to robot waiters, from marketing chatbots to virtual consultants, all are examples of AI.

We can find AI everywhere without even knowing it. It is hard to explain how quickly it has become a part of our daily routine. AI will automatically find suitable searches, foods, and products even without you uttering a word. It is not hard to say that robots will replace humans very shortly.

The evolution of AI has increased the demand for AI experts. With the diversified AI job roles and emerging career opportunities, it won’t be difficult to find a suitable job matching your interests and goals. Here are the top 5 AI jobs picks that may come in handy along with the skills that will help you land them effortlessly.

Must-have skills for AI jobs

To land the AI job you need to train yourself and become an expert in multiple skills. These skills can only be mastered through great zeal of effort, hard work, and enthusiasm to learn them. Every job required its own set of core skills i.e. some may require data analysis, so others might demand expertise in machine learning. But even with the diverse job roles, the core skills needed for AI jobs remain constant which are,

1. Expertise in a programming language (especially in Python, Scala, and Java)
2. Hands-on knowledge of Linear Algebra and Statistics
3. Proficient at Signal Processing Techniques
4. Profound knowledge of the Neural Network Architects

Read blog about AI and Machine learning trends for 2023

 

Our top 5 picks for AI jobs

1. Machine Learning Engineer

Who are they?

They are responsible for discovering and designing self-driven AI systems that can run smoothly without human intervention. Their main task is to automate predictive models.

What do they do?

From designing ML systems, drafting ML algorithms, and selecting appropriate data sets they sand then analyzing large data along with testing and verifying ML algorithms.

Qualifications are required? Individuals with bachelor’s or doctoral degrees in computer science or mathematics along with proficiency in a modern programming language will most likely get this job. Knowledge about cloud applications, expertise in mathematics, computer science, machine learning, programming language, and related certifications are preferred,

2. Robotics Scientist

Who are they? They design and develop robots that can be used to perform the error-free day-to-day task efficiently. Their services are used in space exploration, healthcare, human identification, etc.

What do they do? They design and develop robots to solve problems that can be operated with voice commands. They operate different software and understand the methodology behind it to construct mechanical prototypes. They collaborate with other field specialists to control programming software and use them accordingly.

Qualifications required? A robotics scientist must have a bachelor’s degree in robotics/ mechanical engineering/ electrical engineering or electromechanical engineering. Individuals with expertise in mathematics, AI certifications, and knowledge about CADD will be preferred.

3. Data Scientist

Who are they? They evaluate and analyze data and extract valuable insights that assist organizations in making better decisions.

What do they do? They gather, organize and interpret a large amount of data using ML and predict analytics into much more valuable perspicuity. They use tools and data platforms like Hadoop, Spark, Hive, and programming languages especially Java, SQL, and Python to go beyond statistical analysis.

Qualification required? They must have a master’s or doctoral degree in computer sciences with hands-on knowledge of programming languages, data platforms, and cloud tools.

Master these data science tools to grow your career as Data Scientist

4. Research Scientist

Who are they? They analyze data and evaluate gathered information using restrained-based examinations.

What do they do?  Research scientists have expertise in different AI skills from ML, NLP, data processing and representation, and AI models which they use for solving problems and seeking modern solutions.

Qualifications required? Bachelor or doctoral degree in computer science or other related technical fields. Along with good communication, knowledge about AI, parallel computing, AI algorithms, and models is highly recommended for those who are thinking of pursuing this career opportunity.

5. Business Intelligence Developer

Who are they? They organize and generate the business interface and are responsible for maintaining it.

What do they do? They organize business data, extract insights from it, keep a close eye on market trends and assist organizations in achieving profitable results. They are also responsible for maintaining complex data in cloud base platforms.

Qualifications required? Bachelor’s degree in computer science, and other related technical fields with added AI certifications. Individuals with experience in data mining, SSRS, SSIS, and BI technologies and certifications in data science will be preferred.

Conclusion

A piece of advice for those who want to pursue AI as their career,” invest your time and money”. Take related short courses, acquire ML and AI certifications, and learn about what data science and BI technologies are all about and practices. With all these, you can become an AI expert having a growth-oriented career in no time.

With the surge in demand and interest in AI and machine learning, many contemporary trends are emerging in this space. As a tech professional, this blog will excite you to see what’s next in the realm of Artificial Intelligence and Machine Learning trends.

Data security and regulations 

In today’s economy, data is the main commodity. To rephrase, intellectual capital is the most precious asset that businesses must safeguard. The quantity of data they manage, as well as the hazards connected with it, is only going to expand after the emergence of AI and ML. Large volumes of private information are backed up and archived by many companies nowadays, which poses a growing privacy danger. Don Evans, CEO of Crewe Foundation   

The future currency is data. In other words, it’s the most priceless resource that businesses must safeguard. The amount of data they handle, and the hazards attached to it will only grow when AI and ML are brought into the mix. Today’s businesses, for instance, back up and store enormous volumes of sensitive customer data, which is expected to increase privacy risks by 2023.
 

Overlap of AI and IoT 

There is a blurring of boundaries between AI and the Internet of Things. While each technology has merits of its own, only when they are combined can they offer novel possibilities? Smart voice assistants like Alexa and Siri only exist because AI and the Internet of Things have come together. Why, therefore, do these two technologies complement one another so well?

The Internet of Things (IoT) is the digital nervous system, while Artificial Intelligence (AI) is the decision-making brain. AI’s speed at analyzing large amounts of data for patterns and trends improves the intelligence of IoT devices. As of now, just 10% of commercial IoT initiatives make use of AI, but that number is expected to climb to 80% by 2023. Josh Thill, Founder of Thrive Engine 

Why then do these two technologies complement one other so well? IoT and AI can be compared to the brain and nervous system of the digital world, respectively. IoT systems have become more sophisticated thanks to AI’s capacity to quickly extract insights from data. Software developers and embedded engineers now have another reason to include AI/ML skills in their resumes because of this development in AI and machine learning. 

Augmented Intelligence   

The growth of augmented intelligence should be a relieving trend for individuals who may still be concerned about AI stealing their jobs. It combines the greatest traits of both people and technology, offering businesses the ability to raise the productivity and effectiveness of their staff.

40% of infrastructure and operations teams in big businesses will employ AI-enhanced automation by 2023, increasing efficiency. Naturally, for best results, their staff should be knowledgeable in data science and analytics or have access to training in the newest AI and ML technologies. 

Moving on from the concept of Artificial Intelligence to Augmented Intelligence, where decisions models are blended artificial and human intelligence, where AI finds, summarizes, and collates information from across the information landscape – for example, company’s internal data sources. This information is presented to the human operator, who can make a human decision based on that information. This trend is supported by recent breakthroughs in Natural Language Processing (NLP) and Natural Language Understanding (NLU). Kuba Misiorny, CTO of Untrite Ltd
 

Transparency 

Despite being increasingly commonplace, there are trust problems with AI. Businesses will want to utilize AI systems more frequently, and they will want to do so with greater assurance. Nobody wants to put their trust in a system they don’t fully comprehend.

As a result, in 2023 there will be a stronger push for the deployment of AI in a visible and specified manner. Businesses will work to grasp how AI models and algorithms function, but AI/ML software providers will need to make complex ML solutions easier for consumers to understand.

The importance of experts who work in the trenches of programming and algorithm development will increase as transparency becomes a hot topic in the AI world. 

Composite AI 

Composite AI is a new approach that generates deeper insights from any content and data by fusing different AI technologies. Knowledge graphs are much more symbolic, explicitly modeling domain knowledge and, when combined with the statistical approach of ML, create a compelling proposition. Composite AI expands the quality and scope of AI applications and, as a result, is more accurate, faster, transparent, and understandable, and delivers better results to the user. Dorian Selz, CEO of Squirro

It’s a major advance in the evolution of AI and marrying content with context and intent allows organizations to get enormous value from the ever-increasing volume of enterprise data. Composite AI will be a major trend for 2023 and beyond. 

Continuous focus on healthcare

There has been concern that AI will eventually replace humans in the workforce ever since the concept was first proposed in the 1950s. Throughout 2018, a deep learning algorithm was constructed that demonstrated accurate diagnosis utilizing a dataset consisting of more than 50,000 normal chest pictures and 7,000 scans that revealed active Tuberculosis. Since then, I believe that the healthcare business has mostly made use of Machine Learning (ML) and Deep Learning applications of artificial intelligence. Marie Ysais, Founder of Ysais Digital Marketing

Learn more about the role of AI in healthcare:

AI in healthcare has improved patient care

Pathology-assisted diagnosis, intelligent imaging, medical robotics, and the analysis of patient information are just a few of the many applications of artificial intelligence in the healthcare industry. Leading stakeholders in the healthcare industry have been presented with advancements and machine-learning models from some of the world’s largest technology companies. Next year, 2023, will be an important year to observe developments in the field of artificial intelligence.
 

Algorithmic decision-making 

Advanced algorithms are taking on the skills of human doctors, and while AI may increase productivity in the medical world, nothing can take the place of actual doctors. Even in robotic surgery, the whole procedure is physician-guided. AI is a good supplement to physician-led health care. The future of medicine will be high-tech with a human touch.  

No-code tools   

The low-code/No Code ML revolution accelerates creating a new breed of Citizen AI. These tools fuel mainstream ML adoption in businesses that were previously left out of the first ML wave (mostly taken advantage of by BigTech and other large institutions with even larger resources). Maya Mikhailov Founder of Savvi AI 

Low-code intelligent automation platforms allow business users to build sophisticated solutions that automate tasks, orchestrate workflows, and automate decisions. They offer easy-to-use, intuitive drag-and-drop interfaces, all without the need to write a line of code. As a result, low-code intelligent automation platforms are popular with tech-savvy business users, who no longer need to rely on professional programmers to design their business solutions. 

Cognitive analytics 

Cognitive analytics is another emerging trend that will continue to grow in popularity over the next few years. The ability for computers to analyze data in a way that humans can understand is something that has been around for a while now but is only recently becoming available in applications such as Google Analytics or Siri—and it’ll only get better from here! 

Virtual assistants 

Virtual assistants are another area where NLP is being used to enable more natural human-computer interaction. Virtual assistants like Amazon Alexa and Google Assistant are becoming increasingly common in homes and businesses. In 2023, we can expect to see them become even more widespread as they evolve and improve. Idrees Shafiq-Marketing Research Analyst at Astrill

Virtual assistants are becoming increasingly popular, thanks to their convenience and ability to provide personalized assistance. In 2023, we can expect to see even more people using virtual assistants, as they become more sophisticated and can handle a wider range of tasks. Additionally, we can expect to see businesses increasingly using virtual assistants for customer service, sales, and marketing tasks.
 

Information security (InfoSec)

The methods and devices used by companies to safeguard information fall under the category of information security. It comprises settings for policies that are essentially designed to stop the act of stopping unlawful access to, use of, disclosure of, disruption of, modification of, an inspection of, recording of, or data destruction.

With AI models that cover a broad range of sectors, from network and security architecture to testing and auditing, AI prediction claims that it is a developing and expanding field. To safeguard sensitive data from potential cyberattacks, information security procedures are constructed on the three fundamental goals of confidentiality, integrity, and availability, or the CIA. Daniel Foley, Founder of Daniel Foley SEO 

Wearable devices 

The continued growth of the wearable market. Wearable devices, such as fitness trackers and smartwatches, are becoming more popular as they become more affordable and functional. These devices collect data that can be used by AI applications to provide insights into user behavior. Oberon, Founder, and CEO of Very Informed 

Process discovery

It can be characterized as a combination of tools and methods with heavy reliance on artificial intelligence (AI) and machine learning to assess the performance of persons participating in the business process. In comparison to prior versions of process mining, these goes further in figuring out what occurs when individuals interact in different ways with various objects to produce business process events.

The methodologies and AI models vary widely, from clicks of the mouse for specific reasons to opening files, papers, web pages, and so forth. All of this necessitates various information transformation techniques. The automated procedure using AI models is intended to increase the effectiveness of commercial procedures. Salim Benadel, Director at Storm Internet

Robotic Process Automation, or RPA. 

An emerging tech trend that will start becoming more popular is Robotic Process Automation or RPA. It is like AI and machine learning, and it is used for specific types of job automation. Right now, it is primarily used for things like data handling, dealing with transactions, processing/interpreting job applications, and automated email responses. It makes many businesses processes much faster and more efficient, and as time goes on, increased processes will be taken over by RPA. Maria Britton, CEO of Trade Show Labs 

Robotic process automation is an application of artificial intelligence that configures a robot (software application) to interpret, communicate and analyze data. This form of artificial intelligence helps to automate partially or fully manual operations that are repetitive and rule based. Percy Grunwald, Co-Founder of Hosting Data 

Generative AI 

Most individuals say AI is good for automating normal, repetitive work. AI technologies and applications are being developed to replicate creativity, one of the most distinctive human skills. Generative AI algorithms leverage existing data (video, photos, sounds, or computer code) to create new, non-digital material.

Deepfake films and the Metaphysic act on America’s Got Talent have popularized the technology. In 2023, organizations will increasingly employ it to manufacture fake data. Synthetic audio and video data can eliminate the need to record film and speech on video. Simply write what you want the audience to see and hear, and the AI creates it. Leonidas Sfyris 

With the rise of personalization in video games, new content has become increasingly important. Companies are not able to hire enough artists to constantly create new themes for all the different characters so the ability to put in a concept like a cowboy and then the art assets created for all their characters becomes a powerful tool. 

Observability in practice

By delving deeply into contemporary networked systems, Applied Observability facilitates the discovery and resolution of issues more quickly and automatically. Applied observability is a method for keeping tabs on the health of a sophisticated structure by collecting and analyzing data in real time to identify and fix problems as soon as they arise.

Utilize observability for application monitoring and debugging. Telemetry data including logs, metrics, traces, and dependencies are collected by Observability. The data is then correlated in actuality to provide responders with full context for the incidents they’re called to. Automation, machine learning, and artificial intelligence (AIOps) might be used to eliminate the need for human interaction in problem-solving. Jason Wise, Chief Editor at Earthweb 

Natural Language Processing 

As more and more business processes are conducted through digital channels, including social media, e-commerce, customer service, and chatbots, NLP will become increasingly important for understanding user intent and producing the appropriate response.
 

Read more about NLP tasks and techniques in this blog:

Natural Language Processing – Tasks and techniques

In 2023, we can expect to see increased use of Natural Language Processing (NLP) for communication and data analysis. NLP has already seen widespread adoption in customer service chatbots, but it may also be utilized for data analysis, such as extracting information from unstructured texts or analyzing sentiment in large sets of customer reviews. Additionally, deep learning algorithms have already shown great promise in areas such as image recognition and autonomous vehicles.

In the coming years, we can expect to see these algorithms applied to various industries such as healthcare for medical imaging analysis and finance for stock market prediction. Lastly, the integration of AI tools into various industries will continue to bring about both exciting opportunities and ethical considerations. Nicole Pav, AI Expert.  

 Do you know any other AI and Machine Learning trends

Share with us in comments if you know about any other trending or upcoming AI and machine learning.

What can be a better way to spend your days listening to interesting bits about trending AI and Machine learning topics? Here’s a list of the 10 best AI and ML podcasts.

1. The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Artificial intelligence and machine learning are fundamentally altering how organizations run and how individuals live. It is important to discuss the latest innovations in these fields to gain the most benefit from technology. The TWIML AI Podcast outreaches a large and significant audience of ML/AI academics, data scientists, engineers, tech-savvy business, and IT (Information Technology) leaders, as well as the best minds and gather the best concepts from the area of ML and AI.  

The podcast is hosted by a renowned industry analyst, speaker, commentator, and thought leader Sam Charrington. Artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science, and other technologies are discussed. 

2. The AI Podcast

One individual, one interview, one account. This podcast examines the effects of AI on our world. The AI podcast creates a real-time oral history of AI that has amassed 3.4 million listens and has been hailed as one of the best AI and machine learning podcasts. They always bring you a new story and a new 25-minute interview every two weeks. Consequently, regardless of the difficulties, you are facing in marketing, mathematics, astrophysics, paleo history, or simply trying to discover an automated way to sort out your kid’s growing Lego pile, listen in and get inspired. 

3. Data Skeptic

Data Skeptic launched as a podcast in 2014. Hundreds of interviews and tens of millions of downloads later, we are a widely recognized authoritative source on data science, artificial intelligence, machine learning, and related topics. 

Data Skeptic runs in seasons. By speaking with active scholars and business leaders who are somehow involved in our season’s subject, we probe it. 

We carefully choose each of our visitors using a system internally. Since we do not cooperate with PR firms, we are unable to reply to the daily stream of unsolicited submissions. Publishing quality research to the arxiv is the greatest approach to getting on the show. It is crawled. We will locate you. 

Data Skeptic is a boutique consulting company in addition to its podcast. Kyle participates directly in each project our team undertakes. Our work primarily focuses on end-to-end machine learning, cloud infrastructure, and algorithmic design. 

The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches. 

Pro-tip: Enroll in the data science boot camp today to learn the basics of the industry

4. Podcast.ai 

Podcast.ai is entirely generated by artificial intelligence. Every week, they explore a new topic in-depth, and listeners can suggest topics or even guests and hosts for future episodes. Whether you are a machine learning enthusiast, just want to hear your favorite topics covered in a new way or even just want to listen to voices from the past brought back to life, this is the podcast for you.

The podcast aims to put incremental advances into a broader context and consider the global implications of developing technology. AI is about to change your world, so pay attention. 

5. The Talking Machines

Talking machines is a podcast hosted by Katherine Gorman and Neil Lawrence. The objective of this show is to bring you clear conversations with experts in the field of machine learning, insightful discussions of industry news, and useful answers to your questions. Machine learning is changing the questions we can ask of the world around us, here we explore how to ask the best questions and what to do with the answers. 

6. Linear Digressions

If you are interested in learning about unusual applications of machine learning and data science. In each episode of linear digressions, your hosts explore machine learning and data science through interesting apps. Ben Jaffe and Katie Malone host the show, they assure themselves to produce the most exciting additions in the industry such as AI-driven medical assistants, open policing data, causal trees, the grammar of graphics and a lot more.  

7. Practical AI: Machine Learning, Data Science

Making artificial intelligence practical, productive, and accessible to everyone. Practical AI is a show in which technology professionals, businesspeople, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs (Generative adversarial networks), MLOps (machine learning operations) (machine learning operations), AIOps, and more).

The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you! 

8. Data Stories

Enrico Bertini and Moritz Stefaner discuss the latest developments in data analytics, visualization, and related topics. The data stories podcast consists of regular new episodes on a range of discussion topics related to data visualization. It shares the importance of data stories in different fields including statistics, finance, medicine, computer science, and a lot more to name. The podcast’s hosts Enrico and Moritz invite industry leaders, experienced professionals, and instructors in data visualization to share the stories and the importance of representation of data visuals into appealing charts and graphs. 

9. The Artificial Intelligence Podcast

The Artificial intelligence podcast is hosted by Dr. Tony Hoang. This podcast talks about the latest innovations in the artificial intelligence and machine learning industry. The recent episode of the podcast discusses text-to-image generator, Robot dog, soft robotics, voice bot options, and a lot more.  

10. Learning Machines 101

Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. The objective of this podcast series is to inform students and instructors about the advanced technologies introduced by AI and the following: 

•  How do these devices work? 
• Where do they come from? 
• How can we make them even smarter? 
• And how can we make them even more human-like? 

Have we missed any of your favorite podcasts?

 Do not forget to share in comments the names of your most favorite AI and ML podcasts. Read this amazing blog if you want to know about Data Science podcasts.

Most people have heard the terms “data science” and “AI” at least once in their lives. Indeed, both of these are extremely important in the modern world as they are technologies that help us run quite a few of our industries. 

But even though data science and Artificial Intelligence are somewhat related to one another, they are still very different. There are things they have in common which is why they are often used together, but it is crucial to understand their differences as well. 

What is Data Science? 

As the name suggests, data science is a field that involves studying and processing data in big quantities using a variety of technologies and techniques to detect patterns, make conclusions about the data, and help in the decision-making process. Essentially, it is an intersection of statistics and computer science largely used in business and different industries. 

The standard data science lifecycle includes capturing data and then maintaining, processing, and analyzing it before finally communicating conclusions about it through reporting. This makes data science extremely important for analysis, prediction, decision-making, problem-solving, and many other purposes. 

What is Artificial Intelligence? 

Artificial Intelligence is the field that involves the simulation of human intelligence and the processes within it by machines and computer systems. Today, it is used in a wide variety of industries and allows our society to function as it currently does by using different AI-based technologies. 

Some of the most common examples in action include machine learning, speech recognition, and search engine algorithms. While AI technologies are rapidly developing, there is still a lot of room for their growth and improvement. For instance, there is no powerful enough content generation tool that can write texts that are as good as those written by humans. Therefore, it is always preferred to hire an experienced writer to maintain the quality of work.  

What is Machine Learning? 

As mentioned above, machine learning is a type of AI-based technology that uses data to “learn” and improve specific tasks that a machine or system is programmed to perform. Though machine learning is seen as a part of the greater field of AI, its use of data puts it firmly at the intersection of data science and AI. 

Similarities between Data Science and AI 

By far the most important point of connection between data science and Artificial Intelligence is data. Without data, neither of the two fields would exist and the technologies within them would not be used so widely in all kinds of industries. In many cases, data scientists and AI specialists work together to create new technologies or improve old ones and find better ways to handle data. 

As explained earlier, there is a lot of room for improvement when it comes to AI technologies. The same can be somewhat said about data science. That’s one of the reasons businesses still hire professionals to accomplish certain tasks like custom writing requirements, design requirements, and other administrative work.  

Differences between Data Science and AI 

There are quite a few differences between both. These include: 

• Purpose – It aims to analyze data to make conclusions, predictions, and decisions. Artificial Intelligence aims to enable computers and programs to perform complex processes in a similar way to how humans do. 
• Scope – This includes a variety of data-related operations such as data mining, cleansing, reporting, etc. It primarily focuses on machine learning, but there are other technologies involved too such as robotics, neural networks, etc. 
• Application – Both are used in almost every aspect of our lives, but while data science is predominantly present in business, marketing, and advertising, AI is used in automation, transport, manufacturing, and healthcare. 

Examples of Data Science and Artificial Intelligence in use 

To give you an even better idea of what data science and Artificial Intelligence are used for, here are some of the most interesting examples of their application in practice: 

• Analytics – Analyze customers to better understand the target audience and offer the kind of product or service that the audience is looking for. 
• Monitoring – Monitor the social media activity of specific types of users and analyze their behavior. 
• Prediction – Analyze the market and predict demand for specific products or services in the nearest future. 
• Recommendation – Recommend products and services to customers based on their customer profiles, buying behavior, etc. 

• Forecasting – Predict the weather based on a variety of factors and then use these predictions for better decision-making in the agricultural sector. 
• Communication – Provide high-quality customer service and support with the help of chatbots. 
• Automation – Automate processes in all kinds of industries from retail and manufacturing to email marketing and pop-up on-site optimization. 
• Diagnosing – Identify and predict diseases, give correct diagnoses, and personalize healthcare recommendations. 
• Transportation – Use self-driving cars to get where you need to go. Use self-navigating maps to travel. 

• Assistance – Get assistance from smart voice assistants that can schedule appointments, search for information online, make calls, play music, and more. 
• Filtering – Identify spam emails and automatically get them filtered into the spam folder. 
• Cleaning – Get your home cleaned by a smart vacuum cleaner that moves around on its own and cleans the floor for you. 
• Editing – Check texts for plagiarism and proofread and edit them by detecting grammatical, spelling, punctuation, and other linguistic mistakes. 

It is not always easy to tell which of these examples is about data science and which one is about Artificial Intelligence because many of these applications use both of them. This way, it becomes even clearer just how much overlap there is between these two fields and the technologies that come from them. 

What is your choice?

At the end of the day, data science and AI remain some of the most important technologies in our society and will likely help us invent more things and progress further. As a regular citizen, understanding the similarities and differences between the two will help you better understand how data science and Artificial Intelligence are used in almost all spheres of our lives. 

Applications leveraging AI powered search are on the rise. My colleague, Sam Partee, recently introduced vector similarity search (VSS) in Redis and how it can be applied to common use cases. As he puts it:

“Users have come to expect that nearly every application and website provide some type of search functionality. With effective search becoming ever-increasingly relevant (pun intended), finding new methods and architectures to improve search results is critical for architects and developers. “

–  Sam Partee: Vector Similarity Search: from Basics to Production

For example, in eCommerce, allowing shoppers to browse product inventory with a visual similarity component brings online shopping one step closer to mirroring an in-person experience. 

However, this is only the tip of the iceberg. Here, we will pick up right where Sam left off with another common use case for vector similarity: Document Search.

We will cover:

• Common applications of AI-powered document search
• A typical production workflow
• A hosted example using the arXiv papers dataset
• Scaling embedding workflows

Lastly, we will share about an exciting upcoming hackathon co-hosted by Redis, MLOps Community, and Saturn Cloud from October 24 – November 4 that you can join in the coming weeks!

The use case

Whether we realize it or not, we take advantage of document search and processing capabilities in everyday life. We see its impact while searching for a long-lost text message in our phone, automatically filtering spam from our email inbox, and performing basic Google searches.

Businesses use it for information retrieval (e.g. insurance claims, legal documents, financial records), and even generating content-based recommendations (e.g. articles, tweets, posts). 

Beyond lexical search

Traditional search, i.e. lexical search, emphasizes the intersection of common keywords between docs. However, a search query and document may be very similar to one another in meaning and not share any of the same keywords (or vice versa). For example, in the sentences below, all readers should be able to parse that they are communicating the same thing. But – only two words overlap.

“The weather looks dark and stormy outside.” <> “The sky is threatening thunder and lightning.”

Another example…with pure lexical search, “USA” and “United States” would not trigger a match though these are interchangeable terms.

This is where lexical search breaks down on its own. 

Neural search

Search has evolved from simply finding documents to providing answers. Advances in NLP and large language models (GPT-3, BERT, etc) have made it incredibly easy to overcome this lexical gap AND expose semantic properties of text. Sentence embeddings form a condensed vector-like representation of unstructured data that encodes “meaning”.

These embeddings allow us to compute similarity metrics (e.g. cosine similarity, euclidean distance, and inner product) to find similar documents, i.e. neural (or vector) search.  Neural search respects word order and understands the broader context beyond the explicit terms used.

Immediately this opens up a host of powerful use cases

• Question & Answering Services
• Intelligent Document Search + Retrieval
• Insurance Claim Fraud Detection

What’s even better is that ready-made models from Hugging Face Transformers can fast-track text-to-embedding transformations. Though, it’s worth noting that many use cases require fine-tuning to ensure quality results: 

Production workflow

In a production software environment, document search must take advantage of a low-latency database that persists all docs and manages a search index that can enable nearest neighbors vector similarity operations between documents.

RediSearch was introduced as a module to extend this functionality over a Redis cluster that is likely already handling web request caching or online ML feature serving (for low-latency model inference).

Below we will highlight the core components of a typical production workflow.

Document processing

In this phase, documents must be gathered, embedded, and stored in the vector database. This process happens up front before any client tries to search and will also consistently run in the background on document updates, deletions, and insertions.

Up front, this might be iteratively done in batches from some data warehouse. Also, it’s common to leverage streaming data structures (e.g., Kafka, Kinesis, or Redis Streams) to orchestrate the pipeline in real time.

Scalable document processing services might take advantage of a high-throughput inference server like NVIDIA’s Triton. Triton enables teams to deploy, run, and scale trained AI models from any standard backend on GPU (or CPU) hardware.

Depending on the source, volume, and variety of data, a number of pre-processing steps will also need to be included in the pipeline (including embedding models to create vectors from text).

Serving

After a client enters a query along with some optional filters (e.g. year, category), the query text is converted into an embedding projected into the same vector space as the pre-processed documents. This allows for discovery of the most relevant documents from the entire corpus.

With the right vector database solution, these searches could be performed over hundreds of millions of documents in 100ms or less.

We recently put this into action and built redis-arXiv-search on top of the arXiv dataset (provided by Kaggle) as a live demo. Under the hood, we’re using Redis Vector Similarity Search, a Dockerized Python FastAPI, and a React Typescript single page app (SPA).

Paper abstracts were converted into embeddings and stored in RediSearch. With this app, we show how you can search over these papers with natural language.

Let’s try an example: “machine learning helps me get healthier”. When you enter this query, the text is sent to a Python server that converts the text to an embedding and performs a vector search. 

As you can see, the top four results are all related to health outcomes and policy. If you try to confuse it with something even more complex like: “jay z and beyonce”, the top results are as follows:

1. Elites, communities and the limited benefits of mentorship in electronic music
2. Can Celebrities Burst Your Bubble?
3. Forbidden triads and Creative Success in Jazz: The Miles Davis Factor
4. Popularity and Centrality in Spotify Networks: Critical transitions in eigenvector centrality

We are pretty certain that the names of these two icons don’t show up verbatim in the paper abstracts… Because of the semantic properties encoded in the sentence embeddings, this application is able to associate “Jay Z” and “Beyonce” with topics like Music, Celebrities, and Spotify. 

Scaling embedding workflows

That was the happy path. Realistically, most production-grade document retrieval systems rely on hundreds of millions or even billions of docs. It’s the price to pay for a system that can actually solve real-world problems over unstructured data.

Beyond scaling the embedded workflows, you’ll also need to have a database with enough horsepower to build the search index in a timely fashion. 

GPU acceleration

In 2022, giving out free computers is the best way to make friends with anybody. Thankfully, our friends at Saturn Cloud have partnered with us to share access to GPU hardware.

They have a solid free tier that gives us access to an NVIDIA T4 with the ability to upgrade for a fee. Recently, Google Colab also announced a new pricing structure, a “Pay As You Go” format, which allows users to have flexibility in exhausting their compute quota over time.

These are both great options when running workloads on your CPU bound laptop or instance won’t cut it. 

What’s even better is that Hugging Face Transformers can take advantage of GPU acceleration out-of-the-box. This can speed up ad-hoc embedding workflows quite a bit. However, for production use cases with massive amounts of data, a single GPU may not cut it. 

Multi-GPU with Dask and cuDF

What if data will not fit into RAM of a single GPU instance, and you need the boost? There are many ways a data engineer might address this issue, but here I will focus on one particular approach leveraging Dask and cuDF.

The RAPIDS team at NVIDIA is dedicated to building open-source tools for executing data science and analytics on GPUs. All of the Python libraries have a comfortable feel to them, empowering engineers to take advantage of powerful hardware under the surface. 

Scaling out workloads on multiple GPUs w/ RAPIDS tooling involves leveraging multi-node Dask clusters and cuDF data frames. Most Pythonista’s are familiar with the popular Pandas data frame library. cuDF, built on Apache Arrow, provides an interface very similar to Pandas, running on a GPU, all without having to know the ins and outs of CUDA development.

In the above workflow, a cuDF data frame of arXiv papers was loaded and partitions were created across a 3 node Dask cluster (with each worker node as an NVIDIA T4). In parallel, a user-defined function was applied to each data frame partition that processed and embedded the text using a Sentence Transformer model.

This approach provided linear scalability with the number of nodes in the Dask cluster. With 3 worker nodes, the total runtime decreased by a factor of 3. 

Even with multi-GPU acceleration, data is mapped to and from machines. It’s heavily dependent on RAM, especially after the large embedding vectors have been created.

A few variations to consider:

• Load and process iterative batches of documents from a source database.
• Programmatically load partitions of data from a source database to several Dask workers for parallel execution.
• Perform streaming updates from the Dask workers directly to the vector database rather than loading embeddings back to single GPU RAM.

Call to action – it’s YOUR turn!

Inspired by the initial work on the arXiv search demo, Redis is officially launching a Vector Search Engineering Lab (Hackathon) co-sponsored by MLOps Community and Saturn Cloud. Read more about it here.

This is the future. Vector search & document retrieval is now more accessible than ever before thanks to open-source tools like Redis, RAPIDS, Hugging Face, Pytorch, Kaggle, and more! Take the opportunity to get ahead of the curve and join in on the action. We’ve made it super simple to get started and acquire (or sharpen) an emerging set of skills.

In the end, you will get to showcase what you’ve built and win $$ prizes.

The hackathon will run from October 24 – November 4 and include folks across the globe, professionals and students alike. Register your team (up to 4 people) today! You don’t want to miss it.

The use of AI in culture raises interesting ethical reflections termed as AI ethics nowadays.  

In 2016, a Rembrandt painting, “The Next Rembrandt”, was designed by a computer and created by a 3D printer, 351 years after the painter’s death.  

The achievement of this artistic prowess becomes possible when 346 Rembrandt paintings were together analyzed. The keen analysis of paintings pixel by pixel resulted in an upscale of deep learning algorithms to create a unique database.  

Every detail of Rembrandt’s artistic identity could then be captured and set the foundation for an algorithm capable of creating an unprecedented masterpiece. To bring the painting to life, a 3D printer recreated the texture of brushstrokes and layers of paint on the canvas for a breath-taking result that could trick any art expert. 

The ethical dilemma arose when it came to crediting the author of the painting. Who could it be?  

We cannot overlook the transformations brought by intelligent machine systems in today’s world for the better. To name a few, artificial intelligence contributed to optimizing planning, detecting fraud, composing art, conducting research, and providing translations. 

Undoubtedly, it all contributed to the more efficient and consequently richer world of today. Leading global tech companies emphasize adopting boundless landscape of artificial intelligence and step ahead of the competitive market.  

Amidst the boom of overwhelming technological revolutions, we cannot undermine the new frontier for ethics and risk assessment.  

Regardless of the risks AI offers, there are many real-world problems that are begging to be solved by data scientists. Check out this informative session by Raja Iqbal (Founder and lead instructor at Data Science Dojo) on AI For Social Good 

Some of the key ethical issues in AI you must learn about are: 

1. Privacy & surveillance – Is your sensitive information secured?

Access to personal identifiable information must only be accessible for the authorized users only. The other key aspects of privacy to consider in artificial intelligence are information privacy, privacy as an aspect of personhood, control over information about oneself, and the right to secrecy. 

Business today is going digital. We are associated with the digital sphere. Most digital data available online connects to a single Internet. There is increasingly more sensor technology in use that generates data about non-digital aspects of our lives. AI not only contributes to data collection but also drives possibilities for data analysis.  

Much of the most privacy-sensitive data analysis today–such as search algorithms, recommendation engines, and AdTech networks–are driven by machine learning and decisions by algorithms. However, as artificial intelligence evolves, it defines ways to intrude privacy interests of users.

For instance, facial recognition introduces privacy issues with the increased use of digital photographs. Machine recognition of faces has progressed rapidly from fuzzy images to rapid recognition of individual humans.  

2. Manipulation of behavior – How does the internet know our preferences?

Usage of internet and online activities keep us engaged every day. We do not realize that our data is constantly collected, and information is tracked. Our personal data is used to manipulate our behavior online and offline as well.  

If you are thinking about exactly when businesses make use of the information gathered and how they manipulate us, then marketers and advertisers are the best examples. To sell the right product to the right customer, it is significant to know the behavior of your customer.

Their interests, past purchase history, location, and other key demographics. Therefore, advertisers retrieve the personal information of potential customers that is available online. 

Social media has become the hub of manipulating user behaviors by marketers to maximize profits. AI with its advanced social media algorithms identifies vulnerabilities in human behavior and influences our decision-making process. 

 Artificial intelligence integrates such algorithms with digital media that exploit human biases detected by AI algorithms. It implies personalized addictive strategies for consumption of (online) goods or benefits from the vulnerable state of individuals to promote products and services that match well with their temporary emotions. 

3. Opacity of AI systems – Complexed AI processes

Danaher stated, “we are creating decision-making processes that constrain and limit opportunities for human participation” 

Artificial Intelligence supports automated decision-making, thus neglecting the free will of personnel to speak of their choice. AI processes work in a way that no one knows how the output is generated. Therefore, the decision will remain opaque even for the experts  

AI systems use machine learning techniques in neural networks to retrieve patterns from a given dataset. With or without “correct” solutions provided, i.e., supervised, semi-supervised or unsupervised.

Read this blog to learn more about AI powered document search

 

Machine learning captures existing patterns in the data with the help of these techniques. And then label these patterns in such a way that it gets useful for the decision the system makes, while the programmer does not really know which patterns in the data the system has used. 

4. Human-robot interaction – Are robots more capable than us?

As AI is now widely used to manipulate human behavior, it is also actively driving robots. It can get problematic if their processes or appearance involve deception or threatening human dignity 

The key ethical issue here is, “Should robots be programmed to deceive us?” If we answer this question with a yes, then the next question to ask is “what should be the limits of deception?” If we say that robots can deceive us if it does not seriously harm us, then the robot might lie about its abilities or pretend to have more knowledge than it has.  

If we believe that robots should not be programmed to deceive humans, then the next ethical question becomes “should robots be programmed to lie at all?” The answer would depend on what kind of information they are giving and whether humans are able to provide an alternative source.  

Robots are now being deployed in the workplace to do jobs that are dangerous, difficult, or dirty. The automation of jobs is inevitable in the future, and it can be seen as a benefit to society or a problem that needs to be solved. The problem arises when we start talking about human robot interaction and how robots should behave around humans in the workplace. 

5. Autonomous systems – AI gaining self-sufficiency

An autonomous system can be defined as a self-governing or self-acting entity that operates without external control. It can also be defined as a system that can make its own decisions based on its programming and environment. 

The next step in understanding the ethical implications of AI is to analyze how it affects society, humans, and our economy. This will allow us to predict the future of AI and what kind of impact it will have on society if left unchecked. 

In societies where AI is rapidly replacing humans can get harmed or suffer in the longer run. For instance, thinking of AI writers as a replacement for human copywriters when it is just designed to bring efficiency to a writer’s job, provide assistance, and help in getting rid of writer’s block while generating content ideas at scale.  

Secondly, autonomous vehicles are the most relevant examples for a heated debate topic of ethical issues in AI. It is not yet clear what the future of autonomous vehicles will be. The main ethical concern around autonomous cars is that they could cause accidents and fatalities. 

Some people believe that because these cars are programmed to be safe, they should be given priority on the road. Others think that these vehicles should have the same rules as human drivers. 

Enroll in Data Science Bootcamp today to learn advanced technological revolutions 

6. Machine ethics – Can we infuse good behavior in machines?

Before we get into the ethical issues associated with machines, we need to know that machine ethics is not about humans using machines. But it is solely related to the machines operating independently as subjects. 

The topic of machine ethics is a broad and complex one that includes a few areas of inquiry. It touches on the nature of what it means for something to be intelligent, the capacity for artificial intelligence to perform tasks that would otherwise require human intelligence, the moral status of artificially intelligent agents, and more. 

Read this blog to learn about Big Data Ethics

The field is still in its infancy, but it has already shown promise in helping us understand how we should deal with certain moral dilemmas. 

In the past few years, there has been a lot of research on how to make AI more ethical. But how can we define ethics for machines? 

AI programmed machines with rules for good behavior and to avoid making bad decisions based on the principles. It is not difficult to imagine that in the future, we will be able to tell if an AI has ethical values by observing its behavior and its decision-making process. 

Three laws of robotics by Isaac for machine ethics are: 

First Law—A robot may not injure a human being or, through inaction, allow a human being to come to harm.  

Second Law—A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.  

Third Law—A robot must protect its own existence if such protection does not conflict with the First or Second Laws. 

Artificial Moral Agents 

The development of artificial moral agents (AMA) is a hot topic in the AI space. The AMA has been designed to be a moral agent that can make moral decisions and act according to these decisions. As such, it has the potential to have significant impacts on human lives. 

The development of AMA is not without ethical issues. The first issue is that AMAs (Artificial Moral Agents) will have to be programmed with some form of morality system which could be based on human values or principles from other sources.  

This means that there are many possibilities for diverse types of AMAs and several types of morality systems, which may lead to disagreements about what an AMA should do in each situation. Secondly, we need to consider how and when these AMAs should be used as they could cause significant harm if they are not used properly 

Closing on AI ethics 

Over the years, we went from, “AI is impossible” (Dreyfus 1972) and “AI is just automation” (Lighthill 1973) to “AI will solve all problems” (Kurzweil 1999) and “AI may kill us all” (Bostrom 2014). 

Several questions arise with the increasing dependency on AI and robotics. Before we rely on these systems further, we must have clarity about what the systems themselves should do, and what risks they have in the long term.  

Let us know in the comments if you also think it also challenges the human view of humanity as the intelligent and dominant species on Earth.  

There is so much to explore when it comes to spatial visualization using Python’s Folium library.

Spatial visualization

For problems related to crime mapping, housing prices or travel route optimization, spatial visualization could be the most resourceful tool in getting a glimpse of how the instances are geographically located. This is beneficial as we are getting massive amounts of data from several sources such as cellphones, smartwatches, trackers, etc. In this case, patterns and correlations, which otherwise might go unrecognized, can be extracted visually.

This blog will attempt to show you the potential of spatial visualization using the Folium library with Python. This tutorial will give you insights into the most important visualization tools that are extremely useful while analyzing spatial data.

Introduction to folium

Folium is an incredible library that allows you to build Leaflet maps. Using latitude and longitude points, Folium can allow you to create a map of any location in the world. Furthermore, Folium creates interactive maps that may allow you to zoom in and out after the map is rendered.

We’ll get some hands-on practice with building a few maps using the Seattle Real-time Fire 911 calls dataset. This dataset provides Seattle Fire Department 911 dispatches, and every instance of this dataset provides information about the address, location, date/time and type of emergency of a particular incident. It’s extensive and we’ll limit the dataset to a few emergency types for the purpose of explanation.

Let’s begin

Folium can be downloaded using the following commands.

Using pip:

$ pip install folium

Using conda:

$ conda install -c conda-forge folium

Start by importing the required libraries.

import pandas as pd
import numpy as np
import folium

Let us now create an object named ‘seattle_map’ which is defined as a folium.Map object. We can add other folium objects on top of the folium.Map to improve the map rendered. The map has been centered to the longitude and latitude points in the location parameters. The zoom parameter sets the magnification level for the map that’s going to be rendered. Moreover, we have also set the tiles parameter to ‘OpenStreetMap’ which is the default tile for this parameter. You can explore more tiles such as StamenTerrain or Mapbox Control in Folium‘s documentation.

seattle_map = folium. Map
(location = [47.6062, -122.3321],
tiles = 'OpenStreetMap',
 zoom_start = 11)
seattle_map

We can observe the map rendered above. Let’s create another map object with a different tile and zoom_level. Through ‘Stamen Terrain’ tile, we can visualize the terrain data which can be used for several important applications.

We’ve also inserted a folium. Marker to our ‘seattle_map2’ map object below. The marker can be placed to any location specified in the square brackets. The string mentioned in the popup parameter will be displayed once the marker is clicked as shown below.

seattle_map2 = folium. Map
(location=[47.6062, -122.3321],
    tiles = 'Stamen Terrain',
    zoom_start = 10)
#inserting marker
folium.Marker(
    [47.6740, -122.1215],
    popup = 'Redmond'
).add_to(seattle_map2)
seattle_map2

We are interested to use the Seattle 911 calls dataset to visualize the 911 calls in the year 2019 only. We are also limiting the emergency types to 3 specific emergencies that took place during this time.

We will now import our dataset which is available through this link (in CSV format). The dataset is huge, therefore, we’ll only import the first 10,000 rows using pandas read_csv method. We’ll use the head method to display the first 5 rows.

(This process will take some time because the data-set is huge. Alternatively, you can download it to your local machine and then insert the file path below)

path = "https://data.seattle.gov/api/views/kzjm-xkqj/rows.csv?accessType=DOWNLOAD"
seattle911 = pd.read_csv(path, nrows = 10000)
seattle911.head()

Using the code below, we’ll convert the datatype of our Datetime variable to Date-time format and extract the year, removing all other instances that occurred before 2019.

seattle911['Datetime'] = pd.to_datetime(seattle911['Datetime'], 
                                        format='%m/%d/%Y %H:%M', utc=True)
seattle911['Year'] = pd.DatetimeIndex(seattle911['Datetime']).year
seattle911 = seattle911[seattle911.Year == 2019]

We’ll now limit the Emergency type to ‘Aid Response Yellow’, ‘Auto Fire Alarm’ and ‘MVI – Motor Vehicle Incident’. The remaining instances will be removed from the ‘seattle911’ dataframe.

seattle911 = seattle911[seattle911.Type.isin(['Aid Response Yellow', 
                                              'Auto Fire Alarm', 
                                              'MVI - Motor Vehicle Incident'])]

We’ll remove any instance that has a missing longitude or latitude coordinate. Without these values, the particular instance cannot be visualized and will cause an error while rendering.

#drop rows with missing latitude/longitude values
seattle911.dropna(subset = ['Longitude', 'Latitude'], inplace = True)

seattle911.head()

Now let’s step towards the most interesting part. We’ll map all the instances onto the map object we created above, ‘seattle_map’. Using the code below, we’ll loop over all our instances up to the length of the dataframe. Following this, we will create a folium.CircleMarker (which is similar to the folium.Marker we added above). We’ll assign the latitude and longitude coordinates to the location parameter for each instance. The radius of the circle has been assigned to 3, whereas the popup will display the address of the particular instance.

As you can notice, the color of the circle depends on the emergency type. We will now render our map.

for i in range(len(seattle911)):

    folium.CircleMarker( location = [seattle911.Latitude.iloc[i], seattle911.Longitude.iloc[i]],
        radius = 3,
        popup = seattle911.Address.iloc[i],
        color = '#3186cc' if seattle911.Type.iloc[i] == 'Aid Response Yellow' else '#6ccc31' 
        if seattle911.Type.iloc[i] =='Auto Fire Alarm' else '#ac31cc',).add_to(seattle_map) 
seattle_map

Voila! The map above gives us insights about where and what emergency took place across Seattle during 2019. This can be extremely helpful for the local government to more efficiently place its emergency combating resources.

Advanced features provided by folium

Let us now move towards slightly advanced features provided by Folium. For this, we will use the National Obesity by State dataset which is also hosted on data.gov. There are 2 types of files we’ll be using, a csv file containing the list of all states and the percentage of obesity in each state, and a geojson file (based on JSON) that contains geographical features in form of polygons.

Before using our dataset, we’ll create a new folium.map object with location parameters including coordinates to center the US on the map, whereas, we’ve set the ‘zoom_start’ level to 4 to visualize all the states.

usa_map = folium.Map(
    location=[37.0902, -95.7129],
    tiles = 'Mapbox Bright',
    zoom_start = 4)
usa_map

We will assign the URLs of our datasets to ‘obesity_link’ and ‘state_boundaries’ variables, respectively.

obesity_link = 'http://data-lakecountyil.opendata.arcgis.com/datasets/3e0c1eb04e5c48b3be9040b0589d3ccf_8.csv'
state_boundaries = 'http://data-lakecountyil.opendata.arcgis.com/datasets/3e0c1eb04e5c48b3be9040b0589d3ccf_8.geojson'

We will use the ‘state_boundaries’ file to visualize the boundaries and areas covered by each state on our folium.Map object. This is an overlay on our original map and similarly, we can visualize multiple layers on the same map. This overlay will assist us in creating our choropleth map that is discussed ahead.

folium.GeoJson(state_boundaries).add_to(usa_map)
usa_map

The ‘obesity_data’ dataframe can be viewed below. It contains 5 variables. However, for the purpose of this demonstration, we are only concerned with the ‘NAME’ and ‘Obesity’ attributes.

obesity_data = pd.read_csv(obesity_link)
obesity_data.head()

Choropleth map

Now comes the most interesting part! Creating a choropleth map. We’ll bind the ‘obesity_data’ data frame with our ‘state_boundaries’ geojson file. We have assigned both the data files to our variables ‘data’ and ‘geo_data’ respectively. The columns parameter indicates which DataFrame columns to use, whereas, the key_on parameter indicates the layer in the GeoJSON on which to key the data.

We have additionally specified several other parameters that will define the color scheme we’re going to use. Colors are generated from Color Brewer’s sequential palettes.

By default, linear binning is used between the min and the max of the values. Custom binning can be achieved with the bins parameter.

folium. Choropleth( geo_data = state_boundaries,
    name = 'choropleth',
    data = obesity_data,
    columns = ['NAME', 'Obesity'],
    key_on = 'feature.properties.NAME',
    fill_color = 'YlOrRd',
    fill_opacity = 0.9,
    line_opacity = 0.5,
    legend_name = 'Obesity Percentage').add_to(usa_map)
folium.LayerControl().add_to(usa_map)
usa_map

You might now feel powerful enough after attaining the skill to visualize spatial data effectively. Go ahead and explore Folium‘s documentation to discover the incredible capabilities that this open-source library has to offer.

Thanks for reading! If you want more datasets to play with, check out this blog post. It consists of 30 free datasets with questions for you to solve.

References:

• Python Visualization Modules
• Python Visualization Quickstart
• Seattle Real time Fire Dataset
• https://catalog.data.gov/dataset/national-obesity-by-state
• National Obesity Dataset

Raja Iqbal, Chief Data Scientist and CEO of Data Science Dojo, held a community talk on AI for Social Good. Let’s look at some key takeaways.

This discussion took place on January 30th in Austin, Texas.  Below, you will find the event abstract and my key takeaways from the talk.I’ve also included the video at the bottom of the page.

Event abstract

“It’s not hard to see machine learning and artificial intelligence in nearly every app we use – from any website we visit, to any mobile device we carry, to any goods or services we use. Where there are commercial applications, data scientists are all over it. What we don’t typically see, however, is how AI could be used for social good to tackle real-world issues such as poverty, social and environmental sustainability, access to healthcare and basic needs, and more.

What if we pulled together a group of data scientists working on cutting-edge commercial apps and used their minds to solve some of the world’s most difficult social challenges? How much of a difference could one data scientist make let alone many?

In this discussion, Raja Iqbal, Chief Data Scientist and CEO of Data Science Dojo, will walk you through the different social applications of AI and how many real-world problems are begging to be solved by data scientists.  You will see how some organizations have made a start on tackling some of the biggest problems to date, the kinds of data and approaches they used, and the benefit these applications have had on thousands of people’s lives. You’ll learn where there’s untapped opportunity in using AI to make impactful change, sparking ideas for your next big project.”

1. We all have a social responsibility to build models that don’t hurt society or people

2. Data scientists don’t always work with commercial applications

• Criminal Justice – Can we build a model that predicts if a person will commit a crime in the future?
• Education – Machine Learning is being used to predict student churn at universities to identify potential dropouts and intervene before it happens.
• Personalized Care – Better diagnosis with personalized health care plans

3. You don’t always realize if you’re creating more harm than good.

“You always ask yourself whether you could do something, but you never asked yourself whether you should do something.”

4. We are still figuring out how to protect society from all the data being gathered by corporations.

5. There is not a better time for data analysis than today. APIs and SKs are easy to use. IT services and data storage are significantly cheaper than 20 years ago, and costs keep decreasing.

6. Laws/Ethics are still being considered for AI and data use. Individuals, researchers, and lawmakers are still trying to work out the kinks. Here are a few situations with legal and ethical dilemmas to consider:

• Granting parole using predictive models
• Detecting disease
• Military strikes
• Availability of data implying consent
• Self-driving car incidents

7. In each stage of data processing there are possible issues that arise. Everyone has inherent bias in their thinking process which effects the objectivity of data.

8. Modeler’s Hippocratic Oath

• I will remember that I didn’t make the world and it doesn’t satisfy my equations.
• Though I will use models boldly to estimate value, I will not be overly impressed by mathematics.
• I will never sacrifice reality for elegance without explaining why I have done so.
• I will not give the people who use my model false comfort about accuracy. Instead, I will make explicit its assumptions and oversights.
• I understand that my work may have an enormous impact on society and the economy, many of them beyond my comprehension.
• I will aim to show how my analysis makes life better or more efficient.

Highlights of AI for social good

US-AI vs China-AI – What does the race for AI mean for data science worldwide? Why is it getting a lot of attention these days?

Although it may still be recovering from the effects of the government shutdown, data science has received a lot of positive attention from the United States Government. Two major recent milestones include the OPEN Government Data Act, which passed in January as part of the Foundations for Evidence-Based Policymaking Act, and the American AI Initiative, which was signed as an executive order on February 11th.

The future of data science and AI

The first thing to consider is why and more specifically the US administration has passed these recent measures. Although it’s not mentioned in either of the documents, any political correspondent who has been following these topics could easily explain that they are intended to stake a claim against China.

China has stated its intention to become the world leader in data science and AI by 2030. And with far more government access, data sets (a benefit of China being a surveillance state), and an estimated $15 billion in machine learning, they seem to be well on their way. In contrast, the US has only $1.1 billion budgeted annually for machine learning.

So rather than compete with the Chinese government directly, the US appears to have taken the approach of convincing the rest of the world to follow their lead, and not China’s. They especially want to direct this message to the top data science companies and researchers in the world (especially Google) to keep their interest in American projects.

So, what do these measures do?

On the surface, both the OPEN Government Data Act and the American AI Initiative strongly encourage government agencies to amp up their data science efforts. The former is somewhat self-explanatory in name, as it requires agencies to publish more machine-readable publicly available data and requires more use of this data in improved decision making. It imposes a few minimal standards for this and also establishes the position of Chief Data Officers at federal agencies. The latter is somewhat similar in that it orders government agencies to re-evaluate and designate more of their existing time and budgets towards AI use and development, also for better decision making.

Critics are quick to point out that the American AI Initiative does not allocate more resources for its intended purpose, nor does either measure directly impose incentives or penalties. This is not much of a surprise given the general trend of cuts to science funding under the Trump administration. Thus, the likelihood that government agencies will follow through with what these laws ‘require’ has been given skeptical estimations.

However, this is where it becomes important to remember the overall strategy of the current US administration. Both documents include copious amounts of values and standards that the US wants to uphold when it comes to data, machine learning, and artificial intelligence. These may be the key aspects that can hold up against China, having a government that receives a hefty share of international criticism for its use of surveillance and censorship. (Again, this has been a major sticking point for companies like Google.)

These are some of the major priorities brought forth in both measures: Make federal resources, especially data and algorithms, available to all data scientists and researchers; Prepare the workforce for technology changes like AI and optimization; Work internationally towards AI goals while maintaining American values; and finally, Create regulatory standards, to protect security and civil liberties in the use of data science.

So there you have it. Both countries are undeniably powerhouses for data science. China may have the numbers in its favor, but the US would like the world to know that they have an American spirit.

Not working for both? –  US-AI vs China-AI

In short, the phrase “a rising tide lifts all ships” seems to fit here. While the US and China compete for data science dominance at the government level, everyone else can stand atop this growing body of innovations and make their own.

The thing data scientists can get excited about in the short term is the release of a lot of new data from US federal sources or the re-release of such data in machine-readable formats. The emphasis is on the public part – meaning that anyone, not just US federal employees or even citizens, can use this data. To briefly explain for those less experienced in the realm of machine learning and AI, having as much data to work with as possible helps scientists to train and test programs for more accurate predictions.

A lot of what made the government shutdown a dark period for data scientists suggests the possibility of a golden age shortly.

Learn about the different types of AI as a Service, including examples from the top three leading cloud providers – Azure, AWS, and GCP.

Artificial Intelligence as a Service (AIaaS) is an AI offering that you can use to incorporate AI functionality without in-house expertise. It enables organizations and teams to benefit from AI capabilities with less risk and investment than would otherwise be required.

Types of AI as a service

Multiple types of AIaaS are currently available. The most common types include:

• Cognitive computing APIs—APIs enable developers to incorporate AI services into applications with API calls. Popular services include natural language processing (NLP), knowledge mapping, computer vision, intelligent searching, and translation.
• Machine learning (ML) frameworks—frameworks enable developers to quickly develop ML models without big data. This allows organizations to build custom models appropriate for smaller amounts of data.
• Fully-managed ML services—fully-managed services can provide pre-built models, custom templates, and code-free interfaces. These services increase the accessibility of ML capabilities to non-technology organizations and enterprises that don’t want to invest in the in-house development of tools.
• Bots and digital assistance—including chatbots, digital assistants, and automated email services. These tools are popular for customer service and marketing and are currently the most popular type of AIaaS.

Why AI as a Service can be transformational for Artificial Intelligence projects

In addition to being a sign of how far AI has advanced in recent years, AIaaS has several wider implications for AI projects and technologies. A few exciting ways that AIaaS can help transform AI are covered below:

Ecosystem growth

Robust AI development requires a complex system of integrations and support. If teams are only able to use AI development tools on a small range of platforms, advancements take longer to achieve because fewer organizations are working on compatible technologies. However, when vendors offer AIaaS, they help development teams overcome these challenges and speed advances.

Several significant AIaaS vendors have already encouraged growth. For example, AWS in partnership with NVIDIA provides access to GPUs used for AI as a Service. Or, Siemens and SAS, have partnered to include AI-based analytics in Siemens’ Industrial Internet of things (IIoT) software. As these vendors implement AI technologies, they help standardize the environmental support of AI.

Increased accessibility

AI as a Service eliminates much of the expertise and resources that are needed to develop and perform AI computations. This elimination can decrease the overall cost and increase the accessibility of AI for smaller organizations. This increased accessibility can drive innovation since teams that were previously prevented from using advanced AI tools can now compete with larger organizations.

Additionally, when small organizations are better equipped to incorporate AI capabilities, it is more likely to be adopted in previously lacking industries. This opens markets for AI that were previously inaccessible or unappealing and can drive the development of new offerings.

Reduced cost

The natural cost curve of technologies decreases as resources become more widely available and demand increases. As demand increases for AIaaS, vendors can reliably invest to scale up their operations, driving down the cost for consumers. Additionally, as demand increases, hardware and software vendors will compete to produce those resources at a more competitive cost, benefiting AIaaS vendors and traditional AI developers alike.

AI as a Service Platforms

Currently, all three major cloud providers offer some form of AIaaS services:

Microsoft Azure

Azure provides AI capabilities in three different offerings—AI Services, AI Tools and Frameworks, and AI Infrastructure. Microsoft also recently announced that it is going to make the Azure Internet of Things Edge Runtime public. This enables developers to modify and customize applications for edge computing.

AI Services include:

• Cognitive Services—enables users without machine learning expertise to add AI to chatbots and web applications It allows you to easily create high-value services, such as chatbots with the ability to provide personalized content. Services include functionality for decision making, language and speech processing, vision processing, and web search improvements.
• Cognitive Search—adds Cognitive Services capabilities to Azure Search to enable more efficient asset exploration. This includes auto-complete, geospatial search, and optical character recognition (OCR).
• Azure Machine Learning (AML)—supports custom AI development, including the training and deployment of models. AML helps make ML development accessible to all levels of expertise. It enables you to create custom AI to meet your organizational or project needs.

AI Tools & Frameworks include Visual Studio tools, Azure Notebooks, virtual machines optimized for data science, various Azure migration tools, and the AI Toolkit for Azure IoT Edge.

Build a predictive model in Azure.

Amazon Web Services (AWS)

Amazon offers AI capabilities focused on AWS services and its consumer devices, including Alexa. These capabilities overlap significantly since many of AWS’ cloud services are built on the resources used for its consumer devices.

AWS’ primary services include:

• Amazon Lex—a service that enables you to perform speech recognition, convert speech to text, and apply natural language processing to content analysis. It uses the same algorithm currently used in Alexa devices.
• Amazon Polly—a service that enables you to convert text to speech. It uses deep learning capabilities to deliver natural-sounding speech and real-time, interactive “conversation”.
• Amazon Rekognition—a computer vision API that you can use to add image analysis, object detection, and facial recognition to your applications. This service uses the algorithm employed by Amazon to analyze Prime Photos.

Google Cloud

Google has made serious efforts to market Google Cloud as an AI-first option, even rebranding its research division as “Google AI”. They have also invested in acquiring a significant number of AI start-ups, including DeepMind and Onward. All of this is reflected in their various offerings, including:

• AI Hub—a repository of plug-and-play components that you can use to experiment with and incorporate AI into your projects. These components can help you train models, perform data analyses, or leverage AI in services and applications.
• AI building blocks—APIs that you can incorporate into application code to add a range of AI capabilities, including computer vision, NLP, and text-to-speech. It also includes functions for working with structured data and training ML models.
• AI Platform—a development environment that you can use to quickly and easily deploy AI projects. Includes a managed notebooks service, VMs and containers pre-configured for deep learning, and an automated data labeling service.

Conclusion

Cloud computing vendors and third-party service providers continue to extend capabilities into more realms, including AI and machine learning. Today, there are cognitive computing APIs that enable developers to leverage ready-made capabilities like NLP and computer vision. If you are into building your own models, you can use machine learning frameworks to fast-track development.

There are also bots and digital assistants that you can use to automate various services. Some services require configuration, but others are fully managed and come with a variety of licensing. Be sure to check the shared responsibility model offered by your provider, to ensure that you are fully compliant with regulatory requirements.

Artificial intelligence and machine learning are part of our everyday lives. These data science movies are my favorite.

Advanced artificial intelligence (AI) systems, humanoid robots, and machine learning are not just in science fiction movies anymore. We come across this technological advancement in our everyday life. Today our cellphones, cars, TV sets, and even household appliances are using machine learning to improve themselves.

As we advance towards faster connectivity and the possibility of making the Internet of Things (IoT) more common, the idea of machines taking over and controlling humans might sound funny, but there are some challenges that need attention, including ethical and moral dimensions of machines thinking and acting like humans.

Here we are going to talk about some amazing movies that bring to life these moral and ethical aspects of machine learning, artificial intelligence, and the power of data science. These data science movies are a must-watch for any enthusiast willing to learn data science.

List of movies on Data Science

2001: A Space Odyssey (1968)

This classic film by Stanley Kubrick addresses the most interesting possibilities that exist within the field of Artificial Intelligence. Scientists, like always, are misled by their pride when they develop a highly advanced 9000 series of computers. This AI system is programmed into a series of memory banks giving it the ability to solve complex problems and think like humans.

What humans don’t comprehend is that this superior and helpful technology has the ability to turn against them and signal the destruction of mankind. The movie is based on the Discovery One space mission to the planet Jupiter. Most aspects of this mission are controlled by H.A.L the advanced A.I program. H.A.L is portrayed as a humanistic control system with an actual voice and ability to communicate with the crew.

Initially, H.A.L seems to be a friendly advanced computer system, making sure the crew is safe and sound. But as we advance into the storyline, we realize that there is a glitch in this system, and what H.A.L is trying to do is fail the mission and kill the entire human crew. As the lead character, Dave tries to dismantle H.A.L we hear the horrifying words “I’m Sorry Dave.” This phrase has become iconic as it serves as a warning against allowing computers to take control of everything.

Interstellar (2014)

Christopher Nolan’s cinematic success won an Oscar for best visual effects and grossed over $677 million worldwide.  The film is centered around astronauts’ journey to the far reaches of our galaxy to find a suitable planet for life as Earth is slowly dying. The lead character played by Oscar winner Matthew McConaughey, an astronaut and spaceship pilot, along with mission commander Brand and science specialists are heading towards a newly discovered wormhole.

The mission takes the astronauts on a spectacular interstellar journey through time and space, but at the same time they miss out on their own life back at home light years away. On board the spaceship, Endurance is a pair of quadrilateral robots called TARS and CASE. They surprisingly resemble the monoliths from 2001: A Space Odyssey.

TARS is one of the crew members of mission Endurance. TARS’ personality is witty, sarcastic, and humorous, traits programmed into him to make him a suitable companion for its human crew on this decades-long journey.

CASE’s mission is maintenance and operations of the Endurance in the absence of human crew members. CASE’s personality is quiet and reserved as opposed to TARS. TARS and CASE are true embodiments of the progress that human beings have made in AI technology, thus promising us great adventures in the future.

The Imitation Game (2014)

Based on the real-life story of Alan Turing, A.K.A. the father of modern computer science, The Imitation Game is centered around Turing and his team of code-breakers at top secret British Government Code and Cipher School. They’re determined to decipher the Nazi German military code called “Enigma”. Enigma is a key part of the Nazi military strategy to safely transmit important information to its units.

To crack this Enigma, Turing creates a primitive computer system that would consider permutations at a faster rate than any human. This achievement helped Allied forces ensure victory over Nazi German in the second world war. The movie not only portrays the impressive life of Alan Turning but also describes the important process of creating the first ever machine of its kind giving birth to the field of cryptography and cyber security.

The Terminator (1984)

The cult classic, Terminator, starring Arnold Schwarzenegger as a cyborg assassin from the future is the perfect combination of action, sci-fi technology, and personification of machine learning.

The humanistic cyborg is created by Cyberdyne Systems and is known as T-800 model 101. Designed specifically for infiltration and combat and is sent on a mission to kill Sarah Connor before she gives birth to John Connor, who would become the ultimate savior for humanity after the robotic uprising.

In this classic, we get to see advanced artificial intelligence in the works and how it has considered humanity the biggest threat to the world. Bent upon total destruction of the human race, only freedom fighters led by John Connor stand in their way. Therefore, sending The Terminator back in time to alter their future is the top priority.

Blade Runner 2049 (2017)

The sequel to the 1982 original Blade runner has impressive visuals capturing the audience’s attention throughout the film. The story is about bio-engineered humans known as “Replicants” after the uprising of 2022 they are being hunted down by LAPD Blade Runner. Blade Runner is an officer who hunts and retires (kills) rogue replicants. Ryan Gosling stars as “K” hunting down replicants who are considered a threat to the world. Every decision he makes is based on analysis. The films explore the relationships and emotions of artificially intelligent beings and raise moral questions regarding freedom to live and the life of self-aware technology.

I, Robot (2004)

Will Smith stars as Chicago policeman Del Spooner in the year 2035. He is highly suspicious of the A.I technology, data science, and robots are being used as household helpers. One of these mass-produced robots (cueing in the data science / AI angle), named Sonny, goes rogue and is held responsible for the death of its owner. Its owner falls from a window on the 15^th floor. Del investigates this murder and discovers a larger threat to humanity by Artificial Intelligence. As the investigation continues, there are multiple murder attempts on Del but he manages to barely escape with his life. The police detective continues to unravel mysterious threats from the A.I technology and tries to stop the mass uprising.

Minority Report (2002)

Minority Report and data science? That is correct! It is a 2002 action thriller directed by Steven Spielberg and starring Tom Cruise. The most common use of data science is using current data to infer new information, but here data are being used to predict crime predispositions. A group of humans gifted with psychic abilities (PreCogs) provide the Washington police force with information about crimes before they are committed.

Using visual data and other information by PreCogs, it is up to the PreCrime police unit to use data to explore the finer details of a crime in order to prevent it. However, things take a turn for the worst when one-day PreCogs predict John Anderson one of their own, is going to commit murder. To prove his innocence, he goes on a mission to find the “Minority Report” which is the prediction of the PreCog Agatha that might tell a different story and prove John’s innocence.

Her (2013)

Her (2013) is a Spike Jones science fiction film starring Joaquin Phoenix as Theodore Twombly, a lonely and depressed writer. He is going through a divorce at the time and, to make things easier, purchases an advanced operating system with an A.I. virtual assistant designed to adapt and evolve. The virtual assistant names itself Samantha. Theodore is amazed at the operating system’s ability to emotionally connect with him. Samantha uses its highly advanced intelligence system to help with every one of Theodore’s needs, but now he’s facing an inner conflict of being in love with a machine.

Ex-Machina (2014)

The story is centered around a 26-year-old programmer, Caleb, who wins a competition to spend a week at a private mountain retreat belonging to the CEO of Blue Book, a search engine company. Soon afterward Caleb realizes he’s participating in an experiment to interact with the world’s first real artificially intelligent robot. In this British science fiction, A.I do not want world domination but simply want the same civil rights as humans.

The Machine (2013)

The Machine is an Indie-British film centered around two artificial intelligence engineers who come together to create the first-ever, self-aware artificial intelligence machines. These machines are created for the Ministry of Defense. The Government’s intention is to create a lethal soldier for war. The cyborg told its designer, “I’m a part of the new world and you’re part of the old.” this chilling statement gives you the idea of what is to come next.

Transcendence (2014)

Transcendence is a story about a brilliant researcher in the field of Artificial Intelligence, Dr. Will Caster, played by Johnny Depp. He’s working on a project to create a conscious machine that combines the collective intelligence of everything along with the full range of human emotions. Dr. Caster has gained fame due to his ambitious project and controversial experiments. He’s also become a target for anti-technology extremists who is willing to do anything to stop him.

However, Dr. Caster becomes more determined to accomplish his ambitious goals and achieve the ultimate power. His wife Evelyn and best friend Max are concerned with Will’s unstoppable appetite for knowledge which is evolving into a terrifying quest for power.

A.I. ARTIFICIAL INTELLIGENCE (2001)

A.I Artificial Intelligence is a science fiction drama directed by Steven Spielberg. The story takes us to the not-so-distant future where ocean waters are rising due to global warming and most coastal cities are flooded. Humans move to the interior of the continents and keep advancing their technology. One of the newest creations is realistic robots known as “Mechas”. Mechas are humanoid robots, very complex but lack emotions.

This changes when David, a prototype Mecha child capable of experiencing love, is developed. He is given to Henry and his wife Monica, whose son contracted a rare disease and has been placed in cryostasis. David is providing all the love and support for his new family, but things get complicated when Monica’s real son returns home after a cure is discovered. The film explores every possible emotional interaction humans could have with an emotionally capable A.I. technology.

Moneyball (2011)

Billy Beane, played by Brad Pitt, and his assistant, Peter Brand (Jonah Hill), are faced with the challenge of building a winning team for the Major League Baseball’s Oakland Athletics’ 2002 season with a limited budget. To overcome this challenge Billy uses Brand’s computer-generated statistical analysis to analyze and score players’ potential and assemble a highly competitive team. Using historical data and predictive modeling they manage to create a playoff-bound MLB team with a limited budget.

Margin Call (2011)

The 2011 American drama film written and directed by J.C. Chandor is based on the events of the 2007-08 global financial crises. The story takes place over a 24-hour period at a large Wall Street investment bank. One of the junior risk analysts discovers a major flaw in the risk models which has led their firm to invest in the wrong things, winding up at the brink of financial disaster.

A seemingly simple error is in fact affecting millions of lives. This is not only limited to the financial world. An economic crisis like this caused by flawed behavior between humans and machines can have trickle-down effects on ordinary people. Technology doesn’t exist in a bubble, it affects everyone around it and spreads exponentially. Margin Call explores the impact of technology and data science on our lives.

21 (2008)

Ben Campbell, a mathematics student at MIT, is accepted at the prestigious Harvard Medical School but he’s unable to afford the $300,000 tuition. One of his professors at MIT, Micky Rosa (Kevin Spacey), asks him to join his blackjack team consisting of five other fellow students. Ben accepts the offer to win enough cash to pay his Harvard tuition. They fly to Las Vegas over the weekend to win millions of dollars using numbers, codes, and hand signals. This movie gives insights into Newton’s method and Fibonacci numbers from the perspective of six brilliant students and their professor.

Thanks for reading we hope you will enjoy our recommendations on data science-based movies. Also, check out the 18 Best Data Science Podcasts.

Want to learn more about AI, Machine Learning, and Data Science? Check out Data Science Dojo’s online Data Science Bootcamp program!

Artificial Intelligence (AI) has added ease to the job of content creators and webmasters. It has wonder us by introducing different inventions for work. Here you will learn how it is helping webmasters and content creators!

Technology has worked wonders for us. From using the earliest generation of computers with the capability of basic calculation to the era of digitization, where everything is digital, the world has changed quite swiftly. How did this happen?

The obvious answer would be “advancement in technology.” However, when you dig deep, the answer “advancement in technology” won’t be substantial. Another question may arise, “how advanced technology made possible and how has it changed the entire landscape?”. The answer to this particular question is the development of advanced algorithms that are capable of solving bigger problems.

These advanced algorithms are developed on the basis of Artificial Intelligence. Although, advanced technologies are often pronounced together and, in some situations, work in tandem with each other.

However, we will keep our focus only on Artificial Intelligence in this writing. You will find several definitions of Artificial Intelligence, but a simple definition of AI will be the ability of machines to work on their own without any input from mankind. This technology has revolutionized the landscape of technology and made the jobs of many people easier.

Content creators and webmasters around the world are also among those people. This writing is mainly focused on the topic of how it is helping content creators and webmasters to make their jobs easier. We put together a massive amount of details to help you understand the said topic.

1. Focused content

Content creators and webmasters around the world want to serve their audience with the type of content they want. The worldwide audience also tends to appreciate the type of content that is capable of answering their questions and resolving their confusion.

This is where AI-backed tools can help webmasters and content creators to get ideas about the content their audience needs. For instance, AI-backed tools will come up with high-ranking queries and keywords searched on google regarding a specific niche or topic, and content creators can articulate content accordingly. Webmasters will also publish the content on their website after getting ideas about the choice of their audience.

2. Easy and quick plagiarism check with AI

The topmost concern of any content creator or webmaster will be the articulation of plagiarism-free content. Just a couple of decades earlier, it was quite problematic and laborious for content creators and webmasters to spot plagiarism in a given content. They had to dig a massive amount of content for this purpose.

This entire task of content validation took a huge amount of effort and time; besides, it was tiresome as well. However, it is not a difficult task these days. Whether you are a webmaster or a content creator, you can simply check plagiarism by pasting the content or its URL on an online plagiarism detector. Once you paste the content, you will get the plagiarism report in a matter of seconds.

It is because of this technology, that this laborious task became so easy and quick. The algorithms of the plagiarism checkers are based on this technology. This technology works on its own to understand the meaning of content given by the user and then find similar content, even if it is in a different language.

Not only that but the AI-backed algorithm of such a tool can also check patch plagiarism (the practice of changing a few words in a phrase). This whole process of finding plagiarism is easy because it enables webmasters and content creators to mold or rephrase content to avoid penalties imposed by search engines.

3. AI reduced the effort of paraphrasing

As mentioned earlier, an effective option to remove plagiarism from content is rephrasing or rewriting. However, in the fast-paced business environment, content creators and webmasters don’t have substantial time to rewrite or rephrase the plagiarized content.

Now a question like “what is the easiest method of paraphrasing plagiarized content?” may strike your mind. The answer to this question will be using a capable paraphrase tool. Advanced rewriting tools these days make it quite easier for everyone to remove plagiarism from their content.

These tools make use of AI-backed algorithms. These AI-backed algorithms first understand the meaning of the whole writing. Once the task of understanding the content is done, the tool rewrites the entire content by changing words where needed to remove plagiarism from it.

The best thing about this entire process is it happens in a quick time. If you try to do it yourself, it will take plenty of time and effort as well. Using an AI-backed paraphrasing tool will allow you to rewrite an article, business copy, blog, or anything else in a few minutes.

4. Searching copied images is far easier with

Another headache for webmasters and content creators is the use of their images by other sources. Not a long time ago, finding images or visuals created by you that are being used by other sources without your consent was difficult. You had to enter relevant queries and various other kinds of methods to find out the culprit.

However, it is quite easier these days, and credit obviously goes to AI. You may ask, “how?”. Well! We have an answer to this question. There are advanced image search methods that make use of machine learning and artificial intelligence to help you find similar images.

Suppose you are a webmaster or a content creator looking for the stolen images published from your end. All you have to do is search by image one by one, and you will get to see similar image results in a matter of seconds.

If you discover that certain sources are utilizing photographs that are your intellectual property without your permission, you can ask them to remove them, give you a backlink, or face the repercussions of copyright laws. This image search solution has made things a lot easier for content creators and webmasters and worried about copied and stolen images. No worries, because AI is here to assist you!

Final words

Artificial intelligence has certainly made a lot of things easier for us. If we focus our lens on the jobs of content creators and webmasters, it is helping them as well. From the creation of content to detecting plagiarism and paraphrasing it to remove plagiarism, it has shown to be quite beneficial to webmasters and content providers. It can also search for stolen or copied images using it. All these factors have made a huge impact on the web content creation industry. We hope it will help them in a number of other ways in the coming days because technology is seeing advancements rather swiftly.

Explore Google DialogFlow, a conversational AI Platform and use it to build a smart, contextually aware Chatbot.

Chatbots have become extremely popular in recent years and their use in the e-commerce industry has skyrocketed. They have found a strong foothold in almost every task that requires text-based public dealing. They have become so critical with customer support, for example that almost 25% of all customer service operations are expected to use them by the end of 2020.

Building a comprehensive and production-ready chatbot from scratch, however, is an almost impossible task. Tech companies like Google and Amazon have been able to achieve this feat after spending years and billions of dollars in research, something that not everyone with a use for a chatbot can afford.

Luckily, almost every player in the tech market (including Google and Amazon) allows businesses to purchase their technology platforms to design customized chatbots for their own use. These platforms have pre-trained language models and easy-to-use interfaces that make it extremely easy for new users to set up and deploy customized chatbots in no time.

In the previous blogs in our series on chatbots, we talked about how to build AI and rule based chatbots in Python. In this blog, we’ll be taking you through how to build a simple AI chatbot using Google’s DialogFlow:

Intro to Google DialogFlow

DialogFlow is a natural language understanding platform (based on Google’s AI) that makes it easy to design and integrate a conversational user interface into your mobile app, web application, device, bot, interactive voice response system, and so on. Using DialogFlow, you can provide new and engaging ways for users to interact with your product.

Fundamentals of DialogFlow

We’re going to run through some of the basics of DialogFlow just so that you understand the vernacular when we build our chatbot.

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Agents

An Agent is what DialogFlow calls your chatbot. A DialogFlow Agent is a trained generative machine learning model that understands natural language flows and the nuances of human conversations. DialogFlow translates input text during a conversation to structured data that your apps and services can understand.

Intents

Intents are the starting point of a conversation in DialogFlow. When a user starts a conversation with a chatbot, DialogFlow matches the input to the best intent available.

A chatbot can have as many intents as required depending on the level of conversational detail a user wants the bot to have. Each intent has the following parameters:

• Training Phrases: These are examples of phrases your chatbot might receive as inputs. When a user input matches one of the phrases in the intent, that specific intent is called. Since all DialogFlow agents use machine learning, you don’t have to define every possible phrase your users might use. DialogFlow automatically learns and expands this list as users interact with your bot.
• Parameters: These are input variables extracted from a user input when a specific intent is called. For example, a user might say: “I want to schedule a haircut appointment on Saturday.” In this situation, “haircut appointment” and “Saturday” could be the possible parameters DialogFlow would extract from the input. Each parameter has a type, like a data type in normal programming, called an Entity. You need to define what parameters you would be expecting in each intent. Parameters can be set to “required”. If a required parameter is not present in the input, DialogFlow will specifically ask the user for it.
• Responses: These are the responses DialogFlow returns to the users when an Intent is matched. They may provide answers, ask the user for more information or serve as conversation terminators.

Entities

Entities are information types of intent parameters which control how data from an input is extracted. They can be thought of as data types used in programming languages. DialogFlow includes many pre-defined entity types corresponding to common information types such as dates, times, days, colors, email addresses etc.

You can also define custom entity types for information that may be specific to your use case. In the example shared above, the “appointment type” would be an example of a custom entity.

Contexts

DialogFlow uses contexts to keep track of where users are in a conversation. During the flow of a conversation, multiple intents may need to be called. DialogFlow uses contexts to carry a conversation between them. To make an intent follow on from another intent, you would create an output context from the first intent and place the same context in the input context field of the second intent.

In the example shared above, the conversation might have flowed in a different way.

In this specific conversation, the agent is performing 2 different tasks: authentication and booking.

When the user initiates the conversation, the Authentication Intent is called that verifies the user’s membership number. Once that has been verified, the Authentication Intent activates the Authentication Context and the Booking Intent is called.

In this situation, the Booking Intent knows that the user is allowed to book appointments because the Authentication Context is active. You can create and use as many contexts as you want in a conversation for your use case.

Conversation in a DialogFlow hatbot

A conversation with a DialogFlow Agent flows in the following way:

Building a chatbot

In this tutorial, we’ll be building a simple customer services agent for a bank. The chatbot (named BankBot) will be able to:

1. Answer Static Pre-Defined Queries
2. Set up an appointment with a Customer Services Agent

Creating a new DialogFlow agent

It’s extremely easy to get started with DialogFlow. The first thing you’ll need to do is log in to DialogFlow. To do that, go to https://dialogflow.cloud.google.com and login with your Google Account (or create one if you don’t have it).

Once you’re logged in, click on ‘Create Agent’ and give it a name.

1. Answering static defined series

To keep things simple, we’ll be focusing on training BankBot to respond to one static query initially; responding to when a user asks the Bank’s operational timings. For this we will teach BankBot a few phrases that it might receive as inputs and their corresponding responses.

Creating an intent

The first thing we’ll do is create a new Intent. That can be done by clicking on the ‘+’ sign next to the ‘Intents’ tab on the left side panel. This intent will specifically be for answering queries about our bank’s working hours. Once on the ‘Create Intent’ Screen (as shown below), fill in the ‘Intent Name’ field.

Training phrases

Once the intent is created, we need to teach BankBot what phrases to look for. A list of sample phrases needs to be entered under ‘Training Phrases’. We don’t need to enter every possible phrase as BankBot will keep on learning from the inputs it receives thanks to Google’s machine learning.

Responses

After the training phrases, we need to tell BankBot how to respond if this intent is matched. Go ahead and type in your response in the ‘Responses’ field.

DialogFlow allows you to customize your responses based on the platform (Google Assistant, Facebook Messenger, Kik, Slack etc.) you will be deploying your chatbot on.

Once you’re happy with the response, go ahead and save the Intent by clicking on the Save button at the top.

Testing the intent

Once you’ve saved your intent, you can see how its working right within DialogFlow.

To test BankBot, type in any user query in the text box labeled ‘Try it Now’.

2. Setting an appointment

Getting BankBot to set an appointment is mostly the same as answering static queries, with one extra step. To book an appointment, BankBot will need to know the date and time the user wants the appointment for. This can be done by teaching BankBot to extract this information from the user query – or to ask the user for this information in-case it is not provide in the initial query.

Creating an intent

This process will be the same as how we created an intent in the previous example.

Training phrases

This will also be same as in the previous example except for one important difference.  In this situation, there are 3 distinct ways in which the user can structure his initial query:

1. Asking for an appointment without mentioning the date or time in the initial query.
2. Asking for an appointment with just the date mentioned in the initial query.
3. Asking for an appointment with both the date and time mentioned in the initial query.

We’ll need to make sure to add examples of all 3 cases in our Training Phrases.  We don’t need to enter every possible phrase as BankBot will keep on learning from the inputs it receives.

Parameters

BankBot will need additional information (the date and time) to book an appointment for the user. This can be done by defining the date and time as ‘Parameters’ in the Intent.

For every defined parameter, DialogFlow requires the following information:

1. Required: If the parameter is set to ‘Required’, DialogFlow will prompt the user for information if it has not been provided in the original query.
2. Parameter Name: Name of the parameter.
3. Entity: The type of data/information that will be stored in the parameter.
4. Value: The variable name that will be used to reference the value of this parameter in ‘Responses.’
5. Prompts: The response to be used in-case the parameter has not been provided in the original query.

DialogFlow automatically extracts any parameters it finds in user inputs (notice that the time and date information in the training phrases has automatically been color-coded according to the parameters).

Responses

After the training phrases, we need to tell BankBot how to respond if this intent is matched. Go ahead and type in your response in the ‘Responses’ field.

DialogFlow allows you to customize your responses based on the platform (Google Assistant, Facebook Messenger, Kik, Slack etc.) you will be deploying your chatbot on.
Once you’re happy with the response, go ahead and save the Intent by clicking on the Save button at the top.

Testing the intent

Once you’ve saved your intent, you can see how its working right within DialogFlow.

To test BankBot, type in any user query in the text box labeled ‘Try it Now’

Example 1: All parameters present in the initial query.

Example 2: When complete information is not present in the initial query.

Conclusion

DialogFlow has made it exceptionally easy to build extremely functional and fully customizable chatbots with little effort. The purpose of this tutorial was to give you an introduction to building chatbots and to help you get familiar with the foundational concepts of the platform.

Other Conversational AI tools use almost the same concepts as were discussed, so these should be transferable to any platform.