In this blog post, we will explore the potential benefits of generative AI for jobs. We will discuss how it will help to improve productivity, creativity, and problem-solving. We will also discuss how it can create new opportunities for workers.
Generative AI is a type of AI that can create new content, such as text, images, and music. It’s still under development, but it has the potential to revolutionize many industries.
Here’s an example let’s say you’re a writer. You have an idea for a new blog post, but you’re not sure how to get started. With generative AI, you could simply tell the AI what you want to write about, and it would generate a first draft for you. You could then edit and refine the draft until it’s perfect.
Are you scared of Generative AI?
There are a few reasons why people might fear that generative AI will replace them.
First, generative AI is becoming increasingly sophisticated. As technology continues to develop, it is likely that it will be able to perform more and more tasks that are currently performed by humans.
Second, it is becoming more affordable. As technology becomes more widely available, it will be within reach of more businesses. This means that more businesses will be able to automate tasks using AI, which could lead to job losses.
Third, it is not biased in the same way that humans are. This means that artificial intelligence could be more efficient and accurate than humans at performing certain tasks. For example, it could be used to make decisions about lending or hiring that are free from human bias.
Of course, there are also reasons to be optimistic about the future of artificial intelligence. For example, it has the potential to create new jobs. With task automation, we will see new opportunities for people to develop new skills and create new products and services.
How generative AI can improve productivity
Generative AI can help improve productivity in a number of ways. For example, artificial intelligence can be used to automate tasks that are currently performed by humans. This can free up human workers to focus on more creative and strategic tasks.
Those who are able to acquire the skills needed to work with generative AI will be well-positioned for success in the future of work.
In addition to the skills listed above, there are a few other things that people can do to prepare for the future of work in a AI world.
Staying up-to-date on the latest developments in generative AI
Learning how to use AI tools
Developing a portfolio of work that demonstrates their skills
Networking with other people who are working in the field of generative AI
By taking these steps, people can increase their chances of success in the future of work.
Here is an example of how generative AI is going to be involved in our jobs everyday:
Content writer: It will help content writers to create high-quality content more quickly and efficiently. For example, a large language model could be used to generate a first draft of a blog post or article, which the content writer could then edit and refine.
Software engineer: Software engineers will be able to write code more quickly and accurately. For example, a generative AI model could be used to generate a skeleton of a new code function, which the software engineer could then fill in with the specific details.
Customer service representative: It will help customer service representatives answer customer questions more quickly and accurately. For example, a generative AI model could be used to generate a response to a customer question based on a database of previous customer support tickets.
Sales representative: Generative AI can help sales representatives generate personalized sales leads and pitches. For example, an AI model could be used to generate a list of potential customers who are likely to be interested in a particular product or service or to generate a personalized sales pitch for a specific customer.
These are just a few examples of how language models and artificial intelligence is already being used to benefit jobs. As technology continues to develop, we can expect to see even more ways in which generative AI can be used to improve the way we work.
In addition, we will see notable improvement in the efficiency of existing processes. For example, generative AI can be used to optimize supply chains or develop new marketing campaigns.
How generative AI can improve creativity
Generative AI can help you be more creative in a few ways. First, it can generate new ideas for you. Just tell it what you’re working on, and it will spit out a bunch of ideas. You can then use these ideas as a starting point or even just to get your creative juices flowing.
Second, we will be able to create new products and services. For example, if you’re a writer, it can help you come up with new story ideas or plot twists. If you’re a designer, it can help you come up with new product designs or marketing campaigns.
Third, it can help brainstorm and come up with new solutions to problems. Just tell it what problem you’re trying to solve, and it will generate a list of possible solutions. You can then use this list as a starting point to find the best solution to your problem.
How generative AI can help with problem-solving
Generative AI can also help you solve problems in a few ways. First, it can help you identify patterns and make predictions. This can be helpful for identifying and solving problems more quickly and efficiently.
For example, if you’re a scientist, you could identify patterns in your data. This could help you discover new insights or develop new theories. If you’re a business owner, you could predict customer demand or identify new market opportunities.
Second, generative AI can help you generate new solutions to problems. This can be helpful for finding creative and innovative solutions to complex problems.
For example, if you’re a software engineer, you could generate new code snippets or design new algorithms. If you’re a product manager, you could use artificial intelligence to generate new product ideas or to design new user interfaces.
How generative AI can create new opportunities for workers
Generative AI is also creating new opportunities for workers. First, it’s creating new jobs in the fields of data science and programming. Its models need to be trained and maintained, and this requires skilled workers.
Second, a number of workers can start their own businesses. For example, businesses could use it to create new marketing campaigns or to develop new products. This is opening up new opportunities for entrepreneurs.
Are you using Generative AI at your work?
Generative AI has the potential to revolutionize the way we work. By automating tasks, creating new possibilities, and helping workers to be more productive, creative, and problem-solving, large language models can help to create a more efficient and innovative workforce.
Imagine you’re trying to translate a menu from French to English, but you don’t know any French. You could use a traditional machine translation system, but these systems can sometimes be inaccurate and produce translations that are difficult to understand.
A better approach would be to use a large language model (LLM) for translation. LLMs are trained on massive datasets of text and code, which allows them to learn the patterns of human language and generate translations that are more natural-sounding and accurate.
The struggle with accurate translations
In today’s globalized world, the need for accurate and efficient translation technology has never been greater. The need for AI translation tools has increased as businesses are expanding into new markets and people are connecting with individuals from diverse cultures.
However, traditional translation methods have their limitations, often resulting in inaccurate or awkward translations. This is where large language models for translation are revolutionizing the translation industry.
What are large language models?
Large language models are NLP models that are trained on vast amounts of text data, allowing them to understand and generate human-like language.
These models are able to learn the nuances and complexities of language, making them incredibly powerful tools for translation. By processing and analyzing large amounts of text data, these models are able to generate translations that are more accurate and natural sounding than ever before.
The power of large language models for translation
by Arthur Osipyan (https://unsplash.com/@arty_nyc)
Large language models are transforming the translation industry in several ways. First and foremost, they are able to handle a wide range of languages, making them a versatile solution for businesses and individuals who need to communicate in multiple languages.
These models are also able to handle complex sentence structures and idiomatic expressions, resulting in more accurate translations that capture the true meaning of the original text.
Another major advantage of large language models is their ability to continuously improve and adapt. As these models are exposed to more and more text data, they are able to refine their understanding of language and produce even more accurate translations. This means that the more these models are used, the better they become at translating.
5 most useful AI translation tools
1. Google Translate
Google Translate uses a number of language models to improve the accuracy and fluency of its translations. These LLMs are trained on massive datasets of text and code, which allows them to learn the patterns of human language and generate translations that are more natural-sounding and accurate.
It also uses several other techniques to improve the quality of its translations, such as statistical machine translation (SMT) and neural machine translation (NMT). SMT is a rule-based approach to translation that uses statistical methods to identify the most likely translation of a given text.
NMT is a deep learning approach to translation that uses neural networks to learn the patterns of human language and generate translations.
Features of Google Translate using LLMs for translation:
Utilizes multiple Large Language Models (LLMs) to enhance translation accuracy and fluency.
Offers translations in numerous languages.
Provides both web-based and mobile app platforms for easy accessibility.
Real-time translation features are available with its mobile app using the device’s camera.
Voice translation capabilities.
2. Bing Microsoft Translator
Bing Microsoft Translator also uses LLMs to improve its translation capabilities. It is trained on a massive dataset of text and code, which allows them to learn the patterns of human language and generate translations that are more natural-sounding and accurate.
It also uses a number of other techniques to improve the quality of its translations, such as SMT and NMT.
Features of Bing Microsoft Translator using LLMs for translation:
Employs LLMs to enhance the quality of translations.
Offers translations in multiple languages.
Integrates with various Microsoft products like Word and PowerPoint.
Provides APIs for developers to integrate into applications.
Features real-time conversation translation on its mobile app.
DeepL is a paid translation tool that is known for its high-quality translations. DeepL uses a proprietary LLM to generate translations that are more accurate, fluent, and natural-sounding than traditional machine translation systems.
Its language model is trained on a massive dataset of text and code, which allows it to learn the patterns of human language and generate translations that are more natural-sounding and accurate.
Features of DeepL using LLMs for translation:
Uses a unique proprietary Large Language Model to produce high-quality translations.
Known for generating more natural-sounding translations compared to other tools.
Provides a free online translator and a Pro version with advanced features.
Offers integrations for Windows and MacOS for seamless translation during tasks.
4. Amazon Translate
Amazon Translate is a cloud-based translation service that supports over 75 languages. Amazon Translate uses a number of LLMs to translate text in a variety of languages.
It is trained on a massive dataset of text and code, which allows them to learn the patterns of human language and generate translations that are more natural-sounding and accurate.
Features of Amazon Translate using LLMs for translation:
Leverages multiple LLMs for translating text across various languages.
Provides real-time and batch translation capabilities.
Scalable translation services suitable for large enterprises.
Offers seamless integration with other AWS services.
Supports content localization for global audiences
Memsource is a paid translation management system that includes a built-in machine translation engine. Memsource’s built-in machine translation engine is powered by a number of LLMs.
Memsource’s LLMs are trained on a massive dataset of text and code, which allows them to learn the patterns of human language and generate translations that are more natural-sounding and accurate.
Features of Memsource using LLMs for translation:
Features an in-built machine translation engine powered by several LLMs.
Offers translation memory to save and reuse previous translations.
Provides a cloud-based translation management system.
Supports multiple file formats and integrations with CMS, marketing, and development platforms.
Offers workflow automation for translation projects.
LLMs are still under development, but they have the potential to revolutionize the field of translation. LLMs can learn from large amounts of data to generate translations that are more accurate, fluent, and natural-sounding than traditional machine translation systems.
NLP models: The future of translation
NLP models are at the forefront of the translation industry, and their potential for growth and improvement is immense. As more and more data become available, these models will continue to evolve and become even more accurate and efficient.
This means that the future of translation is bright, with the potential for seamless and natural communication between people of different languages and cultures.
The use of large language models for translation has a significant impact on both businesses and individuals. For businesses, it means the ability to expand into new markets and communicate with customers and partners in their native language. This can lead to increased sales, improved customer satisfaction, and stronger relationships with international partners.
Knowledge test of large language model
For individuals, large language models make it easier to connect with people from different cultures and backgrounds. Whether it’s for personal or professional reasons, these models allow for more natural and accurate communication, breaking down language barriers and promoting understanding and collaboration.
Which AI translation tool you like the most?
Imagine LLMs as really smart translators who have been trained on a mountain of books and articles. They know the nuances of different languages and can produce translations that are both accurate and fluent.
Large language models are revolutionizing the translation industry, offering a more accurate and efficient solution for businesses and individuals. With the continuous advancement of NLP technology, the future of translation looks bright, with the potential for seamless communication across borders. Embracing these models can lead to improved global communication and a more connected world.
The winds of change are sweeping through the accounting profession, driven by the rapid integration of Artificial Intelligence into our daily business operations. As we embrace the undeniable benefits that AI brings to the table, we must also acknowledge the potential challenges it poses to our traditional roles.
As a finance professional, I’ve penned this blog to delve into the various ways AI is transforming day-to-day operational activities in accounting while discussing the pros and cons of its integration. Moreover, we’ll explore how accountants can navigate this revolution, remain relevant, and preserve the vital human element that defines our profession.
Generative AI in accounting: Role in day-to-day operational activities
AI has permeated nearly every facet of accounting, enhancing efficiency and accuracy in unprecedented ways:
One of the remarkable capabilities of AI is its proficiency in handling repetitive tasks that used to consume a significant portion of our time, such as data entry and reconciliations. By allowing AI to manage these tasks, we can redirect our focus towards more strategic endeavors like analyzing data and making informed decisions.
Fraud detection and risk assessment
AI brings an exceptional skill to the table – the ability to detect irregular patterns within financial data. This unique capability serves as a safeguard, enabling us to identify potential mistakes and even detect fraudulent activities. This plays a pivotal role in ensuring the financial integrity of our organizations.
Financial forecasting and analysis
Leveraging the power of AI-driven algorithms, we are now equipped with tools that can delve into extensive datasets and o er valuable insights into future financial trends. Armed with these insights, we can contribute more effectively to strategic planning, enhancing our role as forward-thinking financial professionals.
The fusion of AI and online accounting provides accountants with a wealth of data-driven insights that aid in making well-informed decisions. This strategic approach to decision-making directly contributes to the growth and profitability of our organizations. Generating reports and visualizations of datasets is made easy while more vibrant compared to the numbers shown before these tools.
Customer Interaction and Service
AI-powered solutions, such as chatbots and automated customer service platforms, provide an always-available avenue for customer support. This not only enhances customer satisfaction but also enables us to allocate our time and effort toward higher-level financial analyses and advisory tasks.
Pros and cons of AI integration
While AI offers a multitude of advantages, it’s essential to recognize its potential drawbacks:
Enhanced Efficiency: AI’s remarkable ability to expedite processes translates into considerable time saved, enabling us to devote more energy to strategic tasks that demand our expertise.
Reduced Errors: The precision that AI brings to data processing minimizes manual errors, resulting in financial records and reports that are notably more accurate. In-Depth Insights: AI’s analytical prowess equips us with insights that go beyond the surface, enriching our decision-making processes and enhancing overall financial outcomes.
Cost Efficiency: By automating repetitive tasks, AI enables organizations to streamline operations, freeing up resources for more impactful initiatives.
Learning and Adoption: Beyond efficiency gains, AI integration offers a unique opportunity for continuous learning. Finance professionals can quickly grasp different software and tools, enabling swift and summarized financial reporting.
Compliance Made Easy: The integration of AI equips finance professionals with a digital rulebook at their fingertips, simplifying the often-complex landscape of compliance and ensuring adherence to standards.
Shift in job landscape: As AI gradually assumes specific tasks, our roles may shift, prompting the need for us to acquire new skills to remain adaptable and valuable.
Skill upgradation: Proficiency in understanding and effectively working with AI systems requires an investment in learning new skills.
Data security: With AI’s capabilities come concerns about safeguarding sensitive financial data. Implementing robust AI systems with stringent security measures is paramount.
Adjustment period: Incorporating AI into our workflows may necessitate an initial adjustment period, demanding time, and effort as we integrate this technology seamlessly.
The power of AI in finance: Real-world examples
Let’s journey into the tangible world of AI applications in finance, where innovation meets everyday operations.
AI in fintech
Imagine a fintech startup that uses AI algorithms to analyze user spending patterns. These algorithms are learned over time, offering personalized budgeting advice and even predicting potential financial pitfalls. This empowers users to make informed financial decisions, and the fintech company gains loyal customers who value their data-driven insights.
Consider a corporation on the verge of launching a new product. AI can quickly analyze market trends, competitor data, and consumer sentiment to predict the product’s potential success. Armed with this information, financial professionals can provide invaluable insights to leadership, guiding them in making strategic decisions that maximize profitability.
Compliance made effortless
In the world of regulatory compliance, AI can shine brightly. A financial institution can utilize AI-powered tools to scan vast amounts of transaction data, quickly identifying any suspicious activities that might point to money laundering or fraud. This not only ensures adherence to industry regulations but also saves time and resources that can be redirected to more value-added tasks.
In the fast-paced realm of trading, AI algorithms can execute trades based on real-time market data, reacting far quicker than any human could. These algorithms can analyze historical data, news articles, and even social media sentiment to make split-second decisions that capitalize on market movements.
Customer service revolution
Imagine a bank utilizing AI-powered chatbots to handle routine customer inquiries. These chatbots not only provide instant responses but also learn from each interaction to improve their accuracy over time. This translates to enhanced customer satisfaction, as clients receive timely assistance around the clock.
Navigating the AI revolution
In the face of this transformative evolution, finance professionals have the power to not just adapt, but to flourish.
Just as staying updated on financial trends is crucial, keeping an eye on AI developments through classes and workshops helps us remain coordinated. This way, we’re always prepared to tackle fresh challenges that arise at the convergence of finance and AI.
By understanding the information AI gives us, we can put the pieces together and figure out what it means. This is super important for making smart choices. Much like dissecting financial data, the ability to interpret AI-generated insights empowers us to extract valuable conclusions—a skill pivotal for making sound financial decisions. Think of it as piecing together a financial puzzle, where AI provides the missing elements.
Just as we translate complex financial concepts for clients, we serve as intermediaries between AI-generated insights and stakeholders. While AI generates valuable data, our role in explaining it in simple terms ensures clarity and alignment among all parties involved.
Like adjusting financial strategies to market shifts, embracing flexibility in our roles lets us collaborate with AI tools. Being adaptable to the evolving AI landscape is akin to adding new steps to a financial dance, maximizing constructive collaboration for best results.
In a nutshell
In conclusion, in the landscape of accounting and finance, AI is not a mere tool; it’s a catalyst for transformation. By embracing the fusion of AI and our ability, we harness the power to elevate our roles, unlock hidden insights, and propel our organizations toward unprecedented growth. As we navigate this AI evolution, let’s remember that AI isn’t here to replace us—it’s here to amplify us.
Armed with a deep understanding of AI’s integration, a commitment to continuous learning, and an unwavering dedication to ethical practices, we pave the way for a harmonious partnership between human intellect and technological innovation. The journey ahead beckons—a journey where the future of finance meets the prowess of AI.
Artificial Intelligence (AI) and Predictive Analytics are revolutionizing the way engineers approach their work. This article explores the fascinating applications of AI and Predictive Analytics in the field of engineering. We’ll dive into the core concepts of AI, with a special focus on Machine Learning and Deep Learning, highlighting their essential distinctions.
By the end of this journey, you’ll have a clear understanding of how Deep Learning utilizes historical data to make precise forecasts, ultimately saving valuable time and resources.
Different Approaches to Analytics
In the realm of analytics, there are diverse strategies: descriptive, diagnostic, predictive, and prescriptive. Descriptive analytics involves summarizing historical data to extract insights into past events. Diagnostic analytics goes further, aiming to uncover the root causes behind these events. In engineering, predictive analytics takes center stage, allowing professionals to forecast future outcomes, greatly assisting in product design and maintenance. Lastly, prescriptive analytics recommends actions to optimize results.
AI: Empowering Engineers
Artificial Intelligence isn’t about replacing engineers; it’s about empowering them. AI provides engineers with a powerful toolset to make more informed decisions and enhance their interactions with the digital world. It serves as a collaborative partner, amplifying human capabilities rather than supplanting them.
AI and Predictive Analytics: Bridging the Gap
AI and Predictive Analytics are two intertwined yet distinct fields. AI encompasses the creation of intelligent machines capable of autonomous decision-making, while Predictive Analytics relies on data, statistics, and machine learning to forecast future events accurately. Predictive Analytics thrives on historical patterns to predict forthcoming outcomes.
Before AI’s advent, engineers employed predictive analytics tools grounded in their expertise and mathematical models. While these tools were effective, they demanded significant time and computational resources.
However, with the introduction of Deep Learning in 2018, predictive analytics in engineering underwent a transformative revolution. Deep Learning, an AI subset, quickly analyzes vast datasets, delivering results in seconds. It replaces complex algorithms with neural networks, streamlining and accelerating the predictive process.
The Role of Data Analysts
Data analysts play a pivotal role in predictive analytics. They are the ones who spot trends and construct models that predict future outcomes based on historical data. Their expertise in deciphering data patterns is indispensable in making accurate forecasts.
Machine Learning and Deep Learning: The Power Duo
Machine Learning (ML) and Deep Learning (DL) are two critical branches of AI that bring exceptional capabilities to predictive analytics. ML encompasses a range of algorithms that enable computers to learn from data without explicit programming. DL, on the other hand, focuses on training deep neural networks to process complex, unstructured data with remarkable precision.
Turbocharging Predictive Analytics with AI
The integration of AI into predictive analytics turbocharges the process, dramatically reducing processing time. This empowerment equips design teams with the ability to explore a wider range of variations, optimizing their products and processes.
In the domain of heat exchanger applications, AI, particularly the NCS AI model, showcases its prowess. It accurately predicts efficiency, temperature, and pressure drop, elevating the efficiency of heat exchanger design through generative design techniques.
Uses historical data to identify patterns and predict future outcomes.
Uses machine learning to learn from data and make decisions without being explicitly programmed.
To predict future events and trends.
To automate tasks, improve decision-making, and create new products and services.
Uses statistical models, machine learning algorithms, and data mining.
Uses deep learning, natural language processing, and computer vision.
Customer behavior analysis, fraud detection, risk assessment, and inventory management.
Self-driving cars, medical diagnosis, and product recommendations.
Can be used to make predictions about complex systems.
Can learn from large amounts of data and make decisions that are more accurate than humans.
Can be biased by the data it is trained on.
Can be expensive to develop and deploy.
Well-established and widely used.
Still emerging, but growing rapidly.
Realizing the Potential: A Use Case
AI aids medical professionals by prioritizing and triaging patients based on real-time data.
It supports early disease diagnosis by analyzing medical history and statistical data.
Medical imaging powered by AI helps visualize the body for quicker and more accurate diagnoses.
AI-driven smart call routing minimizes wait times and ensures customers’ concerns are directed to the right agents.
Online chatbots, powered by AI, handle common customer inquiries efficiently.
Smart Analytics tools provide real-time insights for faster decision-making.
AI assists in fraud detection by monitoring financial behavior patterns and identifying anomalies.
Expense management systems use AI for categorizing expenses, aiding tracking and future projections.
Automated billing streamlines financial processes, saving time and ensuring accuracy.
Machine Learning (ML):
Social Media Moderation:
ML algorithms help social media platforms flag and identify posts violating community standards, though manual review is often required.
Email providers employ ML to detect and filter spam, ensuring cleaner inboxes.
ML algorithms recognize facial patterns for tasks like device unlocking and photo tagging.
Predictive analytics anticipates equipment failures, allowing for proactive maintenance and cost savings.
It uses historical data to identify potential business risks, aiding in risk mitigation and informed decision-making.
Next Best Action:
Predictive analytics analyzes customer behavior data to recommend the best ways to interact with customers, optimizing timing and channels.
The combination of AI, ML, and predictive analytics offers businesses the capability to:
Make informed decisions.
Improve customer service.
Prevent costly equipment breakdowns.
Optimize customer interactions.
Enhance overall decision-making through clear analytics and future predictions.
These technologies empower businesses to navigate the complex landscape of data and derive actionable insights for growth and efficiency.
Enhancing Supply Chain Efficiency with Predictive Analytics and AI
The convergence of predictive analytics and AI holds the key to improving supply chain forecast accuracy, especially in the wake of the pandemic. Real-time data access is critical for every resource in today’s dynamic environment. Consider the example of the plastic supply chain, which can be disrupted by shortages of essential raw materials due to unforeseen events like natural disasters or shipping delays. AI systems can proactively identify potential disruptions, enabling more informed decision-making.
AI is poised to become a $309 billion industry by 2026, and 44% of executives have reported reduced operational costs through AI implementation. Let’s delve deeper into how AI can enhance predictive analytics within the supply chain:
1. Inventory Management:
Even prior to the pandemic, inventory mismanagement led to significant financial losses due to overstocking and understocking. The lack of real-time inventory visibility exacerbated these issues. When you combine real-time data with AI, you move beyond basic reordering.
Technologies like Internet of Things (IoT) devices in warehouses offer real-time alerts for low inventory levels, allowing for proactive restocking. Over time, AI-driven solutions can analyze data and recognize patterns, facilitating more efficient inventory planning.
To kickstart this process, a robust data collection strategy is essential. From basic barcode scanning to advanced warehouse automation technologies, capturing comprehensive data points is vital. When every barcode scan and related data is fed into an AI-powered analytics engine, you gain insights into inventory movement patterns, sales trends, and workforce optimization possibilities.
2. Delivery Optimization:
Predictive analytics has been employed to optimize trucking routes and ensure timely deliveries. However, unexpected events such as accidents, traffic congestion, or severe weather can disrupt supply chain operations. This is where analytics and AI shine.
By analyzing these unforeseen events, AI can provide insights for future preparedness and decision-making. Route optimization software, integrated with AI, enables real-time rerouting based on historical data. AI algorithms can predict optimal delivery times, potential delays, and other transportation factors.
IoT devices on trucks collect real-time sensor data, allowing for further optimization. They can detect cargo shifts, load imbalances, and abrupt stops, offering valuable insights to enhance operational efficiency.
Turning Data into Actionable Insights
The pandemic underscored the potency of predictive analytics combined with AI. Data collection is a cornerstone of supply chain management, but its true value lies in transforming it into predictive, actionable insights. To embark on this journey, a well-thought-out plan and organizational buy-in are essential for capturing data points and deploying the appropriate technology to fully leverage predictive analytics with AI.
AI and Predictive Analytics are ushering in a new era of engineering, where precision, efficiency, and informed decision-making reign supreme. Engineers no longer need extensive data science training to excel in their roles. These technologies empower them to navigate the complex world of product design and decision-making with confidence and agility. As the future unfolds, the possibilities for engineers are limitless, thanks to the dynamic duo of AI and Predictive Analytics.
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.
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.
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
One might wonder as to exactly how prevalent LLMs are in our personal and professional lives. For context, while the world awaited the clash of Barbenheimer on the silver screen, there was a greater conflict brewing in the background.
SAG-AFTRA, the American labor union representing approximately 160,000 media professionals worldwide (some main members include George Clooney. Tom Hanks, and Meryl Streep among many others) launched a strike in part to call for tightening regulations on the use of artificial intelligence in creative projects. This came as the world witnessed growing concern regarding the rapid advancements of artificial intelligence, which in particular is being led by Large Language Models (LLMs).
Few concepts have garnered as much attention and concern as LLMs. These AI-powered systems have taken the stage as linguistic juggernauts, demonstrating remarkable capabilities in understanding and generating human-like text.
However, instead of fearing these advancements, you can harness the power of LLMs to not just survive but thrive in this new era of AI dominance and make sure you stay ahead of the competition. In this article, we’ll show you how. But before we jump into that, it is imperative to gain a basic understanding of what LLM’s primarily are.
What are large language models?
Picture this: an AI assistant who can converse with you as if a seasoned expert in countless subjects. That’s the essence of a Large Language Model (LLM). This AI marvel is trained on an extensive array of texts from books, articles, websites, and conversations.
It learns the intricate nuances of language, grammar, and context, enabling it to answer queries, draft content, and even engage in creative pursuits like storytelling and poetry. While LLMs might seem intimidating at first glance, they’re tools that can be adapted to enhance your profession.
Embracing large language models across professions
1. Large language models and software development
Automating code generation: LLMs can be used to generate code automatically, which can save developers a significant amount of time and effort. For example, LLMs can be used to generate boilerplate code, such as class declarations and function definitions. They can also be used to generate code that is customized to specific requirements.
Generating test cases: LLMs can be used to generate test cases for software. This can help to ensure that software is thoroughly tested and that bugs are caught early in the development process. For example, LLMs can be used to generate inputs that are likely to cause errors, or they can be used to generate test cases that cover all possible paths through a piece of code.
Writing documentation: LLMs can be used to write documentation for software. This can help to make documentation more comprehensive and easier to understand. For example, LLMs can be used to generate summaries of code, or they can be used to generate interactive documentation that allows users to explore the code in a more dynamic way.
Designing software architectures: LLMs can be used to design software architectures. This can help to ensure that software is architected in a way that is efficient, scalable, and secure. For example, LLMs can be used to analyze code to identify potential bottlenecks, or they can be used to generate designs that are compliant with specific security standards.
Real-life use cases in software development
Google AI has used LLMs to develop a tool called Bard that can help developers write code more efficiently. Bard can generate code, translate languages, and answer questions about code.
Microsoft has used LLMs to develop a tool called GitHub Copilot that can help developers write code faster and with fewer errors. Copilot can generate code suggestions, complete unfinished code, and fix bugs.
The company AppSheet has used LLMs to develop a tool called AppSheet AI that can help developers create mobile apps without writing any code. AI can generate code, design user interfaces, and test apps.
2. Building beyond imagination: Large language models and architectural innovation
Analyzing crop data: LLMs can be used to analyze crop data, such as yield data, weather data, and soil data. This can help farmers to identify patterns and trends, and to make better decisions about crop rotation, planting, and irrigation.
Optimizing yields: LLMs can be used to optimize yields by predicting crop yields, identifying pests and diseases, and recommending optimal farming practices.
Managing pests: LLMs can be used to manage pests by identifying pests, predicting pest outbreaks, and recommending pest control methods.
Personalizing recommendations: LLMs can be used to personalize recommendations for farmers, such as recommending crops to plant, fertilizers to use, and pest control methods to employ.
Generating reports: LLMs can be used to generate reports on crop yields, pest outbreaks, and other agricultural data. This can help farmers to track their progress and make informed decisions.
Chatbots: LLMs can be used to create chatbots that can answer farmers’ questions about agriculture. This can help farmers to get the information they need quickly and easily.
Real-life scenarios in agriculture
The company Indigo Agriculture is using LLMs to develop a tool called Indigo Scout that can help farmers to identify pests and diseases in their crops. Indigo Scout uses LLMs to analyze images of crops and to identify pests and diseases that are not visible to the naked eye.
The company BASF is using LLMs to develop a tool called BASF FieldView Advisor that can help farmers to optimize their crop yields. BASF FieldView Advisor uses LLMs to analyze crop data and to recommend optimal farming practices.
The company John Deere is using LLMs to develop a tool called John Deere See & Spray that can help farmers to apply pesticides more accurately. John Deere See & Spray uses LLMs to analyze images of crops and to identify areas that need to be sprayed.
3. Powering progress: Large language models and energy industry
Analyzing energy data: LLMs can be used to analyze energy data, such as power grid data, weather data, and demand data. This can help energy companies to identify patterns and trends, and to make better decisions about energy production, distribution, and consumption.
Optimizing power grids: LLMs can be used to optimize power grids by predicting demand, identifying outages, and routing power. This can help to improve the efficiency and reliability of power grids.
Developing new energy technologies: LLMs can be used to develop new energy technologies, such as solar panels, wind turbines, and batteries. This can help to reduce our reliance on fossil fuels and to transition to a clean energy future.
Managing energy efficiency: LLMs can be used to manage energy efficiency by identifying energy leaks, recommending energy-saving measures, and providing feedback on energy consumption. This can help to reduce energy costs and emissions.
Creating educational content: LLMs can be used to create educational content about energy, such as videos, articles, and quizzes. This can help to raise awareness about energy issues and to promote energy literacy.
Real-life scenarios in the energy sector
The company Griddy is using LLMs to develop a tool called Griddy Insights that can help energy consumers to understand their energy usage and to make better decisions about their energy consumption. Griddy Insights uses LLMs to analyze energy data and to provide personalized recommendations for energy saving.
The company Siemens is using LLMs to develop a tool called MindSphere Asset Analytics that can help energy companies to monitor and maintain their assets. MindSphere Asset Analytics uses LLMs to analyze sensor data and to identify potential problems before they occur.
The company Google is using LLMs to develop a tool called DeepMind Energy that can help energy companies to develop new energy technologies. DeepMind Energy uses LLMs to simulate energy systems and to identify potential improvements.
4. LLMs: The Future of Architecture and Construction?
Generating designs: LLMs can be used to generate designs for buildings, structures, and other infrastructure. This can help architects and engineers to explore different possibilities and to come up with more creative and innovative designs.
Optimizing designs: LLMs can be used to optimize designs for efficiency, sustainability, and cost-effectiveness. This can help to ensure that buildings are designed to meet the needs of their users and to minimize their environmental impact.
Automating tasks: LLMs can be used to automate many of the tasks involved in architecture and construction, such as drafting plans, generating estimates, and managing projects. This can save time and money, and it can also help to improve accuracy and efficiency.
Communicating with stakeholders: LLMs can be used to communicate with stakeholders, such as clients, engineers, and contractors. This can help to ensure that everyone is on the same page and that the project is completed on time and within budget.
Analyzing data: LLMs can be used to analyze data related to architecture and construction, such as building codes, environmental regulations, and cost data. This can help to make better decisions about design, construction, and maintenance.
Real-life scenarios in architecture and construction
The company Gensler is using LLMs to develop a tool called Gensler AI that can help architects design more efficient and sustainable buildings. Gensler AI can analyze data on building performance and generate design recommendations.
The company Houzz has used LLMs to develop a tool called Houzz IQ that can help users find real estate properties that match their needs. Houzz IQ can analyze data on property prices, market trends, and zoning regulations to generate personalized recommendations.
The company Opendoor has used LLMs to develop a chatbot called Opendoor Bot that can answer questions about real estate. Opendoor Bot can be used to provide 24/7 customer service and to help users find real estate properties.
5. LLMs: The future of logistics
Optimizing supply chains: LLMs can be used to optimize supply chains by identifying bottlenecks, predicting demand, and routing shipments. This can help to improve the efficiency and reliability of supply chains.
Managing inventory: LLMs can be used to manage inventory by forecasting demand, tracking stock levels, and identifying out-of-stock items. This can help to reduce costs and improve customer satisfaction.
Planning deliveries: LLMs can be used to plan deliveries by taking into account factors such as traffic conditions, weather, and fuel prices. This can help to ensure that deliveries are made on time and within budget.
Communicating with customers: LLMs can be used to communicate with customers about shipments, delays, and other issues. This can help to improve customer satisfaction and reduce the risk of complaints.
Automating tasks: LLMs can be used to automate many of the tasks involved in logistics, such as processing orders, generating invoices, and tracking shipments. This can save time and money, and it can also help to improve accuracy and efficiency.
Real-life scenarios and logistics
The company DHL is using LLMs to develop a tool called DHL Blue Ivy that can help to optimize supply chains. DHL Blue Ivy uses LLMs to analyze data on demand, inventory, and transportation costs to identify ways to improve efficiency.
The company Amazon is using LLMs to develop a tool called Amazon Scout that can deliver packages autonomously. Amazon Scout uses LLMs to navigate around obstacles and to avoid accidents.
The company Uber Freight is using LLMs to develop a tool called Uber Freight Einstein that can help to match shippers with carriers. Uber Freight Einstein uses LLMs to analyze data on shipments, carriers, and rates to find the best possible match.
6. Crafting connection: Large Language Models and Marketing
If you are a journalist or content creator, chances are that you’ve faced the challenge of sifting through an overwhelming volume of data to uncover compelling stories. Here’s how LLMs can offer you more than just assistance:
Enhanced Research Efficiency: Imagine having a virtual assistant that can swiftly scan through extensive databases, articles, and reports to identify relevant information for your stories. LLMs excel in data processing and retrieval, ensuring that you have the most accurate and up-to-date facts at your fingertips. This efficiency not only accelerates the research process but also enables you to focus on in-depth investigative journalism.
Deep-Dive Analysis: LLMs go beyond skimming the surface. They can analyze patterns and correlations within data that might be challenging for humans to spot. By utilizing these insights, you can uncover hidden trends and connections that form the backbone of groundbreaking stories. For instance, if you’re investigating customer buying habits in the last fiscal quarter, LLMs can identify patterns that might lead to a new perspective or angle for your study.
Generating Data-Driven Content: In addition to assisting with research, LLMs can generate data-driven content based on large datasets. They can create reports, summaries, and infographics that distill complex information into easily understandable formats. This skill becomes particularly handy when covering topics such as scientific research, economic trends, or public health data, where presenting numbers and statistics in an accessible manner is crucial.
Hyper-Personalization: LLMs can help tailor content to specific target audiences. By analyzing past engagement and user preferences, these models can suggest the most relevant angles, language, and tone for your content. This not only enhances engagement but also ensures that your stories resonate with diverse readerships.
Fact-Checking and Verification: Ensuring the accuracy of information is paramount in journalism. LLMs can assist in fact-checking and verification by cross-referencing information from multiple sources. This process not only saves time but also enhances the credibility of your work, bolstering trust with your audience.
7. Words unleashed: Large language models and content
8 seconds. That is all the time you have as a marketer to catch the attention of your subject. If you are successful, you then have to retain it. LLMs offer you a wealth of possibilities that can elevate your campaigns to new heights:
Efficient Copy Generation: LLMs excel at generating textual content quickly. Whether it’s drafting ad copy, social media posts, or email subject lines, these models can help marketers create a vast amount of content in a short time. This efficiency proves particularly beneficial during time-sensitive campaigns and product launches.
A/B Testing Variations: With LLMs, you can rapidly generate different versions of ad copies, headlines, or taglines. This enables you to perform A/B testing on a larger scale, exploring a variety of messaging approaches to identify which resonates best with your audience. By fine-tuning your content through data-driven experimentation, you can optimize your marketing strategies for maximum impact.
Adapting to Platform Specifics: Different platforms have unique engagement dynamics. LLMs can assist in tailoring content to suit the nuances of various platforms, ensuring that your message aligns seamlessly with each channel’s characteristics. For instance, a tweet might require concise wording, while a blog post can be more in-depth. LLMs can adapt content length, tone, and style accordingly.
Content Ideation: Stuck in a creative rut? LLMs can be a valuable brainstorming partner. By feeding them relevant keywords or concepts, you can prompt them to generate a range of creative ideas for campaigns, slogans, or content themes. While these generated ideas serve as starting points, your creative vision remains pivotal in shaping the final concept.
Enhancing SEO Strategy: LLMs can assist in optimizing content for search engines. They can identify relevant keywords and phrases that align with trending search queries. Tools such as Ahref for Keyword search are already commonly used by SEO strategists which use LLM strategies at the backend. This ensures that your content is not only engaging but also discoverable, enhancing your brand’s online visibility.
8. Healing with data: Large language models in healthcare
The healthcare industry is also witnessing the transformative influence of LLMs. If you are in the healthcare profession, here’s how these AI agents can be of use to you:
Staying Current with Research: LLMs serve as valuable research assistants, efficiently scouring through a sea of articles, clinical trials, and studies to provide summaries and insights. This allows healthcare professionals to remain updated with the latest breakthroughs, ensuring that patient care is aligned with the most recent medical advancements.
Efficient Documentation: The administrative workload on healthcare providers can be overwhelming. LLMs step in by assisting in transcribing patient notes, generating reports, and documenting medical histories. This streamlined documentation process ensures that medical professionals can devote more time to direct patient interaction and critical decision-making.
Patient-Centered Communication: Explaining intricate medical concepts to patients in an easily understandable manner is an art. LLMs aid in transforming complex jargon into accessible language, allowing patients to comprehend their conditions, treatment options, and potential outcomes. This improved communication fosters trust and empowers patients to actively participate in their healthcare decisions.
9. Knowledge amplified: Large language models in education
Perhaps the possibilities with LLMs are nowhere as exciting as in the Edtech Industry. These AI tools hold the potential to reshape the way educators impart knowledge, empower students, and tailor learning experiences. If you are related to academia, here’s what LLMs may hold for you:
Diverse Content Generation: LLMs are adept at generating a variety of educational content, ranging from textbooks and study guides to interactive lessons and practice quizzes. This enables educators to access a broader spectrum of teaching materials that cater to different learning styles and abilities.
Simplified Complex Concepts: Difficult concepts that often leave students perplexed can be presented in a more digestible manner through LLMs. These AI models have the ability to break down intricate subjects into simpler terms, using relatable examples that resonate with students. This ensures that students grasp foundational concepts before delving into more complex topics.
Adaptive Learning: LLMs can assess students’ performance and adapt learning materials accordingly. If a student struggles with a particular concept, the AI can offer additional explanations, resources, and practice problems tailored to their learning needs. Conversely, if a student excels, the AI can provide more challenging content to keep them engaged.
Personalized Feedback: LLMs can provide instant feedback on assignments and assessments. They can point out areas that need improvement and suggest resources for further study. This timely feedback loop accelerates the learning process and allows students to address gaps in their understanding promptly.
Enriching Interactive Learning: LLMs can contribute to interactive learning experiences. They can design simulations, virtual labs, and interactive exercises that engage students and promote hands-on learning. This interactivity fosters deeper understanding and retention.
Engaging Content Creation: Educators can collaborate with LLMs to co-create engaging educational content. For instance, an AI can help a history teacher craft captivating narratives or a science teacher can use an AI to design interactive experiments that bring concepts to life.
A collaborative future
It’s undeniable that LLMs are changing the professional landscape. Even now, proactive software companies are taking steps to update their SDLC’s to integrate AI and LLM’s as much as possible to increase efficiency. Marketers are also at the forefront, using LLMs to test tons of copies to find just the right one. It is incredibly likely that LLMs have already seeped into your industry; you just have to enter a few search strings on your search engine to find out.
However, it’s crucial to view them not as adversaries but as collaborators. Just as calculators did not replace mathematicians but enhanced their work, LLMs can augment your capabilities. They provide efficiency, data analysis, and generation support, but the core expertise and creativity that you bring to your profession remain invaluable.
Empowering the future
In the face of concerns about AI’s impact on the job market, a proactive approach is essential. Large Language Models, far from being a threat, are tools that can empower you to deliver better results. Rather than replacing jobs, they redefine roles and offer avenues for growth and innovation. The key lies in understanding the potential of these AI systems and utilizing them to augment your capabilities, ultimately shaping a future where collaboration between humans and AI is the driving force behind progress.
So, instead of fearing change, harness the potential of LLMs to pioneer a new era of professional excellence.
Prompt engineering guides AI models like ChatGPT and DALLE-2 by refining input instructions to generate specific outputs.
It is a crucial process to ensure that AI models produce desired results aligned with certain criteria or parameters.
Prompt engineering includes the task of fine-tuning the input data used to train AI models, where careful selection and structuring of data maximize its usefulness for training.
The importance of prompt engineering
The importance of prompt engineering lies in its ability to enhance the accuracy and performance of AI models. By understanding the flaws through prompt engineering, developers can identify and address issues that arise during model training.
Moreover, prompt engineering can transform simple inputs into unique outputs, improving the overall model performance. In cases where data availability is limited, like in medical imaging, prompt engineering helps make the most of available data by optimizing its use in training the model.
Ensuring user expectations and positive user experience
Prompt engineering plays a critical role in ensuring software applications meet user expectations, providing a positive user experience by quickly responding to user input. Timely development and deployment of software applications contribute to project success, making prompt engineering an essential aspect of AI projects.
Prompt engineering as a career
As a career path, prompt engineering offers exciting opportunities for individuals with a deep understanding of natural language processing and a creative mindset. With the increasing prevalence of AI and NLP technologies across industries, the demand for skilled prompt engineers is expected to rise.
Significance of transparency and responsibility
As companies adopt language models to offer user-friendly solutions, transparency and responsibility in prompt engineering become even more critical, making experienced prompt engineers highly valuable. Given the rise of AI and ML, prompt engineering promises to be one of the top career choices for the future.
Embracing the Future of AI
We stand at the brink of a new era in AI, with state-of-the-art tools like ChatGPT leading the advancements in the field. The possibilities for AI development are limitless, and the enthusiasm surrounding it is evident. For those aspiring to be at the forefront of AI innovation, prompt engineering is the key to joining the wave of progress in the world of AI.
Roadmap to becoming a prompt engineer
Becoming a proficient prompt engineer requires following a structured path and gaining expertise in various areas. Below are the essential steps to embark on this journey and start your career as a prompt engineer:
1. Grasp the fundamentals of NLP
Begin by understanding the basics of natural language processing (NLP), which focuses on the interaction between computers and human language. Familiarize yourself with key concepts like tokenization, part-of-speech tagging, named entity recognition, and syntactic parsing. These form the foundation for working with conversational AI systems like ChatGPT.
2. Master Python
Python is the primary language for NLP and AI tasks. Master Python’s fundamentals, including variables, data types, control flow, and functions. Progress to advanced topics like file handling, modules, and packages. Familiarize yourself with essential libraries like TensorFlow and PyTorch, which play a vital role in working with ChatGPT.
3. Explore NLP libraries and frameworks
Dive into popular NLP libraries and frameworks such as Natural Language Toolkit (NLTK), spaCy, and Transformers.
NLTK offers a comprehensive set of tools and datasets for NLP tasks. spaCy provides efficient NLP processing with pre-trained models, while Transformers, developed by Hugging Face, offers access to state-of-the-art transformer models like ChatGPT. Practice text preprocessing, sentiment analysis, text classification, and language generation using these tools.
4. Understand ChatGPT and transformer models
Gain a thorough understanding of the underlying architecture and functioning of transformer models, including the one used in ChatGPT. Dive into the self-attention mechanism, encoder-decoder structure, and positional encoding. This knowledge will help you comprehend how ChatGPT generates coherent and contextually relevant responses.
Test your prompting knowledge
5. Experiment with pre-trained ChatGPT models
Take advantage of pre-trained ChatGPT models like GPT-2 or GPT-3. Experiment with different prompts to observe the model’s text generation capabilities and limitations. Hands-on practice will deepen your understanding of ChatGPT’s behavior.
6. Fine-tune ChatGPT for custom applications
Learn the process of fine-tuning pre-trained models like ChatGPT to suit specific tasks and use cases. Familiarize yourself with transfer learning, data preprocessing, and hyperparameter tuning techniques. Explore domain adaptation, context handling, and response generation to optimize ChatGPT’s performance in conversational AI applications.
7. Be aware of ethical considerations and bias in AI
As a prompt engineer, it is crucial to be mindful of ethical considerations and potential biases associated with AI models. Understand responsible AI development and the impact of biases in training data and model outputs. Stay updated on guidelines and best practices to mitigate biases and ensure fair AI systems.
8. Stay current with latest research
NLP and AI are evolving rapidly, with new research and advancements occurring frequently. Stay updated by following reputable sources, attending conferences, and engaging with the AI community. Keep abreast of the latest techniques, models, and research breakthroughs related to ChatGPT.
9. Collaborate and contribute to open-source projects
Participate actively in open-source projects related to NLP and AI. Collaborate with other professionals in the field, contribute to libraries, frameworks, or research initiatives that enhance ChatGPT’s capabilities. This collaborative approach will provide practical experience, exposure to different perspectives, and professional growth opportunities.
10. Apply skills to real-world projects
Solidify your expertise by applying your skills to real-world NLP and conversational AI projects. Seek opportunities to work on practical problems and use ChatGPT to address specific use cases. Building a portfolio of successful projects will showcase your capabilities to potential employers and further enhance your proficiency in ChatGPT.
By following this roadmap, you can become a skilled prompt engineer ready to make significant contributions in the dynamic world of AI and NLP.
When using language models like ChatGPT, there are several types of prompting techniques that you can utilize to guide the model’s responses. Here are some common prompt types:
Instructional prompts provide explicit instructions to the model about the desired behavior or response. You can specify the format, style, or tone of the answer or ask the model to think step-by-step before generating a response. Instructional prompts help set clear expectations and guide the model’s output accordingly.
Example: “Please provide a detailed explanation of the process involved in solving this math problem.”
Socratic prompts aim to guide the model’s thinking by asking leading questions or providing hints. This prompts the model to reason through the problem and arrive at a well-thought-out response. Socratic prompts are useful when you want the model to demonstrate understanding or critical thinking.
Example: “What are the advantages and disadvantages of using renewable energy sources?”
Priming prompts involve providing specific example responses that align with the desired output. By showcasing the style or tone you’re aiming for, you can guide the model to generate similar responses. Priming helps shape the model’s behavior and encourages it to produce outputs consistent with the provided examples.
Example: “Here are a few responses I’m looking for: ‘That’s great!’ or ‘I completely agree with you.'”
Mixed prompts combine multiple types of prompts to provide a comprehensive guiding framework. By incorporating instructional, contextual, and other types of prompts together, you can provide a rich context and precise instructions for the model’s responses.
Example: “Based on our previous conversation (contextual prompt), please explain the advantages and disadvantages of using renewable energy sources (instructional prompt). Additionally, consider providing examples to support your points (Socratic prompt).”
Example-based prompts involve providing specific examples or sample inputs and desired outputs to guide the model’s behavior. By showing the model concrete examples of what you expect, you help it learn patterns and generate responses that align with those examples.
Example: “Here’s an example of the type of response I’m looking for: When asked about your favorite book, mention ‘To Kill a Mockingbird’ and explain why it resonated with you.”
The effectiveness of each prompt type can vary depending on the specific use case and context. It’s essential to experiment with different prompt types and iterate to find the most effective approach for obtaining accurate and desired outputs from the language model.
5 essential skills for becoming a prompt engineer
The role of a prompt engineer demands a unique skill set that combines technical expertise with effective communication and problem-solving abilities. As this emerging field continues to evolve, prompt engineers must possess the following five key skills to excel in their roles:
1. Strong verbal and written communication skills
Prompt engineers need to communicate effectively with AI systems using words and phrases. Crafting detailed prompts can be complex, requiring careful selection of hundreds or even thousands of words. Moreover, the cross-disciplinary nature of prompt engineering makes communication and collaboration vital in the development process.
2. Programming proficiency
While prompt engineering is distinct from traditional programming, many prompt engineers are involved in coding tasks. This involvement may include developing the AI platform itself or using programming skills to automate testing and other functions. Proficiency in well-established languages such as Python is commonly expected, alongside familiarity with APIs, operating systems, and command-line interfaces, tailored to the specific AI platform and company requirements.
3. Prior prompt experience
Given the novelty of prompt engineering, there is no fixed benchmark for prior experience. However, most employers seek prompt engineers with demonstrated experience in building and testing AI prompts, particularly in major models like GPT and platforms such as ChatGPT. Practical experience in these areas is highly valued.
4. AI technology knowledge
While language skills are essential for prompt engineers, they also require a comprehensive understanding of natural language processing (NLP), large language models (LLMs), machine learning, and AI-generated content development. Familiarity with coding and AI platform development is crucial for hands-on involvement in certain responsibilities.
5. Data analysis experience
A fundamental skill for prompt engineers is the ability to comprehend the data used by the AI platform, the data employed in prompts, and the data generated or provided by the AI in response. Proficiency in data analytics techniques and tools is necessary to identify data biases and objectively assess the quality of AI outputs. Employers often seek candidates with several years of experience analyzing structured and unstructured data sources.
In addition to technical skills, prompt engineers must possess soft skills like problem-solving, analytical thinking, and effective collaboration with cross-functional teams.
The salary outlook for prompt engineers
The demand for prompt engineers is on a steady rise as organizations across various industries increasingly rely on software systems to optimize their operations and improve user experiences. Industry reports project that the global software development market is expected to reach $1.5 trillion by 2027, leading to a significant demand for skilled prompt engineers.
In terms of remuneration, prompt engineers are well-rewarded for their specialized expertise. In the United States, the average annual salary for a prompt engineer stands at approximately $98,000, with experienced professionals earning salaries exceeding $120,000 per year. These salary figures highlight the lucrative nature of the prompt engineering field, making it an appealing career choice for aspiring technologists.
Future of prompt engineers
In this comprehensive guide, we have explored the world of prompt engineering and its significance in guiding AI models like ChatGPT and DALLE-2 to generate specific outputs aligned with desired criteria. We have seen the various types of prompts that prompt engineers can use to influence the model’s behavior effectively.
Prompt engineering empowers developers to enhance the accuracy and performance of AI models by providing clear instructions, guiding questions, example responses, and more. It plays a vital role in shaping the AI system’s behavior, making it user-friendly and aligned with user expectations.
Roadmap to becoming a prompt engineer
The roadmap to becoming a prompt engineer highlights the essential steps one must take to embark on this exciting career path. From mastering the fundamentals of NLP and Python programming to experimenting with pre-trained models, fine-tuning, and staying updated with the latest research, each step contributes to building a well-rounded prompt engineer.
As AI and NLP technologies continue to advance, prompt engineering will remain a crucial aspect of the industry. Ethical considerations, transparency, and responsibility in AI development will become increasingly important, making experienced prompt engineers invaluable contributors to responsible AI solutions.
Embracing the future of AI, we recognize the limitless possibilities that lie ahead with state-of-the-art tools like ChatGPT leading the way. Aspiring prompt engineers have the opportunity to be at the forefront of AI innovation, leveraging their skills and creativity to shape the world of conversational AI.
Learn best prompting methods with us
In conclusion, prompt engineering is an exciting and rapidly growing field that holds significant promise. By following this guide and staying dedicated to continuous learning and exploration, individuals can become proficient prompt engineers, driving AI advancements and contributing to the dynamic world of natural language processing and artificial intelligence.
This is your cue to level up your skills with our Generative AI Roadmap for beginners! No need to search any further – we’ve got you covered with the basics, career paths, and learning strategies. Let’s dive in!
For the unknown, generative AI, an exciting field of Artificial Intelligence, promises to transform automation, creativity, and decision-making. With a foundation in math, statistics, and programming, learning Generative AI requires dedication and patience as the technology evolves.
Back to basics: What is Generative AI?
Generative AI harnesses deep learning algorithms to generate human-like data in response to user input. It goes beyond traditional programming, empowering machines with creativity and curiosity. This technology finds applications in NLP, computer vision, autonomous driving, robotics, and more.
What is Generative AI used for?
Generative AI revolutionizes data creation and management, transforming media experiences across channels. It benefits businesses in image generation, facial recognition, NLP tools, and automated marketing campaigns. It’s potential for innovation drives higher revenue growth and engagement.
How does Generative AI work?
Before we dive into the generative AI roadmap, let’s understand what it does. Open AI (ChatGPT) collects and analyzes data to create new content. By automating creative processes, it accelerates design workflows and ad campaigns while maintaining quality. Its accessible capabilities unlock untapped opportunities in product development and customer experience.
Generative AI Roadmap: How to learn it?
Why do people want to learn Generative AI? The answer is simple. Generative AI will save you time, no matter what your job is. So this generative AI roadmap provides you a direction on how you need to jump on this tech bandwagon.
Set Exciting Goals: What do you want to achieve with generative AI? Create innovative products? Automate tasks? Set clear goals that inspire you and tailor your learning journey accordingly.
Discover Quality Resources: Explore a variety of resources like online courses, books, and tutorials. Find the ones that resonate with your learning style and are relevant to your goals. Don’t settle for average—choose the best!
Craft a Learning Adventure: Design a learning plan that breaks down your goals into manageable tasks. Schedule regular time for exploration and growth. Make it an exciting adventure rather than a mundane task.
Dive Deep and Practice: Immerse yourself in generative AI. Take deep dives into the concepts and practice your skills. Code, experiment, participate in hackathons, and contribute to real-world projects. It’s hands-on fun!
Seek Clarity and Question: Ask questions, seek answers, and engage with others in the field. Join online forums, connect on Slack, and interact with mentors. Keep your curiosity alive and embrace the joy of learning.
Reflect and Review: Reflect on your progress, celebrate your achievements, and brainstorm new ideas. Unleash your creativity to envision exciting projects and possibilities. Let your imagination soar!
Embrace Challenges: Challenges are stepping stones to growth. Embrace them as opportunities to learn and overcome obstacles. Stay motivated by setting small goals, celebrating successes, and connecting with fellow learners.
Apply and Innovate: Put your knowledge to work! Apply generative AI in real-world scenarios. Explore its potential applications, solve problems creatively, and consider ethical implications. Be a catalyst for innovation!
Embrace Continous Learning: Generative AI is a dynamic field. Stay ahead by embracing continuous learning. Stay updated with advancements, expand your knowledge base, and develop problem-solving skills.
Careers in Generative AI
Since we are talking about a Generative AI roadmap, the discussion cannot be concluded without inspecting the future jobs that might storm the market due to the huge demand for generative AI.
1. AI Engineer
AI Engineers build AI solutions to complex problems. Their responsibilities can range from building chatbots and smart assistants with natural language processing (NLP) to developing internal algorithms and programs that help automate a company’s processes.
An AI Engineer’s tools will depend on their specific role and specialization, but generally, the role requires strong programming, data science, and math skills. Python is one of the most popular programming languages used for machine learning and AI, and it’s a great place to start if you want to get into the field. You can learn the basics of the language in our Learn Python course.
2. Prompt Engineer
This list includes a lot of tech-heavy roles, but you don’t need to be a programmer to work with AI. Being really good at writing prompts for chatbots is an in-demand skill to have on your resume if you want to become a Prompt Engineer.
AI needs to understand its users, which is no easy task considering the ambiguities of human communication. The way we ask ChatGPT for information can affect the types of responses we get. Prompt Engineers figure out exactly how to word a command to achieve a desired result, and they help evaluate AI performance and uncover flaws by testing models with specialized and specific prompts.
Prompt engineering helps ensure that AI can properly interpret and respond to our commands, and companies will doubtlessly need native speakers of different languages and dialects worldwide to help train their models.
3. Algorithm Engineer
Algorithms underlie an AI’s ability to learn from data, and algorithm engineering requires extensive knowledge of computer science and architecture, data structures, programming, and development. Algorithm Engineers build and fine-tune algorithms for machine learning and AI systems and applications, and while the tools they use will depend on the projects they work on, Java and C++ are used extensively in the field.
4. NLP Engineer
NLP sits at the heart of human-computer interaction, and NLP Engineers build tools and systems for parsing and processing text and language. While the most common NLP tools include virtual assistants like Siri and Alexa, NLP is also used in search engines, email filters, and recommender systems.
Generative AI has the potential to revolutionize daily life. It enables accurate decisions, cost reduction, and improved efficiency in various fields. Recommended roadmaps for beginners, intermediates, and advanced users are available to learn generative AI effectively. As we explore this technology further, it will become indispensable in all industries, providing new opportunities for growth and innovation through automated decision-making. We hope this Generative AI Roadmap blog is helpful.
OpenAI is a research company that specializes in artificial intelligence (AI) and machine learning (ML) technologies. Its goal is to develop safe AI systems that can benefit humanity as a whole. OpenAI offers a range of AI and ML tools that can be integrated into mobile app development, making it easier for developers to create intelligent and responsive apps.
The purpose of this blog post is to discuss the advantages and disadvantages of using OpenAI in mobile app development. We will explore the benefits and potential drawbacks of OpenAI in terms of enhanced user experience, time-saving, cost-effectiveness, increased accuracy, and predictive analysis.
How OpenAI works in mobile app development?
OpenAI provides developers with a range of tools and APIs that can be used to incorporate AI and ML into their mobile apps. These tools include natural language processing (NLP), image recognition, predictive analytics, and more.
OpenAI’s NLP tools can help improve the user experience by providing personalized recommendations, chatbot functionality, and natural language search capabilities. Image recognition tools can be used to identify objects, people, and places within images, enabling developers to create apps that can recognize and respond to visual cues.
OpenAI’s predictive analytics tools can analyze data to provide insights that can be used to enhance user engagement. For example, predictive analytics can be used to identify which users are most likely to churn and to provide targeted offers or promotions to those users.
OpenAI’s machine learning algorithms can also automate certain tasks, such as image or voice recognition, allowing developers to focus on other aspects of the app.
Advantages of using OpenAI in mobile app development
1. Enhanced user experience:
OpenAI can help improve the user experience by providing personalized recommendations, chatbot functionality, and natural language search capabilities. For instance, using OpenAI algorithms, a mobile app can analyze user data to provide tailored recommendations, making the user experience more intuitive and enjoyable. Additionally, OpenAI can enhance the user interface of an app by providing natural language processing that allows users to interact with the app using their voice or text. This feature can make apps more accessible to people with disabilities or those who prefer not to use touch screens.
OpenAI’s machine learning algorithms can automate certain tasks, such as image or voice recognition, which can save developers time and effort. This allows developers to focus on other aspects of the app, such as design and functionality. For instance, using OpenAI image recognition, a mobile app can automatically tag images uploaded by users, which saves time for both the developer and the user.
OpenAI can reduce development costs by automating tasks that would otherwise require manual labor. This can be particularly beneficial for smaller businesses that may not have the resources to hire a large development team. Additionally, OpenAI provides a range of pre-built tools and APIs that developers can use to create apps quickly and efficiently.
4. Increased accuracy:
OpenAI algorithms can perform complex calculations with a higher level of accuracy than humans. This can be particularly useful for tasks such as predictive analytics or image recognition, where accuracy is essential. For example, using OpenAI predictive analytics, a mobile app can analyze user data to predict which products a user is likely to buy, enabling the app to provide personalized offers or promotions.
5. Predictive analysis:
OpenAI’s predictive analytics tools can analyze data and provide insights that can be used to enhance user engagement. For example, predictive analytics can be used to identify which users are most likely to churn and to provide targeted offers or promotions to those users. Additionally, OpenAI can be used to analyze user behavior to identify patterns and trends that can inform app development decisions.
Disadvantages of using OpenAI in mobile app development:
Integrating OpenAI into mobile app development can be complex and time-consuming. Developers need to have a deep understanding of AI and machine learning concepts to create effective algorithms. Additionally, the integration process can be challenging, as developers need to ensure that OpenAI is compatible with the app’s existing infrastructure.
2. Data privacy concerns:
OpenAI relies on data to learn and make predictions, which can raise privacy concerns. Developers need to ensure that user data is protected and not misused. Additionally, OpenAI algorithms can create bias if the data used to train them is not diverse or representative. This can lead to unfair or inaccurate predictions.
3. Limited compatibility:
OpenAI may not be compatible with all mobile devices or operating systems. This can limit the number of users who can use the app and affect its popularity. Developers need to ensure that OpenAI is compatible with the target devices and operating systems before integrating it into the app.
4. Reliance on third-party APIs:
OpenAI may rely on third-party APIs, which can affect app performance and security. Developers need to ensure that these APIs are reliable and secure, as they can be a potential vulnerability in the app’s security. Additionally, the performance of the app can be affected if the third-party APIs are not optimized.
Implementing OpenAI into mobile app development can be expensive, especially for smaller businesses. Developers need to consider the cost of developing and maintaining the AI algorithms, as well as the cost of integrating and testing them. Additionally, OpenAI may require additional hardware or infrastructure to run effectively, which can further increase costs.
It is essential for developers to carefully consider these factors before implementing OpenAI into mobile app development.
For developers who are considering using OpenAI in their mobile apps, we recommend conducting thorough research into the AI algorithms and their potential impact on the app. It may also be helpful to seek guidance from AI experts or consultants to ensure that the integration process is smooth and successful.
In conclusion, while OpenAI can be a powerful tool for enhancing mobile app functionality and user experience, developers must carefully consider its advantages and disadvantages before integrating it into their apps. By doing so, they can create more intelligent and responsive apps that meet the needs of their users, while also ensuring the app’s security, privacy, and performance.
Despite major layoffs in 2022, there are many optimistic fintech trends to look out for in 2023. Every crisis bespells new opportunities. In this blog, let’s see what the future holds for fintech trends in 2023.(more…)
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,
Expertise in a programming language (especially in Python, Scala, and Java)
Hands-on knowledge of Linear Algebra and Statistics
Proficient at Signal Processing Techniques
Profound knowledge of the Neural Network Architects
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.
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.
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.
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.
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.
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.
In this blog, we will discuss how Artificial Intelligence and computer vision are contributing to improving road safety for people.
Each year, about 1.35 million people are killed in crashes on the world’s roads, and as many as 50 million others are seriously injured, according to the World Health Organization. With the increase in population and access to motor vehicles over the years, rising traffic and its harsh effects on the streets can be vividly observed with the growing number of fatalities.
We call this suffering traffic “accidents” — but, in reality, they can be prevented. Governments all over the world are resolving to reduce them with the help of artificial intelligence and computer vision.
Humans make mistakes, as it is in their nature to do so, but when small mistakes can lead to huge losses in the form of traffic accidents, necessary changes are to be made in the design of the system.
A technology deep-dive into this problem will show how a lack of technological innovations has failed to lower this trend over the past 20 years. However, with the adoption of the ‘Vision Zero’ program by governments worldwide, we may finally see a shift in this unfortunate trend.
Role of Artificial Intelligence for improving road traffic
AI can improve road traffic by reducing human error, speeding up the process of detection and response to accidents, as well as improving safety. With the advancement of computer vision, the quality of data and predictions made with video analytics has increased ten-folds.
Artificial Intelligence is already leveraging the power of vision analytics in scenarios like identifying mobile phone usage by the driver on highways and recognize human errors much faster. But what lies ahead to be used in our everyday life? Will progress be fast enough to tackle the complexities self-driving cars bring with them?
In recent studies, it’s been inferred through data that subtle distractions on a busy road are correlated to the traffic accidents there. Experts believe that in order to minimize the risk of an accident, the system must be planned with the help of architects, engineers, transport authorities, city planners and AI.
With the help of AI, it becomes easier to identify the problems at hand, however they will not solve them on their own. Designing the streets in a way that can eliminate certain factors of accidents could be the essential step to overcome the situation at hand.
AI also has a potential to help increase efficiency during peak hours by optimizing traffic flow. Road traffic management has undergone a fundamental shift because of the quick development of artificial intelligence (AI). With increasing accuracy, AI is now able to predict and manage the movement of people, vehicles, and goods at various locations along the transportation network.
As we make advancements into the field, simple AI programs along with machine learning and data science, are enabling better service for citizens than ever before while also reducing accidents by streamlining traffic at intersections and enhancing safety during times when roads are closed due to construction or other events.
Deep learning impact on improved infrastructure for road safety
Deep learning system’s capacity for processing, analyzing, and making quick decisions from enormous amounts of data has also facilitated the development of efficient mass transit systems like ride-sharing services. With the advent of cloud-edge devices, the process of gathering and analyzing data has become much more efficient.
Increase in the number of different sources of data collection has led to an increase of not only quality but quantity of variety of data as well. These systems leverage the data from real-time edge devices and can tackle them effectively by retrofitting existing camera infrastructure for road safety.
Join our upcoming webinar
In our upcoming webinar on 29th November, we will summarize the challenges in the industry and how AI plays its part in making a safe environment by solutions catering to avoiding human errors.
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
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.
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.
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.
This blog discusses the applications of AI in healthcare. We will learn about some businesses and startups that are using AI to revolutionize the healthcare industry. This advancement in AI has helped in fighting against Covid19.
COVID-19 was first recognized on December 30, 2019, by BlueDot. It did so nine days before the World Health Organization released its alert for coronavirus. How did BlueDot do it? BlueDot used the power of AI and data science to predict and track infectious diseases. It identified an emerging risk of unusual pneumonia happening around a market in Wuhan.
The role of data science and AI in the Healthcare industry is not limited to that. Now, it has become possible to learn the causes of whatever symptoms you are experiencing, such as cough, fever, and body pain, without visiting a doctor and self-treating it at home. Platforms like Ada Health and Sensely can diagnose the symptoms you report.
The Healthcare industry generates 30% of 1.145 trillion MB of data generated every day. This enormous amount of data is the driving force for revolutionizing the industry and bringing convenience to people’s lives.
Applications of Data Science in Healthcare:
1. Prediction and spread of diseases
Predictive analysis, using historical data to find patterns and predict future outcomes, can find the correlation between symptoms, patients’ habits, and diseases to derive meaningful predictions from the data. Here are some examples of how predictive analytics plays a role in improving the quality of life and medical condition of the patients:
Magic Box, built by the UNICEF office of innovation, uses real-time data from public sources and private sector partners to generate actionable insights. It provides health workers with disease spread predictions and countermeasures. During the early stage of COVID-19, Magic box correctly predicted which the African States are most likely to see imported cases using airline data. This prediction proved beneficial in planning and strategizing quarantine, travel restrictions, and enforcing social distancing.
Another use of analytics in healthcare is AIME. It is an AI platform that helps health professionals in tackling mosquito-borne diseases like dengue. AIME uses data like health center notification of dengue, population density, and water accumulation spots to predict outbreaks in advance with an accuracy of 80%. It aids health professionals in Malaysia, Brazil, and the Philippines. The Penang district of Malaysia saw a cost reduction of USD 500,000 by using AIME.
BlueDot is an intelligent platform that warns about the spread of infectious diseases. In 2014, it identified the Ebola outbreak risk in West Africa accurately. It also predicted the spread of the Zika virus in Florida six months before the official reports.
Sensely uses data from trusted sources like Mayo Clinic and NHS to diagnose the disease. The patient enters symptoms through a chatbot used for diagnosis. Sensely launched a series of customized COVID-19 screening and education tools with enterprises around the world which played their role in supplying trusted advice urgently.
According to a survey carried out in January 2020, 85 percent of the respondents working in smart hospitals reported being satisfied with their work compared to 80 percent of the respondents from digital hospitals. Similarly, 74 percent of the respondents from smart hospitals would recommend the medical profession to others, while only 66 percent of the respondents from digital hospitals recommend it.
Staff retention has been a challenge but is now becoming an enormous challenge, especially post-pandemic. For instance, after six months of the COVID-19 outbreak, almost a quarter of care staff quit their job in Flanders & Belgium. The care staff felt exhausted, experienced sleep deprivation, and could not relax properly. A Smart healthcare system can solve these issues.
Smart healthcare systems can help optimize operations and provide prompt service to patients. It forecasts the patient load at a particular time and plans resources to improve patient care. It can optimize clinic staff scheduling and supply, which reduces the waiting time and overall experience.
Getting data from partners and other third-party sources can be beneficial too. Data from various sources can help in process management, real-time monitoring, and operational efficiency. It leads to overall clinic performance optimization. We can perform deep analytics of this data to make predictions for the next 24 hours, which helps the staff focus on delivering care.
3. Data science for medical imaging
According to the World Health Organization (WHO), radiology services are not accessible to two-thirds of the world population. Patients must wait for weeks and travel distances for simple ultrasound scans. One of the foremost uses of data science in the healthcare industry is medical imaging. Data Science is now used to inspect images from X-rays, MRIs, and CT scan to find irregularities. Traditionally, radiologists did this task manually, but it was difficult for them to find microscopic deformities. The patient’s treatment depends highly on insights gained from these images.
Data science can help radiologists with image segmentation to identify different anatomical regions. Applying some image processing techniques like noise reduction & removal, edge detection, image recognition, image enhancement, and reconstruction can also help with inspecting images and gaining insights.
One example of a platform that uses data science for medical imaging is Medo. It provides a fully automated platform that enables quick and accurate imaging evaluations. Medo transforms scans taken from different angles into a 3D model. They compare this 3D model against a database of millions of other scans using machine learning to produce a recommended diagnosis in real-time. Platforms like Medo make radiology services more accessible to the population worldwide.
4. Drug discovery with data science
Traditionally, it took decades to discover a new drug, but the time has now been reduced to less than a year using data science. Drug discovery is a complex task. Pharmaceutical industries rely heavily on data science to develop better drugs. Researchers need to identify the causative agent and understand its characteristics which may require millions of test cases to understand. This is a huge problem for pharmaceutical companies because it can take decades to perform these tests. Data science solved this problem and can perform this task in a month or even a few weeks.
For example, the causative agent for COVID-19 is the SARS-CoV-2 virus. For discovering an effective drug for COVID-19, deep learning is used to identify and design a molecule that binds to SARS-CoV-2 to inhibit its function by using extracted data from scientific literature through NLP (Natural Language Processing).
5. Monitoring patients’ health
The human body generates two terabytes of data daily. Humans are trying to collect most of this data using smart home devices and wearables. The data these devices collect includes heart rate, blood sugar, and even brain activity. Data can revolutionize the healthcare industry if known how to use it.
Every 36 seconds, a person dies from cardiovascular disease in the United States. Data science can identify common conditions and predict disorders by identifying the slightest change in the health indicators. Timely alert of changes in health indicators can save thousands of lives. Personal health coaches are designed to help to gain deep insights into the patient’s health and alert if the health indicator reaches a dangerous level.
Companies like Corti can detect cardiac arrest in 48 seconds through phone calls. This solution uses real-time natural language processing to listen to emergency calls and look out for several verbal and non-verbal patterns of communication. It is trained on a dataset of emergency calls and acts as a personal assistant of the call responder. It helps the responder ask relevant questions, provide insights, and predict if the caller is suffering from cardiac arrest. Corti finds cardiac arrest more accurately and faster than humans.
6. Virtual assistants in healthcare
The WHO estimated that by 2030, the world will need an extra 18 million health workers worldwide. Using virtual assistant platforms can fulfill this need. According to a survey by Nuance, 92% of clinicians believe virtual assistant capabilities would reduce the burden on the care team and patient experience.
Patients can enter their symptoms as input to the platform and ask questions. The platform would tell you about your medical condition using the data of symptoms and causes. It is possible because of the predictive modeling of disease. These platforms can also assist patients in many other ways, like reminding them to take medication on time.
An example of such a platform is Ada Health, an AI-enabled symptom checker. A person enters symptoms through a chatbot, and Ada uses all available data from patients, past medical history, EHR implementation, and other sources to predict a potential health issue. Over 11 million people (about twice the population of Arizona) use this platform.
Other examples of health chatbots are Babylon Health, Sensely, and Florence.
In this blog, we discussed the applications of AI in healthcare. We learned about some businesses and startups that are using AI to revolutionize the healthcare industry. This advancement in AI has helped in fighting against Covid19. To learn more about data science enroll in our Data Science Bootcamp, a remote instructor-led Bootcamp where you will learn data science through a series of lectures and hands-on exercises. Next, we will be creating a prognosis prediction system in python. You can follow along with my next blog post here.
Learn how to use Chatterbot, the Python library, to build and train AI-based chatbots.
Chatbots have become extremely popular in recent years and their use in the industry has skyrocketed. The chatbot market is projected to grow from $2.6 billion in 2019 to $9.4 billion by 2024. This doesn’t come as a surprise when you look at the immense benefits chatbots bring to businesses. According to a study by IBM, chatbots can reduce customer services cost by up to 30%.
In the third blog of A Beginners Guide to Chatbots, we’ll be taking you through how to build a simple AI-based chatbot with Chatterbot; a Python library for building chatbots.
Chatterbot is a python-based library that makes it easy to build AI-based chatbots. The library uses machine learning to learn from conversation datasets and generate responses to user inputs. The library allows developers to train their chatbot instances with pre-provided language datasets as well as build their datasets.
A newly initialized Chatterbot instance starts with no knowledge of how to communicate. To allow it to properly respond to user inputs, the instance needs to be trained to understand how conversations flow. Since conversational chatbot Python relies on machine learning at its backend, it can very easily be taught conversations by providing it with datasets of conversations.
Chatterbot’s training process works by loading example conversations from provided datasets into its database. The bot uses the information to build a knowledge graph of known input statements and their probable responses. This graph is constantly improved and upgraded as the chatbot is used.
The Chatterbot Corpus is an open-source user-built project that contains conversational datasets on a variety of topics in 22 languages. These datasets are perfect for training a chatbot on the nuances of languages – such as all the different ways a user could greet the bot. This means that developers can jump right to training the chatbot on their customer data without having to spend time teaching common greetings.
Chatterbot has built-in functions to download and use datasets from the Chatterbot Corpus for initial training.
Chatterbot logic adapters
Conversational chatbot Python uses Logic Adapters to determine the logic for how a response to a given input statement is selected.
A typical logic adapter designed to return a response to an input statement will use two main steps to do this. The first step involves searching the database for a known statement that matches or closely matches the input statement. Once a match is selected, the second step involves selecting a known response to the selected match. Frequently, there will be several existing statements that are responses to the known match. In such situations, the Logic Adapter will select a response randomly. If more than one Logic Adapter is used, the response with the highest cumulative confidence score from all Logic Adapters will be selected.
Chatterbot storage adapters
Chatterbot stores its knowledge graph and user conversation data in an SQLite database. Developers can interface with this database using Chatterbot’s Storage Adapters.
Storage Adapters allow developers to change the default database from SQLite to MongoDB or any other database supported by the SQLAlchemy ORM. Developers can also use these Adapters to add, remove, search, and modify user statements and responses in the Knowledge Graph as well as create, modify and query other databases that Chatterbot might use.
Building an AI-based chatbot
In this tutorial, we will be using the Chatterbot Python library to build an AI-based Chatbot.
We will be following the steps below to build our chatbot
Instantiating a ChatBot Instance
Training on Chatbot-Corpus Data
Training on Custom Data
Building a front end
The first thing we’ll need to do is import the modules we’ll be using. The ChatBot module contains the fundamental Chatbot class that will be used to instantiate our chatbot object. The ListTrainer module allows us to train our chatbot on a custom list of statements that we will define. The ChatterBotCorpusTrainer module contains code to download and train our chatbot on datasets part of the ChatterBot Corpus Project.
from chatterbot import ChatBot
from chatterbot.trainers import ListTrainer
from chatterbot.trainers import ChatterBotCorpusTrainer
Instantiating chatbots instance
A chatbot instance can be created by creating a Chatbot object. The Chatbot object needs to have the name of the chatbot and must reference any logic or storage adapters you might want to use.
In the case you don’t want your chatbot to learn from user inputs after it has been trained, you can set the read-only parameter to True.
Training your chatbot agent on data from the Chatterbot-Corpus project is relatively simple. To do that, you need to instantiate a ChatterBotCorpusTrainer object and call the train() method. The ChatterBotCorpusTrainer takes in the name of your ChatBot object as an argument. The train() method takes in the name of the dataset you want to use for training as an argument.
You can also train ChatterBot on custom conversations. This can be done by using the module’s ListTrainer class.
In this case, you will need to pass in a list of statements where the order of each statement is based on its placement in a given conversation. Each statement in the list is a possible response to its predecessor in the list.
The training can be undertaken by instantiating a ListTrainer object and calling the train() method. It is important to note that the train() method must be individually called for each list to be used.
greet_conversation = [
"How are you doing?",
"I'm doing great.",
"That is good to hear",
open_timings_conversation = [
"What time does the Bank open?",
"The Bank opens at 9AM",
close_timings_conversation = [
"What time does the Bank close?",
"The Bank closes at 5PM",
#Initializing Trainer Object
trainer = ListTrainer(BankBot)
Building a front end
Once the chatbot has been trained, it can be used by calling Chatterbot’s get response() method. The method takes a user string as an input and returns a response string.
user_input = input()
if (user_input == 'quit'):
response = BankBot.get_response(user_input)
This blog was hands-on to building a simple AI-based chatbot in Python. The functionality of this bot can easily be increased by adding more training examples. You could, for example, add more lists of custom responses related to your application.
As we saw, building an AI-based chatbot is easy compared to building and maintaining a Rule-based Chatbot. Despite this ease, chatbots such as this are very prone to mistakes and usually give robotic responses because of a lack of good training data.
A better way of building robust AI-based Chatbots is to use Conversational AI Tools offered by companies like Google and Amazon. These tools are based on complex machine learning models with AI that has been trained on millions of datasets. This makes them extremely intelligent and, in most cases, are almost indistinguishable from human operators.
In the next blog to learn data science, we’ll be looking at how to create a Dialog Flow Chatbot using Google’s Conversational AI Platform.
Learn how to create a bird recognition app using Custom Vision AI and Power BI for application to track the effect of climate change on bird populations.
Imagine a world without birds: the ecosystem would fall apart, bug populations would skyrocket, erosion would be catastrophic, crops would be torn down by insects, and so many other damages. Did you know that 1,200 species are facing extinction over the next century, and many more are suffering from severe habitat loss? (source).
Birds are fascinating and beautiful creatures who keep the ecosystem organized and balanced. They have emergent properties that help them react spontaneously in many situations, which are unique to other organisms.
Here are some fun facts: Parasitic jaegers ( a type of bird species) obtain food by stealing it directly from the beaks of other birds. The Bassian Thrush finds its food using the most unique way possible: they have adapted their foraging methods to depend on creating a large amount of gas to surprise earworms and trigger them to start moving (so the birds can find and eat it).
Due to the intriguing behaviors of birds, I got inspired and lifted to create an app that could identify any bird which you are captivated by in real-time. I also built this app to raise awareness of the heart-breaking reality that most birds face around the world.
Global trends of bird species survival chart
I first researched bird populations and their global trends from the data that contains the information of the past 24 years. I then analyzed this data set and created interactive visuals using Power BI.
This chart displays the Red List Index (RLI) of species survival from 1988 to 2012. RLI values range from 1 (no species at risk of extinction in the near term) down to 0 (all species are extinct).
As you click on the Power BI Line Chart you will notice that since 1988, bird species have faced a steadily increasing risk of extinction in every major region of the world (change being more rapid in certain regions). 1 in 8 currently known bird species in the world are at the threshold of extinction. The main reasons are degradation/loss of habitat (due to deforestation, sea-level rise, more frequent wildfires, droughts, flooding, loss of snow and ice, and more), bird trafficking, pollution, and global warming. As figured, most of these are a result of us humans.
Due to industrialization, more than 542,390,438 birds have lost their lives. Climate change is causing the natural food chain to fall apart. Birds starve with lesser food (therefore must fly longer distances), choke on human-made pollutants, and end up becoming weaker. Change is necessary, and with change comes compassion. This web app can help to build an understanding and empathy toward birds.
Let’s look at the Power BI reports and the web app:
Power BI report: Bird attributes / Bird Recognition
As you can see in this report, along with recognizing a specific bird in real-time, interactive visualizations from Power BI display the unique attributes and information about each bird and its status in the wild. The fun facts on the visualization about each bird will linger in your mind for days.
AI web app – To create a bird recognition app
In this webapp, I used cognitive services to upload the images (of the 85 bird species), tagged them, trained the model, and evaluated the results. With Microsoft Custom Vision AI, I could
train the model to recognize 85 bird species. You can upload an image from your file explorer, and it will then predict the species name of the bird and the accuracy tied to that tag.
The Custom Vision Service uses machine learning to classify the images I uploaded. The only thing I was required to do was specify the correct tag for each image. You can also tag thousands of images at a time. The AI algorithm is immensely powerful as it gives us great accuracy and once the model is trained, we can use the same model to classify new images according to the needs of our app.
Choose a bird image from your PC
Upload a bird image URL
Take a picture of a bird in real-time (only works on the phone app as described later in the blog)
Once you upload an image, it will call the Custom Vision Prediction API (which was already trained by Custom Vision, powered by Microsoft) to get the species of the bird.
I also created a phone application, called ‘AI for Birds’, that you can use with camera integration for taking pictures of birds in real-time. After using the built-in camera to take a picture, the name of the bird species will be identified and shown. As of now, I added 85 bird species into the AI model, however that number will increase.
The journey of building my own custom model, training it, and deploying it has been noteworthy. Here is the link to my other blog for how to build your own AI custom model. You can also follow along with these steps and use it as a tutorial: Instructions for how to create Power BI reports and publish them to the web will also be provided in the other blog.
The grim statistics are not just sad news for bird populations. They are sad news for the planet because the health of bird species is a key- measure for the state of ecosystems and biodiversity on planet earth in general.
I believe in: Exploring- Learning- Teaching- Sharing. There are several thousands of other bird species that are critical to biodiversity on planet earth.
Consider looking at my app and supporting organizations that work to fight the constant threats of habitat destruction and global warming today.
Our Earth is full of unique birds which took millions of years to evolve into the striking bird species we see today. We do not want to destroy organisms which took millions of years to evolve in just a couple of decades.
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.
“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
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.