chatgpt

Have you ever read a sentence in a book that caught you off guard with its meaning? Maybe it started in one direction and then, suddenly, the meaning changed, making you stumble and re-read it. These are known as garden-path sentences, and they are at the heart of a fascinating study on human cognition—a study that also sheds light on the capabilities of AI, specifically the language model ChatGPT.

Certainly! Here is a comparison table outlining the key aspects of language processing in ChatGPT versus humans based on the study:

This comparison table synthesizes the congruencies and distinctions discussed in the research, providing a broad understanding of how ChatGPT and humans process language and the potential for AI-assisted advancements in cognitive performance.

The Intrigue of Garden-Path Sentences

Certainly! Garden-path sentences are a unique and useful tool for linguists and psychologists studying human language processing and memory. These sentences are constructed in a way that initially leads the reader to interpret them incorrectly, often causing confusion or a momentary misunderstanding. The term “garden-path” refers to the idiom “to be led down the garden path,” meaning to be deceived or misled.

Usually, the first part of a garden-path sentence sets up an expectation that is violated by the later part, which forces the reader to go back and reinterpret the sentence structure to make sense of it. This reanalysis process is of great interest to researchers because it reveals how people construct meaning from language, how they deal with syntactic ambiguity, and how comprehension and memory interact.

The classic example given,

“The old man the boat,”

relies on the structural ambiguity of the word “man.”

Initially, “The old man” reads like a noun phrase, leading you to expect a verb to follow.

But as you read “the boat,” confusion arises because “the boat” doesn’t function as a verb.

Here’s where the garden-path effect comes into play:

To make sense of the sentence, you must realize “man” is being used as a verb, meaning to operate or staff, and “the old” functions as the subject. The corrected interpretation is that older individuals are the ones operating the boat.

Other examples of garden-path sentences might include:

• “The horse raced past the barn and fell.” At first read, you might think the sentence is complete after “barn,” making “fell” seem out of place. However, the sentence means the horse that was raced past the barn is the one that fell.
• “The complex houses married and single soldiers and their families.” Initially, “complex” might seem to be an adjective modifying “houses,” but “houses” is in fact a verb, and “the complex” refers to a housing complex.

These sentences demonstrate the cognitive work involved in parsing and understanding language. By examining how people react to and remember such sentences, researchers can gain insights into the psychological processes underlying language comprehension and memory formation

ChatGPT’s Predictive Capability

Garden-path sentences, with their inherent complexity and potential to mislead readers temporarily, have allowed researchers to observe the processes involved in human language comprehension and memory. The study at the core of this discussion aimed to push boundaries further by exploring whether an AI model, specifically ChatGPT, could predict human memory performance concerning these sentences.

The study presented participants with pairs of sentences, where the second sentence was a challenging garden-path sentence, and the first sentence provided context. This context was either fitting, meaning it was supportive and related to the garden-path sentence, making it easier to comprehend, or unfitting, where the context was not supportive and made comprehension more challenging.

ChatGPT, mirroring human cognitive processes to some extent, was used to assess the relatedness of these two sentences and to predict the memorability of the garden-path sentence.

The participants then participated in a memory task to see how well they recalled the garden-path sentences. The correlation between ChatGPT’s predictions and human performance was significant, suggesting that ChatGPT could indeed forecast how well humans would remember sentences based on the context provided.

For instance, if the first sentence was

“Jane gave up on the diet,” followed by the garden-path sentence

“Eating carrots sticks to your ribs,” the fitting context (“sticks” refers to adhering to a diet plan), makes it easier for both humans and

ChatGPT to make the sentence memorable. On the contrary, an unfitting context like

“The weather is changing” would offer no clarity, making the garden-path sentence less memorable due to a lack of relatability.

This reveals the role of context and relatability in language processing and memory. Sentences placed in a fitting context were rated as more memorable and, indeed, better remembered in subsequent tests. This alignment between AI assessments and human memory performance underscores ChatGPT’s predictive capability and the importance of cohesive information in language retention.

Memory Performance in Fitting vs. Unfitting Contexts

In the study under discussion, the experiment involved presenting participants with two types of sentence pairs. Each pair consisted of an initial context-setting sentence (Sentence 1) and a subsequent garden-path sentence (Sentence 2), which is a type of sentence designed to lead the reader to an initial misinterpretation.

In a “fitting” context, the first sentence provided would logically lead into the garden-path sentence, aiding comprehension by setting up the correct framework for interpretation.

For example, if Sentence 1 was “The city has no parks,” and Sentence 2 was “The ducks the children feed are at the lake,” the concept of feed here would fit with the absence of city parks, and the readers can easily understand that “the children feed” is a descriptive action relating to “the ducks.”

Conversely, in an “unfitting” context, the first sentence would not provide a supportive backdrop for the garden-path sentence, making it harder to parse and potentially less memorable.

If Sentence 1 was “John is a skilled carpenter,” and Sentence 2 remained “The ducks the children feed are at the lake,” the relationship between Sentence 1 and Sentence 2 is not clear because carpentry has no apparent connection to feeding ducks or the lake.

Participants in the study were asked to first rate the relatedness of these two sentences on a scale. The study found that participants rated fitting contexts as more related than unfitting ones.

The second part of the task was a surprise memory test where only garden-path sentences were presented, and the participants were required to recall them. It was discovered that the garden-path sentences that had a preceding fitting context were better remembered than those with an unfitting context—this indicated that context plays a critical role in how we process and retain sentences.

ChatGPT, a generative AI system, predicted this outcome. The model also rated garden-path sentences as more memorable when they had a fitting context, similar to human participants, demonstrating its capability to forecast memory performance based on context.

This highlights not only the role of context in human memory but also the potential for AI to predict human cognitive processes.

Stochastic Reasoning: A Potential Cognitive Mechanism

The study in question introduces the notion of stochastic reasoning as a potential cognitive mechanism affecting memory performance. Stochastic reasoning involves a probabilistic approach to understanding the availability of familiar information, also known as retrieval cues, which are instrumental in bolstering memory recall.

The presence of related, coherent information can elevate activation within our cognitive processes, leading to an increased likelihood of recalling that information later on.

Let’s consider an example to elucidate this concept. Imagine you are provided with the following two sentences as part of the study:

“The lawyer argued the case.”
“The evidence was compelling.”

In this case, the two sentences provide a fitting context where the first sentence creates a foundation of understanding related to legal scenarios and the second sentence builds upon that context by introducing “compelling evidence,” which is a familiar concept within the realm of law.

This clear and potent relation between the two sentences forms strong retrieval cues that enhance memory performance, as your brain more easily links “compelling evidence” with “lawyer argued the case,” which aids in later recollection.

Alternatively, if the second sentence was entirely unrelated, such as “The roses in the garden are in full bloom,” the lack of a fitting context would mean weak or absent retrieval cues. As the information related to law does not connect well with the concept of blooming roses, this results in less effective memory performance due to the disjointed nature of the information being processed.

The study found that when sentences are placed within a fitting context that aligns well with our existing knowledge and background, the relationship between the sentences is clear, thus providing stronger cues that streamline the retrieval process and lead to better retention and recall of information.

This reflects the significance of stochastic reasoning and the role of familiarity and coherence in enhancing memory performance.

ChatGPT vs. Human Language Processing

The paragraph delves into the intriguing observation that ChatGPT, a language model developed by OpenAI, and humans share a commonality in how they process language despite the underlying differences in their “operating systems” or cognitive architectures 1. Both seem to rely significantly on the surrounding context to comprehend incoming information and to integrate it coherently with the preceding context.

To illustrate, consider the following example of a garden-path sentence: “The old man the boat.” This sentence is confusing at first because “man” is often used as a verb, and the reader initially interprets “the old man” as a noun phrase.

The confusion is cleared up when provided with a fitting context, such as “elderly people are in control.” Now, the phrase makes sense—’man’ is understood as a verb meaning ‘to staff,’ and the garden-path sentence is interpreted correctly to mean that elderly people are the ones operating the boat.

However, if the preceding sentence was unrelated, such as “The birds flew to the south,” there is no helpful context to parse “The old man the boat” correctly, and it remains confusing, illustrating an unfitting context. This unfitness affects the recall of the garden-path sentence in the memory task, as it lacks clear, coherent links to preexisting knowledge or context that facilitate understanding and later recall.

The study’s findings depicted that when humans assess two sentences as being more related, which is naturally higher in fitting contexts than in unfitting ones, the memory performance for the ambiguous (garden-path) sentence also improves.

In a compelling parallel, ChatGPT generated similar assessments when given the same sentences, assigning higher relatedness values to fitting contexts over unfitting ones. This correlation suggests a similarity in how ChatGPT and humans use context to parse and remember new information.

Furthermore, the relatedness ratings were not just abstract assessments but tied directly to the actual memorability of the sentences. As with humans, ChatGPT’s predictions of memorability were also higher for sentences in fitting contexts, a phenomenon that may stem from its sophisticated language processing capabilities that crudely mimic cognitive processes involved in human memory.

This similarity in the use of context and its impact on memory retention is remarkable, considering the different mechanisms through which humans and machine learning models operate.

Broader Implications and the Future

The paragraph outlines the wider ramifications of the research findings on the predictive capabilities of generative AI like ChatGPT regarding human memory performance in language tasks. The research suggests that these AI models could have practical applications in several domains, including:

Education:

AI could be used to tailor learning experiences for students with diverse cognitive needs. By understanding how different students retain information, AI applications could guide educators in adjusting teaching materials, pace, and instructional approaches to cater to individual learning styles and abilities.

For example, if a student is struggling with remembering historical dates, the AI might suggest teaching methods or materials that align with their learning patterns to improve retention.

Eldercare:

The study indicates that older adults often face a cognitive slowdown, which could lead to more frequent memory problems. AI, once trained on data taking into account individual cognitive differences, could aid in developing personalized cognitive training and therapy plans aimed at enhancing mental functions in the elderly.

For instance, a cognitive enhancement program might be customized for an older adult who has difficulty recalling names or recent events by using strategies found effective through AI analysis.

Impact of AI on human cognition

The implications here go beyond just predicting human behavior; they extend to potentially improving cognitive processes through the intervention of AI.

These potential applications represent a synergistic relationship between AI and human cognitive research, where the insights gained from one field can materially benefit the other.

Furthermore, adaptive AI systems could continually learn and improve their predictions and recommendations based on new data, thereby creating a dynamic and responsive tool for cognitive enhancement and education.

Welcome to the world of open source (LLMs) large language models, where the future of technology meets community spirit. By breaking down the barriers of proprietary systems, open language models invite developers, researchers, and enthusiasts from around the globe to contribute to, modify, and improve upon the foundational models.

This collaborative spirit not only accelerates advancements in the field but also ensures that the benefits of AI technology are accessible to a broader audience. As we navigate through the intricacies of open-source language models, we’ll uncover the challenges and opportunities that come with adopting an open-source model, the ecosystems that support these endeavors, and the real-world applications that are transforming industries.

Benefits of open source LLMs

As soon as ChatGPT was revealed, OpenAI’s GPT models quickly rose to prominence. However, businesses began to recognize the high costs associated with closed-source models, questioning the value of investing in large models that lacked specific knowledge about their operations.

In response, many opted for smaller open LLMs, utilizing Retriever-And-Generator (RAG) pipelines to integrate their data, achieving comparable or even superior efficiency.

There are several advantages to closed-source large language models worth considering.

1. Cost-effectiveness:

Open-source Large Language Models (LLMs) present a cost-effective alternative to their proprietary counterparts, offering organizations a financially viable means to harness AI capabilities.

• No licensing fees are required, significantly lowering initial and ongoing expenses.
• Organizations can freely deploy these models, leading to direct cost reductions.
• Open large language models allow for specific customization, enhancing efficiency without the need for vendor-specific customization services.

2. Flexibility:

Companies are increasingly preferring the flexibility to switch between open and proprietary (closed) models to mitigate risks associated with relying solely on one type of model.

This flexibility is crucial because a model provider’s unexpected update or failure to keep the model current can negatively affect a company’s operations and customer experience.

Companies often lean towards open language models when they want more control over their data and the ability to fine-tune models for specific tasks using their data, making the model more effective for their unique needs.

3. Data ownership and control:

Companies leveraging open-source language models gain significant control and ownership over their data, enhancing security and compliance through various mechanisms. Here’s a concise overview of the benefits and controls offered by using open large language models:

Data hosting control:

• Choice of data hosting on-premises or with trusted cloud providers.
• Crucial for protecting sensitive data and ensuring regulatory compliance.

Internal data processing:

• Avoids sending sensitive data to external servers.
• Reduces the risk of data breaches and enhances privacy.

Customizable data security features:

• Flexibility to implement data anonymization and encryption.
• Helps comply with data protection laws like GDPR and CCPA.

Transparency and audibility:

• The open-source nature allows for code and process audits.
• Ensures alignment with internal and external compliance standards.

Examples of enterprises leveraging open source LLMs

Here are examples of how different companies around the globe have started leveraging open language models.

1. VMWare

VMWare, a noted enterprise in the field of cloud computing and digitalization, has deployed an open language model called the HuggingFace StarCoder. Their motivation for using this model is to enhance the productivity of their developers by assisting them in generating code.

This strategic move suggests VMware’s priority for internal code security and the desire to host the model on their infrastructure. It contrasts with using an external system like Microsoft-owned GitHub’s Copilot, possibly due to sensitivities around their codebase and not wanting to give Microsoft access to it

2. Brave

Brave, the security-focused web browser company, has deployed an open-source large language model called Mixtral 8x7B from Mistral AI for their conversational assistant named Leo, which aims to differentiate the company by emphasizing privacy.

Previously, Leo utilized the Llama 2 model, but Brave has since updated the assistant to default to the Mixtral 8x7B model. This move illustrates the company’s commitment to integrating open LLM technologies to maintain user privacy and enhance their browser’s functionality.

3. Gab Wireless

Gab Wireless, the company focused on child-friendly mobile phone services, is using a suite of open-source models from Hugging Face to add a security layer to its messaging system. The aim is to screen the messages sent and received by children to ensure that no inappropriate content is involved in their communications. This usage of open language models helps Gab Wireless ensure safety and security in children’s interactions, particularly with individuals they do not know.

4. IBM

IBM actively incorporates open models across various operational areas.

• AskHR application: Utilizes IBM’s Watson Orchestration and open language models for efficient HR query resolution.
• Consulting advantage tool: Features a “Library of Assistants” powered by IBM’s wasonx platform and open-source large language models, aiding consultants.
• Marketing initiatives: Employs an LLM-driven application, integrated with Adobe Firefly, for innovative content and image generation in marketing.

5. Intuit

Intuit, the company behind TurboTax, QuickBooks, and Mailchimp, has developed its language models incorporating open LLMs into the mix. These models are key components of Intuit Assist, a feature designed to help users with customer support, analysis, and completing various tasks. The company’s approach to building these large language models involves using open-source frameworks, augmented with Intuit’s unique, proprietary data.

6. Shopify

Shopify has employed publically available language models in the form of Shopify Sidekick, an AI-powered tool that utilizes Llama 2. This tool assists small business owners with automating tasks related to managing their commerce websites. It can generate product descriptions, respond to customer inquiries, and create marketing content, thereby helping merchants save time and streamline their operations.

7. LyRise

LyRise, a U.S.-based talent-matching startup, utilizes open language models by employing a chatbot built on Llama, which operates similarly to a human recruiter. This chatbot assists businesses in finding and hiring top AI and data talent, drawing from a pool of high-quality profiles in Africa across various industries.

8. Niantic

Niantic, known for creating Pokémon Go, has integrated open-source large language models into its game through the new feature called Peridot. This feature uses Llama 2 to generate environment-specific reactions and animations for the pet characters, enhancing the gaming experience by making character interactions more dynamic and context-aware.

9. Perplexity

Here’s how Perplexity leverages open source LLMs

• Response generation process:

When a user poses a question, Perplexity’s engine executes approximately six steps to craft a response. This process involves the use of multiple language models, showcasing the company’s commitment to delivering comprehensive and accurate answers.

In a crucial phase of response preparation, specifically the second-to-last step, Perplexity employs its own specially developed open-source language models. These models, which are enhancements of existing frameworks like Mistral and Llama, are tailored to succinctly summarize content relevant to the user’s inquiry.

The fine-tuning of these models is conducted on AWS Bedrock, emphasizing the choice of open models for greater customization and control. This strategy underlines Perplexity’s dedication to refining its technology to produce superior outcomes.

• Partnership and API integration:

Expanding its technological reach, Perplexity has entered into a partnership with Rabbit to incorporate its open-source large language models into the R1, a compact AI device. This collaboration facilitated through an API, extends the application of Perplexity’s innovative models, marking a significant stride in practical AI deployment.

10. CyberAgent

CyberAgent, a Japanese digital advertising firm, leverages open language models with its OpenCALM initiative, a customizable Japanese language model enhancing its AI-driven advertising services like Kiwami Prediction AI. By adopting an open-source approach, CyberAgent aims to encourage collaborative AI development and gain external insights, fostering AI advancements in Japan. Furthermore, a partnership with Dell Technologies has upgraded their server and GPU capabilities, significantly boosting model performance (up to 5.14 times faster), thereby streamlining service updates and enhancements for greater efficiency and cost-effectiveness.

Challenges of open source LLMs

While open LLMs offer numerous benefits, there are substantial challenges that can plague the users.

1. Customization necessity:

Open language models often come as general-purpose models, necessitating significant customization to align with an enterprise’s unique workflows and operational processes. This customization is crucial for the models to deliver value, requiring enterprises to invest in development resources to adapt these models to their specific needs.

2. Support and governance:

Unlike proprietary models that offer dedicated support and clear governance structures, publically available large language models present challenges in managing support and ensuring proper governance. Enterprises must navigate these challenges by either developing internal expertise or engaging with the open-source community for support, which can vary in responsiveness and expertise.

3. Reliability of techniques:

Techniques like Retrieval-Augmented Generation aim to enhance language models by incorporating proprietary data. However, these techniques are not foolproof and can sometimes introduce inaccuracies or inconsistencies, posing challenges in ensuring the reliability of the model outputs.

4. Language support:

While proprietary models like GPT are known for their robust performance across various languages, open-source large language models may exhibit variable performance levels. This inconsistency can affect enterprises aiming to deploy language models in multilingual environments, necessitating additional effort to ensure adequate language support.

5. Deployment complexity:

Deploying publically available language models, especially at scale, involves complex technical challenges. These range from infrastructure considerations to optimizing model performance, requiring significant technical expertise and resources to overcome.

6. Uncertainty and risk:

Relying solely on one type of model, whether open or closed source, introduces risks such as the potential for unexpected updates by the provider that could affect model behavior or compliance with regulatory standards.

7. Legal and ethical considerations:

Deploying LLMs entails navigating legal and ethical considerations, from ensuring compliance with data protection regulations to addressing the potential impact of AI on customer experiences. Enterprises must consider these factors to avoid legal repercussions and maintain trust with their users.

8. Lack of public examples:

The scarcity of publicly available case studies on the deployment of publically available LLMs in enterprise settings makes it challenging for organizations to gauge the effectiveness and potential return on investment of these models in similar contexts.

Overall, while there are significant potential benefits to using publically available language models in enterprise settings, including cost savings and the flexibility to fine-tune models, addressing these challenges is critical for successful deployment

Embracing open source LLMs: A path to innovation and flexibility

In conclusion, open-source language models represent a pivotal shift towards more accessible, customizable, and cost-effective AI solutions for enterprises. They offer a unique blend of benefits, including significant cost savings, enhanced data control, and the ability to tailor AI tools to specific business needs, while also presenting challenges such as the need for customization and navigating support complexities.

Through the collaborative efforts of the global open-source community and the innovative use of these models across various industries, enterprises are finding new ways to leverage AI for growth and efficiency.

However, success in this endeavor requires a strategic approach to overcome inherent challenges, ensuring that businesses can fully harness the potential of publically available LLMs to drive innovation and maintain a competitive edge in the fast-evolving digital landscape.

In the rapidly evolving landscape of technology, small businesses are continually looking for tools that can give them a competitive edge. One such tool that has garnered significant attention is ChatGPT Team by OpenAI.

Designed to cater to small and medium-sized businesses (SMBs), ChatGPT Team offers a range of functionalities that can transform various aspects of business operations. Here are three compelling reasons why your small business should consider signing up for ChatGPT Team, along with real-world use cases and the value it adds.

Read more about how to boost your business with ChatGPT

They promise not to use your business data for training purposes, which is a big plus for privacy. You also get to work together on custom GPT projects and have a handy admin panel to keep everything organized. On top of that, you get access to some pretty advanced tools like DALL·E, Browsing, and GPT-4, all with a generous 32k context window to work with.

The best part? It’s only $25 for each person in your team. Considering it’s like having an extra helping hand for each employee, that’s a pretty sweet deal!

The official announcement explains:

“Integrating AI into everyday organizational workflows can make your team more productive.

In a recent study by the Harvard Business School, employees at Boston Consulting Group who were given access to GPT-4 reported completing tasks 25% faster and achieved a 40% higher quality in their work as compared to their peers who did not have access.”

Features of ChatGPT Team

ChatGPT Team, a recent offering from OpenAI, is specifically tailored for small and medium-sized team collaborations. Here’s a detailed look at its features:

1. Advanced AI Models Access: ChatGPT Team provides access to OpenAI’s advanced models like GPT-4 and DALL·E 3, ensuring state-of-the-art AI capabilities for various tasks.
2. Dedicated Workspace for Collaboration: It offers a dedicated workspace for up to 149 team members, facilitating seamless collaboration on AI-related tasks.
3. Administration Tools: The subscription includes administrative tools for team management, allowing for efficient control and organization of team activities.
4. Advanced Data Analysis Tools: ChatGPT Team includes tools for advanced data analysis, aiding in processing and interpreting large volumes of data effectively.
5. Enhanced Context Window: The service features a 32K context window for conversations, providing a broader range of data for AI to reference and work with, leading to more coherent and extensive interactions.
6. Affordability for SMEs: Aimed at small and medium enterprises, the plan offers an affordable subscription model, making it accessible for smaller teams with budget constraints.
7. Collaboration on Threads & Prompts: Team members can collaborate on threads and prompts, enhancing the ideation and creative process.
8. Usage-Based Charging: Teams are charged based on usage, which can be a cost-effective approach for businesses that have fluctuating AI usage needs.
9. Public Sharing of Conversations: There is an option to publicly share ChatGPT conversations, which can be beneficial for transparency or marketing purposes.
10. Similar Features to ChatGPT Enterprise: Despite being targeted at smaller teams, ChatGPT Team still retains many features found in the more expansive ChatGPT Enterprise version.

These features collectively make ChatGPT Team an adaptable and powerful tool for small to medium-sized teams, enhancing their AI capabilities while providing a platform for efficient collaboration.

Enhanced Customer Service and Support

One of the most immediate benefits of ChatGPT Team is its ability to revolutionize customer service. By leveraging AI-driven chatbots, small businesses can provide instant, 24/7 support to their customers. This not only improves customer satisfaction but also frees up human resources to focus on more complex tasks.

Real Use Case:

A retail company implemented ChatGPT Team to manage their customer inquiries. The AI chatbot efficiently handled common questions about product availability, shipping, and returns. This led to a 40% reduction in customer wait times and a significant increase in customer satisfaction scores.

Value for Small Businesses:

• Reduces response times for customer inquiries.
• Frees up human customer service agents to handle more complex issues.
• Provides round-the-clock support without additional staffing costs.

Streamlining Content Creation and Digital Marketing

In the digital age, content is king. ChatGPT Team can assist small businesses in generating creative and engaging content for their digital marketing campaigns. From blog posts to social media updates, the tool can help generate ideas, create drafts, and even suggest SEO-friendly keywords.

Real Use Case:

A boutique marketing agency used ChatGPT Team to generate content ideas and draft blog posts for their clients. This not only improved the efficiency of their content creation process but also enhanced the quality of the content, resulting in better engagement rates for their clients.

Value for Small Businesses:

• Accelerates the content creation process.
• Helps in generating creative and relevant content ideas.
• Assists in SEO optimization to improve online visibility.

Automation of Repetitive Tasks and Data Analysis

Small businesses often struggle with the resource-intensive nature of repetitive tasks and data analysis. ChatGPT Team can automate these processes, enabling businesses to focus on strategic growth and innovation. This includes tasks like data entry, scheduling, and even analyzing customer feedback or market trends.

Real Use Case:

A small e-commerce store utilized ChatGPT Team to analyze customer feedback and market trends. This provided them with actionable insights, which they used to optimize their product offerings and marketing strategies. As a result, they saw a 30% increase in sales over six months.

Value for Small Businesses:

• Automates time-consuming, repetitive tasks.
• Provides valuable insights through data analysis.
• Enables better decision-making and strategy development.

Conclusion

For small businesses looking to stay ahead in a competitive market, ChatGPT Team offers a range of solutions that enhance efficiency, creativity, and customer engagement. By embracing this AI-driven tool, small businesses can not only streamline their operations but also unlock new opportunities for growth and innovation.

In the rapidly evolving world of artificial intelligence, OpenAI has marked yet another milestone with the launch of the GPT Store. This innovative platform ushers in a new era for AI enthusiasts, developers, and businesses alike, offering a unique space to explore, create, and share custom versions of ChatGPT models.

These models are crafted not only by OpenAI but also by community members, enabling a wide range of applications and customizations.

The store facilitates easy exploration of these models, organized into different categories to suit various needs, such as productivity, education, and lifestyle. Visit chat.openai.com/gpts to explore.

This initiative represents a significant step in democratizing AI technology, allowing both developers and enthusiasts to share and leverage AI advancements in a more collaborative and innovative environment.

In this blog, we will delve into the exciting features of the GPT Store, its potential impact on various sectors, and what it means for the future of AI applications.

Features of GPT Store

1. Platform for custom GPTs: It is an innovative platform where users can find, use, and share custom versions of ChatGPT, also known as GPTs. These GPTs are essentially custom versions of the standard ChatGPT, tailored for a specific purpose and enhanced with their additional information.
2. Diverse range and weekly highlights: The store features a diverse range of GPTs, developed by both OpenAI’s partners and the broader community. Additionally, it offers weekly highlights of useful and impactful GPTs, serving as a showcase of the best and most interesting applications of the technology.
3. Availability and enhanced controls: It is accessible to ChatGPT Plus, Teams and Enterprise For these users, the platform provides enhanced administrative controls. This includes the ability to choose how internal-only GPTs are shared and which external GPTs may be used within their businesses.
4. User-created GPTs: It also empowers subscribers to create their own GPTs, even without any programming expertise.
   For those who want to share a GPT in the store, they are required to save their GPT for everyone and verify their Builder Profile. This facilitates a continuous evolution and enrichment of the platform’s offerings.
5. Revenue-sharing program: An exciting feature is its planned revenue-sharing program. This program intends to reward GPT creators based on the user engagement their GPTs generate. This feature is expected to provide a new lucrative avenue for them.
6. Management for team and enterprise customers: It offers special features for Team and Enterprise customers, including private sections with securely published GPTs and enhanced admin controls.

Examples of custom GPTs available on the GPT Store

The earliest featured GPTs on the platform include the following:

1. AllTrails: This platform offers personalized recommendations for hiking and walking trails, catering to outdoor enthusiasts.
2. Khan Academy Code Tutor: An educational tool that provides programming tutoring, making learning code more accessible.
3. Canva: A GPT designed to assist in digital design, integrated into the popular design platform, Canva.
4. Books: This GPT is tuned to provide advice on what to read and field questions about reading, making it an ideal tool for avid readers.

What is the significance of the GPT Store in OpenAI’s business strategy?

This is a significant component of OpenAI’s business strategy as it aims to expand OpenAI’s ecosystem, stay competitive in the AI industry, and serve as a new revenue source.

The Store likened to Apple’s App Store, is a marketplace that allows users to list personalized chatbots, or GPTs, that they’ve built for others to download.

By offering a range of GPTs developed by both OpenAI business partners and the broader ChatGPT community, this platform democratizes AI technology, making it more accessible and useful to a wide range of users.

Importantly, it is positioned as a potential profit-making avenue for GPT creators through a planned revenue-sharing program based on user engagement. This aspect might foster a more vibrant and innovative community around the platform.

By providing these platforms, OpenAI aims to stay ahead of rivals such as Anthropic, Google, and Meta in the AI industry. As of November, ChatGPT had about 100 million weekly active users and more than 92% of Fortune 500 companies use the platform, underlining its market penetration and potential for growth.

Boost your business with ChatGPT: 10 innovative ways to monetize using AI

Looking ahead: GPT Store’s role in shaping the future of AI

The launch of the platform by OpenAI is a significant milestone in the realm of AI. By offering a platform where various GPT models, both from OpenAI and the community, are available, the AI platform opens up new possibilities for innovation and application across different sectors.

It’s not just a marketplace; it’s a breeding ground for creativity and a step forward in making AI more user-friendly and adaptable to diverse needs.

The potential of the newly launched Store extends far beyond its current offerings. It signifies a future where AI can be more personalized and integrated into various aspects of work and life.

With the advent of language models like ChatGPT, improving your data science skills has never been easier. 

Data science has become an increasingly important field in recent years, as the amount of data generated by businesses, organizations, and individuals has grown exponentially.

With the help of artificial intelligence (AI) and machine learning (ML), data scientists are able to extract valuable insights from this data to inform decision-making and drive business success.

However, becoming a skilled data scientist requires a lot of time and effort, as well as a deep understanding of statistics, programming, and data analysis techniques. 

ChatGPT is a large language model that has been trained on a massive amount of text data, making it an incredibly powerful tool for natural language processing (NLP).

Uses of generative AI for data scientists

Generative AI can help data scientists with their projects in a number of ways.

Test your knowledge of generative AI

Data cleaning and preparation

Generative AI can be used to clean and prepare data by identifying and correcting errors, filling in missing values, and deduplicating data. This can free up data scientists to focus on more complex tasks.

Example: A data scientist working on a project to predict customer churn could use generative AI to identify and correct errors in customer data, such as misspelled names or incorrect email addresses. This would ensure that the model is trained on accurate data, which would improve its performance.

Feature engineering

Generative AI can be used to create new features from existing data. This can help data scientists to improve the performance of their models.

Example: A data scientist working on a project to predict fraud could use generative AI to create a new feature that represents the similarity between a transaction and known fraudulent transactions. This feature could then be used to train a model to predict whether a new transaction is fraudulent.

Read more about feature engineering

Model development

Generative AI can be used to develop new models or improve existing models. For example, generative AI can be used to generate synthetic data to train models on, or to develop new model architectures.

Example: A data scientist working on a project to develop a new model for image classification could use generative AI to generate synthetic images of different objects. This synthetic data could then be used to train the model, even if there is not a lot of real-world data available.

Model evaluation

Generative AI can be used to evaluate the performance of models on data that is not used to train the model. This can help data scientists to identify and address any overfitting in the model.

Example: A data scientist working on a project to develop a model for predicting customer churn could use generative AI to generate synthetic data of customers who have churned and customers who have not churned.

This synthetic data could then be used to evaluate the model’s performance on unseen data.

Communication and explanation

Generative AI can be used to communicate and explain the results of data science projects to non-technical audiences. For example, generative AI can be used to generate text or images that explain the predictions of a model.

Example: A data scientist working on a project to predict customer churn could use generative AI to generate a report that explains the factors that are most likely to lead to customer churn. This report could then be shared with the company’s sales and marketing teams to help them to develop strategies to reduce customer churn.

How to use ChatGPT for Data Science projects

With its ability to understand and respond to natural language queries, ChatGPT can be used to help you improve your data science skills in a number of ways. Here are just a few examples: 

Answering data science-related questions 

One of the most obvious ways in which ChatGPT can help you improve your data science skills is by answering your data science-related questions.

Whether you’re struggling to understand a particular statistical concept, looking for guidance on a programming problem, or trying to figure out how to implement a specific ML algorithm, ChatGPT can provide you with clear and concise answers that will help you deepen your understanding of the subject. 

Providing personalized learning resources 

In addition to answering your questions, ChatGPT can also provide you with personalized learning resources based on your specific interests and skill level.

Read more about ChatGPT plugins

For example, if you’re just starting out in data science, ChatGPT can recommend introductory courses or tutorials to help you build a strong foundation. If you’re more advanced, ChatGPT can recommend more specialized resources or research papers to help you deepen your knowledge in a particular area. 

Offering real-time feedback 

Another way in which ChatGPT can help you improve your data science skills is by offering real-time feedback on your work.

For example, if you’re working on a programming project and you’re not sure if your code is correct, you can ask ChatGPT to review your code and provide feedback on any errors or issues it finds. This can help you catch mistakes early on and improve your coding skills over time. 

Generating data science projects and ideas 

Finally, ChatGPT can also help you generate data science projects and ideas to work on. By analyzing your interests, skill level, and current knowledge, ChatGPT can suggest project ideas that will challenge you and help you build new skills.

Additionally, if you’re stuck on a project and need inspiration, ChatGPT can provide you with creative ideas or alternative approaches that you may not have considered. 

Improve your data science skills with generative AI

In conclusion, ChatGPT is an incredibly powerful tool for improving your data science skills. Whether you’re just starting out or you’re a seasoned professional, ChatGPT can help you deepen your understanding of data science concepts, provide you with personalized learning resources, offer real-time feedback on your work, and generate new project ideas.

By leveraging the power of language models like ChatGPT, you can accelerate your learning and become a more skilled and knowledgeable data scientist. 

ChatGPT made a significant market entrance, shattering records by swiftly reaching 100 million monthly active users in just two months. Its trajectory has since been on a consistent growth. Notably, ChatGPT has embraced a range of plugins that extend its capabilities, enabling users to do more than merely generate textual responses. 

What are ChatGPT Plugins? 

ChatGPT plugins serve as supplementary features that amplify the functionality of ChatGPT. These plugins are crafted by third-party developers and are readily accessible in the ChatGPT plugins store. 

ChatGPT plugins can be used to extend the capabilities of ChatGPT in a variety of ways, such as: 

• Accessing and processing external data 
• Performing complex computations 
• Using third-party services 

In this article, we’ll dive into the top 6 ChatGPT plugins tailored for data science. These plugins encompass a wide array of functions, spanning tasks such as web browsing, automation, code interpretation, and streamlining workflow processes. 

1. Wolfram 

The Wolfram plugin for ChatGPT is a powerful tool that makes ChatGPT smarter by giving it access to the Wolfram Alpha Knowledgebase and Wolfram programming language. This means that ChatGPT can now perform complex computations, access real-time data, and generate visualizations, all from within ChatGPT. 

Here are some of the things that the Wolfram plugin for ChatGPT can do: 

• Perform complex computations: You can ask ChatGPT to calculate the factorial of a large number or to find the roots of a polynomial equation. ChatGPT can also use Wolfram Language to perform more complex tasks, such as simulating physical systems or training machine learning models. Here’s an example of Wolfram enabling ChatGPT to solve complex integrations. 

Source: Stephen Wolfram Writings 

• Generate visualizations: You can ask ChatGPT to generate a plot of a function or to create a map of a specific region. ChatGPT can also use Wolfram Language to create more complex visualizations, such as interactive charts and 3D models. 

Source: Stephen Wolfram Writings 

Read this blog to Master ChatGPT cheatsheet

2. Noteable: 

The Noteable Notebook plugin for ChatGPT is a powerful tool that makes it possible to use ChatGPT within the Noteable computational notebook environment. This means that you can use natural language prompts to perform advanced data analysis tasks, generate visualizations, and train machine learning models without the need for complex coding knowledge. 

Here are some examples of how you can use the Noteable Notebook plugin for ChatGPT: 

• Exploratory Data Analysis (EDA): You can use the plugin to generate descriptive statistics, create visualizations, and identify patterns in your data. 
• Deploy machine learning Models:  You can use the plugin to train and deploy machine learning models. This can be useful for tasks such as classification, regression, and forecasting. 
• Data manipulation: You can use the plugin to perform data cleaning, transformation, and feature engineering tasks. 
• Data visualization: You can use the plugin to create interactive charts, maps, and other visualizations. 

Here’s an example of a Noteable plugin enabling ChatGPT to help perform geospatial analysis: 

Source: Noteable.io 

3. Code Interpreter 

ChatGPT Code Interpreter is a part of ChatGPT that allows you to run Python code in a live working environment. With Code Interpreter, you can perform tasks such as data analysis, visualization, coding, math, and more. You can also upload and download files to and from ChatGPT with this feature. To use Code Interpreter, you must have a “ChatGPT Plus” subscription and activate the plugin in the settings. 

Here’s an example of data visualization through Code Interpreter. 

4. ChatWithGit

ChatWithGit is a ChatGPT plugin that allows you to search for code on GitHub repositories using natural language queries. It is a powerful tool that can help you find code quickly and easily, even if you are not familiar with the codebase. 

To use ChatWithGit, you first need to install the plugin. You can do this by following the instructions on the ChatWithGit GitHub page. Once the plugin is installed, you can start using it to search for code by simply typing a natural language query into the ChatGPT chat box. 

For example, you could type “find Python code for web scraping” or “find JavaScript code for sorting an array.” ChatGPT will then query the Chat with Git plugin, which will return a list of code results from GitHub repositories. 

Learn more about ChatGPT enterprise

5. Zapier 

The Zapier plugin allows you to connect ChatGPT with other cloud-based applications, automating workflows and integrating data. This can be useful for data scientists who need to streamline their data science pipeline or automate repetitive tasks. 

For example, you can use Zapier to automatically trigger a data pipeline in ChatGPT when a new dataset is uploaded to Google Drive or to automatically send a notification to Slack when a machine learning model finishes training. 

Here’s a detailed article on how you can use Zapier for automating tasks using ChatGPT: 

6 ways to use the Zapier ChatGPT Plugin 

6. ScholarAI 

The ScholarAI plugin is designed to help people with academic and research-related tasks. It provides access to a vast database of scholarly articles and books, as well as tools for literature review and data analysis. 

For example, you could use ScholarAI to identify relevant research papers on a given topic or to extract data from academic papers and generate citations. 

Source: ScholarAI 

Experiment with ChatGPT now!

From computational capabilities to code interpretation and automation, ChatGPT is now a versatile tool spanning data science, coding, academic research, and workflow automation. This journey marks the rise of an AI powerhouse, promising continued innovation and utility in the realm of AI-powered assistance 

The evolution of the GPT Series culminates in ChatGPT, delivering more intuitive and contextually aware conversations than ever before.

What are chatbots?  

AI chatbots are smart computer programs that can process and understand users’ requests and queries in voice and text. It mimics and generates responses in a human conversational manner. AI chatbots are widely used today from personal assistance to customer service and much more. They are assisting humans in every field making the work more productive and creative. 

Deep learning And NLP

Deep Learning and Natural Language Processing (NLP) are like best friends in the world of computers and language. Deep Learning is when computers use their brains, called neural networks, to learn lots of things from a ton of information.

NLP is all about teaching computers to understand and talk like humans. When Deep Learning and NLP work together, computers can understand what we say, translate languages, make chatbots, and even write sentences that sound like a person. This teamwork between Deep Learning and NLP helps computers and people talk to each other better in the most efficient manner.  

How are chatbots built? 

Building Chatbots involves creating AI systems that employ deep learning techniques and natural language processing to simulate natural conversational behavior.

The machine learning models are trained on huge datasets to figure out and process the context and semantics of human language and produce relevant results accordingly. Through deep learning and NLP, the machine can recognize the patterns from text and generate useful responses. 

Transformers in chatbots 

Transformers are advanced models used in AI for understanding and generating language. This efficient neural network architecture was developed by Google in 2015. They consist of two parts: the encoder, which understands input text, and the decoder, which generates responses.

The encoder pays attention to words’ relationships, while the decoder uses this information to produce a coherent text. These models greatly enhance chatbots by allowing them to understand user messages (encoding) and create fitting replies (decoding).

With Transformers, chatbots engage in more contextually relevant and natural conversations, improving user interactions. This is achieved by efficiently tracking conversation history and generating meaningful responses, making chatbots more effective and lifelike. 

GPT Series – Generative pre trained transformer 

 GPT is a large language model (LLM) which uses the architecture of Transformers. I was developed by OpenAI in 2018. GPT is pre-trained on a huge amount of text dataset. This means it learns patterns, grammar, and even some reasoning abilities from this data. Once trained, it can then be “fine-tuned” on specific tasks, like generating text, answering questions, or translating languages.

This process of fine-tuning comes under the concept of transfer learning. The “generative” part means it can create new content, like writing paragraphs or stories, based on the patterns it learned during training. GPT has become widely used because of its ability to generate coherent and contextually relevant text, making it a valuable tool in a variety of applications such as content creation, chatbots, and more.  

The advent of ChatGPT: 

ChatGPT is a chatbot designed by OpenAI. It uses the “Generative Pre-Trained Transformer” (GPT) series to chat with the user analogously as people talk to each other. This chatbot quickly went viral because of its unique capability to learn complications of natural language and interactions and give responses accordingly.

ChatGPT is a powerful chatbot capable of producing relevant answers to questions, text summarization, drafting creative essays and stories, giving coded solutions, providing personal recommendations, and many other things. It attracted millions of users in a noticeably short period. 

ChatGPT’s story is a journey of growth, starting with earlier versions in the GPT series. In this blog, we will explore how each version from the series of GPT has added something special to the way computers understand and use language and how GPT-3 serves as the foundation for ChatGPT’s innovative conversational abilities. 

GPT-1: 

GPT-1 was the first model of the GPT series developed by OpenAI. This innovative model demonstrated the concept that text can be generated using transformer design. GPT-1 introduced the concept of generative pre-training, where the model is first trained on a broad range of text data to develop a comprehensive understanding of language. It consisted of 117 million parameters and produced much more coherent results as compared to other models of its time. It was the foundation of the GPT series, and it paved a path for advancement and revolution in the domain of text generation. 

GPT-2: 

GPT-2 was much bigger as compared to GPT-1 trained on 1.5 billion parameters. It makes the model have a stronger grasp of the context and semantics of real-world language as compared to GPT-1. It introduces the concept of “Task conditioning.” This enables GTP-2 to learn multiple tasks within a single unsupervised model by conditioning its outputs on both input and task information.

GPT-2 highlighted zero-shot learning by carrying out tasks without prior examples, solely guided by task instructions. Moreover, it achieved remarkable zero-shot task transfer, demonstrating its capacity to seamlessly comprehend and execute tasks with minimal or no specific examples, highlighting its adaptability and versatile problem-solving capabilities. 

As the ChatGPT model was getting more advanced it started to have new qualities of writing long creative essays, answering complex questions instead of just predicting the next word. So, it was becoming more human-like and attracted many users for their day-to-day tasks. 

GPT-3: 

GPT-3 was trained on an even larger dataset and has 175 billion parameters. It gives a more natural-looking response making the model conversational. It was better at common sense reasoning than the earlier models. GTP-3 can not only generate human-like text but is also capable of generating programming code snippets providing more innovative solutions. 

GPT-3’s enhanced capacity, compared to GPT-2, extends its zero-shot and few-shot learning capabilities. It can give relevant and accurate solutions to uncommon problems, requiring training on minimal examples or even performing without prior training.  

Instruct GPT: 

An improved version of GPT-3 also known as InstructGPT(GPT-3.5) produces results that align with human expectations. It uses a “Human Feedback Model” to make the neural network respond in a way that is according to real-world expectations.

It begins by creating a supervised policy via demonstrations on input prompts. Comparison data is then collected to build a reward model based on human-preferred model outputs. This reward model guides the fine-tuning of the policy using Proximal Policy Optimization.

Iteratively, the process refines the policy by continuously collecting comparison data, training an updated reward model, and enhancing the policy’s performance. This iterative approach ensures that the model progressively adapts to preferences and optimizes its outputs to align with human expectations. The figure below gives a clearer depiction of the process discussed. 

GPT-3.5 stands as the default model for ChatGPT, while the GPT-3.5-Turbo Model empowers users to construct their own custom chatbots with similar abilities as ChatGPT. It is worth noting that large language models like ChatGPT occasionally generate responses that are inaccurate, impolite, or not helpful.

This is often due to their training in predicting subsequent words in sentences without always grasping the context. To remedy this, InstructGPT was devised to steer model responses toward better alignment with user preferences.

Read more –> FraudGPT: Evolution of ChatGPT into an AI weapon for cybercriminals in 2023

GPT-4 and beyond: 

After GTP-3.5 comes GPT-4. According to some resources, GPT-4 is estimated to have 1.7 trillion parameters. These enormous number of parameters make the model more efficient and make it able to process up to 25000 words at once.

This means that GPT-4 can understand texts that are more complex and realistic. The model has multimodal capabilities which means it can process both images and text. It can not only interpret the images and label them but can also understand the context of images and give relevant suggestions and conclusions. The GPT-4 model is available in ChatGPT Plus, a premium version of ChatGPT. 

So, after going through the developments that are currently done by OpenAI, we can expect that OpenAI will be making more improvements in the models in the coming years. Enabling it to handle voice commands, make changes to web apps according to user instruction, and aid people in the most efficient way that has never been done before. 

Watch: ChatGPT Unleashed: Live Demo and Best Practices for NLP Applications 

This live presentation from Data Science Dojo gives more understanding of ChatGPT and its use cases. It demonstrates smart prompting techniques for ChatGPT to get the desired responses and ChatGPT’s ability to assist with tasks like data labeling and generating data for NLP models and applications. Additionally, the demo acknowledges the limitations of ChatGPT and explores potential strategies to overcome them.  

Wrapping up: 

ChatGPT developed by OpenAI is a powerful chatbot. It uses the GPT series as its neural network, which is improving quickly. From generating one-liner responses to generating multiple paragraphs with relevant information, and summarizing long detailed reports, the model is capable of interpreting and understanding visual inputs and generating responses that align with human expectations.

With more advancement, the GPT series is getting more grip on the structure and semantics of the human language. It not only relies on its training information but can also use real-time data given by the user to generate results. In the future, we expect to see more breakthrough advancements by OpenAI in this domain empowering this chatbot to assist us in the most effective manner like ever before. 

Master ChatGPT to automate repetitive tasks, including answering frequently asked questions, allowing businesses to provide efficient and round-the-clock customer support. It assists in generating content such as articles, blog posts, and product descriptions, saving time and resources for content creation.

AI-driven chatbots like ChatGPT can analyze customer data to provide personalized marketing recommendations and engage customers in real time. By automating various tasks and processes, businesses can reduce operational costs and allocate resources to more strategic activities.

Key use cases:

 1. Summarizing: ChatGPT is highly effective at summarizing long texts, transcripts, articles, and reports. It can condense lengthy content into concise summaries, making it a valuable tool for quickly extracting key information from extensive documents.

Prompt Example: “Please summarize the key findings from this 20-page research report on climate change.”

2. Brainstorming: ChatGPT assists in generating ideas, outlines, and new concepts. It can provide creative suggestions and help users explore different angles and approaches to various topics or projects.

Prompt Example: “Generate ideas for a marketing campaign promoting our new product.”

3. Synthesizing: This use case involves extracting insights and takeaways from the text. ChatGPT can analyze and consolidate information from multiple sources, helping users distill complex data into actionable conclusions.

Prompt Example: “Extract the main insights and recommendations from this business strategy document.”

4. Writing: ChatGPT can be a helpful tool for writing tasks, including blog posts, articles, press releases, and procedures. It can provide content suggestions, help with structuring ideas, and even generate draft text for various purposes.

Prompt Example: “Write a blog post about the benefits of regular exercise and healthy eating.”

5. Coding: For coding tasks, ChatGPT can assist in writing scripts and small programs. It can help with generating code snippets, troubleshooting programming issues, and offering coding-related advice.

Prompt Example: “Create a Python script that calculates the Fibonacci sequence up to the 20th term.”

6. Extracting: ChatGPT is capable of extracting data and patterns from messy text. This is particularly useful in data mining and analysis, where it can identify relevant information and relationships within unstructured text data.

Prompt Example: “Extract all email addresses from this unstructured text data.”

7. Reformatting: Another valuable use case is reformatting text or data from messy sources into structured formats or tables. ChatGPT can assist in converting disorganized information into organized and presentable formats.

Prompt Example: “Convert this messy financial data into a structured table with columns for date, transaction type, and amount.”

Read more about -> 10 innovative ways to monetize business using ChatGPT

Tones used in ChatGPT prompts

Tone: [x] Writing using [x] tone

1. Conversational

Description: Conversational tone is friendly, informal, and resembles everyday spoken language. It’s suitable for casual interactions and discussions.

Example prompt: “Can you explain the concept of blockchain technology in simple terms?”

2. Lighthearted

Description: Lighthearted tone adds a touch of humor, playfulness, and positivity to the content. It’s engaging and cheerful.

Example prompt: “Tell me a joke to brighten my day.”

3. Persuasive

Description: Persuasive tone aims to convince or influence the reader. It uses compelling language to present arguments and opinions.

Example prompt: “Write a persuasive article on the benefits of renewable energy.”

4. Spartan

Description: Spartan tone is minimalist and to the point. It avoids unnecessary details and focuses on essential information.

Example prompt: “Provide a brief summary of the key features of the new software update.”

5. Formal

Description: Formal tone is professional, structured, and often used in academic or business contexts. It maintains a serious and respectful tone.

Example prompt: “Compose a formal email to inquire about job opportunities at your company.”

6. Firm

Description: Firm tone is assertive and direct. It’s used when a clear and authoritative message needs to be conveyed.

Example prompt: “Draft a letter of complaint regarding the recent service issues with our internet provider.”

These tones can be adjusted to suit specific communication goals and audiences, offering a versatile way to interact with ChatGPT effectively in various situations.

Format

The format of prompts used in ChatGPT plays a crucial role in obtaining desired responses. Here are different formatting styles and their descriptions:

1. Be concise. Minimize excess prose

Description: This format emphasizes brevity and clarity. Avoid long-winded questions and get to the point.

Example: “Explain the concept of photosynthesis.”

2. Use less corporate jargon

Description: Simplify language and avoid technical or business-specific terms for a more understandable response.

Example: “Describe our company’s growth strategy without using industry buzzwords.”

3. Output as bullet points in short sentences

Description: Present prompts in a bullet-point format with short and direct sentences, making it easy for ChatGPT to understand and respond.

Example:

• “Benefits of recycling:”
• “Reduces pollution.”
• “Conserves resources.”
• “Saves energy.”

4. Output as a table with columns: (x). (y), (z). [a]

Description: Format prompts as a table with specified columns and content in a structured manner.

Example:

5. Be extremely detailed

Description: Request comprehensive and in-depth responses with all relevant information.

Example: “Provide a step-by-step guide on setting up a home theater system, including product recommendations and wiring diagrams.”

Using these prompt formats effectively can help you receive more accurate and tailored responses from ChatGPT, improving the quality of information and insights provided. It’s essential to choose the right format based on your communication goals and the type of information you need

Chained prompting

Chained prompting is a technique used with ChatGPT to break down complex tasks into multiple sequential steps, guiding the AI model to provide detailed and structured responses. In the provided example, here’s how chained prompting works:

1. Write an article about ChatGPT.

This is the initial prompt, requesting an article on a specific topic.

2. First give me the outline, which consists of a headline, a teaser, and several subheadings.

In response to the first prompt, ChatGPT is instructed to provide the outline of the article, which includes a headline, teaser, and subheadings.

[Output]: ChatGPT generates the outline as requested.

3. Now write 5 different subheadings.

After receiving the outline, the next step is to ask ChatGPT to generate five subheadings for the article.

[Output]: ChatGPT provides five subheadings for the article.

4. Add 5 keywords for each subheading.

Following the subheadings, ChatGPT is directed to add five keywords for each subheading to enhance the article’s SEO and content structure.

[Output]: ChatGPT generates keywords for each of the subheadings.

Chained prompting allows users to guide ChatGPT through a series of related tasks, ensuring that the generated content aligns with specific requirements. It’s a valuable technique for obtaining well-structured and detailed responses from the AI model, making it useful for tasks like content generation, outlining, and more.

This approach helps streamline the content creation process, starting with a broad request and progressively refining it until the desired output is achieved.

Prompts for designers

The prompts provided are designed to assist designers in various aspects of their work, from generating UI design requirements to seeking advice on conveying specific qualities through design. Here’s a description of each prompt:

1. Generate examples of UI design requirements for a [mobile app].

This prompt seeks assistance in defining UI design requirements for a mobile app. It helps designers outline the specific elements and features that should be part of the app’s user interface.

Example: UI design requirements for a mobile app could include responsive layouts, intuitive navigation, touch-friendly buttons, and accessible color schemes.

2. How can I design a [law firm website] in a way that conveys [trust and authority].

This prompt requests guidance on designing a law firm website that effectively communicates trust and authority, two essential qualities in the legal field.

Example: Design choices like a professional color palette, clear typography, client testimonials, and certifications can convey trust and authority.

3. What are some micro-interactions to consider when designing fintech app.

This prompt focuses on micro-interactions, small animations or feedback elements in a fintech app’s user interface that enhance user experience.

Example: Micro-interactions in a fintech app might include subtle hover effects on financial data, smooth transitions between screens, or informative tooltips.

4. Create a text-based excel sheet to input your copy suggestions. Assume you have 3 members in your UX writing team.

This prompt instructs the creation of a text-based Excel sheet for collaborative copywriting among a UX writing team.

Example: The Excel sheet can have columns for copy suggestions, status (e.g., draft, approved), author names, and deadlines, facilitating efficient content collaboration.

These prompts are valuable tools for designers, providing a structured approach to seeking assistance and generating ideas, whether it’s for UI design, conveying specific qualities, considering micro-interactions, or managing collaborative writing efforts. They help streamline the design process and ensure designers receive relevant and actionable guidance

Modes

These modes are designed to guide interactions with an AI, such as ChatGPT, in various ways, allowing users to leverage AI in different roles. Let’s describe each of these modes with examples:

1. Intern: “Come up with new fundraising ideas.”

In this mode, the AI acts as an intern, tasked with generating fresh ideas.

Example: Requesting fundraising ideas for a cause or organization.

2. Thought Partner: “What should we think about when generating new fundraising ideas?”

When set as a thought partner, the AI helps users brainstorm and consider key aspects of a task.

Example: Seeking guidance on the critical factors to consider when brainstorming fundraising ideas.

3. Critic: “Here’s a list of 10 fundraising ideas I created. Are there any I missed? Which ones seem particularly good or bad?”

In critic mode, the AI evaluates and provides feedback on a list of ideas or concepts.

Example: Requesting a critique of a list of fundraising ideas and identifying strengths and weaknesses.

4. Teacher: “Teach me about [xl. Assume I know [x] and adjust your language.”

This mode transforms the AI into a teacher, providing explanations and information.

Example: Asking the AI to teach a topic, adjusting the complexity of the language based on the user’s knowledge.

Read more about -> Prompt Engineering 

Prompts for marketers

These prompts are designed to assist marketers in various aspects of their work, from content creation to product descriptions and marketing strategies. Let’s describe each prompt and provide examples where necessary:

1. Can you provide me with some ideas for blog posts about [topics]?

This prompt seeks content ideas for blog posts, helping marketers generate engaging and relevant topics for their audience.

Example: Requesting blog post ideas about “content marketing strategies.”

2. Write a product description for my product or service or company.

This prompt is aimed at generating compelling product or service descriptions, essential for marketing materials.

Example: Asking for a product description for a new smartphone model.

3. Suggest inexpensive ways I can promote my [company] without using social media.”

This prompt focuses on cost-effective marketing strategies outside of social media to increase brand visibility.

Example: Seeking low-cost marketing ideas for a small bakery without using social media.

4. How can I obtain high-quality backlinks to raise the SEO of [website name]?

Here, the focus is on improving website SEO by acquiring authoritative backlinks, a crucial aspect of digital marketing.

Example: Inquiring about strategies to gain high-quality backlinks for an e-commerce website.

These prompts provide marketers with AI-driven assistance for a range of marketing tasks, from content creation to SEO optimization and cost-effective promotion strategies. They facilitate more efficient and creative marketing efforts.

Read about -> How to become a Prompt engineer in 10 steps

Prompts for developers

These prompts are designed to assist developers in various aspects of their work, from coding to debugging and implementing specific website features. Let’s describe each prompt and provide examples where needed:

1. Develop architecture and code for a (descriptions website with JavaScript.

This prompt asks developers to create both the architectural design and code for a website that likely involves presenting various descriptions using JavaScript.

Example: Requesting the development of a movie descriptions website with JavaScript.

2. Help me find mistakes in the following code <paste code below>>.

This prompt seeks assistance in identifying errors or bugs in a given piece of code that the developer will paste.

Example: Pasting a JavaScript code snippet with issues and asking for debugging help.

3. I want to implement a sticky header on my website. Can you provide an example using CSS and JavaScript?

Here, the developer requests an example of implementing a sticky (fixed-position) header on a website using a combination of CSS and JavaScript.

Example: Asking for a code example to create a sticky navigation bar for a webpage.

4. Please continue writing this code for JavaScript <post code below>>.

This prompt is for extending an existing JavaScript code snippet by providing additional code to complete a specific task.

Example: Extending JavaScript code for a form validation feature.

These prompts offer valuable assistance to developers, covering a range of tasks from website architecture and coding to debugging and implementing interactive features using JavaScript and CSS. They aim to streamline the development process and resolve coding challenges.

These modes offer flexibility in how users interact with AI, enabling them to tap into AI capabilities for various purposes, including idea generation, brainstorming, evaluation, and learning. They facilitate productive and tailored interactions with AI, making it a versatile tool for a wide range of tasks and roles.

Master ChatGPT to upscale your business

ChatGPT serves as a versatile tool for a wide range of tasks, leveraging its natural language processing capabilities to enhance productivity and streamline various processes. Users can harness its power to save time, improve content quality, and make sense of complex information.

ChatGPT has become popular, changing the way people work and what they may find online. Many people are intrigued by the potential of AI chatbots, even those who haven’t tried them. Cybercriminals are looking for ways to profit from this trend.

Netenrich researchers have discovered a new artificial intelligence tool called “FraudGPT.” This AI bot was created specifically for malicious activities, such as sending spear phishing emails, developing cracking tools, and doing carding. It is available for purchase on several Dark Web marketplaces and the Telegram app.

What is FraudGPT?

FraudGPT is similar to ChatGPT, but it can also generate content for use in cyberattacks. It was first advertised by Netenrich threat researchers in July 2023. One of FraudGPT’s selling points is that it does not have the safeguards and restrictions that make ChatGPT unresponsive to questionable queries.

According to the information provided, the tool is updated every week or two and uses several different types of artificial intelligence. FraudGPT is primarily subscription-based, with monthly subscriptions costing $200 and annual memberships costing $1,700.

How does FraudGPT work?

Netenrich researchers purchased and tested FraudGPT. The layout is very similar to ChatGPT’s, with a history of the user’s requests in the left sidebar and the chat window taking up most of the screen real estate. To get a response, users simply need to type their question into the box provided and hit “Enter.”

One of the test cases for the tool was a phishing email related to a bank. The user input was minimal; simply including the bank’s name in the query format was all that was required for FraudGPT to complete its task. It even indicated where a malicious link could be placed in the text. Scam landing pages that actively solicit personal information from visitors are also within FraudGPT’s capabilities.

FraudGPT was also asked to name the most frequently visited or exploited online resources. This information could be useful for hackers to use in planning future attacks. An online ad for the software boasted that it could generate harmful code to assemble undetectable malware to search for vulnerabilities and identify targets.

The Netenrich team also discovered that the seller of FraudGPT had previously advertised hacking services for hire. They also linked the same person to a similar program called WormGPT.

The FraudGPT investigation highlights the importance of vigilance.

It is still unknown whether hackers have already used these technologies to develop new threats. However, FraudGPT and similar malicious programs could help hackers save time by creating phishing emails and landing pages in seconds.

Therefore, consumers should be wary of any requests for their personal information and follow other cybersecurity best practices. Cybersecurity professionals would be wise to keep their threat-detection tools up to date, as malicious actors may use programs like FraudGPT to target and enter critical computer networks directly.

Read more –> Unraveling the phenomenon of ChatGPT: Understanding the revolutionary AI technology

The analysis of FraudGPT is a sobering reminder that hackers will continue to adapt their methods over time. However, open-source software also has security flaws. Anyone who uses the internet or is responsible for securing online infrastructure must stay up-to-date on emerging technologies and the threats they pose. The key is to be aware of the risks involved when using programs like ChatGPT.

Tips for enhancing cybersecurity amid the rise of FraudGPT

The examination of FraudGPT underscores the importance of maintaining a vigilant stance. Given the novelty of these tools, it remains uncertain when hackers might leverage them to concoct previously unseen threats, or if they have already done so. Nevertheless, FraudGPT and comparable products designed for malevolent purposes could significantly expedite hackers’ activities, enabling them to compose phishing emails or craft entire landing pages within seconds.

As a result, it is imperative for individuals to persist in adhering to cybersecurity best practices, which encompass perpetually harboring suspicion towards requests for personal data. Professionals in the cybersecurity domain should ensure their threat-detection utilities are up to date, recognizing that malicious actors may deploy tools like FraudGPT to directly target and infiltrate online infrastructures.

Beyond hackers: Other threats abound

The integration of ChatGPT into more job roles may not bode well for cybersecurity. Employees could inadvertently jeopardize sensitive corporate information by copying and pasting it into ChatGPT. Notably, several companies, including Apple and Samsung, have already imposed limitations on how employees can utilize this tool within their respective roles.

One study has indicated that a staggering 72% of small businesses fold within two years of data loss. Often, individuals only associate criminal activity with the loss of information. However, forward-thinking individuals recognize the inherent risk associated with pasting confidential or proprietary data into ChatGPT.

Concerned about how Generative AI is reshaping the society? This podcast discusses in detail, the risks and considerations regarding Generative AI, along with a cautiously optimistic outlook for the future. Watch it now!

These concerns are not without merit. In March 2023, a ChatGPT glitch resulted in the inadvertent disclosure of payment details for users who had accessed the tool during a nine-hour window and subscribed to the premium version.

Furthermore, forthcoming iterations of ChatGPT draw from the data entered by prior users, raising concerns about the consequences should confidential information become integrated into the training dataset. While users can opt out of having their prompts used for training purposes, this is not the default setting.

Moreover, complications may arise if employees presume that any information obtained from ChatGPT is infallible. Individuals using the tool for programming and coding tasks have cautioned that it often provides erroneous responses, which may be erroneously accepted as factual by less experienced professionals.

A research paper published by Purdue University in August 2023 validated this assertion by subjecting ChatGPT to programming queries. The findings were startling, revealing that the tool produced incorrect answers in 52% of cases and tended to be overly verbose 77% of the time. If ChatGPT were to similarly err in cybersecurity-related queries, it could pose significant challenges for IT teams endeavoring to educate staff on preventing security breaches.

ChatGPT: A potential haven for cybercriminals

It’s crucial to recognize that hackers possess the capability to inflict substantial harm even without resorting to paid products like FraudGPT. Cybersecurity experts have underscored that the free version of ChatGPT offers similar capabilities. Although this version includes inherent safeguards that may initially impede malicious intent, cybercriminals are adept at creativity and could manipulate ChatGPT to suit their purposes.

The advent of AI has the potential to expand cybercriminals’ scope and accelerate their attack strategies. Conversely, numerous cybersecurity professionals harness AI to heighten threat awareness and expedite remediation efforts. Consequently, technology becomes a double-edged sword, both fortifying and undermining protective measures. It comes as no surprise that a June 2023 survey revealed that 81% of respondents expressed concerns regarding the safety and security implications associated with ChatGPT.

Another concerning scenario is the possibility of individuals downloading what they believe to be the authentic ChatGPT app only to receive malware in its stead. The proliferation of applications resembling ChatGPT in app stores occurred swiftly. While some mimicked the tool’s functionality without deceptive intent, others adopted names closely resembling ChatGPT, such as “Chat GBT,” with the potential to deceive unsuspecting users.

It is common practice for hackers to embed malware within seemingly legitimate applications, and one should anticipate them leveraging the popularity of ChatGPT for such malicious purposes.

Adapting cybersecurity to evolving technologies

The investigation into FraudGPT serves as a poignant reminder of cybercriminals’ agility in evolving their tactics for maximum impact. However, the cybersecurity landscape is not immune to risks posed by freely available tools. Those navigating the internet or engaged in safeguarding online infrastructures must remain vigilant regarding emerging technologies and their associated risks. The key lies in utilizing tools like ChatGPT responsibly while maintaining an acute awareness of potential threats.

Large language models and generative AI jokes are a testament to the fusion of creativity and technology, where lines of code birth lines of laughter.

Large language models (LLMs) and generative AI are rapidly evolving technologies that have the potential to revolutionize the way we interact with computers. These models can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.

But LLMs and generative AI are not without their flaws. One of the biggest challenges facing these models is generating humor. Humor is a complex phenomenon that relies on a number of factors, including unexpected twists, wordplay, and cultural references. LLMs and generative AI models are still struggling to master these factors, which can lead to some pretty funny (or not-so-funny) results.

Read more –> Guide to LLM chatbots: Real-life applications and more.

Large Language Models and Generative AI Jokes

Why did the large language model cross the road? To get to the other dataset.

What do you call a large language model that’s always getting into trouble? A rule breaker.

What do you call a large language model that’s always late? A procrastinator.

How do you know if a large language model is lying? Its lips are moving.

What do you call a large language model that can’t generate any jokes? A dud.

Why did the large language model quit its job? It was too wordy.

What’s the difference between a large language model and a broken record? A broken record doesn’t repeat itself.

What do you call a large language model that can’t understand humor? A literal AI.

Why did the large language model get fired from the comedy club? It kept bombing.

Why did the large language model go to the doctor? It was feeling under the weather.

What did the large language model say when it saw a mirror? “Wow, I’m a lot smarter than I thought I was.”

What did the large language model say when it saw a cat? “I’m not sure if I should pet it or ask it to translate this for me.”

What did the large language model say when it saw a human? “I wonder if they’re as smart as I am.”

What did the large language model say when it saw a computer? “I’m not sure if I should be jealous of it or pity it.”

What do you call a large language model that’s always trying to one-up you? A show-off.

What do you call a large language model that’s always trying to impress you? A charmer.

What do you call a large language model that’s always trying to get your attention? A needy.

What do you call a large language model that’s always trying to get into your head? A manipulator.

Here are some of the AI memes and jokes you wouldn’t want to miss

Chat GPT : Punch and humour

Why did the GPT-3 model get kicked out of the library? It kept talking to itself.

What do you call a GPT-3 model that’s always getting into arguments? A troll.

Why did the Megatron-Turing NLG model get banned from the internet? It was too big and powerful.

What do you call a Megatron-Turing NLG model that’s always crashing? A diva.

Why did the LaMDA model get fired from its job? It was too chatty.

What do you call a Generative AI that’s been trained on too much bad data? A troll.

What do you call a Generative AI that’s been trained on too much cat videos? A purr-fect machine.

What do you call a Generative AI that’s been trained on too much Shakespeare? A Bard.

How do you know if a Generative AI is in love? It keeps generating poems about you.

What do you call a Generative AI that’s been trained on too much code? A hacker.

What do you call a large language model that’s always trying to be helpful? A good samaritan.

What do you call a large language model that’s always trying to be funny? A wisecracker.

What do you call a large language model that’s always trying to be clever? A wit.

What do you call a large language model that’s always trying to be creative? A mastermind.

What do you call a large language model that’s always trying to be original? A genius.

What do you call a large language model that’s always trying to be helpful, funny, clever, creative, and original?A Bard.

Why did ChatGPT cross the road? To get to the other model.

What do you call a ChatGPT that can’t generate jokes? A bore.

What’s the difference between ChatGPT and a comedian? A comedian knows when to stop.

Hilarious wars: Chat GPT vs Bard

How do you know if ChatGPT is lying? Its lips are moving.

What do you call a ChatGPT that’s been trained on too much data? A conspiracy theorist.

What do you call a large language model that’s so good at being a Chat GPT that it becomes the world’s first artificial superintelligence? A god.

What do you call a large language model that’s so good at being a Bard that it takes over the world? A tyrant.

What do you call a large language model that’s so good at being a Chat GPT that it destroys the world? A monster.

What do you call a large language model that’s so good at being a Bard that it saves the world? A hero.

What do you call a large language model that’s so good at being a Chat GPT that it becomes the world’s first artificial

friend? A companion.

What do you call a large language model that’s so good at being a Bard that it helps humans to understand each other better? A diplomat.

What do you call a large language model that’s so good at being a Bard that it creates new forms of art and literature? A visionary.

What do you call a large language model that’s so good at being a Bard that it solves the world’s most pressing problems? A savior.

What do you call a large language model that’s so good at being a Chat GPT that it becomes the world’s first artificial god? A deity.

What do you call a ChatGPT that’s been trained on too much Shakespeare? A Bard.

Read more –> Hilarious Data Science jokes

Share your thoughts

Safe to say, the intersection of humor with large language models and generative AI is fascinating. There is no denying that AI has the potential to revolutionize the way we create and consume humor.

However, there are still many challenges that need to be addressed before generative AI jokes can truly master the art of humor vicinity.

Want to find out more about Large Language Models? Click below:

Large Language Model (LLM) Bootcamps are designed for learners to grasp the hands-on experience of working with Open AI. Popularly known as the brains behind ChatGPT, LLMs are advanced artificial intelligence (AI) systems capable of understanding and generating human language.

They utilize deep learning algorithms and extensive data to grasp language nuances and produce coherent responses. LLM power of platforms like, Google’s BERT and OpenAI’s ChatGPT, demonstrate remarkable accuracy in predicting and generating text based on input.

ChatGPT, in particular, gained massive popularity within a short period due to its ability to mimic human-like responses. It leverages machine learning algorithms trained on an extensive dataset, surpassing BERT in terms of training capacity.

LLMs like ChatGPT excel in generating personalized and contextually relevant responses, making them valuable in customer service applications. Compared to intent-based chatbots, LLM-powered chatbots can handle more complex and multi-touch inquiries, including product questions, conversational commerce, and technical support.

The benefits of LLM-powered chatbots include their ability to provide conversational support and emulate human-like interactions. However, there are also risks associated with LLMs that need to be considered.

Practical applications of LLM power and chatbots

• Enhancing e-Commerce: LLM chatbots allow customers to interact directly with brands, receiving tailored product recommendations and human-like assistance.
• Brand consistency: LLM chatbots maintain a brand’s personality and tone consistently, reducing the need for extensive training and quality assurance checks.
• Segmentation: LLM chatbots identify customer personas based on interactions and adapt responses and recommendations for a hyper-personalized experience.
• Multilingual capabilities: LLM chatbots can respond to customers in any language, enabling global support for diverse customer bases.
• Text-to-voice: LLM chatbots can create a digital avatar experience, simulating human-like conversations and enhancing the user experience.

Read about –> Unleash LlamaIndex: The key to uncovering deeper insights in text exploration

Other reasons why you need a LLM Bootcamp

You might want to sign up for a LLM bootcamp for many reasons. Here are a few of the most common reasons:

• To learn about the latest LLM technologies: LLM bootcamps teach you about the latest LLM technologies, such as GPT-3, LaMDA, and Jurassic-1 Jumbo. This knowledge can help you stay ahead of the curve in the rapidly evolving field of LLMs.
• To build your own LLM applications: LLM bootcamps teach you how to build your own LLM applications. This can be a valuable skill, as LLM applications have the potential to revolutionize many industries.
• To get hands-on experience with LLMs: LLM bootcamps allow you to get hands-on experience with LLMs. This experience can help you develop your skills and become an expert in LLMs.
• To network with other LLM professionals: LLM bootcamps allow you to network with other LLM professionals. This networking can help you stay up-to-date on the latest trends in LLMs and find opportunities to collaborate with other professionals.

Data Science Dojo’s Large Language Model LLM Bootcamp

The Large Language Model (LLM) Bootcamp is a focused program dedicated to building LLM-powered applications. This intensive course offers participants the opportunity to acquire the necessary skills in just 40 hours.

Centered around the practical applications of LLMs in natural language processing, the bootcamp emphasizes the utilization of libraries like Hugging Face and LangChain.

It enables participants to develop expertise in text analytics techniques, such as semantic search and Generative AI. The bootcamp also offers hands-on experience in deploying web applications on cloud services. It is designed to cater to professionals who aim to enhance their understanding of Generative AI, covering essential principles and real-world implementation, without requiring extensive coding skills.

Who is this LLM Bootcamp for?

1. Individuals with Interest in LLM Application Development:

This course is suitable for anyone interested in gaining practical experience and a headstart in building LLM (Language Model) applications.

2. Data Professionals Seeking Advanced AI Skills:

Data professionals aiming to enhance their data skills with the latest generative AI tools and techniques will find this course beneficial.

3. Product Leaders from Enterprises and Startups:

Product leaders working in enterprises or startups who wish to harness the power of LLMs to improve their products, processes, and services can benefit from this course.

What will you learn in this LLM Bootcamp?

In this Large Language Models Bootcamp, you will learn a comprehensive set of skills and techniques to build and deploy custom Large Language Model (LLM) applications. Over 5 days and 40 hours of hands-on learning, you’ll gain the following knowledge:

Generative AI and LLM Fundamentals: You will receive a thorough introduction to the foundations of generative AI, including the workings of transformers and attention mechanisms in text and image-based models.

Canonical Architectures of LLM Applications: Understand various LLM-powered application architectures and learn about their trade-offs to make informed design decisions.

Embeddings and Vector Databases: Gain practical experience in working with vector databases and embeddings, allowing efficient storage and retrieval of vector representations.

Read more –> Guide to vector embeddings and vector database pipeline

Prompt Engineering: Master the art of prompt engineering, enabling you to effectively control LLM model outputs and generate captivating content across different domains and tasks.

Orchestration Frameworks: Explore orchestration frameworks like LangChain and Llama Index, and learn how to utilize them for LLM application development.

Deployment of LLM Applications: Learn how to deploy your custom LLM applications using Azure and Hugging Face cloud services.

Customizing Large Language Models: Acquire practical experience in fine-tuning LLMs to suit specific tasks and domains, using parameter-efficient tuning and retrieval parameter-efficient + retrieval-augmented approaches.

Building An End-to-End Custom LLM Application: Put your knowledge into practice by creating a custom LLM application on your own selected datasets.

Building your own custom LLM application

After completing the Large Language Models Bootcamp, you will be well-prepared to build your own ChatGPT-like application with confidence and expertise. Throughout the comprehensive 5-day program, you will have gained a deep understanding of the underlying principles and practical skills required for LLM application development. Here’s how you’ll be able to build your own ChatGPT-like application:

Foundational Knowledge: The bootcamp will start with an introduction to generative AI, LLMs, and foundation models. You’ll learn how transformers and attention mechanisms work behind text-based models, which is crucial for understanding the core principles of LLM applications.

Customization and Fine-Tuning: You will acquire hands-on experience in customizing Large Language Models. Fine-tuning techniques will be covered in-depth, allowing you to adapt pre-trained models to your specific use case, just like how ChatGPT was built upon a pre-trained language model.

Prompt Engineering: You’ll master the art of prompt engineering, a key aspect of building ChatGPT-like applications. By effectively crafting prompts, you can control the model’s output and generate tailored responses to user inputs, making your application more dynamic and interactive.

Read more –> 10 steps to become a prompt engineer: A comprehensive guide

Orchestration Frameworks: Understanding orchestration frameworks like LangChain and Llama Index will empower you to structure and manage the components of your application, ensuring seamless execution and scalability – a crucial aspect when building applications like ChatGPT.

Deployment and Integration: The bootcamp covers the deployment of LLM applications using cloud services like Azure and Hugging Face cloud. This knowledge will enable you to deploy your own ChatGPT-like application, making it accessible to users on various platforms.

Project-Based Learning: Towards the end of the bootcamp, you will have the opportunity to apply your knowledge by building an end-to-end custom LLM application. The project will challenge you to create a functional and interactive application, similar to building your own ChatGPT from scratch.

Access to Resources: After completing the bootcamp, you’ll have access to course materials, coding labs, Jupyter notebooks, and additional learning resources for one year. These resources will serve as valuable references as you work on your ChatGPT-like application.

Furthermore, the LLM bootcamp employs advanced technology and tools such as OpenAI Cohere, Pinecone, Llama Index, Zilliz Chroma, LangChain, Hugging Face, Redis, and Streamlit.

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

How to curate a good prompt?

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

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

Here’s a simple example to illustrate prompt engineering:

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

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

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

Quick exercise –> Choose the most suitable prompt

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

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

Why everyone should learn prompt engineering:

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

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

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

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

Top characteristics of a good prompt with examples

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

1. Clarity:

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

Example:

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

Poor Prompt: “Climate change and the environment.”

2. Specificity:

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

Example:

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

3. Context:

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

Example: 

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

Poor Prompt: “Climate change effects

4. Open-endedness:

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

Example:

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

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

5. Conciseness:

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

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

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

6. Correct grammar and syntax:

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

Example:

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

7. Balanced complexity:

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

Example:

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

Poor Prompt: “Climate change and weather.”

8. Diversity in phrasing:

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

Example:

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

Poor Prompt: “Climate change and water.

9. Avoiding leading or biased language:

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

Example:

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

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

10. Iterative refinement:

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

Example:

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

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

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

Two different approaches of prompting

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

1. Prompting by instruction:

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

Examples of prompting by instruction:

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

Tips for prompting by instruction:

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

2. Prompting by example:

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

Examples of prompting by example:

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

Tips for prompting by example:

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

Which prompting approach is right for you?

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

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

The ongoing battle ‘ChatGPT vs Bard’ continues as the two prominent contenders in the generative AI landscape which have garnered substantial interest. As the rivalry between these platforms escalates, it continues to captivate the attention of both enthusiasts and experts.

What are chatbots?

Chatbots are revolutionizing the way we interact with technology. These artificial intelligence (AI) programs can carry on conversations with humans, and they are becoming increasingly sophisticated. Two of the most popular chatbots on the market today are ChatGPT and Bard. Both chatbots are capable of carrying on conversations with humans, but they have different strengths and weaknesses. 

1. ChatGPT 

ChatGPT was created by OpenAI and is based on the GPT-3 language model. It is trained on a massive dataset of text and code, and is able to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. 

ChatGPT: Strenght and weaknesses

One of ChatGPT’s strengths is its ability to generate creative text formats. It can write poems, code, scripts, musical pieces, email, letters, etc., and its output is often indistinguishable from human-written text. ChatGPT is also good at answering questions, and can provide comprehensive and informative answers even to open-ended, challenging, or strange questions. 

However, ChatGPT also has some weaknesses. One of its biggest weaknesses is its tendency to generate text that is factually incorrect. This is because ChatGPT is trained on a massive dataset of text, and not all of that text is accurate. As a result, ChatGPT can sometimes generate text that is factually incorrect or misleading. 

Another weakness of ChatGPT is its lack of access to real-time information. ChatGPT is trained on a dataset of text that was collected up to 2021, and it does not have access to real-time information. This means that ChatGPT can sometimes provide outdated or inaccurate information.  

2. Bard 

Bard is a large language model from Google AI, trained on a massive dataset of text and code. It can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.  

One of Bard’s strengths is its access to real-time information. Bard is able to access and process information from the internet in real time, which means that it can provide up-to-date information on a wide range of topics. Bard is also able to access and process information from other sources, such as books, articles, and websites. This gives Bard a much wider range of knowledge than ChatGPT. 

Bard: Strenght and weaknesses

Another strength of Bard is its ability to generate accurate text. Bard is trained on a massive dataset of text that is carefully curated to ensure accuracy. As a result, Bard is much less likely to generate text that is factually incorrect than ChatGPT. 

However, Bard also has some weaknesses. One of its biggest weaknesses is its lack of creativity. Bard is good at generating text that is factually accurate, but it is not as good at generating text that is creative or engaging. Bard’s output is often dry and boring, and it can sometimes be difficult to follow. 

Another weakness of Bard is its limited availability. Bard is currently only available to a select group of users, and it is not yet clear when it will be made available to the general public.  

How chatbots are revolutionary 

Chatbots are revolutionary because they have the potential to change the way we interact with technology in a number of ways. 

First, chatbots can make technology more accessible to people who are not comfortable using computers or smartphones. For example, chatbots can be used to provide customer service or technical support to people who are not able to use a website or app. 

Second, chatbots can make technology more personalized. For example, chatbots can be used to provide recommendations or suggestions based on a user’s past behavior. This can help users to find the information or services that they are looking for more quickly and easily. 

Third, chatbots can make technology more engaging. For example, chatbots can be used to play games or tell stories. This can help to make technology more fun and enjoyable to use. 

Does the future belong to chatbots?

Chatbots are still in their early stages of development, but they have the potential to revolutionize the way we interact with technology. As chatbots become more sophisticated, they will become increasingly useful and popular.  

In the future, it is likely that chatbots will be used in a wide variety of settings, including customer service, education, healthcare, and entertainment. Chatbots have the potential to make our lives easier, more efficient, and more enjoyable. 

ChatGPT vs Bard: Which AI chatbot is right for you? 

When it comes to AI language models, the battle of ChatGPT vs Bard is a hot topic in the tech community. But, which AI chatbot is right for you? It depends on what you are looking for. If you are looking for a chatbot that can generate creative text formats, then ChatGPT is a good option. However, if you are looking for a chatbot that can provide accurate information, then Bard is a better option. 

Ultimately, the best way to decide which AI chatbot is right for you is to try them both out and see which one you prefer. 

For the unversed – What is ChatGPT?

ChatGPT is an artificial intelligence language model developed by OpenAI. It is designed to generate human-like responses to natural language inputs, making it an ideal candidate for chatbot applications. ChatGPT is trained on vast amounts of text data and is capable of understanding and responding to a wide range of topics and questions.

While ChatGPT is a powerful tool, it’s important to be able to distinguish between real and fake chatbots, which is why tools for detecting ChatGPT and other fake chatbots have become increasingly important.

Read more about ChatGPT and how this AI tool is a game changer for businesses.

Overrated or underrated – Is ChatGPT reshaping the world?

ChatGPT, as an advanced language model, is reshaping the world in a number of ways. Here are some of the ways it is making an impact: 

• Improving customer service – ChatGPT is being used by companies to improve their customer service by creating chatbots that can provide human-like responses to customer queries. This helps to reduce response times and improve the overall customer experience. 

• Revolutionizing language translation – It is being used to improve language translation services by creating chatbots that can translate between languages in real-time, making communication between people who speak different languages easier. 

• Advancing healthcare – Chat GPT is being used to create chatbots that can assist healthcare professionals by providing medical advice and answering patient queries. 

• Transforming education –  The popular AI tool is being used to create chatbots that can assist students with their studies by providing answers to questions and offering personalized feedback.

5 free tools for detecting ChatGPT 

As artificial intelligence (AI) continues to advance, the use of chatbots and virtual assistants has become increasingly common. However, with the rise of AI, there has also been an increase in the use of fake chatbots, which can be used to deceive users for fraudulent purposes. As a result, it’s important to be able to detect whether you’re interacting with a real chatbot or a fake one. In this article, we’ll look at five free tools for detecting ChatGPT.

1. Botometer:

Botometer is a free online tool developed by the University of Southern California’s Information Sciences Institute. It uses machine learning algorithms to detect whether a Twitter account is a bot or a human. It considers a range of factors, including the frequency and timing of tweets, the language used in tweets, and the presence of certain hashtags or URLs. Botometer can also detect the likelihood that the bot is using ChatGPT or another language model.

2. Bot Sentinel:

Bot Sentinel is another free online tool that can detect and analyze Twitter accounts that exhibit bot-like behavior. It uses a variety of factors to identify accounts that are likely to be bots, such as the frequency of tweets, the similarity of tweets to other bots, and the use of certain keywords or hashtags. Bot Sentinel can also identify accounts that are likely to be using ChatGPT or other language models.

3. Botcheck.me:

Botcheck.me is a free tool that analyzes Twitter accounts to determine the likelihood that they are bots. It considers a range of factors, such as the frequency and timing of tweets, the similarity of tweets to other bots, and the presence of certain hashtags or URLs. Botcheck.me can also detect whether a bot is using ChatGPT or other language models.

4. OpenAI’s GPT-3 Detector:

OpenAI has developed a tool that can detect whether a given text was generated by their GPT-3 language model or a human. While it’s not specifically designed to detect ChatGPT, it can be useful for identifying text generated by language models. The tool uses a deep neural network to analyze the language in the text and compare it to known patterns of human language and GPT-3-generated language.

5. Hugging Face Transformers:

Hugging Face offers a free, open-source library of natural language processing tools, including several models that can detect language-based chatbots. Their “pipeline” tool can be used to quickly detect whether a given text was generated by ChatGPT or other language models. Hugging Face Transformers is used by researchers, developers, and other professionals working with natural language processing and machine learning.

Why chatbot detectors are essential for professionals?

There are several groups of people who may want chatbot detectors, including: 

• Business owners: Business owners who rely on chatbots for customer service may want detectors to ensure that their customers are interacting with a genuine chatbot and not a fake one. This can help to protect their customers from scams or fraud. 
• Consumers: Consumers who interact with chatbots may want detectors to protect themselves from fraudulent chatbots or phishing scams. This can help them to avoid sharing personal information with a fake chatbot. 
• Researchers: Researchers who are studying chatbots may want detectors to help them identify which chatbots are powered by ChatGPT or other language models. This can help them to understand how language models are being used in chatbot development and how they are being integrated into different applications. 
• Developers: Chatbot developers who are working with ChatGPT may want detectors to ensure that their chatbots are providing accurate and reliable responses to users. This can help them to build better chatbots that can provide a more satisfying user experience.
  

Wrapping up 

Love it or hate it – ChatGPT is here to stay. However, with the increasing use of AI in chatbots and virtual assistants, it’s important to be able to detect whether you’re interacting with a real chatbot or a fake one. These five free tools can help you detect ChatGPT and other fake chatbots, helping you to stay safe online.

In this blog, we asked ChatGPT to come up with some interesting and fun facts about the core areas related to data science, Artificial Intelligence, machine learning, and Statistics. The new AI tool surely knows the struggles of professionals and how to put it in a fun way.

Let’s take a look at some fun facts for data scientists put down by ChatGPT.

Some fun facts for Mathematicians by ChatGPT

Here are some fun facts for all statisticians to make you laugh by ChatGPT:

• Statisticians have a sense of humor too! The probability of someone laughing at a joke you tell is directly proportional to the number of statistical jokes you know.  
• A statistician’s favorite thing to do is to take a sample. 
• A statistician’s favorite drink is a “mean” martini. 
• A statistician’s favorite time of day is “meantime.” 
• Statisticians tend to “mean” well. 
• Statisticians are always looking for a “standard deviation” from the norm. 
• Statisticians are always looking for a “means” to get ahead. 
• Statisticians are never “mean” to each other – they are always “normally distributed.”
• Mathematicians are always on the lookout for new “equations” to solve.  
• A mathematician’s favorite thing is to “prove” a theorem.  
• Mathematicians are known to have a “matrix” of connections in the industry. 
• Mathematicians have been known to “calculate” the most unlikely scenarios. 
• A mathematician’s favorite type of math is “abstract” math – it keeps things interesting! 
• Mathematicians are always looking for ways to “optimize” their equations. 
• Mathematicians have a saying: “The more complex the problem, the better!” 
• Mathematicians are known to be “precise” – they never leave room for error in their calculations.  

Some fun facts for Data Scientists by ChatGPT  

Here are a few funny facts about data scientists: 

• Data scientists have been known to “mine” for data in the most unlikely places. 
• A data scientist’s favorite type of data is “big data” – the bigger, the better! 
• A data scientist’s favorite tool is the “data hammer” – they can pound any data into submission. 
• Data scientists have a saying: “The data never lies, but it can be misleading.” 
• Data scientists have been known to “data dunk” their colleagues – throwing them into a pool of data and seeing if they can swim. 
• Data scientists are always “data mining” for new insights and discovering “data gold.” 
• Data scientists are known to have “data-phoria” – a state of excitement or euphoria when they uncover a particularly interesting or valuable piece of data. 
• Data scientists have been known to “data mash” – combining different datasets to create something new and interesting. 

 Enroll in our Data Science Bootcamp course to become a Data Scientist today

Some fun facts for Machine Learning professionals by ChatGPT 

Here are some fun facts about machine learning professionals   

• Machine learning professionals are always on the lookout for new “learning opportunities.” 
• A machine learning professional’s favorite thing is to “train” their algorithms. 
• Machine learning professionals are known to have a “neural network” of friends in the industry. 
• Machine learning professionals have been known to “deep learn” on the job – immersing themselves in their work and picking up new skills along the way. 
• A machine learning professional’s favorite type of data is “clean” data – it makes their job much easier! 
• Machine learning professionals are always looking for ways to “optimize” their algorithms. 
• Machine learning professionals have a saying: “The more data, the merrier!” 
• Machine learning professionals are known to be “adaptive” – they can quickly adjust to new technologies and techniques. 

Some fun facts for AI experts by ChatGPT 

Here are a few funny facts about artificial intelligence experts:   

• AI experts are always on the lookout for new “intelligent” ideas. 
• AI experts have been known to “teach” their algorithms to do new tasks. 
• AI experts are known to have a “neural network” of connections in the industry. 
• AI experts have been known to “deep learn” on the job – immersing themselves in their work and picking up new skills along the way. 
• AI experts are always looking for ways to “optimize” their algorithms. 
• AI experts have a saying: “The more data, the smarter the AI!” 
• AI experts are known to be “adaptive” – they can quickly adjust to new technologies and techniques. 
• AI experts are always looking for ways to make their algorithms more “human-like.”  
• The term “artificial intelligence” was first coined in 1956 by computer scientist John McCarthy. 
• The first recorded instance of artificial intelligence was in the early 1800s when mathematician Charles Babbage designed a machine that could perform basic mathematical calculations. 
• One of the earliest demonstrations of artificial intelligence was the “Turing Test,” developed by Alan Turing in 1950. The test is a measure of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. 
• The first self-driving car was developed in the 1980s by researchers at Carnegie Mellon University. 
• In 1997, a computer program called Deep Blue defeated world chess champion Garry Kasparov, marking the first time a computer had beaten a human at chess. 
• In 2011, a machine translation system developed by Google called Google Translate was able to translate entire documents from Chinese to English with near-human accuracy. 
• In 2016, a machine learning algorithm developed by Google DeepMind called AlphaGo defeated the world champion at the ancient Chinese board game Go, which is considered to be much more complex than chess. 
• Artificial intelligence has the potential to revolutionize a wide range of industries, including healthcare, finance, and transportation.  

Some fun facts for Data Engineers by ChatGPT 

Here are a few funny facts about data engineers by ChatGPT: 

• Data engineers are always on the lookout for new “pipelines” to build. 
• A data engineer’s favorite thing is to “ingest” large amounts of data. 
• Data engineers are known to have a “data infrastructure” of connections in the industry. 
• Data engineers have been known to “scrape” the internet for new data sources. 
• A data engineer’s favorite type of data is “structured” data – it makes their job much easier! 
• Data engineers are always looking for ways to “optimize” their data pipelines. 
• Data engineers have a saying: “The more data, the merrier!” 
• Data engineers are known to be “adaptive” – they can quickly adjust to new technologies and techniques. 

Do you have a more interesting answer by ChatGPT?

People across the world are generating interesting responses using ChatGPT. The new AI tool has an immense contribution to the knowledge of professionals associated with different industries. Not only does it produce witty responses but also shares information that is not known by many. Share with us your use of this amazing AI tool as a Data Scientist.

ChatGPT is being hailed across the globe for disrupting major jobs and businesses. In this blog, we see how much of that hype is fair. 

After raging headlines like “Google is done” and “The college essay is dead”, ChatGPT is busy churning sonnets and limericks about its downtime caused due to heavy traffic. The news spreading like wildfire around town is that it will bring an end to jobs from insurance agents to court reporters. Let’s dive in and assess how much of the hype is true. 

Did ChatGPT kill the essay? 

OpenAI’s latest release large learning model (LLM), ChatGPT claims to provide natural and conversational communication. It also claims to assist with providing advice, information, performing writing and coding tasks, and admitting mistakes. Naturally, people across the globe have been bombarding the bot with requests to check how great it really is. 

Let’s consider the “death of the college essay“. The first read will show well-written essays to subjects on nearly anything. Consider, for example, the academic essay on theories of nationalism being hailed as a “solid A- essay”. However, a closer look shows that this AI tool works by using existing templates and so, college essays are churned out as per five-paragraph formulas.  

These academic essays also lack the sophistication provided by critical thinking skills. They reproduce existing content online and refashion it to fit a specific template. In style, they are dreadfully dull, lacking stylistic human expressions.

Similarly, ChatGPT’s poetic output conveys a similar emulation of formulas being rewired, with technical obeyance of rhyme scheme, while a lack of ingenuity is evident.  

An obvious conclusion, then, appears that, while great at reorganizing text to fit templates, is deeply unaware of what it means. This comes as no surprise to those familiar with even a rudimentary understanding of natural language processing and its applications.

The function of large learning models (LLM) is far from epistemological and is rather based on identifying patterns and replicating them.  

Here, it should be noted that AI tools such as it can be used as tools for humans to perform routinized, well-formulated tasks such as producing well-structured poetry or college essays. However, they lack the essential key insights provided via human intelligence regardless of the field of study. 

Is ChatGPT a source of information or misinformation? 

A feature that allows ChatGPT’s performance across a range of writing tasks is its ability to fast-fetch information. Because of its ability to fetch information immediately, it is being hailed as the end of Google. However, a few considerations regarding the differences between large learning models and search engines are important. 

For example, search engines work by hunting the web for all weblinks that are related to the search query. Their selling point here is accuracy, as they only connect you to other sources. ChatGPT, however, can provide responses to nearly any nonsensical queries.

Consider, for example, a user’s search query on designing “an electron app that is hosted on a remote server to give a desktop user notification.” As a response to this query, it came up with a completely fake method, revealing ChatGPT’s susceptibility to being a source of misinformation. 

This tool would only admit to mistakes if prompted to do so via further inquiry, making it a rather risky tool. Opposed to this, an SEO engine would provide accurate information from original sources. This ranks the practical utility of an SEO engine far above it. This settles the debate on whether ChatGPT is to replace Google any time soon.  

Furthermore, ChatGPT’s ability to construct nonsensical ideas and arguments about nearly anything can make it unsafe for a first onlooker. Only a trained eye will then be capable of nitpicking factually plausible ideas from the mere fictional constructs. Here, again, the relevance of human ingenuity and intelligence is needed to ensure tools like this, are used in meaningful ways.  

ChatGPT’s release a signal to rethink education 

ChatGPT’s advances are, however, relevant in considering the value of human creative output rethinking conventional education and training at schools that rely on memorizing and reproducing routine tasks. For circumstances where these tasks or skills are deemed essential, it’s simple to enforce testing practices that prevent access to such sources.  

At the same time, it’s an unfair stretch to suggest that ChatGPT means the end of optimized search engines like Google and creative human tasks such as writing. At best, it can be used to assist humans in their projects, be it their daily tasks or work-related queries.

It is, at the end of the day, only a mere tool that can be integrated in a plethora of human initiatives.  

Final words 

With limitations ranging from verbosity in communication, inaccurate information, and an obvious lack of sophisticated opinions, ChatGPT’s performance doesn’t quite meet the hype. Similarly, instead of offering natural conversations, ChatGPT has offered boring and dull essays, even when it comes to imitating a writer’s style.

At the same time, it is a tool that can be used by trained experts to perform certain routine tasks including writing, coding, and information fetching more easily.