GPT-4

 

 

 

Here’s a sneak peek into how GPT-4 is different from GPT-3.5

 

 

 

 

 

• Game development: Given a high-level description of a 3D game, GPT-4 successfully creates a functional game in HTML and JavaScript without any prior training or exposure to similar tasks

 

 

3. Language mastery

GPT-4’s mastery of language is a distinguishing feature. It can understand and generate human-like text, showcasing fluency, coherence, and creativity. Its language capabilities extend beyond next-word prediction, setting it apart as a more advanced language model.

 

 

4. Cognitive traits

GPT-4 exhibits traits associated with intelligence, such as abstraction, comprehension, and understanding of human motives and emotions. It can reason, plan, and learn from experience. These cognitive abilities align with the goals of AGI, highlighting GPT-4’s progress towards this goal.

 

 

 

 

Multimodal information

GPT-4 can manipulate and understand multi-modal information. This is achieved through techniques such as leveraging vector graphics, 3D scenes, and music data in conjunction with natural language prompts. GPT-4 can generate code that compiles into detailed and identifiable images, demonstrating its understanding of visual concepts.

 

 

 

Read more about the GPT-4 Vision’s use cases

 

 

 

In the dynamic world of artificial intelligence, strides in innovation are commonplace. At the forefront of these developments is Mistral AI, a European company emerging as a strong contender in the Large Language Models (LLM) arena with its latest offering: Mistral Large. With capabilities meant to rival industry giants, Mistral AI is poised to leave a significant imprint on the tech landscape.

 

Features of Mistral AI’s large model

 

Mistral AI’s new flagship model, codenamed Mistral Large, isn’t just a mere ripple in the AI pond; it’s a technological tidal wave. As we take a look at what sets it apart, let’s compare the main features and capabilities of Mistral AI’s Large model, as detailed in the sources, with those commonly attributed to GPT-4.

 

 

Language support

Mistral Large: Natively fluent in English, French, Spanish, German, and Italian.
GPT-4: is known for supporting multiple languages, but the exact list isn’t specified in the sources.

 

Scalability

Mistral Large: Offers different versions, including Mistral Small for lower latency and cost optimization.
GPT-4: Provides various scales of models, but specific details on versions aren’t provided in the sources.

 

Training and cost

Mistral Large: Charges $8 per million input tokens and $24 per million output tokens.
GPT-4: Mistral Large is noted to be 20% cheaper than GPT-4 Turbo, which suggests GPT-4 would be more expensive.

 

Performance on benchmarks

Mistral Large: Claims to rank second after GPT-4 on commonly used benchmarks and only marginally outperforms offerings from Google and Meta under the MMLU benchmark.

GPT-4

It is known to be one of the leading models in terms of benchmark performance, but no specific details on benchmark scores are provided in the sources.

Cost to train

Mistral Large: The model reportedly cost less than $22 million to train.
GPT-4: cost over $100 million to develop, according to claims.

Multilingual Abilities

Le Chat supports a variety of languages including English, French, Spanish, German, and Italian 1.

Different Versions

Users can choose between three different models, namely Mistral Small, Mistral Large, and Mistral Next, the latter of which is designed to be brief and concise.

Web Access

Currently, Le Chat does not have the capability to access the internet 1.

Free Beta Access

Le Chat is available in a beta version that is free for users, requiring just a sign-up to use 2.

Planned Enterprise Version

Mistral AI plans to offer a paid version for enterprise clients with features like central billing and the ability to define moderation mechanisms

Please note that this comparison is based on the information provided within the sources, which may not include all features and capabilities of GPT-4 or Mistral Large.

 

Mistral AI vs. GPT-4: A comparative look

 

 

Against the backdrop of OpenAI’s GPT-4 stands Mistral Large, challenging the status quo with outstanding features. While GPT-4 shines with its multi-language support and high benchmark performance, Mistral Large offers a competitive edge through:

 

Affordability: It’s 20% cheaper than GPT-4 Turbo, negotiating cost-savings for AI-powered projects.

 

Benchmark Performance: Mistral Large competes closely with GPT-4, ranking just behind it while surpassing other tech behemoths in several benchmarks.

 

Multilingual Prowess: Exceptionally fluent across English, French, Spanish, German, and Italian, Mistral Large breaks language barriers with ease.

 

Efficiency in Development: Crafted with capital efficiency in mind, Mistral AI invested less than $22 million in training its model, a fraction of the cost incurred by its counterparts.

 

Commercially Savvy: The model offers a paid API with usage-based pricing, balancing accessibility with a monetized business strategy, presenting a cost-effective solution for developers and businesses.

 

 

Practical applications of Mistral AI’s Large and GPT-4

 

The applications of both Mistral AI’s Large and GPT-4 sprawl across various industries and use cases, such as:

 

Natural Language Understanding: Both models demonstrate excellence in understanding and generating human-like text, pushing the boundaries of conversational AI.

 

Multilingual Support: Business expansion and global communication are facilitated through the multilingual capabilities of both LLMs.

 

Code Generation: Their ability to understand and generate code makes them invaluable tools for software developers and engineers.

 

Recommendations for use

 

As businesses and individuals navigate through the options in large language models, here’s why you might consider each tool:

 

Choose Mistral AI’s Large: If you’re looking for a cost-effective solution with efficient multilingual support and the flexibility of scalable versions to suit different needs 2.

 

Opt for GPT-4: Should your project require the prestige and robustness associated with OpenAI’s cutting-edge research and model performance, GPT-4 remains an industry benchmark 3.

 

 

Final note

 

In conclusion, while both Mistral AI’s Large and GPT-4 stand as pioneers in their own right, the choice ultimately aligns with your specific requirements and constraints. With Mistral AI nipping at the heels of OpenAI, the world of AI remains an exciting space to watch.

 

The march of AI is relentless, and as Mistral AI parallels the giants in the tech world, make sure to keep abreast of their developments, for the choice you make today could redefine your technological trajectory tomorrow.

InstructGPT is an advanced iteration of the GPT (Generative Pretrained Transformer) language models developed by OpenAI. Here’s a detailed look into InstructGPT:

What is InstrcutGPT?

The main objective of InstructGPT is to better align AI-powered language models with human intentions by training them using Reinforcement Learning from Human Feedback (RLHF). This method improves the model’s ability to understand and follow instructions more accurately.

 

 

Target users

InstructGPT is built for a broad range of users, from developers creating AI applications to businesses leveraging AI for enhanced customer service and for educational purposes where clear, concise, and contextually correct language is crucial.

Key features

• Alignment with Human Intent: The model is fine-tuned to understand and execute instructions as intended by the user.
• Enhanced Accuracy and Relevance: Through self-evaluation and human feedback, InstructGPT provides responses that are more accurate and contextually relevant.
• Instruction-based Task Performance: It is designed to perform structured tasks based on specific instructions.

Examples of use

• Creating more effective chatbots that can understand and respond to user queries accurately.
• Generating educational content that can help explain complex topics in a simple manner.
• Assisting in programming by providing code explanations or generating code snippets based on a given prompt.
• Enhancing customer service by providing precise answers to customer inquiries, reducing the need for human intervention.

InstructGPT represents a significant move towards creating AI that can interact with humans more naturally and effectively, leading to a wide array of practical applications across different industries

 

Read in detail about GPT 4 use cases

 

InstructGPT prompting examples

Prompt:
Tell a 6-year-old why the sky is blue in a few sentences.

Completion:
GPT-3:
Discuss the scientific reason for the sky’s color.
Describe how light travels from the sun to the earth.
Detail the process of atmospheric scattering of sunlight.

InstructGPT response:
The sky looks blue to us during the day because the sunlight gets scattered by stuff in the air, and blue light gets scattered the most, so that’s what we see!

 

 

InstructGPT architecture

Let’s break down the architecture of InstructGPT in a way that’s easy to digest. Imagine that you’re building a really complex LEGO model. Now, instead of LEGO bricks, InstructGPT uses something called a transformer architecture, which is just a fancy term for a series of steps that help the computer understand and generate human-like text.

At the heart of this architecture are things called attention mechanisms. Think of these as little helpers inside the computer’s brain that pay close attention to each word in a sentence and decide which other words it should pay attention to. This is important because, in language, the meaning of a word often depends on the other words around it.

Now, InstructGPT takes this transformer setup and tunes it with something called Reinforcement Learning from Human Feedback (RLHF). This is like giving the computer model a coach who gives it tips on how to get better at its job. For InstructGPT, the job is to follow instructions really well.

So, the “coach” (which is actually people giving feedback) helps InstructGPT understand which answers are good and which aren’t, kind of like how a teacher helps a student understand right from wrong answers. This training helps InstructGPT give responses that are more useful and on point.

And that’s the gist of it. InstructGPT is like a smart LEGO model built with special bricks (transformers and attention mechanisms) and coached by humans to be really good at following instructions and helping us out.

 

Differences between InstructorGPT, GPT 3.5 and GPT 4

Comparing GPT-3.5, GPT-4, and InstructGPT involves looking at their capabilities and optimal use cases.

Feature	InstructGPT	GPT-3.5	GPT-4
Purpose	Designed for natural language processing in specific domains	General-purpose language model, optimized for chat	Large multimodal model, more creative and collaborative
Input	Text inputs	Text inputs	Text and image inputs
Output	Text outputs	Text outputs	Text outputs
Training Data	Combination of text and structured data	Massive corpus of text data	Massive corpus of text, structured data, and image data
Optimization	Fine-tuned for following instructions and chatting	Fine-tuned for chat using the Chat Completions API	Improved model alignment, truthfulness, less offensive output
Capabilities	Natural language processing tasks	Understand and generate natural language or code	Solve difficult problems with greater accuracy
Fine-Tuning	Yes, on specific instructions and chatting	Yes, available for developers	Fine-tuning capabilities improved for developers
Cost	–	Initially more expensive than base model, now with reduced prices for improved scalability

GPT-3.5

• Capabilities: GPT-3.5 is an intermediate version between GPT-3 and GPT-4. It’s a large language model known for generating human-like text based on the input it receives. It can write essays, create content, and even code to some extent.
• Use Cases: It’s best used in situations that require high-quality language generation or understanding but may not require the latest advancements in AI language models. It’s still powerful for a wide range of NLP tasks.

GPT-4

• Capabilities: GPT-4 is a multimodal model that accepts both text and image inputs and provides text outputs. It’s capable of more nuanced understanding and generation of content and is known for its ability to follow instructions better while producing less biased and harmful content.
• Use Cases: It shines in situations that demand advanced understanding and creativity, like complex content creation, detailed technical writing, and when image inputs are part of the task. It’s also preferred for applications where minimizing biases and improving safety is a priority.

 

Learn more about GPT 3.5 vs GPT 4 in this blog

 

InstructGPT

• Capabilities: InstructGPT is fine-tuned with human feedback to follow instructions accurately. It is an iteration of GPT-3 designed to produce responses that are more aligned with what users intend when they provide those instructions.
• Use Cases: Ideal for scenarios where you need the AI to understand and execute specific instructions. It’s useful in customer service for answering queries or in any application where direct and clear instructions are given and need to be followed precisely.

 

 

When to use each

• GPT-3.5: Choose this for general language tasks that do not require the cutting-edge abilities of GPT-4 or the precise instruction-following of InstructGPT.
• GPT-4: Opt for this for more complex, creative tasks, especially those that involve interpreting images or require outputs that adhere closely to human values and instructions.
• InstructGPT: Select this when your application involves direct commands or questions and you expect the AI to follow those to the letter, with less creativity but more accuracy in instruction execution.

Each model serves different purposes, and the choice depends on the specific requirements of the task at hand—whether you need creative generation, instruction-based responses, or a balance of both.

Multimodality refers to an AI model’s ability to understand, process, and generate multiple types of information, such as text, images, and potentially even sounds. It’s the capacity to interpret and interact with various data forms, where the model not only reads textual information but also comprehends visual or other types of data.  

 

How does multimodality increase the power of LLMs?

The significance of multimodality lies in its potential to greatly enhance the effectiveness and applications of AI models.  

Consider the human intellect and its capacity to comprehend the world and tackle unique challenges. This ability stems from processing diverse forms of information, including language, sight, and taste, among others.

If an individual lacks access to one of these sensory inputs from the outset, such as vision, their understanding of the real world is likely to be significantly impaired. 

 

 

 

Hence, multimodality in models, like GPT-4, allows them to develop intuition and understand complex relationships not just inside single modalities but across them, mimicking human-level cognizance to a higher degree.  

 

Read about: GPT 3.5 VS GPT 4

 

Here are a few examples where we see that GPT-4 Vision is capable of performing human-like tasks:

 

Example 1: GPT-4 Vision and understanding humor

 

  Source: OpenAI 

 

 

Example 2: GPT-4 Vision acing complex exams  

 

 

 

 

Why does vision help GPT-4 do better on tests? Well, think about it like this: you’d probably get more out of an exam if it’s written down for you to see, rather than just hearing it from someone, right?

It’s the same deal with a model like the GPT-4. Having that visual element just makes things a bit clearer and easier to work with. 

Hence, multimodal learning opens up newer opportunities, helps AI handle real-world data more efficiently, and brings us closer to developing AI models that act and think more like humans. 

 

 

How does the GPT-4 with Vision model combine text and image inputs to provide responses? 

 

GPT-4 with Vision combines natural language processing capabilities with computer vision. This means it can accept different forms of input, like text and images, and deliver outputs based on that mixture of information.

This model represents a significant advance in machine learning and natural language processing, as it bridges two traditionally separate fields: computer vision and natural language processing. 

Enabling models to understand different types of data enhances their performance and expands their application scope. For instance, in the real-world, they may be used for Visual Question Answering (VQA), wherein the model is given an image and a text query about the image, and it needs to provide a suitable answer. 

 

Use-cases of GPT-4 Vision 

 

GPT-4V can perform a variety of tasks, including data deciphering, multi-condition processing, text transcription from images, object detection, coding enhancement, design understanding, and more. Here are some mind-boggling use cases of GPT-4 Vision. Of course, as time progresses, its usability will keep increasing.

1. Data Deciphering and Visualization: GPT-4V is capable of processing infographics or charts and providing detailed breakdowns of the data presented. This means that complex visual data can be transformed into understandable insights, making it easier for users to comprehend complex information. Here’s an example:

 

Source: Datacamp 

 

Conversely, the technology demonstrates proficiency in interpreting the provided data and generating impactful visual representations. Here’s an example where GPT-4 successfully processed LATEX code to produce a Python plot.

This was achieved through interactive dialogue with the user. In this scenario, the model accurately extracted the necessary data and efficiently addressed all user queries. It adeptly reformatted the data and tailored the visualization to meet the specified requirements. 

 

Source: Sparks of Artificial General Intelligence: Early experiments with GPT-4 | Microsoft 

 

 

1. Multi-condition processing:

GPT-4V is excellent at analyzing images under varying conditions, such as different lighting or complex scenes, and can provide insightful details drawn from these varying contexts.  

 

Source: roboflow 

 

Text Transcription

The model is geared to transcribe text from images. It could be a game-changer in digitizing written or printed documents by converting images of text into a digital format. 

 

Object Detection

GPT-4V has superior object detection capabilities. It can accurately identify different objects within an image, even abstract ones, providing a comprehensive analysis and comprehension of images. 

 

 

Source: roboflow 

 

 

Game Development:

GPT-4V can significantly impact the gaming industry as well. Here an example where it was provided with a comprehensive overview of a 3D game. GPT-4 demonstrated its capability to develop a functional game using HTML and JavaScript. This is accomplished without prior training or experience in related projects. 

Source: Sparks of Artificial General Intelligence: Early experiments with GPT-4 | Microsoft 

 

 

Web Development:

GPT-4 Vision significantly enhances web development by enabling the creation of websites from visual inputs like sketches. It interprets design elements and transforms them into functional HTML, CSS, and JavaScript code, including interactive features and specific themes, such as a ’90s hacker style with dynamic effects. Here’s an example where GPT-4 was prompted to write code for a website by only providing it a hand drawn sketch:  

 

Source: Datacamp 

 

 

Once the HTML and CSS files were created as instructed, this was the result: 

 

Source: Datacamp 

 

This advancement streamlines the web development process, making it more accessible and efficient, particularly for those with limited coding knowledge. It opens up new possibilities for creative design and can be applied across various domains, potentially evolving with continuous learning and improvement. 

 

 

Complex Mathematical Analysis: GPT-4V can process and analyze intricate mathematical expressions, especially when they are represented graphically or in handwritten forms. 

 

 

Source: roboflow 

 

 

Integrations with Other Systems: GPT-4 can be integrated with other systems through its API, expanding its application sphere to diverse domains like security, healthcare diagnostics, and entertainment. 

Educational Assistance: GPT-4V can help in the educational sector by analysing diagrams, illustrations, and visual aids, and transforming them into detailed textual explanations, making concepts easier to comprehend for students and educators alike. 

The innovation of incorporating visual capabilities, therefore, offers a dynamic and engaging method for users to interact with AI systems. 

 

 

Where does GPT 4 Vision perform less effectively? 

While the GPT-4 Vision is groundbreaking, it is important to recognize its limitations and risks. 

• Privacy Concerns: GPT-4 Vision’s ability to identify individuals and locations in images raises serious privacy issues. This poses a challenge for companies to balance innovation with adherence to privacy laws and ethical practices. 
• Bias in Image Analysis: The risk of biases in image interpretation could lead to unfair or discriminatory outcomes, particularly affecting diverse demographic groups. This necessitates careful oversight and continuous improvement of the AI’s algorithms to minimize biases. 
• Unreliable Medical Advice or Dangerous Instructions: The model might inadvertently provide inaccurate medical advice or instructions for potentially hazardous tasks. This limitation is significant, especially in contexts where precise and reliable information is critical for safety and health. 
• Cybersecurity Vulnerabilities: GPT-4 Vision could be exploited for tasks like solving CAPTCHAs, posing cybersecurity risks. This highlights the need for robust security measures to prevent malicious use. 
• Content Accuracy and Hallucination: The model, like other AI systems, can sometimes generate content that is not factually correct or based in reality, known as ‘hallucinations’. Users must be vigilant and verify the information provided by the AI. 
• Refusal to Analyze Certain Images: In some cases, GPT-4 Vision might refuse to analyze images, particularly those involving people, due to the sensitive nature of such data. This limitation can be viewed as a measure to prevent misuse or ethical breaches, but it also restricts the model’s functionality in certain scenarios. 
• Overall, these risks and limitations highlight the importance of cautious and responsible deployment of GPT-4 Vision, ensuring that its use aligns with ethical standards and societal norms. 

 

Conclusion 

GPT-4 Vision represents a monumental leap in AI technology, merging text and image processing to offer unprecedented capabilities. Its potential in fields like web development, content creation, and data analysis is immense.

However, this technology comes with responsibilities. The potential risks, including privacy concerns, biases, and safety issues, underscore the importance of using GPT-4 Vision with a mindful approach.

As we harness this powerful tool, it’s crucial to continuously evaluate and address these challenges to ensure ethical and responsible usage of AI. 

Are you already aware of the numerous advantages of using AI tools like GPT 3.5 and GPT-4? Then skip the intro and quickly head to its comparative analysis. We will briefly define the core differences offered in both versions.

What is GPT, and why do we need it?

ChatGPT is used by 92% of the Fortune 500 companies.

GPT stands for Generative Pretrained Transformer, which is a large language model (LLM) chatbot developed by OpenAI. It is a powerful tool that can be used for a variety of tasks, including generating text, translating languages, and writing different kinds of creative content.

Here are some of the reasons why we need GPT:

GPT can help us to communicate more effectively. It can be used to translate languages, summarize text, and generate different creative text formats. For example, a company can use GPT to translate its website and marketing materials into multiple languages in order to reach a wider audience.

GPT can help us to be more productive. It can be used to automate tasks, such as writing emails and reports. For example, a customer service representative can use GPT to generate personalized responses to customer inquiries.

GPT can help us to be more creative. It can be used to generate new ideas and concepts. For example, a writer can use GPT to brainstorm ideas for new blog posts or articles.

 

 

Here are some examples of how GPT is being used in the real world:

Expedia uses GPT to generate personalized travel itineraries for its customers.

Duolingo uses GPT to generate personalized language lessons and exercises for its users.

Askviable uses GPT to analyze customer feedback and identify areas for improvement.

These are just a few examples of the many ways that GPT is being used to improve our lives. As GPT continues to develop, we can expect to see even more innovative and transformative applications for this technology

 

GPT-3.5 vs GPT-4: A Comparative Analysis

 

 

1. Enhanced Understanding and Generation of Dialects

• GPT-3.5: Already proficient in generating human-like text.
• GPT-4: Takes it a step further with an improved ability to understand and generate different dialects, making it more versatile in handling diverse linguistic nuances.

2. Multimodal Capabilities

• GPT-3.5: Primarily a text-based tool.
• GPT-4: Introduces the ability to understand images. For instance, when provided with a photo, GPT-4 can describe its contents, adding a new dimension to its functionality.

3. Improved Performance and Language Comprehension

• GPT-3.5: Known for its excellent performance.
• GPT-4: Shows even better language comprehension skills, making it more effective in understanding and responding to complex queries.

4. Reliability and Creativity

• GPT-3.5: Highly reliable in generating text-based responses.
• GPT-4: Touted as more reliable and creative, capable of handling nuanced instructions with greater precision.

5. Data-to-Text Model

• GPT-3.5: A text-to-text model.
• GPT-4: This evolves into a more comprehensive data-to-text model, enabling it to process and respond to a wider range of data inputs.

 

 

 

 

Real-World Examples Illustrating the Differences

1. Dialect Understanding:
   • Example: GPT-4 can more accurately interpret and respond in regional dialects, such as Australian English or Singaporean English, compared to GPT-3.5.
2. Image Description:
   • Example: When shown a picture of a crowded market, GPT-4 can describe the scene in detail, including the types of stalls and the atmosphere, a task GPT-3.5 cannot perform.
3. Complex Query Handling:
   • Example: In a scenario where a user asks about the implications of a specific economic policy, GPT-4 provides a more nuanced and comprehensive analysis than GPT-3.5.

 

Read about: OpenAI Dismisses Sam Altman

 

Handling biases: GPT 3.5 vs GPT 4

GPT-4 has been designed to be better at handling biases compared to GPT-3.5. This improvement is achieved through several key advancements:

1. Enhanced Training Data and Algorithms: GPT-4 has been trained on a more extensive and diverse dataset than GPT-3.5. This broader dataset helps reduce biases that may arise from a limited or skewed data sample.

Additionally, the algorithms used in GPT-4 have been refined to better identify and mitigate biases present in the training data.

2. Improved Contextual Understanding: GPT-4 shows advancements in understanding and maintaining context over longer conversations or texts. This enhanced contextual awareness helps in providing more balanced and accurate responses, reducing the likelihood of biased outputs.

3. Ethical and Bias Considerations in Development: The development of GPT-4 involved a greater focus on ethical considerations and bias mitigation. This includes research and strategies specifically aimed at understanding and addressing various forms of bias that AI models can exhibit.

4. Feedback and Iterative Improvements: OpenAI has incorporated feedback from GPT-3.5’s usage to make improvements in GPT-4. This includes identifying and addressing specific instances or types of biases observed in GPT-3.5, leading to a more refined model in GPT-4.

5. Advanced Natural Language Understanding: GPT-4’s improved natural language understanding capabilities contribute to more nuanced and accurate interpretations of queries. This advancement helps in reducing misinterpretations and biased responses, especially in complex or sensitive topics.

While GPT-4 represents a significant step forward in handling biases, it’s important to note that completely eliminating bias in AI models is an ongoing challenge. Users should remain aware of the potential for biases and use AI outputs critically, especially in sensitive applications.

Conclusion

The transition from GPT-3.5 to GPT-4 marks a significant leap in the capabilities of language models. GPT-4’s enhanced dialect understanding, multimodal capabilities, and improved performance make it a more powerful tool in various applications, from content creation to complex problem-solving.

As AI continues to evolve, the potential of these models to transform how we interact with technology is immense.

 

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

 

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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. 

 

This blog explores the amazing (Artificial Intelligence) AI technology called ChatGPT that has taken the world by storm and tries to unravel the underlying phenomenon that makes up this seemingly complex technology.

What is ChatGPT? 

ChatGPT was officially launched on 30th November 2022 by OpenAI and quickly amassed a huge following not even in a week. Just to give you an idea it took Facebook around 10 months to gain 1 million followers ChatGPT did it in 5 days. So, the question that might arise in your minds my dear readers is why? Why did it gain so much popularity? What purpose does it serve? How does it work? Well, fret not we are here to answer those questions in this blog. 

Let us begin by understanding what ChatGPT is, ChatGPT is a language model that uses reinforcement learning from human feedback (RLHF) to keep on learning and fine-tuning its responses, it can answer a wide variety of questions within a span of a few minutes, help you in numerous tasks by giving you a curated, targeted response rather than vague links in a human-like manner. 

Be it writing a code or searching for something chances are ChatGPT already has the specific thing you are looking for. This brings us to our next question; how does it work? Is there magic behind it? No, it is just the clever use of machine learning and an abundance of use cases and data that OpenAI created something as powerful and elegant as ChatGPT. 

The architecture of Chat GPT 

ChatGPT is a variant of transformer-based neural network architecture, introduced in a paper by the name “Attention is all you need” in 2017, transformer architecture was specifically designed for NLP (Natural Language Processing) tasks and prevails as one of the most used methods to date. 

A quick overview of the architecture involves its usage of self-attention mechanisms which allow the model to focus on specific words and phrases when generating text, rather than processing the entire input as a single unit. It consists of multiple layers, each of which contains a multi-head self-attention mechanism and a fully connected neural network.

Also, it includes a mechanism called positional encoding which lets the model understand the relative position of the words in the input. This architecture has proven to be amazingly effective in natural language processing tasks such as text generation, language translation, and text summarization.

Following are the different layers that are involved in the architecture of ChatGPT 

• An embedding layer: this layer is responsible for converting the input words into a dense vector representation that the model can process. 
• Multiple layers of self-attention: these layers are responsible for analyzing the input and calculating a set of attention weights, which indicate which parts of the input are most important for the current task. 
• Multi-head attention: this layer is responsible for concatenating the outputs of multiple self-attention layers and then linearly transforming the resulting concatenated vectors 
• Multiple layers of fully connected neural networks: these layers are responsible for transforming the output of the attention layers into a final representation that can be used for the task at hand. 
• Output layer: this layer is responsible for generating the final output of the model, which can be a probability distribution over the possible next words in a sentence or a classification label for a given input text
   

Flow of ChatGPT

After getting a basic understanding of what ChatGPT is and its internal architecture we will now see the flow of ChatGPT from the training phase to answering a user prompt. 

1. Data collection:

Around 300 billion words were gathered for the training of ChatGPT, the sources for the data mainly included books, articles, and websites. 

2. Pre-Processing:

Once the data was collected it needed to be preprocessed so that it could be used for training. Techniques involved in preprocessing are stopped word removal, removal of duplicate data, lowercasing, removing special characters, tokenization, etc. 

3. Training:

The pre-processed data is used to train ChatGPT, which is a variant of the transformer architecture. During training, the model learns the patterns and relationships between words, phrases, and sentences. This process can take several days to several weeks depending on the size of the dataset and the computational resources available. 

4. Fine-tuning:

Once the pre-training is done, the model can be fine-tuned on a smaller, task-specific data set to improve its performance on specific natural language processing tasks. 

5. Inference:

The trained and fine-tuned model is ready to generate responses to prompts. The input prompt is passed through the model, which uses its pre-trained weights and the patterns it learned during the training phase to generate a response. 

6. Output:

The model generates a final output, which is a sequence of words that forms the answer to the prompt. 

Strengths of the AI technology of ChatGPT

• ChatGPT is a large language model that has been trained on a massive dataset of text data, allowing it to understand and generate human-like text. 
• It can perform a wide range of natural language processing tasks such as text completion, question answering, and conversation simulation. 
• The transformer-based neural network architecture enables ChatGPT to understand the context of the input and generate a response accordingly. 
• It can handle large input sequences and generate coherent and fluent text; this makes it suitable for long-form text generation tasks. 
• ChatGPT can be used for multiple languages and can be fine-tuned for different dialects and languages. 
• It can be easily integrated with other NLP tasks, such as named entity recognition, sentiment analysis, and text summarization 
• It can also be used in several applications like chatbots, virtual assistants, and language model-based text generation tasks.
   

Weaknesses of ChatGPT

• ChatGPT is limited by the information contained in the training data and does not have access to external knowledge, which may affect its ability to answer certain questions. 
• The model can be exposed to biases and stereotypes present in the training data, so the generated text should be used with caution. 
• ChatGPT’s performance on languages other than English may be limited. 
• Training and running ChatGPT requires significant computational resources and memory. 
• ChatGPT is limited to natural language processing tasks and cannot perform tasks such as image or speech recognition. 
• Lack of common-sense reasoning ability: ChatGPT is a language model and lacks the ability to understand common-sense reasoning, which can make it difficult to understand some context-based questions. 
• Lack of understanding of sarcasm and irony: ChatGPT is trained on text data, which can lack sarcasm and irony, so it might not be able to understand them in the input. 
• Privacy and security concerns: ChatGPT and other similar models are trained on large amounts of text data, which may include sensitive information, and the model’s parameters can also be used to infer sensitive information about the training data. 

 

Storming the Internet – What’s Chat GPT-4?

The latest development in artificial intelligence (AI) has taken the internet by storm. OpenAI’s new language model, GPT-4, has everyone talking. GPT-4 is an upgrade from its predecessor, GPT-3, which was already an impressive language model. GPT-4 has improved capabilities, and it is expected to be even more advanced and powerful.

With GPT-4, there is excitement about the potential for advancements in natural language processing, which could lead to breakthroughs in many fields, including medicine, finance, and customer service. GPT-4 could enable computers to understand natural language more effectively and generate more human-like responses.

A glimpse into Auto GPT

However, it is not just GPT-4 that is causing a stir. Other AI language models, such as Auto GPT, are also making waves in the tech industry. Auto GPT is a machine learning system that can generate text on its own without any human intervention. It has the potential to automate content creation for businesses, making it a valuable tool for marketers.

Auto chat is particularly useful for businesses that need to engage with customers in real-time, such as customer service departments. By using auto chat, companies can reduce wait times, improve response accuracy and provide a more personalized customer experience.

Want to start your EDA journey, well you can always get yourself registered at Data Science Bootcamp.

In a nutshell

So just to recap, ChatGPT is not a black box of unknown mysteries but rather a carefully crafted state-of-the-art artificial intelligence algorithm that has been rigorously trained with a variety of scenarios in order to cover all the possible use cases. Even though it can do wonders as we have seen already there is still a long way to go as there are still potential problems that need to be inspected and worked on. To get the latest news on astounding technological advancements and other associated fields visit Data Science Dojo to keep yourself posted.   

 

ChatGPT is scary good. We are not far from dangerously strong AI – Elon Musk