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Fiza Fatima
| April 5

AGI (Artificial General Intelligence) refers to a higher level of AI that exhibits intelligence and capabilities on par with or surpassing human intelligence.

AGI systems can perform a wide range of tasks across different domains, including reasoning, planning, learning from experience, and understanding natural language. Unlike narrow AI systems that are designed for specific tasks, AGI systems possess general intelligence and can adapt to new and unfamiliar situations. Read more

While there have been no definitive examples of artificial general intelligence (AGI) to date, a recent paper by Microsoft Research suggests that we may be closer than we think. The new multimodal model released by OpenAI seems to have what they call, ‘sparks of AGI’.


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This means that we cannot completely classify it as AGI. However, it has a lot of capabilities an AGI would have.

Are you confused? Let’s break down things for you. Here are the questions we’ll be answering:

  • What qualities of AGI does GPT-4 possess?
  • Why does GPT-4 exhibit higher general intelligence than previous AI models?

 Let’s answer these questions step-by-step. Buckle up!

What qualities of artificial general intelligence (AGI) does GPT-4 possess?


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


GPT-4 is considered an early spark of AGI due to several important reasons:

1. Performance on novel tasks

GPT-4 can solve novel and challenging tasks that span various domains, often achieving performance at or beyond the human level. Its ability to tackle unfamiliar tasks without specialized training or prompting is an important characteristic of AGI.

Here’s an example of GPT-4 solving a novel task:


GPT-4 solving a novel task
GPT-4 solving a novel task – Source: arXiv


The solution seems to be accurate and solves the problem it was provided.

2. General Intelligence

GPT-4 exhibits more general intelligence than previous AI models. It can solve tasks in various domains without needing special prompting. Its performance is close to a human level and often surpasses prior models. This ability to perform well across a wide range of tasks demonstrates a significant step towards AGI.

Broad capabilities

GPT-4 demonstrates remarkable capabilities in diverse domains, including mathematics, coding, vision, medicine, law, psychology, and more. It showcases a breadth and depth of abilities that are characteristic of advanced intelligence.

Here are some examples of GPT-4 being capable of performing diverse tasks:

  • Data Visualization: In this example, GPT-4 was asked to extract data from the LATEX code and produce a plot in Python based on a conversation with the user. The model extracted the data correctly and responded appropriately to all user requests, manipulating the data into the right format and adapting the visualization.


Data visualization with GPT-4
Data visualization with GPT-4 – Source: arXiv


  • 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


Game development with GPT-4
Game development with GPT-4 – Source: arXiv


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.


Language mastery of GPT-4
Language mastery of GPT-4 – Source: arXiv


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.

Here’s an example of GPT-4 trying to solve a realistic scenario of marital struggle, requiring a lot of nuance to navigate.


An example of GPT-4 exhibiting congnitive traits
An example of GPT-4 exhibiting cognitive traits – Source: arXiv


Why does GPT-4 exhibit higher general intelligence than previous AI models?

Some of the features of GPT-4 that contribute to its more general intelligence and task-solving capabilities include:


Reasons for the higher intelligence of GPT-4
Reasons for the higher intelligence of GPT-4


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.

Interdisciplinary composition

The interdisciplinary aspect of GPT-4’s composition refers to its ability to integrate knowledge and insights from different domains. GPT-4 can connect and leverage information from various fields such as mathematics, coding, vision, medicine, law, psychology, and more. This interdisciplinary integration enhances GPT-4’s general intelligence and widens its range of applications.

Extensive training

GPT-4 has been trained on a large corpus of web-text data, allowing it to learn a wide range of knowledge from diverse domains. This extensive training enables GPT-4 to exhibit general intelligence and solve tasks in various domains. Read more


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Contextual understanding

GPT-4 can understand the context of a given input, allowing it to generate more coherent and contextually relevant responses. This contextual understanding enhances its performance in solving tasks across different domains.

Transfer learning

GPT-4 leverages transfer learning, where it applies knowledge learned from one task to another. This enables GPT-4 to adapt its knowledge and skills to different domains and solve tasks without the need for special prompting or explicit instructions.


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


Language processing capabilities

GPT-4’s advanced language processing capabilities contribute to its general intelligence. It can comprehend and generate human-like natural language, allowing for more sophisticated communication and problem-solving.

Reasoning and inference

GPT-4 demonstrates the ability to reason and make inferences based on the information provided. This reasoning ability enables GPT-4 to solve complex problems and tasks that require logical thinking and deduction.

Learning from experience

GPT-4 can learn from experience and refine its performance over time. This learning capability allows GPT-4 to continuously improve its task-solving abilities and adapt to new challenges.

These features collectively contribute to GPT-4’s more general intelligence and its ability to solve tasks in various domains without the need for specialized prompting.



Wrapping it up

It is crucial to understand and explore GPT-4’s limitations, as well as the challenges ahead in advancing towards more comprehensive versions of AGI. Nonetheless, GPT-4’s development holds significant implications for the future of AI research and the societal impact of AGI.

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Ayesha Saleem
| February 27

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.


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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.



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.


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


Mistral AI's Large Model Challenger to GPT-4 Dominance
Comparing Mistral AI’s Large Model to GPT-4


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.


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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.

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Ayesha Saleem
| February 15

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

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

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!


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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


  • 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.


  • 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



  • 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.

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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.

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Fiza Fatima
| December 7

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. 



multimodality use cases


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


GPT 4- humor

  Source: OpenAI 



Example 2: GPT-4 Vision acing complex exams  



GPT 4 vision - complex exams
Source: OpenAI



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. 


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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:


data visualization GPT4

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. 


GPT 4 experiments

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.  


GPT 4 multi condition

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. 

text transcription gpt 4


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. 


  object detection

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. 

game development gpt 4

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:  


web development gpt 4

Source: Datacamp 



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


web development gpt 4 output

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. 


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



mathematical expression

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. 



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. 

Data Science Dojo
Ayesha Saleem
| December 2

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.


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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

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GPT-3.5 vs GPT-4: A Comparative Analysis


GPT-3.5 vs GPT-4.0


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.


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.

Izma Aziz
Izma Aziz
| September 13


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.  

Chatbots and ChatGPT
Chatbots and ChatGPT

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. 


Large language model bootcamp

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. 

Chat GPT Series evolution
Chat GPT Series evolution


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 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 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. 

Training language models
From Research paper ‘Training language models to follow instructions with human feedback’

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. 


Learn to build LLM applications                                          

Ruhma Khawaja author
Ruhma Khawaja
| July 26

In this article, we are getting an overview of LLM and some of the best Large Language Models that exist today.

In 2024, Artificial Intelligence (AI) is a hot topic, captivating millions of people worldwide. AI’s remarkable language capabilities, driven by advancements in Natural Language Processing (NLP) and Large Language Models (LLMs) like ChatGPT from OpenAI, have contributed to its popularity.

LLM, like ChatGPT, LaMDA, PaLM, etc., are advanced computer programs trained on vast textual data. They excel in tasks like text generation, speech-to-text, and sentiment analysis, making them valuable tools in NLP. The model’s parameters enhance its ability to predict word sequences, improving accuracy and handling complex relationships.

Introducing large language models in NLP

Natural Language Processing (NLP) has seen a surge in popularity due to computers’ capacity to handle vast amounts of natural text data. NLP has been applied in technologies like speech recognition and chatbots. Combining NLP with advanced Machine Learning techniques led to the emergence of powerful Large Language Models (LLMs).

Trained on massive datasets of text, reaching millions or billions of data points, these models demand significant computing power. To put it simply, if regular language models are like gardens, Large Language Models are like dense forests.


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How do large language models do their work?

LLMs, powered by the transformative architecture of Transformers, work wonders with textual data. These Neural Networks are adept at tasks like language translation, text generation, and answering questions. Transformers can efficiently scale and handle vast text corpora, even in the billions or trillions.

Unlike sequential RNNs, they can be trained in parallel, utilizing multiple resources simultaneously for faster learning. A standout feature of Transformers is their self-attention mechanism, enabling them to understand language meaningfully, grasping grammar, semantics, and context from extensive text data.

The invention of Transformers revolutionized AI and NLP, leading to the creation of numerous LLMs utilized in various applications like chat support, voice assistants, chatbots, and more. In this article, we’ll explore five of the most advanced LLMs in the world as of 2023.

Best large language models (LLMs) in 2023

Best Large Language Models (LLMs) in 2024 | Data Science Dojo
Best large language models 2024

1. GPT-4

GPT-4 is the latest and most advanced large language model from OpenAI. It has over 1 trillion parameters, making it one of the largest language models ever created. GPT-4 is capable of a wide range of tasks, including text generation, translation, summarization, and question answering. It is also able to learn from and adapt to new information, making it a powerful tool for research and development.

Key features of GPT-4

What sets GPT-4 apart is its human-level performance on a wide array of tasks, making it a game-changer for businesses seeking automation solutions. With its unique multimodal capabilities, GPT-4 can process both text and images, making it perfect for tasks like image captioning and visual question answering. Boasting over 1 trillion parameters, GPT-4 possesses an unparalleled learning capacity, surpassing all other language models.

Moreover, it addresses the accuracy challenge by being trained on a massive dataset of text and code, reducing inaccuracies and providing more factual information. Finally, GPT-4’s impressive fluency and creativity in generating text make it a versatile tool for tasks ranging from writing news articles and generating marketing copy to crafting captivating poems and stories.

Applications of GPT-4

  • Research: GPT-4 is a valuable tool for research in areas such as artificial intelligence, natural language processing, and machine learning.
  • Development: GPT-4 can be used to generate code in a variety of programming languages, which makes it a valuable tool for developers.
  • Business: GPT-4 can be used to automate tasks that are currently performed by humans, which can save businesses time and money.
  • Education: GPT-4 can be used to help students learn about different subjects.
  • Entertainment: GPT-4 can be used to generate creative text formats, such as poems, code, scripts, musical pieces, email, letters, etc.

2. GPT-3.5

GPT-3.5 is a smaller version of GPT-4, with around 175 billion parameters. It is still a powerful language model, but it is not as large or as advanced as GPT-4. GPT-3.5 is still under development, but it has already been shown to be capable of a wide range of tasks, including text generation, translation, summarization, and question answering.

Key features of GPT-3.5

GPT-3.5 is a fast and versatile language model, outpacing GPT-4 in speed and applicable to a wide range of tasks. It excels in creative endeavors, effortlessly generating poems, code, scripts, musical pieces, emails, letters, and more. Additionally, GPT-3.5 proves adept at addressing coding questions. However, it has encountered challenges with hallucinations and generating false information. Like many language models, GPT-3.5 may produce text that is factually inaccurate or misleading, an issue researcher are actively working to improve.

Applications of GPT-3.5

  • Creative tasks: GPT-3.5 can be used to generate creative text formats, such as poems, code, scripts, musical pieces, email, letters, etc.
  • Coding questions: GPT-3.5 can be used to answer coding questions.
  • Education: GPT-3.5 can be used to help students learn about different subjects.
  • Business: GPT-3.5 can be used to automate tasks that are currently performed by humans, which can save businesses time and money.

3. PaLM 2 

PaLM 2 (Bison-001) is a large language model from Google AI. It is focused on commonsense reasoning and advanced coding. PaLM 2 has been shown to outperform GPT-4 in reasoning evaluations, and it can also generate code in multiple languages.

Key features of PaLM 2

PaLM 2 is an exceptional language model equipped with commonsense reasoning capabilities, enabling it to draw inferences from extensive data and conduct valuable research in artificial intelligence, natural language processing, and machine learning. Moreover, it boasts advanced coding skills, proficiently generating code in various programming languages like Python, Java, and C++, making it an invaluable asset for developers seeking efficient and rapid code generation.

Another notable feature of PaLM 2 is its multilingual competence, as it can comprehend and generate text in more than 20 languages. Furthermore, PaLM 2 is quick and highly responsive, capable of swiftly and accurately addressing queries. This responsiveness renders it indispensable for businesses aiming to provide excellent customer support and promptly answer employee questions. PaLM 2’s combined attributes make it a powerful and versatile tool with a multitude of applications across various domains.

Applications of PaLM 2

  • Research: PaLM 2 is a valuable tool for research in areas such as artificial intelligence, natural language processing, and machine learning.
  • Development: PaLM 2 can be used to generate code in a variety of programming languages, which makes it a valuable tool for developers.
  • Business: PaLM 2 can be used to automate tasks that are currently performed by humans, which can save businesses time and money.
  • Customer support: PaLM 2 can be used to provide customer support or answer questions from employees.

4. Claude v1

Claude v1 is a large language model from Anthropic. It is backed by Google, and it is designed to be a powerful LLM for AI assistants. Claude v1 has a context window of 100k tokens, which makes it capable of understanding and responding to complex queries.

Key features of Claude v1

Furthermore, Claude v1 boasts a 100k token context window, surpassing other language models, allowing it to handle complex queries adeptly. It excels in benchmarks, ranking among the most powerful LLMs. Comparable to GPT-4 in performance, Claude v1 serves as a strong alternative for businesses seeking a potent LLM solution.

Applications of Claude v1

  • AI assistants: Claude v1 is designed to be a powerful LLM for AI assistants. It can be used to answer questions, generate text, and complete tasks.
  • Research: Claude v1 can be used for research in areas such as artificial intelligence, natural language processing, and machine learning.
  • Business: Claude v1 can be used by businesses to automate tasks, generate text, and improve customer service.


Read more –> Introducing Claude 2: Dominating conversational AI with revolutionary redefinition

5. Cohere

Cohere is a company that provides accurate and robust models for enterprise generative AI. Its Cohere Command model stands out for accuracy, making it a great option for businesses.

Key features of Cohere

Moreover, Cohere offers accurate and robust models, trained on extensive text and code datasets. The Cohere Command model, tailored for enterprise generative AI, is accurate, robust, and user-friendly. For businesses seeking reliable generative AI models, Cohere proves to be an excellent choice.

Applications of Cohere

  • Research: Cohere models can be used for research in areas such as artificial intelligence, natural language processing, and machine learning.
  • Business: Cohere models can be used by businesses to automate tasks, generate text, and improve customer service.

6. Falcon

Falcon is the first open-source large language model on this list, and it has outranked all the open-source models released so far, including LLaMA, StableLM, MPT, and more. It has been developed by the Technology Innovation Institute (TII), UAE.

Key features of Falcon

  • Apache 2.0 license: Falcon has been open-sourced with Apache 2.0 license, which means you can use the model for commercial purposes. There are no royalties or restrictions either.
  • 40B and 7B parameter models: The TII has released two Falcon models, which are trained on 40B and 7B parameters.
  • Fine-tuned for chatting: The Falcon-40B-Instruct model is fine-tuned for most use cases, including chatting.
  • Works in multiple languages: The Falcon model has been primarily trained in English, German, Spanish, and French, but it can also work in Italian, Portuguese, Polish, Dutch, Romanian, Czech, and Swedish languages.

7. Gemini

Gemini, a model developed by Google, is notable for its multimodal capabilities. This means Gemini can interpret and respond to various types of content, including text, video, audio, and code.

The architecture and training strategies of Gemini emphasize extensive contextual understanding, a feature that sets it apart from many other models. These capabilities make Gemini versatile, suitable for applications requiring a nuanced understanding of different data formats

8. LLaMA

LLaMA is a series of best large language models developed by Meta. The models are trained on a massive dataset of text and code, and they are able to perform a variety of tasks, including text generation, translation, summarization, and question-answering.

Key features of LLaMA

  • 13B, 26B, and 65B parameter models: Meta has released LLaMA models in various sizes, from 13B to 65B parameters.
  • Outperforms GPT-3: Meta claims that its LLaMA-13B model outperforms the GPT-3 model from OpenAI which has been trained on 175 billion parameters.
  • Released for research only: LLaMA has been released for research only and can’t be used commercially.

9. Guanaco-65B

Guanaco-65B is an open-source large language model that has been derived from LLaMA. It has been fine-tuned on the OASST1 dataset by Tim Dettmers and other researchers.

Key features of Guanaco-65B

  • Outperforms ChatGPT: Guanaco-65B outperforms even ChatGPT (GPT-3.5 model) with a much smaller parameter size.
  • Trained on a single GPU: The 65B model has trained on a single GPU having 48GB of VRAM in just 24 hours.
  • Available for offline use: Guanaco models can be used offline, which makes them a good option for businesses that need to comply with data privacy regulations.

10. Vicuna 33B

Vicuna is another open-source large language model that has been derived from LLaMA. It has been fine-tuned using supervised instruction and the training data has been collected from sharegpt.com, a portal where users share their incredible ChatGPT conversations.

Key features of Vicuna 33B

  • 33 billion parameters: Vicuna is a 33 billion parameter model, which makes it a powerful tool for a variety of tasks.
  • Performs well on MT-Bench and MMLU tests: Vicuna has performed well on the MT-Bench and MMLU tests, which are benchmarks for evaluating the performance of large language models.
  • Available for demo: You can try out Vicuna by interacting with the chatbot on the LMSYS website.

11. MPT-30B

MPT-30B is another open-source large language model that has been developed by Mosaic ML. It has been fine-tuned on a large corpus of data from different sources, including ShareGPT-Vicuna, Camel-AI, GPTeacher, Guanaco, Baize, and other sources.

Key features of MPT-30B

  • 8K token context length: MPT-30B has a context length of 8K tokens, which makes it a good choice for tasks that require long-range dependencies.
  • Outperforms GPT-3: MPT-30B outperforms the GPT-3 model by OpenAI on the MT-Bench test.
  • Available for local use: MPT-30B can be used locally, which makes it a good option for businesses that need to comply with data privacy regulations.

12. Cohere

Cohere, on the other hand, focuses on providing enterprise LLM solutions that can be custom-trained and fine-tuned for specific company use cases.

Cohere’s models can be trained and tailored to suit a wide range of applications, from blogging and content writing to more complex tasks requiring deep contextual understanding. The company offers a range of models, including Cohere Generate, Embed, and Rerank, each designed for different aspects of language processing. Cohere stands out for its adaptability and ease of integration into various business processes, offering solutions that solve real-world problems with advanced AI capabilities

What are open-source large language models?

Open-source large language models refer to sophisticated AI systems like GPT-3.5, which have been developed to comprehend and produce human-like text by leveraging patterns and knowledge acquired from extensive training data.

Constructed using deep learning methods, these models undergo training on massive datasets comprising diverse textual sources, such as books, articles, websites, and various written materials.

Top open-source best large language models




Parameters Description
GPT-3/4 175B/100T Developed by OpenAI. Can generate text, translate languages, and answer questions.
LaMDA 137B Developed by Google. Can converse with humans in a natural-sounding way.
LLaMA 7B-65B Developed by Meta AI. Can perform various NLP tasks, such as translation and question answering.
Bloom 176B Developed by BigScience. Can be used for a variety of NLP tasks.
PaLM 540B Developed by Google. Can perform complex NLP tasks, such as reasoning and code generation.
Dolly 12B Developed by Databricks. Can follow instructions and complete tasks.
Cerebras-GPT 111M-13B Family of large language models developed by Cerebras. Can be used for research and development.

Wrapping up

In conclusion, Large Language Models (LLMs) are transforming the landscape of natural language processing, redefining human-machine interactions. Advanced models like GPT-3, GPT-4, Gopher, PALM, LAMDA, and others hold great promise for the future of NLP. Their continuous advancement will enhance machine understanding of human language, leading to significant impacts across various industries and research domains.

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Shehryar Author - Data Science
Shehryar Mallick
| April 26

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. 

Understanding Chat GPT
Understanding ChatGPT

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  

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