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Evolution of GPT series: The GPT revolution from 1 to 4 trillion
Izma Aziz
| September 13, 2023


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. 


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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                                          

Best Large Language Models (LLMs) in 2023
Ruhma Khawaja
| July 26, 2023

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

In 2023, 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
Best Large Language Models

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. 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.

8. 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.

9. 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.

10. 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.

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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Unraveling the phenomenon of ChatGPT: Understanding the revolutionary AI technology 
Shehryar Mallick
| April 26, 2023

This blog explores the amazing AI (Artificial Intelligence) technology called ChatGPT that has taken the world by storm and try to unravel the underlying phenomenon which 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 which 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 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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