ChatGPT Evolution

From GPT-1 to GPT-4: A Look at the Evolution of Generative AI

ChatGPT, has undeniably garnered widespread acclaim and an impressive level of demand since its debut in late 2022. What may not be widely known is that ChatGPT’s predecessors have been around for quite some time. While they may not have exhibited the same level of proficiency as their successor, they still outshone many other chatbots and AI models introduced during their era.

Now, with many iterations before its time, Generative Pre-trained Transformer 4 (GPT-4) launched on March 14, 2023. It would be highly erroneous to say that GPT-4 is just an ordinary language model. What sets GPT-4 apart is its astounding capacity to generate text spanning up to 25,000 words, its ability to comprehend images, and its aptitude for reasoning, enabling it to analyze visual content and provide pertinent responses to users. Therefore, it is safe to conclude that GPT-4 has the potential to revolutionize education in subjects like Mathematics, Science, History, Literature, and Business even more so than its predecessors. dsets and preferences beforehand. Data analytics enables companies to identify distinct customer types and tailor experiences based on past choices.

GPT series model, an introduction

GPT-1: GPT-1 was introduced in 2018 with roughly 117 million model parameters. GPT-1 was trained on 40GB of data available on the internet with an approximate count of 600 billion words. With GPT-1, it was possible to ask generic questions, rephrase and generate new text, and translate languages. However, the model was not at par with its subsequent successors at comprehending long pieces of text i.e., it was good at responding to relatively short sentences or small extracts per request.

GPT-2: Built upon the foundation of the GPT-1 model, the GPT-2 model was made to retain its core architectural characteristics. It was trained on a larger corpus of textual data in comparison to GPT-1. GPT-2 was capable of handling input that is twice the size of what GPT-1 could do, allowing it to process more extensive textual samples effectively. With nearly 1.5 billion parameters, GPT-2 demonstrates a substantial increase in its capability for language modeling.

GPT-2 underwent a process known as ‘Modified Objective Training,’ with the aim of improving language models and ensuring that their responses maintain coherence and relevance. This was achieved by integrating additional contextual elements, including ‘Parts of Speech’ such as verbs and nouns, and the identification of subjects and objects.

GPT-3: Released in 2020, GPT-3 was hailed for its ability to generate text that had a lot of depth and came across as more realistic. It was trained on more than 570 GB of text data, scraped from the Internet. The sources included books from various genres, Wikipedia, BookCorpus, Common Crawl, and more. The GPT-3 model represented an advanced version of the GPT-2 model, surpassing it in various aspects. It underwent training on a substantially larger text dataset and had a maximum of 175 billion parameters. This is why it was and is capable of responding to a good range of prompts and queries. However, some of GPT-3’s limitations were also highlighted predominantly. It showcased a few instances of biases and inaccuracies.

GPT-3.5: GPT-3.5, like its recent predecessor, was trained on over 570 GB of data from various sources like the Internet, Wikipedia, and E-books. It was released in 2022 with the same number of parameters as GPT-3. What sets GPT-3.5 apart is its compliance with some guidelines that were made keeping the human value system in mind. It was incorporated through a technique called Reinforcement Learning with Human Feedback (RLHF).

GPT-3.5 was modified to align with human intent and ensure accuracy and verity at the same time.

GPT-4: A masterstroke by OpenAI, GPT-4 is the next generation of advanced language models that can brilliantly co-relate to prompts, support ideas and thoughts, and transform concepts into a text format. GPT-4, unlike its predecessors, can understand objects in an image and generate a brief opinion on its subject matter or theme.

While OpenAI has not provided a comprehensive report describing the architecture of GPT-4, its capability to generate contextually relevant text from visual inputs suggests that GPT-4 has undergone training on both textual and visual data. GPT-4 employs dual-stream transformers, allowing it to simultaneously process visual and textual information. This includes a visual encoder for analyzing visual input and a decoder model for generating text-based outputs. Consequently, GPT-4 excels at deciphering documents containing images, schematics, infographics, and diagrams.

Like previous models, GPT-4 incorporates the Reinforcement Learning with Human Feedback (RLHF) technique into its training process. While OpenAI has not disclosed the exact size and sources of the data used to train GPT-4, it is reasonable to assume that they have compiled a diverse and extensive dataset from various web pages and digital sources to enrich the model’s knowledge base.

What are the capabilities of GPT-4?

OpenAI has trained and exposed the GPT-4 model to a broad range of textual and visual information. Consequently, it can produce refined specimens of text and respond to prompts in a human way. It is capable of many other things like:

  1. Completing incomplete sentences and predicting the right set of alternatives for insufficient inputs.
  2. Perfectly describing images and highlighting their central idea agreeably.
  3. Accurately comprehending jokes most of the time.
  4. Writing codes in multiple programming languages.
  5. Generating long-form conversational texts for emails and clauses for legal documents.

OpenAI and its team of engineers and AI scientists have utilized the last five years to develop GPT models into something that can help humans excel in various fields and tackle a wide range of challenges. The noteworthy advancements can be credited to ongoing improvements in various aspects, including the size and quality of training data, the diversity of data sources, the number of parameters, and training methodologies.

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