Chat GPT is an application of machine learning, specifically based on the GPT-3.5 architecture developed by Open AI. Machine learning is a subfield of artificial intelligence (AI) that focuses on creating algorithms and models that can learn and make predictions or decisions based on data.
In the case of Chat GPT, it has been trained on a vast amount of text data to understand and generate human-like responses to user inputs. The training process involves exposing the model to large datasets and using techniques such as deep learning to learn patterns and relationships within the data.
Machine learning algorithms like the one used in Chat GPT are typically designed to generalize from the training data to make predictions or generate outputs on new, unseen data. In the case of Chat GPT, it has learned to understand natural language inputs and produce coherent and contextually relevant responses.
The training process for Chat GPT involves presenting the model with input-output pairs, where the input is a prompt or a portion of text, and the output is the expected response. The model learns to map the input to the output by adjusting its internal parameters through an optimization process called backpropagation and gradient descent. This iterative process helps the model improve its performance over time.
It’s important to note that Chat GPT is a specific instance of a machine learning model trained for conversational tasks. Machine learning encompasses a wide range of algorithms and techniques beyond just language models and it is a rapidly evolving field with ongoing research and advancements.
Let’s us talk about the evolution of Chat GPT starting from GPT-1 to GPT-4.
It was released in 2018 and It had 117 million parameters. Its core strength was to generate fluent, logical and consistent language when given a prompt or context. This model was a combination of two datasets: Common Crawl (a set of web pages with billions of words) and the Book Corpus (a collection of over 11,000 books on various genres). These datasets allow GPT-1 to develop strong language modeling abilities. But GPT-1 also had some limitations, like it provides solutions to only short text only and longer passages would lack logic. It also failed to reason over multiple turns of dialogue and could not track long-term dependencies in text.
After GPT-1, Open AI was set to release GPT-2 as a better chatbot named as GPT-2. It was released in 2019 as a successor to GPT-1. It contained 1.5 billion parameters which are larger than GPT-1. This model was trained on a great dataset than GPT-1 combining Common Crawl, Book Corpus, and Web Text. One of its abilities is to generate logical and real-time texts sequence. It also generates human-like responses which makes it more valuable than different NLP technologies. It also had some limitations like it found difficulties with complex reasoning and understanding. While it excelled in short paragraphs, it also failed to maintain logical reasoning in long paragraphs.
NLP models made exponential leaps with the release of GPT-3 in 2020. It contains 175 billion parameters. GPT-3 is about 100 times larger than GPT-1 and 10 times larger than GPT-2. It is trained on a large range of data sources including Common Crawl, Book Corpus, Wikipedia, Books, Articles, and more. It contains trillions of words that generate sophisticated responses on NLP tasks, even without providing any prior example data. GPT-3 is the improved version of GPT-1 and GPT-2.
The main improvement of GPT-3 is that, it has a great ability to provide logical reasoning, write codes, and logical texts and even create art. It understands the context and gives answers according to that. It also creates a natural-sounding text which has huge implications for applications like language translation.
Where GPT-3 has a lot of advantages, it also has flaws in it. For example, it can provide inappropriate responses sometimes. It is because of this, GPT-3 is based on a massive amount of text that contains biased and inappropriate information. Misuse of such a powerful language model also arose in this era to create malware, fake news, and phishing emails.
It is the latest model of the GPT series, which is launched on March 14, 2023. It is a better version of GPT-3 which already impresses everyone. As its datasets are not announced yet but we all know that it builds upon the strength of GPT-3 and overcome some of its limitations. However, it is exclusive to Chat GPT Plus users, but its usage limit is restricted. By joining GPT-4 API waitlist, we can also gain its access, which might take some time due to the high volume of applications. But the easiest way to get your hands on GPT-4 is using “Microsoft Bing Chat” because it’s completely free and there is no need to join a waitlist.
The best and improved feature of GPT-4 is a multimedia module, which means it can accept images as input and understand them like prompt text. It also understands complex code and exhibits human-level performance. GPT-4 is pushing the boundaries of what we can do with AI tools and applications.
Chat-GPT models have evolved beautifully in the field of AI. It grows bigger and better toward learning technologies. The capability, Complexity and Large Scale of these models have made them incredible. GPT models evolve and become better, more reliable and more useful in today’s world. It continues to give shape to AI, NLP, and MLT.
From its inception as GPT-3.5 to its current form as an advanced AI conversational agent, Chat GPT has come a long way. The evolution of Chat GPT has seen enhancements in contextual understanding, knowledge expansion, ethical considerations, user-driven customization and more. As Open AI continues to push the boundaries of AI language models, we can expect Chat GPT to evolve further, empowering users with increasingly sophisticated conversational capabilities.