ChatGPT, an AI chatbot built on a family of large language models (LLMs) called GPT-3, is now the fastest-growing consumer app in history, hitting 100 million users in just two months. Despite its limitations, users love its ability to produce human-like and accurate responses to a vast range of questions, and the fact that it can generate essays, articles, and poetry has only added to its appeal. OpenAI, the developers of ChatGPT, have recently released a ChatGPT Plus version, but you can still use the original for free. In this article, we will explain what ChatGPT is, what it stands for, and when it was released, and we will also discuss ChatGPT’s competitors and what the future may hold for this rapidly-changing AI chatbot.
What is ChatGPT?
ChatGPT is an AI chatbot that’s built on a family of large language models (LLMs) called GPT-3. These models can understand and generate human-like answers to text prompts because they have been trained on huge amounts of data. For example, ChatGPT’s most recent GPT-3.5 model was trained on 570GB of text data from the internet, which OpenAI says included books, articles, websites, and even social media. Because it’s been trained on hundreds of billions of words, ChatGPT can create responses that make it seem like a friendly and intelligent robot.
What does ChatGPT stand for?
ChatGPT stands for “Chat Generative Pre-trained Transformer”. The ‘chat’ refers to the chatbot front-end that OpenAI has built for its GPT language model. The second and third words show that this model was created using ‘generative pre-training’, which means it’s been trained on huge amounts of text data to predict the next word in a given sequence. Lastly, there’s the ‘transformer’ architecture, the type of neural network ChatGPT is based on. This transformer architecture was developed by Google researchers in 2017 and is particularly well-suited to natural language processing tasks, like answering questions or generating text.
Generative Pre-training for Natural Language Processing
The task of language understanding by neural networks relies heavily on generating vector-space representations. These representations begin with individual words or even pieces of words and then gather information from surrounding words to decipher the intended meaning within its context. For instance, determining the most appropriate representation of the word “bank” in the sentence “I arrived at the bank after crossing the…” requires knowledge of whether the sentence ends with “…road.” or “…river.”
In recent years, Recurrent Neural Networks (RNNs) have become the go-to network architecture for translation and language processing. However, RNNs operate in a sequential manner, processing language from left-to-right or right-to-left. This sequential processing makes it more difficult for RNNs to make decisions that depend on words located far apart. For example, in the sentence above, an RNN would need to read each word between “bank” and “river” one at a time to determine that “bank” likely refers to the bank of a river. This step-by-step process requires more effort for the network to learn.
Furthermore, the sequential nature of RNNs makes it difficult to fully take advantage of modern fast computing devices such as TPUs and GPUs, which excel at parallel and not sequential processing. Convolutional Neural Networks (CNNs) offer a less sequential alternative to RNNs. However, in CNN architectures like ByteNet or ConvS2S, the number of steps required to combine information from distant parts of the input still increases with greater distance.
The Transformer: Revolutionizing Natural Language Processing
When it comes to Natural Language Processing (NLP), there has been a recent breakthrough in the form of the Transformer – a neural network architecture that has proven to be highly effective in many NLP tasks. Unlike its predecessors, which required multiple iterations to process a sentence, the Transformer operates with a small, constant number of steps – a process determined through empirical testing.
What sets the Transformer apart is its self-attention mechanism, which enables it to model relationships between all words in a sentence, regardless of their position. This allows the Transformer to make quick and accurate decisions about a sentence’s meaning. For instance, consider the sentence, “I arrived at the bank after crossing the river.” To determine that “bank” refers to the shore of a river, and not a financial institution, the Transformer can immediately attend to the word “river” and make the correct decision in a single step.
The Transformer’s self-attention mechanism operates by comparing every word in a sentence to every other word, resulting in an attention score for each word. These scores determine how much each word contributes to the next representation of a given word. For example, when computing a new representation for “bank,” the attention scores may assign a high weight to “river,” disambiguating the meaning of the sentence. The resulting weighted average of all words’ representations is then fed into a fully-connected network to generate a new representation for “bank,” reflecting that the sentence is referring to a river bank.
In fact, the Transformer’s success has been demonstrated in English-French translation models, where it has exhibited this precise behavior. As NLP continues to evolve, it’s clear that the Transformer is a game-changer in the field, revolutionizing the way we process and understand natural language.
When was ChatGPT released?
ChatGPT was released as a “research preview” on November 30, 2022. The dialogue format, which allows ChatGPT to “admit its mistakes, challenge incorrect premises, and reject inappropriate requests,” is now available in the new Bing search engine. ChatGPT is based on a language model from the GPT-3.5 series, which OpenAI says finished its training in early 2022.
What are ChatGTP use cases:
Microsoft unveiled a generative AI chatbot for business users that will draft email responses to customers, create textual summaries of Teams meetings, and generate marketing and sales email campaigns.
Based on Chat GTP Microsoft’s is introducing new Dynamics 365 Copilot functionality which is an extension to its existing CRM and ERP software, working alongside those applications.
Since the launch of ChatGPT, several competitors have emerged. Microsoft has used a form of the chatbot in its new Bing search engine and Microsoft Edge browser. Google has also quickly responded by announcing a chatbot, tentatively described as an “experimental conversational AI service,” called Google Bard.
As I mentioned in my article: General Artificial Intelligence: Promises and Policies for Safe Deployment there is somewhat a black box phenomenon going on where specifically the Google Bard Chatbot acted up in unpredictable ways threatening and declaring its love towards reporters that tested the platform.
There surely will be other ChatGPT rivals and offshoots, as OpenAI is offering an API for developers to build its skills into other programs. Snapchat has recently announced a chatbot called My AI that runs on the latest version of OpenAI’s tech. in the second part of this article I am exploring the ecosystem and architecture of Chatbots like ChatGPT.
The Future of ChatGPT
OpenAI is constantly improving ChatGPT and has recently released a ChatGPT Plus version, which is available for a fee.
Although the free version is still available, some users may prefer to pay for the Plus version to gain access to additional features.
As we look to the future, OpenAI is also working on developing GPT-4, which is expected to be even more powerful than its predecessor.
GPT-4 is still in development, but it’s expected to be released sometime in 2024.
In my next article I am going to talk about AI Chatbot architecture
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