If you’re looking for what is ChatGPT-4 | The New Game Changer then your search ends here. After the ChatGPT 3.0 release that we posted earlier here, OpenAI released another version which is ChatGPT-4 | The New Game Changer. What new things and evolutions are taken into consideration in this release, will be discussed in this article.
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OpenAI’s ChatGPT-4 (Generative Pre-trained Transformer 4) is the company’s latest generation of a language model. It’s gained a lot of attention in the NLP community for its ability to generate text that sounds like human speech. OpenAI introduced the latest DALLE2 text-to-image model in July 2022.
Recently, AI released a free, public-source implementation of DALLE-2 called Stable Diffusion. The Automatic Speech Recognition (ASR) model Whisper is new to this release from OpenAI. Both of these models are widely used and have proven useful capabilities in terms of both quality and understanding of the prompt.
In terms of reliability and precision, it is superior to any other models that have been developed so far. From these things, we were expecting OpenAI to unveil GPT-4 within the next few months.
The market has shown a need for large language models. The success of GPT-3 indicates that users wanted to see advancements in areas like accuracy, compute optimization, bias reduction, and security in GPT-4 and it happened.
In this post, we’ll explore the ChatGPT-4 | The New Game Changer, based on current AI developments and OpenAI’s information. Furthermore, we’ll explore the potential of using additional large language models. You can read more about the ChatGPT and Google Bard in a separate post here.
Features of ChatGPT-4 | The New Game Changer
In this section, we’ll examine some features that take place in ChatGPT-4 | The New Game Changer. So, let’s examine them below:
1. Model Size
Altman claims GPT-4 will be roughly the same size as GPT-3. Like Deepmind’s Gopher language model, we can therefore say that its size is in the range of 175B to 280B parameters. The Megatron NLG big model’s performance was not better than that of the GPT-3 with 530B parameters. The sequel, a smaller model, actually performed better. The larger the size, the less likely it is to perform well.
2. Optimal Parameterization
Many huge models are not optimized to their full potential. Companies must balance the cost of training the model against the value of its predictions. In a recent study, researchers from Microsoft and OpenAI showed that GPT-3 might have been optimized if it had been trained with the best possible hyperparameters.
It was found that the performance of a 6.7B GPT-3 model may be enhanced to the same level as a 13B GPT-3 model by optimizing its hyperparameters. They have found a new parameterization (P) where the optimal hyperparameters for small models are identical to those for large models of the same architecture. It’s helped scientists reduce the expense of optimizing big models.
3. Compute Model
In a recent study, DeepMind found that the quantity of training tokens had almost as much of an impact on model performance as the model’s overall size. They have shown this by training a 70B model in Chinchilla, which is four times smaller than Gopher and four times more data than major language models since GPT-3.
The OpenAI training tokens for a compute-optimal model are expected to increase by 5 trillion. This suggests that training the model to achieve low loss will require 10-20X more FLOPs than GPT-3.
4. Text-Based Model
Altman clarified during Q&A that, unlike DALL-E, the GPT-4 doesn’t support multiple transport modes. As such, it simply consists of text. The question is why. It is more difficult to develop a good multimodal system than a language-only or vision-only system. It can be difficult to find the right balance between using text and images to convey information. It means they have to outperform GPT-3 and DALL-E 2, which are already rather impressive.
5. AI Alignment
GPT-4 also includes improved alignment compared to GPT-3. AI alignment is a problem for OpenAI. In other words, they want language models that support our objectives and ideals. By training ChatGPT, they’ve already made the first move. It’s a GPT-3 model that was taught to obey commands by observing human behavior. Users rated the model higher than GPT-3, suggesting that it is superior.
As a result, GPT-4 is a huge language model that just uses text and will outperform GPT-3 on the same dataset. GPT-4, which reportedly has 100 trillion parameters and is entirely dedicated to code generation, can have received different reports.
Like GPT-3, GPT-4 uses a variety of language contexts, including code creation, text summarization, translation, classification, a chatbot, and the correction of grammatical and spelling errors. With the updated model, we’d expect better safety, reliability, precision, and consistency. It will be strong and efficient in the future!
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