Well, there is a good news for fans of generative AI. But also a bad news for those who worry about a future full of cheap, procedurally generated content. OpenAI’s GPT-4 language model is better than GPT-3, which was used to run ChatGPT, the chatbot that went viral at the end of last year. The differences are very clear, according to OpenAI’s own reports.
For example, OpenAI says that GPT-3 failed a simulated bar exam with terrible scores in the bottom 10%, while GPT-4 scored in the top 10% on the same exam. Most people who have never done this “simulated bar exam” will be impressed just by seeing how it works.
And compared to other models, the new one does well, but not as well as its test scores might make you think. In fact, on some of our tests, GPT-3 gave the better answer. To be clear, the public can’t try out all of the things that OpenAI talked about at its launch yesterday.
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Especially interesting is that it can take images as input and turn them into text. This means that, in theory, it could answer questions like, where should you build a shop on a screenshot from Google Earth? We haven’t been able to be sure, though.
What is Better Between ChatGPT 4 and ChatGPT 3?
In this post, we’ll go deep into the world of artificial intelligence (AI) and take a closer look at two of the most advanced AI algorithms being worked on right now: GPT-3 and GPT-4. We’ll compare these algorithms based on their features, how well they work, and what they can be used for. This will show you how they are different and which one is more exciting. This post will give you useful information about the future of AI, whether you are interested in technology or work in business. So, let’s get started!
A Revolution in Optimization
The fact that they take a lot of time and money to train is one of the biggest problems with language models. Companies often choose to trade accuracy for a lower price, which makes AI models not as good as they could be. AI is often only taught once, which means it doesn’t get the best set of hyperparameters for things like learning rate, batch size, and sequence length, among other things.
People used to believe that the size of a model was the most important factor in how well it worked. This is why big companies like Google, Microsoft, Facebook, and others have spent a lot of money building the biggest systems they can. But this method didn’t consider how much information was given to the models.
Recently, it has been shown that one of the most important ways to improve performance is to tune the hyperparameters. But bigger models can’t be built this way. On a smaller scale, it costs a lot less to train new parameterization models. Then, the hyperparameters can be moved to a bigger system almost for free.
Since this is the case, GPT-4 doesn’t need to be much bigger than GPT-3 to be stronger. Its optimization is based on improving things other than model size, like getting better data, but we won’t know the whole picture until it comes out. A well-tuned GPT-4 that uses the best model sizes, the right number of parameters, and the right set of hyperparameters can make huge gains in all benchmarks.
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Smaller but More Powerful
GPT-4 might not be much larger than GPT-3. The newer model disproves the idea that the only way to get better is to get bigger by giving machine learning parameters more weight than size. Even though it will still be bigger than most neural networks from the past, its size will not be as important to how well it works. Some of the newest language software uses models that are more than three times as big as GPT-3.
But bigger doesn’t always mean that something works better. On the other hand, it seems that training digital intelligence is best done with smaller models. Many companies are getting better results by switching to smaller systems. They can not only improve their performance, but also lower their costs for computing, their carbon footprint, and the barriers to entry.
A Huge Improvement in Performance Over GPT 3
GPT-4 is supposed to be much faster than GPT-3, and it will also make it easier to make text that moves and acts like a person. GPT-4 is more flexible and can do more things. It can translate languages, summarize texts, and do other things. When it is used to train software, it will be better able to figure out what users want, even when humans make mistakes.
Lesser Hallucinations Than GPT 3
GPT-3 still loves to hallucinate so much. To make wrong answers sound right, you have to change a lot about geography. For example, the symbolic bridge that is talked about in the Koreas is close to North Korea, but both sides of it are in South Korea. GPT-4 was more careful.
It said it didn’t know anything about the present and gave a much shorter list that was also a little off. People have different ideas about whether or not the line on a map between Gaza and Israel is even a national border, but GPT-4’s answer is better than GPT-3’s. In a few tests, GPT-3 falls into some other logical traps that GPT-4 was able to avoid.
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Also most of the poetry people write isn’t very good. Since GPT-3 is supposed to act like a person, making fun of its bad poetry wasn’t really a dig at the technology itself. Still, it’s a lot easier to read the nonsense in GPT-4 than it was in GPT-3.
GPT-4 is likely to keep having an effect on the business of making software. AI can help developers when they are writing code for new software programs. This will help them automate most of the tasks they used to do by hand that they did over and over again. In the field of language models, GPT-3 and GPT-4 are very important steps forward.
Even though GPT-4 hasn’t been released yet, it is likely to have many changes that will make these powerful language models even more flexible. It will be interesting to see how these models change in the future, because they could completely change how we talk to robots and how they understand natural language.
GPT-4 is a big step forward for technology that works with natural language. It could be a very useful tool for anyone who needs to make text. GPT-4’s main goal is to make things work better and make better use of resources. GPT-3 has been used in a lot of different ways, which shows how popular the technology is and how much more it can do.
It is set up to make the most of smaller models instead of relying on big ones. If they are optimized enough, small models can keep up with and even beat big ones. Also, using smaller models lets you make solutions that are both cheaper and better for the environment.