GPT-4: All about the latest update, and how it changes ChatGPT

OpenAI recently released their latest AI language model, GPT-4. Considering the stir GPT-3 caused, many people are curious about how powerful this new model is compared to its predecessor.

In this article, we'll dive into the differences between GPT-3 and GPT-4, and show off some new features that GPT-4 brings to ChatGPT.

• A quick primer: The difference between GPT models and implementations
• GPT-4 vs GPT-3: What's changed
• Getting hands-on with ChatGPT (powered by GPT-4)
• How much better is ChatGPT at coding with GPT-4 than GPT-3?
• Could the new version of ChatGPT pass my tech exam for me?
• Could the new version of ChatGPT write me a blog post or novel?
• How do I try out the GPT-4 version of ChatGPT?
• How close is GPT-4 to an Artificial General Intelligence (AGI) Model?
• How to learn more about ChatGPT, AI and ML

To start, let’s clear up something a lot of tech bloggers are not clarifying: there’s a difference between GPT models and implementations. It’s important not to conflate the two.

GPT-4 stands for Generative Pre-trained Transformer 4.  It is a model, specifically an advanced version of OpenAI's state-of-the-art large language model (LLM). A large language model is an AI model trained on massive amounts of text data to act and sound like a human.

This is different from ChatGPT, which is an application of the GPT model explicitly designed for conversational language. It has been trained on a large data set of conversational data to give human-like responses. You can use these for text generation, code generation, language translation, summarizing, and answering questions.

If you’re not familiar with ChatGPT, I highly recommend checking out the following articles:

• What are ChatGPT and Generative AI (and how can I use them)?

• How to use ChatGPT to write code

… Or just jump onto ChatGPT and play around with it yourself! Anyway, to summarize:

• A GPT model like GPT-4 is the underlying AI technology.

• The implementation is how that model has been used (Like ChatGPT)

As of the time of writing, the free version of ChatGPT is powered by GPT-3, while the premium version (ChatGPT Plus) uses GPT-4, so any release of a new model does impact the ChatGPT implementation.

GPT-4 is now “Multimodal”, meaning you can input images as well as text. It still doesn’t output images (Like Midjourney or DALL-E), but it can interpret the images it is provided. For example, this extends to being able to check out a meme and tell you why it’s funny.

It’s worth noting that ChatGPT has not implemented this feature yet, even when you use the paid version (ChatGPT Plus) which uses GPT-4 — another good reason to highlight the difference between the model and the implementation. 

Many people on the internet are suggesting you can just put a URL into the chat field to do it, but this is incorrect. We even tested it! We sent it a link to an image from an article about computer vision, which had a sasquatch riding a skateboard through Times Square:

If GPT-4 mistook it as Chewbacca, I'd understand.

And here’s what ChatGPT Plus had to say:

As you can see, it crawled the text of the article for context, but didn’t really check out the image itself — there is no mention of Sasquatch, a skateboard, or Times Square. Instead, it accurately described how the image is being used (and lied about being able to see it, but that’s not unusual).

Previous versions of GPT were limited by the amount of text they could keep in their short-term memory, both in the length of the questions you could ask and the answers it could give. However, GPT-4 can now process and handle up to 25,000 words of text from the user. 

In practical terms, that means you could hand it a novella and ask it to process it in one go (but not The Fellowship of The Ring, which would blow its mind at 187k words). Additionally, you can also send it a web link and ask it to digest the text from that page. This also means it can comprehend and retain a conversation better, especially long ones.

All of this is really good news for programmers who are using tools like ChatGPT to code, because larger context windows allow GPT-4 to generate more advanced code. Now it can understand context better and build complete functions in multiple languages.

A GPT model's parameters define its ability to learn and predict. Your answer depends on the weight or bias of each parameter. Its accuracy depends on how many parameters it uses.

GPT-3 uses 175 billion parameters in its training, while GPT-4 uses trillions! It's nearly impossible to wrap your head around. The new design also brings better performance, scalability, and efficiency.

Accoding to OpenAI’s own research, one indication of the difference between the GPT 3.5 — a “first run” of the system — and GPT-4 was how well it could pass exams meant for humans.

On traditional benchmarks for machine learning models, OpenAI reported that GPT-4 far outstripped not only its predecessors, but best selected learning models.

Reinforcement learning is a type of machine learning in which an agent learns how to behave in an environment by performing actions and receiving rewards. The agent takes actions in the environment, receives feedback through rewards or penalties, and uses it to update its understanding and improve future behavior.

GPT-3 has limited reinforcement learning capabilities and does not perform reinforcement learning traditionally. It uses "unsupervised learning," where the model is exposed to large amounts of text data and learns to predict the next word in a sentence based on context.

GPT-4 has added enhanced reinforcement learning, which provides more effective learning from user interactions and preferences.

Complex Problem Solving: GPT-4 can now address more complex problems, even ones requiring multiple steps.

Advanced Natural Language Processing (NLP): GPT-4's advanced engine can perform sentiment analysis, translation, and text summarization at a far higher accuracy than before.

Fewer Mistakes: GPT-4 "Hallucinates" much less than GPT-3. It's approximately 40% less.

Many people are less interested in the GPT-4 models and more about what this means for the implementation, specifically, what it means for using ChatGPT itself. We tested it out to answer people’s most burning questions.

It's still a little early on this front, but we know that the new model has been designed to generate code better. Let's take a look at a difference with a quick sort algorithm. Here's a simple prompt:

I want you to act as a software developer, write out a demonstration of a quick sort in Python.

ChatGPT powered by GPT-3.5 comes up with this:

This looks like a typical quick sort implementation. I then ask further about the code:

I want you to act as a computer scientist. What is the time complexity of the algorithm you provided above?

ChatGPT returns this:

The time complexity of the Quick Sort algorithm provided above is O(n log n) in the average case, and O(n^2) in the worst case.

Brief, but helpful. I'll send the same prompt to ChatGPT powered by GPT-4. It returns this:

You'll notice, it provides the same code. However, it now adds a test to it. So I ask it again about time complexity, and ChatGPT gives a very thorough explanation:

The time complexity of the quick sort algorithm depends on the choice of the pivot element and how balanced the partitioning is. In the best case, the pivot element always divides the array into two roughly equal-sized subarrays, which leads to a balanced recursion tree. In this case, the time complexity is O(n*log(n)), where n is the number of elements in the array.

In the worst case, the pivot always selects the smallest or largest element, causing one partition to have a single element and the other to have n-1 elements. This results in an unbalanced recursion tree, and the time complexity is O(n^2).

The average case time complexity of quick sort is also O(n*log(n)). This assumes that the pivot selection results in a reasonably balanced partitioning on average. In practice, quick sort is often faster than other sorting algorithms, such as bubble sort or insertion sort, because of its good average-case performance and because it has smaller constant factors.

In the provided implementation, the pivot is chosen as the middle element of the array. This choice can lead to poor performance for certain input sequences (e.g., already sorted or reverse sorted arrays). A better approach to pivot selection, which can help avoid worst-case scenarios, is to choose a random pivot or use the "median-of-three" method, where the pivot is chosen as the median of the first, middle, and last elements of the array.

To summarize, the time complexity of the quick sort algorithm is:

Best case: O(n*log(n))

Worst case: O(n^2)

Average case: O(n*log(n))

This is only a single test, but it appears that ChatGPT "understands" the code better, at least in the sense that a Large Language Model can understand anything (For an interesting debate on that, read this article: Pigeon VS AI: Is a wild bird smarter than artificial intelligence?).

It's yet to be seen if the code generated is "better", but the explanations seem to be. We’ll be exploring this more and writing about it here soon.

This is a great question! Let’s ask it:

As an AI language model, I can provide assistance, explanations, and guidance on a wide range of technical topics. However, I cannot physically take an exam for you or directly answer questions on a real-time exam. My purpose is to help you learn, understand, and prepare for exams by providing explanations and resources related to the subject matter.

If you have specific questions or need clarification on a topic, feel free to ask, and I will do my best to help you. Remember, it's important to follow academic integrity guidelines and avoid cheating on exams. Properly preparing and studying for your exams will help you achieve long-term success and a deeper understanding of the material.

I like this answer. ChatGPT cannot physically take an exam for you. Ethical concerns aside, it may be able to answer the questions correctly enough to pass (like Google can). Most certification test centers don’t allow you to bring in anything that can access ChatGPT. 

However we shouldn’t put ethical concerns aside: cheating is wrong and it helps nobody. Instead of using ChatGPT to pass a tech exam, use it to learn the material! There are many ways you can use it to structure your study time, give you the answers you need and help you learn and understand the material. It's always best to pass an exam.

Again, let’s ask ChatGPT (Seeing a pattern?):

As an AI language model, I can certainly help you generate content for a blog post or assist with writing a novel. For a blog post, you can provide a topic, and for a novel, you can give me a plot summary, character descriptions, or any other relevant information you'd like me to include. I will then generate content based on your input.

Keep in mind that while I can produce coherent and creative text, it may not be perfect and may require some editing and refinement from you to align with your specific vision and style. Additionally, due to the limitations of my training data, some of the content I generate might not be completely up-to-date or accurate.

• It produces content with mostly accurate information

• It organizes it well

• It’s very boring!

My biggest complaint about ChatGPT creating blog posts and other forms of writing is that it’s very dry. If you ask it to “spice things up” and make them more interesting, it feels very fake and forced. Human writers are sought after for a reason: they know how to write things that other humans want to read. ChatGPT is still in the early stages of trying to accomplish this goal. 

So, to summarise, the latest model of ChatGPT using GPT-4 is good at:

• Helping you outline your writing

• Suggesting topics

• Rewording things

• Providing explanations 

Using these to your advantage is helpful. You can have ChatGPT help you organize your thoughts, but they’re still your words in the end.

At this time, you need to have the paid version of ChatGPT, which is $20USD a month. As a paid member, you’ll be shown the options to switch between models:

Artificial General Intelligence is the “holy grail” for AI scientists. It’s the point where a model can think like a human. Specifically, it’s where a model can perform any intellectual task that a human can. Learning, reasoning, problem solving, understanding, emotions, etc

In the words of Sam Altman, CEO of OpenAI, it’s not even close. ChatGPT can generate contextually relevant text, but has no understanding of the topics it discusses. The knowledge it shares comes from patterns in the text data it was trained on. 

It’s primarily focused on generating text, and improving the text it generates. ChatGPT cannot “think” for itself, and doesn’t have the cognitive abilities humans do. It also doesn’t have “common sense”. This is evident in some of the conversations folks have posted online where there is no logic to the conversation. 

So we’ll have to wait for a while for a true AGI model. We can enjoy what ChatGPT is good at providing, which is text conversations that can teach us things.

GPT-4 marks a new era in the world of AI language models. It’s certainly pushing the boundaries of what we thought was possible just a few months ago. GPT-4 is great at generating code and explaining it, crafting interesting writing, and assisting with research. It will no doubt make us smarter over time, but may cause us to forget a few things too. 

We must remain mindful of what GPT-4 is: A language model. It’s an AGI, and it’s not self aware. Keep in mind we’re just at the beginning of this movement. There are plenty of challenges ahead and exciting things are coming. This is undoubtedly a major turning point in human lives. It’s right up there with the invention of the internet, electricity, and automobiles. 

This is the point of the article where I could say “surprise! This whole article was written by ChatGPT!” However, that’s not true.  I am a real human being. I used ChatGPT in the examples above, and I used to organize some thoughts and reword a few things. This is just one of the ways ChatGPT can be your super smart assistant. In the words of Scott Hanselman: “It’s not a person. It’s a clever rubber duck.”

Bookmark this blog for future posts about ChatGPT, OpenAI, and other topics.

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