I got my hands on the new model and did some experiments.
When I logged into ChatGPT Plus, I was welcomed with a big smile and a friendly message saying “Hey there! OpenAI’s new GPT-4 is so smart it can do all kinds of tricky thinking stuff!”
When I clicked on the link, I saw some cool stuff about the three different models they had to offer: Legacy, Turbo (also known as Default) and GPT-4. It was like a comparison chart!
I was excited to compare the new Turbo model’s intelligence to the old one and find out how much better it is!
I asked two models some questions to see how smart they were. The first one was a tricky one about family, the second was a riddle, and the third was like something a salesman would ask. Let’s see if they can figure it out!
Here are the results:
Question #1: Wolf, Chicken, and Feed Riddle
This riddle is easy for most people to solve, but GPT-3.5 gave a confusing answer. However, GPT-4 was able to solve the riddle correctly, giving the right steps in the right order.
Question #2: Traveling Salesman
Even though there were only five cities, there were 24 possible routes, making it an NP-hard problem. GPT-3.5 used the Nearest Neighbor Algorithm, which gave the wrong answer because it wasn’t the shortest possible path. I asked it to use the brute-force approach, but it still gave the wrong answer.
GPT-4 was able to solve the traveling salesman problem by using a method called brute-force, which means it looked at all 24 possible routes and found the correct one.
Question #3: Family Relationships
I was so confused by this question that even the advanced artificial intelligence programs GPT-3.5 and GPT-4 couldn’t get it right. However, the correct answer is that my two friends are related as first cousins once removed.
GPT-4 is still making mistakes, but they are much less noticeable than the mistakes made by GPT-3.5. It is amazing that this model can do so much with probability calculations.
I will look at how well GPT-4 can do coding tasks once I have some good tasks for it to do. I will let you know when I have done this.
As always, I hope you enjoyed this article and learned something new.
Thank you and see you in the next articles!