From Chatting to Innovating: How ChatGPT and GPT-4 Differ in AI Development

Joud Baddawi

March 6th, 2023

ChatGPT has dominated the digital landscape in the past few months, showcasing its capabilities and continuously amazing both the general public and tech enthusiasts. OpenAI has recently unveiled the new release of GPT ( Generative Pre-trained Transformer ), GPT-4.

GPT-4 in the new powerful multimodal large language model. "multimodel" means that GPT-4 can respond to both text and images, giving it a lead over ChatGPT which can only respond to text.

Not many details are shared about GPT-4, which is understandable. Ilya Sutskever, OpenAI’s chief scientist, said: "That’s something that, you know, we can’t really comment on at this time, it’s pretty competitive out there."

But OpenAI did say that GPT-4 is smoother and more human-like in its responses. It can explain jokes and tell you why they are funny, another advantage over ChatGPT. GPT-4 can analyze an image of your fridge contents and give you recipes based on what you have. Developers also hope that GPT-4 will perform better with multilingual and non-English texts.

Via its website, OpenAI says that " In a casual conversation, the distinction between GPT-3.5 and GPT-4 can be subtle. The difference comes out when the complexity of the task reaches a sufficient threshold—GPT-4 is more reliable, creative, and able to handle much more nuanced instructions than GPT-3.5." GPT-4 has outperformed its previous releases on many exams in various fields, such as the Bar exam and many AP tests.

A contributing factor in building this better AI was human feedback. OpenAI used the same approach in building GPT-4 and enhanced it with the feedback it got from ChatGPT users.

In conclusion, despite their shared foundation as advanced language models created by OpenAI, the differences between ChatGPT and GPT-4 are significant. ChatGPT prioritizes conversational ability, with a focus on engaging and natural user interactions, whereas GPT-4 is expected to push the boundaries of language processing and advance the state-of-the-art in a variety of applications. Both models represent significant advances in artificial intelligence and demonstrate the field's potential for future growth and innovation.