Sparks of AGI: early experiments with GPT-4

If you (or anyone else is) interested in the topic, I'd highly recommend giving some of these a look: Bubeck, Sébastien, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, et al. “Sparks of Artificial General Intelligence: Early Experiments with GPT-4.” arXiv, March 27, 2023. [LINK] . or watching Sebastien Bubeck's recent talk he gave describing what GPT-4 can do that previous LLMs couldn't: [VIDEO] Geoffrey Hinton recently gave a very interesting interview and he specifically wanted to address the "auto-complete" topic: [LINK] Here's another way that Ilya Sutskever recently described it (comparing GPT 4 to 3): [LINK] I'd also recommend this recent Sam Bowman article that does a goood job reviewing some of the surprising recent developments/properties of the current crop of LLMs that's pretty fascinating: Bowman, Samuel R. “Eight Things to Know about Large Language Models.” arXiv, April 2, 2023. [LINK] .

I used to say this as well, I’ve written blog posts and made videos saying exactly this. But - I would strongly recommend trying GPT4. There’s a very good video that is worth watching as well that may not change your mind, but will certainly make you think: [VIDEO] The thing to remember is that these models are unbelievably huge and deep. We don’t know what is really happening in the layers or what it has really learned. Thinking that it’s a simple language model that is just predicting the next most likely word is unwise.

Highly highly highly recommend listen to this lecture by Sebastien Bubeck (the dude who wrote the paper), it's extremely interesting: [VIDEO]

It goes back to Sparks of AGI [0] unless I am mistaken. Can recommend the talk, one that has stayed in the back of my mind since I first saw it two years ago. Personally, still have major reservations about throwing claims of intelligence or understanding around, but I do agree that SVG code generation can be a very effective source to get a quick and easy to present understanding of a models ability to output code with a rather open ended prompt that needs a high degree of coherence and were a lot of layers depend/build on each other. Helps that these are eye catching (literally as the output is visual) and easy to grasp. Same reason a lot of hype is created around the web desktops. [0] [VIDEO]

Here are two. First one is Nathan Labenz who ended up Red Teaming for OpenAI during the six months after the base model was fully trained but before it was released (especially relevant parts are after 45 minutes): [LINK] Other one is Sebastien Bubeck who put Sydney into Bing before she was tamed down some notches; he explains the lobotomization process in a particularly poignant way, where they had the GPT keep trying to draw a unicorn even as it was progressively deadened: [VIDEO]

You might find this video[1] on why "GPT is smarter than you think" interesting. They're older, but the "Sparks of AGI" paper[2] and video[3] are also well worth your time. [1] - [LINK] [2] - [LINK] [3] - [VIDEO]

You should take a look at this video. He is a researcher at Microsoft and had accès to private version of ChatGPT. He literally claims that ChatGPT 4 is not as good as before. His talk actually demonstrates the different evolutions. [VIDEO]

I just watched a video where the guy touches on GPT-4 limitations and one of those is simple math. He asks it some order of operations question and it outputs the correct answer but only if it does it step by step. It then apologizes and says its original incorrect answer was “a typo.” Video: [VIDEO]