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: [this 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] .

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] [this video]

[this video] about 5 minutes in. > People say it doesn't have a world model but it's not as clean cut as that, it absolutely could build an internal representation of the world and act on it as it progresses through the sentence temporally. Beware of trillion-dimensional space and its surprises, it's very hard for humans to reason about. [...] We shouldn't think about those neural networks as learning simple concepts like 'Paris is the capital of France'; it's doing much more like operators, it's learning algorithms. Inside it, it's not just retrieving information, not at all, it's built internal representation that allows it to reproduce the data that it has seen succinctly. Really you shouldn't think about it as pattern matching and just trying to predict the next word, yes it was trained to predict the next word but what emerged out of this is a lot more than just a statistical pattern matching object. We need to think about it as learning algorithms. [..] it's something very different from what we are used to. - Sebastien Bubeck, Sr. Principal Research Manager in the Machine Learning Foundations group at Microsoft Research

And there's an army of people that dismiss concerns, discussion, and evidence presented on this topic regardless of what is provided. On subjective topics people can always dispute examples. Some people abuse the courtesy of those that provide such examples, you've further proven that here. Regardless of if we should benchmark imagery with something that was claimed to be multimodal, can you genuinely not see the difference here? [link] Maybe your internal prompt is primed to disagree regardless of what is presented? Edit: [this video] even mentions what you claim not to see. Safety degrades the model.