AI - Ongoing Model Improvements

LeCun points to four essential characteristics of human intelligence that current AI systems, including LLMs, can’t replicate: reasoning, planning, persistent memory, and understanding the physical world. He stresses that LLMs’ reliance on textual data severely limits their understanding of reality: “We’re easily fooled into thinking they are intel... See more

t doesn’t help that the two most impressive implementations of AI for real work - Claude’s artifacts and ChatGPT’s Code Interpreter - are often hidden and opaque

As Ethan said a year ago, it takes 10 hours of monkeying around to get into it. But I think a good AI executive training program can dramatically shorten the window, assuming a growth mindset on the exec’s part. I ran into a retired architect the other day who was bemoaning wanting to work in retirement but he had never learned CAD-CAM! Now he’s tw... See more

Even though the underlying model is no different than the usual GPT-4o, the addition of voice has a lot of implications. A voice-powered tutor works very differently than one that communicates via typing, for example. It can also speak many other languages providing new approaches to cross-cultural communication. And I have no doubt people will hav... See more

it is kind of surprising that none of the major AI labs seem to have put out any deep documentation aimed at non-specialists. There are some guides for programmers or serious prompt engineers, but remarkably little aimed at non-technical folks who actually want to use these systems to do stuff - the vast majority of users

This is all early, and based on first impressions, but I think that voice capabilities like GPT-4o’s are going to change how most people interact with AI systems. Voice and visual interactions are more natural than text and will have broader appeal to a wider audience. The future will involve talking to AI.

I still think that we are thinking about scaling of network-based intelligence looking at this particular network. An extended network of machines and humans in larger networks facilitated by smaller and increasingly embodied devices might in themselves add an AI-augmented neocortex to our (human-machine) systems. Intelligence will continue to emer... See more

o, alternative pathways to building Type-2 reasoning-capable AI systems, likely using neurosymbolic approaches, have become much more attractive. People like Gary Marcus have argued for neurosymbolic approaches for decades. Such approaches combine the pattern recognition of neural nets, like LLMs, with symbolic reasoning’s logic and rules. Vinod Kh... See more

But there's just something about the immediacy and interactivity of Claude's "Artifacts" window, combined with the model's less stilted tone that brings it home.

For the most avarege users, these seemingly minor difference matter so much more than raw performance on abstract LLM benchmarks.