It's absolutely not the same.
If you think llms and brains works the same way you clearly don't know how either works.
For a LLM learning what you wrote your last session would be update the weights with the new relationships and factual knowledge created in the session. That doesn't happen. The weights are static and fixed after training. There's no online training in the transformer architecture or any variant. If the weights don't update, the network doesn't learn. Period.
By the very way this technology works they can't learn anything after training. What you think is "learning" it's just a session log written back to the context when you resume the session.
Is this article targeted just to an American audience? Because the US isolationism will only hurt them. Unless they truly have achieved AGI or ASI, the Chinese models soon will catch up. I'm pretty sure we will have an open weights frontier model this year.
>I wonder if our common expectation that true theories somehow had to be beautiful and elegant is going to survive the coming century.
That's the layman's idea of physics theories. They are beautiful and elegant only on the surface, that's why they're technically models and approximations of the real world. The standard model renormalization techniques are a mess of patches and ad-hoc heuristics, pretty far from the "this lagrangian literally contains all physics". Generally you just _ignore_ higher order terms and just call it a day. The famous E=mc^2 it's just the first term of a Taylor expansion. The beautiful form of physics it's what you would call "good enough" and often just a pedagogical tool.
Web apps and CRUDs, if may I ask? Or is AI helping you with something that you couldn't ever do by yourself? I have mixed results across different technologies like frontend, backend, infra and hardware.
The submitted site runs smoother, has less annoying popups, don't use the entire screen (on mobile) to show info of the selected startup and overall I think it has better UX than yours.
You can write a rebuttal to address what's wrong with the article, from your point of view. Maybe I'm old but the whole "live reaction in twitch" thing doesn't help how the scientific community perceives your area of expertise.
Ha. When I found that problem I draw the grids and paths from the example, left for a coffee and when came back I just look at the drawings at an angle and thought "well this is just Pascal's triangle". And the solution was obvious.
That's my point. This "open source" doesn't feel like the real open source. It's open just for the few ones with ton of capital, and mostly in the US, or US adyacent markets. It's like if SpaceX publish an open source rocket design and people celebrating like it's the new Linux. Feels more like a goodwill gesture than something with real impact for the benefit of mankind, like the spirit of open source software as commonly understood.
Please correct me if I'm wrong, I'm totally out of my field here but what's the point of sota models that can be run only by hyperscalers? I mean, glm-5.2 is open source but with 1.5TB in weights who can run it really? It still needs dozens of H100s. Those 753B quantized down to Q4 (~400Gb) would require datacenter levels of hardware. Down to Q2 still would require serious hardware, way out of reach for most users, and you'll be far from the sota benchmark of the full precision model. I get it, it's open source but not quite democratizing LLM for everyone except compute providers. It's no like, let's say, Kubernetes. I can run k8s fully in my shitty homelab, without "quantization" exactly like Google does in their datacenters.
While 80% of Europe is subservient to the US?