Support Vector Machines involve solving a large QP optimization problem... which is often done by running a variant of gradient descent (or one of the second- or quasi-second-order optimization algorithms, which involves finding the Hessian as well as the gradient)
Back when I was a researcher, I had a classification problem where the default random forest classifier built into scikitlearn completely dominated the neural method. The best numbers we got were something like a ~30% improvement from baseline for the neural network to ~95% for the random forest.
The disparity was so large that I was certain I must have made a mistake and I spent a few hours debugging, and then a few hours more trying different neural architectures.
Turns out this is just a super common experience for anyone in NNs who would also try the more established learning algorithms.
But that's not the same thing at all. Searle used the terms "strong AI" and "weak AI" to distinguish between systems emulating humans that were "actually conscious" and systems emulating humans in behavior only. The whole distinction was bogus.
If you google the terms you will see that the meanings have shifted since then to mean AGI and ANI ... such shifts occur because people aren't familiar with the original technical meanings ... e.g., the way "meme" now means a gif you post on social media, as opposed to Dawkins' original concept of a cultural thought component akin to a gene.
It must have had shifted already by 96-97. I remember how I used it in AI discussions back then and it meant “the opposite of narrow AI”, which makes it very similar to how we use “AGI” term today. Consciousness was considered to be a potentially emergent phenomenon, not a required property.
There is a world where scale and experience means it's about the same though, is the thing.
And memory is already expensive. It's downright hard to even get it though - you frequently would prefer not what's cheapest, but whatever is in largest scale production.
Scale and experience almost entirely share between HBM and normal memory. And they're both in large-enough scale production to not have a big difference on availability; if you're willing to pay HBM prices you should find even more sellers of DDR.
The only way I see HBM becoming competitive for consumer CPUs is if they solve the yield issues. Or if AI crashes so hard that people are putting those GPUs on fire sale and salvaging mass quantities of HBM off of them.
It seems unlikely as the entire machines with HBM on them are apt to be sold whole on the market and snapped up quickly. With how much demand is in the current market machines 3 years old may not be sold if they can't get newer faster machines fast enough.
And the purpose there was research right. And the implication is that once they figured out it was causing problems it was turned off for forensic analysis.
Meanwhile in the real world hundreds of billions are being invested into humanoid robot development. In 2-3 years my Optimus 4 will do all the gardening and plumbing I need.
Look at the whole robot vacuum market. These aren't exactly great devices. They have low suction small bins and dont do a great job. People love them and think that they work so well. Why? Because they keep their house clean and avoid making the sorts of messes that would be easy to address with a larger vacuums.
We have nuclear reactors, helicopters, and self driving cars already. And we finally have powerful AI that we could only dream about just 5 years ago.
We now have everything we need to scale up humanoid robot training: algorithms, hardware, and money to buy a lot of training data/compute. Fierce competition and strong economic motivation will force rapid progress in this field.
It is rivaling Astra, on their own benchmark that they made (FrontierCode), that they ran themselves in their own closed-source ecosystem that isn’t reproducible by anyone.
I’ve never played against a grandmaster, but I have a feeling that he/she would play very different compared to me and would never make mistakes I could notice. Though admittedly I’m not very good at chess.
I've played both. The GM plays tremendously differently than Stockfish.
Engines - specifically heuristically-driven ones like Stockfish - don't play like a strong GM. They play engine-perfect chess, which isn't how a GM plays with any consistency.
I'm only a decent amateur (1550 USCF) but when I lose to a titled player it's largely explainable in human terms how it happened.
I'm currently running two 24/7 semi-autonomous AI research projects using Fable 5.1. It's on track to burn through my weekly quota in about 3 days. I check progress in the morning and in the evening, and provide some light steering.
See my sibling comment, you can probably robo-code yourself some tooling to alleviate a lot of that in a few hours but if you want help shoot me an e-mail. I'm interested in seeing other people's workflows.
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