>The human brain manages to self-organize with only a fraction of the information that LLMs get trained on.
So? The question isnt can we get to ASI as efficiently as a brain, the question is can we get there, which we likely can. The inefficiencies can also be fixed after that.
>No matter how many trillions of dollars get thrown at the problem, they still don't learn like humans do.
Again, so? Humans are efficient but also bad at many things that transformers are already better at because of it. You are looking at the wrong thing if you think it needs to be like humans.
You can watch the whole Lex Friedman interview, it's on youtube. It's not out of context at all. He goes on about how LLMs will never be able to do things that they do trivially. And he has just doubled down for years.
Ive read and watched more of his interviews and lectures it seems, it feels like you just have a rosier idea of his views than the views he repeatedly presents.
He said years ago even 'GPT 5000' couldnt do things that they ended up doing fine a month later, let alone by 5000. His later predictions are just moving that goal post including towards them not being able to do more general, harder problems of which Arc AGI is a counter-example.
His main anti-LLM predictions have been consistently either wrong or misleading.
There's many ways to skin a cat so you can probably do something with a JEPA approach as well, but I doubt he actually catches up to having agents on the level of where Anthropic/OpenAI will be at any point.
Is any of those comparisons about Pro vs non-Pro (Pro is only available in $100+ plans)? I am curious about that but I think Sol, Terra, Luna are different sizes of it without the Pro part, and I want to know how much worse do I have it on the $20 plan compared to if I upgrade.
If you are imagining that, you could imagine it with search doing the same 10 years ago, which would have more thoroughly prevented you from researching things.
He's been interested in forecasting since a long time before there was any money in it, and still hyped Metacalculus (free research project) and Manifold (play money) because he is interested in the subject. If it smells like an ad it is because your ad sense is faulty.
In theory but in this specific example you can make a lot more money on the stock market instead if it's that doable.
In general you should judge different prediction markets differently and read the fulfilment criteria though - some are very ripe for abuse, AI or no, while others are much more ironclad.
Yes, in the very long term, but in the medium term where the listed capacities even matter we are not close to that. Really, the cost per gb of vram on flagships hasnt went down since 1080ti and thats not accouting for the recent increases which will likely last for years.
I looked for one and it's impossible to find, let alone at a reasonable price + it does suffer from being harder to train/use less common models and workflows (e.g. arbitrary comfyui ones). Spark at least doesnt have that drawback, while AMD has both drawbacks.
Waiting for the market to be less insane is somewhat akin to waiting for the s&p500 to drop a decent amount so you can buy in.
I really want a 128gb+ machine but it's brutal to be at only 256 GB/s for $4k (especially with the drawbacks of both ARM and AMD).
I fear that by the time the RTX Spark comes out it'd have to be $6k, and by the time a 128gb or more machine with 700+
GB/s comes out it'd be at $10k, way out of most consumers' hands.
Measuring aircraft production per weight doesnt sound like that bad of a proxy. If I hear that Boeing produced 300 kilotons worth of aircrafts more this year, I'd be right to suspect they've ramped up production.
Yeah I like the idea, but I'd rather just not use most of these plugins, superpowers included. Code seems to include the best tricks in itself anyway at a fast pace.
Do you mean that Gemini is most token-efficent at watching videos? Is that the case for e.g. just giving it a video in the browser? I admit, I dont give LLMs videos as I just assume it'll burn too many tokens.
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