@Lee715495232368@newstart_2024 1000-step solution is nothing, he has no idea what he's talking about. Yes LLM might be able to make 1000 steps but that's not nearly enough. Logical solvers can already do millions of steps *per second*.
This tweet is +1y old, but I've said it for much longer. ChatGPT came years ago, and "AI" is still more or less the same. Against all "expert" predictions, it didn't cure cancer, it didn't colonize Mars, it didn't discover new physics, it didn't really improve much. Because I was right all along: machine learning is around its peak, and it is a huge bubble. Nowadays it's obvious, but back then I was a single voice against the whole world. Not only that machine learning is fundamentally incapable of logical reasoning, but the architecture of those giant data centers, is also incapable of logical reasoning. And I keep telling you: the real deal is Logical AI, and we are the leaders of this segment. Machine learning is only for translation.
1. Yes. Machine learning models are more or less at their peak, and the bubble is about to pop.
2. Yes and no. Although it can be mathematically proven that there are things impossible to do, it is also proven that for any given intelligence ability, there's another one above. But the truly exceptional abilities got nothing to do with machine learning, but with logic.
3. Easy. Humans are not that smart at all. The only thing we understand better is human nature.
@Krawlarr We did launch a version of the logical AI engine. And testnet is around the corner. This post is about AI. It is clear that you are a troll paid by the scared "competition".
Really really funny. Not even close. Maybe his analogy holds for extremely simple logical systems, far from enough for software specification. He thinks he's the first to find the connection between Datalog (polynomial time) and matrix multiplication (while seemingly he missed expressing recursion using matrix inverse). Can kill his argument using undergrad complexity theory!
@Freshbt2 Atomless Boolean algebras are much beyond just 0s & 1s. For another thing, Tarski's result of course holds, but we were able to achieve the goal by relaxing his assumptions and weakening his requirements, in a way that is good enough for practical applications.
@arnau00296942 It can prove anything as long as you express it in the Tau language. This is already a very big limitation, there are only certain things that the language supports. For example, it cannot express problems that require above double exponential space to solve.
Machine learning models / LLMs excel at patterns but will never offer logical correctness for non-trivial/complex problems.
I'm excited about formal software synthesis from logical requirements, where correctness is guaranteed by construction rather than hoped for.
Imagine software that adapts to you—individualized to your needs.
Ohad Asor and the Tau Team are building the first and ONLY blockchain that its users fully control:
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$AGRS
Quantum mechanics works very well (except when it doesn't), but whenever a quantum theory is illogical, they say "find a better logic". LLM works very well (except when it doesn't), but whenever it is illogical, they say "what is logic? there isn't such a thing". Coincidence? No. Human nature, yes. Same human nature that made humanity susceptible to religion.
Show me one blockchain project which is not just more of the same thing. Show me one AI project which is not just more of the same thing. I'll show you one that ticks both boxes: @TauLogicAI $AGRS. Read my two recent articles here to see why it's the case