That battle was fought in 2012-2015 and except for a few exceptions, IBM lost. The only real advantage IBM had was in mainframes with 1-10TB RAM. For example, customers who like doing database joins on a time-slice of countrywide telecoms data. Only a few customers have that use case, unsurprisingly.
abstract inductive reasoning requires dynamic learning in unfamiliar contexts. All the benchmarks which measure this specifically (in particular, task composition) see LLMs fail catastrophically. There is quite a lot of research published on these limitations now.
LLMs don't have the executive control to dynamically manage and compose mental sub-routines, when it has never seen those sub-routines in its training corpus. That requires fluid intelligence. ARAOC benchmark measures this specifically.
Interpolation vs extrapolation? It's very simple distinction and fundamental. Look at ARC-AGI-3 for empirical evidence, if that's important to you. My point however is purely an a priori one.
> That's a common meme but it's the opposite of true.
But you're restating what I just wrote - We're training a status quo machine and the probability of anything outside that distribution rapidly drops to zero.
> so the proof to the unit-distance problem was on the manifold, given it was outputed by a LLM?
By design this must be the case, even when you account for stochastic sampling i.e. 'temperature'. All it's outputs are a highly-dimensional combinatorial interpolation (I'm talking about GPTs here)
That's probably why Claude is very good at producing plausible nonsense rather than the often correct response of 'I don't know'.
The output of a GPT is an interpolation (an estimation of new data points inside the range of known data) rather than extrapolation (estimations outside that range).
99% of the time we don't need a true intellectual breakthrough to get the job done, and often 'new ideas' are simply riffs on or blends of old ones, like fashion or music genres.
The worry to me, however, is that if society comes to rely on this form of 'AI' then eventually the model collapse bleeds into academia (e.g. grant proposals reviewed by AI?) causing a kind of incremental sociocognitive atrophy. Everything becomes a reaffirmation of the status quo.
That being said I think people said something similar about electronic calculators (that if you couldn't do long division by hand then you'd be too incompetent for higher-level calculus.)
Nazism is as eschatological as Marxism (class vs racial struggle) and many other secular/religious ideologies with utopian endpoints.
Depending on your point of view there's no difference (because the end justifies any means), but I think the True Believers of these ideologies would beg to differ.
He's a Christian Zionist which is the belief in the fulfilment of old testament biblical prophecy. This seems diametrically opposed to Nazism to me, ideologically. I don't know how you'd square the two.
I'm actually talking about both. WSJ publishes Anthropic artificial profitability. Days later the reason for the profitability appears in SpaceX S-1; it's compute costs were artificially suppressed. Both are going public. It's a quid pro quo.
They need to financially engineer a good looking quarter beforehand.
Perhaps Larry Ellison can cut them a nice quid pro quo for a few months to make OpenAI look profitable (like the SpaceX/Anthropic deal), although that's probably unlikely given the debt Oracle is taking on to build it's infra.
Do you mean the marginal cost by the producer, or the cost on the consumer? I can't see the price of electricity falling much, and the demand curve is apparently exponential if the hype is to be believed.
I like the alliteration but the article is trying to pitch a dichotomy where one need not exist. China is a good example of classical fascism with 21st century characteristics and according to the article it's working great for them.