How AI knows things no one told it

You are using their study to claim that LLMs can not learn and reason about novel tasks. Their study doesn't make any claims about what LLMs can and can't do. The study says that when it appears that LLMs suddenly became able to reason about novel tasks, what actually happened is that the ability of the LLM to perform reasoning improved gradually and smoothly during its training until it could do those things. They are not saying that LLMs don't have emergent capabilities: they are saying that those capabilities emerged at a continuous rate rather than a discontinuous one.

Do you see?

The bain also has a bunch of other cells that dont appear to encode information. They may supply structural scaffolding and chemicals to mediate neuron function.

In the context of optimization, does the generalized assignment problem satisfy total unimodularity?

Assistant:

The generalized assignment problem (GAP) is an NP-hard optimization problem that can be formulated as an integer linear program. The problem involves assigning a set of tasks to a set of agents, subject to capacity constraints, with the goal of maximizing the total profit or minimizing the total cost. The integer linear programming formulation of GAP is as follows:

Objective function:

Maximize ∑_(i=1)^m ∑_(j=1)^n c_ij * x_ij

Subject to:

1. ∑_(j=1)^n a_ij * x_ij ≤ b_i, for all i (capacity constraints for each agent)

2. ∑_(i=1)^m x_ij = 1, for all j (each task must be assigned to exactly one agent)

3. x_ij ∈ {0, 1}, for all i and j (binary assignment variables)

Total unimodularity (TU) is a property of a matrix that guarantees that the linear programming relaxation of an integer linear program will result in integer optimal solutions. If the constraint matrix is totally unimodular, you can solve the linear programming relaxation and obtain an integer solution without using specialized integer programming algorithms.

In the case of the generalized assignment problem, the constraint matrix does not satisfy

There's no underlying theory of mind here. There's a lot of opinion and belief .. mainly in emergent behaviour or properties of scale.

My problem is that I'm old enough to have been a child during the years of the lighthill report, and the AI winter(s) which followed. I've seen too many prior claims that the magic was being seen.

I admit, what this cycle of GPT and LLMs do is pretty bloody impressive. I don't see inductive reasoning, directed drive or any evidence of what I think intelligence is. I do see good approximations. But that winds up in "well it's a different kind of intelligence" which I find unsatisfying.

I hate analogies. But I'm constantly reminded of one person acts at the Edinburgh Festival fringe "channeling" shakespear or dickens in dialogue. They're great, but they don't write new shakespear. I don't see any act of creation in this stuff, any massive inferential leap in large problems. I do see massive improvements in some things like diagnostic image analysis and that's heartening, but there's a light yearg gap between image analysis of cancerous cell forms in xrays and being "alive" as a mind.

I totally get I do anything but define what intelligence is, and for a good reason: nobody knows yet. We're in the meese report "I know it when I see it" territory disagreeing if we are seeing it. (But about intelligence, not pornography).

I don't know what intelligence is. Dolphins and apes have some. Elephants and pigs too. They display affection and preplanned behaviour, empathy, memory, a sense of future, ant hills less so although some have argued rhetorically the anthill has will even if individual ants do not.

I certainly don't think animals inability to speak makes them "unintelligent" but there's a qualitative and quantitative difference between them and us, humans. And I continue to believe (and I stress believe, not have evidence or scientific proof) there are no signs of latent intelligence in what we're seeing.