81–90 of 96 posts
Art is going to be just fine.
Earlier quoted context omitted.
Technically, they are. There was a post on HN recently about how every model is equivalent to a decision tree.
I mean, if it can be specified by a computational process, that’s trivially true, right?
Decision trees are finite set of if-else branches, and work on finite input - much like models. I assume many of the classic arbitrary-size-input algorithms cannot be represented by a decision tree.
Adding two arbitrary-sized numbers comes to mind - how would one model it as a decision tree?
if (a[0] + b[0] == 0) {
c[0] = 0
} else if (a[0] + b[0] == 1) {
c[0] = 1
} else if ...
...
} else if (a[0] + b[0] == 10) {
c[0] = 0
if (a[1] + b[1] == 0) {
c[1] = 1
} else ...
...
}
...
I can't think of a proof that such a computation cannot be represented by a decision tree, and it's possible that my definitions aren't quite correct. But intuitively, I think this is the way it works.
Of course, if I'm wrong, any explanation on why and how would be very welcome.
The "new-fangled" AI, as the article calls it, is often useful when the stakes are low, and you can accept mistakes in outcomes. Examples of such applications are: trying to determine which of your friends occur in a photo, which movies a subscriber may be interested in, or which action could lead to victory in a computer game. Getting a rough translation of a newspaper entry, as mentioned in the article, is also a g…
> As soon as you need reliable outcomes, such as certainty whether an erroneous state can arise in a program, whether a proof for a mathematical conjecture exists, or whether a counterexample exists, exhaustive search is often necessary. Proof checking requires 100% reliability. But if you are searching the space of all possible proofs for a valid one, that process does not require 100% reliability. On the contrary,…
For me, GOFAI was simply the earliest attempts at experimenting with the Physical Symbol System Hypothesis: A physical symbol system has the necessary and sufficient means for general intelligent action - Newell & Simon[0]
And like any hypothesis, all we have to do is falsify it...
[0] https://en.wikipedia.org/wiki/Physical_symbol_system#Argumen...
Earlier quoted context omitted.
The tragedy is that GOFAI did all these things as built-ins. Procedural expert systems have been doing introspection, backtracing, declaring confidence intervals etc since the 1960s. Layering "assurance" on top of inherently jittery statistical/stochastic and neural systems seems to misunderstand how these models evolved, where they come from and why there are alternatives.
While horses don't consume fossil fuels, they also don't solve the problem of transoceanic flight. Yet for some reason every discussion of airplane design ends up dominated by a vocal contingent of buggy whip salesmen.
Same thing. Different parochial perspective.
Earlier quoted context omitted.
The only barrier for higher stakes applications is going to be the frequency of errors. Flying an airplane or running a factory has a lot less margin for error, but humans don't do those things perfectly either (Chernobyl, Three Mile Island, Union Carbide-Bhopal disaster). It doesn't have to be perfect, just better than humans. And in fact, I'd argue that by having no deterministic outcomes prevents systemic failure,…
> It doesn't have to be perfect, just better than humans. I have a different opinion on this. Humans don’t like uncertainty. We like to feel like our mental model of reality can predict future outcomes. When it doesn’t, we get very uneasy. It’s why we don’t like dealing with erratic humans. Part of the problem with AI is it’s lack of interpretability. People aren’t going to want to interact with AI if they can’t intu…
Also, on average is not a great target either, sometimes it makes sense, but there are plenty of examples where we definitely don't want more average work.
Earlier quoted context omitted.
The tragedy is that GOFAI did all these things as built-ins. Procedural expert systems have been doing introspection, backtracing, declaring confidence intervals etc since the 1960s. Layering "assurance" on top of inherently jittery statistical/stochastic and neural systems seems to misunderstand how these models evolved, where they come from and why there are alternatives.
While horses don't consume fossil fuels, they also don't solve the problem of transoceanic flight. Yet for some reason every discussion of airplane design ends up dominated by a vocal contingent of buggy whip salesmen.
Is deep blue really considered AI? I thought that was more of a brute force algo?
Also, why can't a brute force algorithm not be AI? Machine learning is arguably brute force too; a neural network just brute forces some realistic sentences by doing a bunch of matrix multiplications, if you get down to it.
I think it might be just around the corner, a few years maybe.
I imagine a consultant interacting with an AI-powered solution to rapidly create a unique website, with text generated from bullet points and a design generated from a description and a short sketch. Those generated designs could be refined based on feedback from the client until a compromise is reached. I think it can be really low-cost.