[readyplayeremma]

AI is not a well-defined term. This is more likely a specific machine learning technique that is designed to identify all the boring "normal stuff" and ignore it, and then do that at very large scale. By doing that, you can find the interesting parts so that humans with more limited resources can determine if those newly flagged things need to be added to the boring list, or if they do represent something truly interesting.

edit: After reading the article a bit more, it is using a random forest classifier. This is almost certainly not meeting the definition of what many here are thinking when the term "AI" is being used in the title. The term is clearly used here for marketing purposes.

[nl]

If you read the paper itself[1] the random forest is only used in the final stage. The main approach is a convolutional variational autoencoder (which of course is a deep learning model).

The VAE model itself is defined in step 6 in [2]

[1] https://www.nature.com/articles/s41550-022-01872-z.epdf?shar...

[2] https://github.com/PetchMa/ML_GBT_SETI/blob/4096_pipeline/te...

[nyrikki]

And deep learning models have no understanding of the underlying data because they are either classification or regression.

[nl]

Well the humans are doing classification too so I'm not sure what this is supposed to mean.

It's true that this model doesn't know anything outside its training dataset, but that's a different objection.

It's an interesting question as to if there is anything humans do that isn't just classification or regression (and of course regression is just classification over an infinite set where we assign labels to certain ranges and then select the highest probability density).

[mr_toad]

For certain definitions of understanding.

[nyrikki]

They are talking about ML, which is statistical pattern matching and finding.

Gödel ruined the fun of automated theorem testing a century ago unless we make significant discoveries in math.

Type inference is an accessable example of SOTA automated reasoning if you want a more realistic idea of what our current constraints are.

We will be restricted to Human assisted Turing machines for the foreseeable future unless there is a major development in pure math.

Remember we can't even build a logically consistent model of arithmetic due to Gödel.

[uncletaco]

Nitpicky and probably universalist or whatever but the fact that we can't build a logically consistent model was always the case, Gödel just discovered it.

[nyrikki]

No, you are most likely correct, I personally haven't found anything that even gives a reason to hope. But as reductionism and Laplacian Determinism are taught as cannon and not as the best option I sugar coated it.

The Wada property arising in simple models like predator/prey models with simple added factors like fear/cover probably also suggests that even simple models may be indeterminate at least with binary operations like modern algebra is based on.

Three attractors or exit basins can make this unintuitive topological feature pop up. Note that that fractal behavior is topological and not scale invariant noise like is typically studied with topics like fractal scattering.

Indecomposable continua like the Wada property aren't solvable with probabilistic models using automata like in the standard model, which is lucky enough to have less than three exit basins.

Here is one fairly accessable paper on the Wada property.

Hopefully some n-ary algebras may come forward to deal with restricted problems, but even if we could create a logically consistent model of arithmetic, binary operations will still be indeterminate with feature like the Wada property even with perfect knowledge of initial conditions.

https://www.researchgate.net/publication/365233050_Organized...

[gowld]

There is nothing human can do that Turing proved a computer can't do. Gödel ruined the fun of manual theorem testing the same way.

[nyrikki]

A human with a piece of paper and a pen could do. AKA writing down an algorithm.

Human reason isn't limited to written algorithms. The missing text problem from NLU is an example of a problem that is thought to be impossible for Turing machines/algorithms but is trivial in most cases for humans.

[drdeca]

> The missing text problem from NLU is an example of a problem that is thought to be impossible for Turing machines/algorithms but is trivial in most cases for humans.

Not thought to be impossible by people who believe in scientific materialism, and current mainstream ideas in theoretical physics (like the Bekenstein bound and such), and who have thought carefully about the issue.

The laws of physics are believed to be computable, and the information content in a bounded region of space, finite. Therefore, it is believed that, in principle, a Turing machine could run an accurate physical simulation of a person, and could therefore do any cognitive task (as far as input/output correspondence goes) that a human can.

If you’d like to explicitly reject scientific materialism though, I’d have no complaints about you doing so.

[nyrikki]

Physics models are models.

All models are wrong, some are useful.

I am not claiming that useful models need to be computable, in fact the problem with the MTP is that it induces cycles into something that needs to be recursively enumerable to be decidable.

"The trophy wouldn't fit in the suitcase because it was too [large,small]" is a nice toy case to consider how NLP can deal with that easily but NLP would have issues.

It all relates to VC dimensionality and decidablity in the end.

But the math is hard to demonstrate without actually using math.

[marcosdumay]

If you would like to reject scientific materialism (and the empiricism and the dispassionate utilitarianism that go with it), go ahead.

Just don't try to frame your conclusions as objective truth, or known scientific results like the GP.

[nl]

> Just don't try to frame your conclusions as objective truth, or known scientific results like the GP.

To clarify this objection is to @nyrikki's (incorrect) claim that "NLU is an example of a problem that is thought to be impossible for Turing machines/algorithms" and not to anything that @drdeca said right?

I think you are agreeing emphatically with @drdeca but it's possible to read this comment as an objection to @drdeca (NLU may not be as trivial as claimed - ha).

[drdeca]

Right, or..

Ok, uh, I don’t think one has to really reject empiricism to reject scientific materialism?

Or, err, by “empiricism” do you mean like, “support for doing experiments, and keeping track of the results and what models work good to explain them, etc.”, or do you mean stuff like “rejecting anything that doesn’t have good scientific evidence behind it”? One can do the former without doing the latter.

When I express a belief I have that doesn’t fit with scientific materialism, I make sure to mark it as such, so that people can take that into account. I don’t anticipate any clear externally-verifiable refutation of scientific materialism within my lifetime, and so I don’t anticipate predictions that follow from it to be refuted anytime soon. And I definitely wouldn’t present those beliefs of mine as being the scientific consensus.

I suppose one might accuse me of having a “belief in belief”, seeing as I don’t expect these supposed “beliefs” of mine to be predictively useful any time soon.

But I think it is right that there are goals/values that I place higher than pure predictive accuracy. And beliefs about purpose, and meaning, and what is good, etc. fall into that.

(You mentioned utilitarianism. I’m not a utilitarian, but I do think it is often a very good heuristic, and in many contexts it would be good for it to be used more.)

[nl]

> The missing text problem from NLU is an example of a problem that is thought to be impossible for Turing machines/algorithms but is trivial in most cases for humans.

I'm not aware of any mainstream researcher who thinks the missing text problem[1] is impossible for Turing machines. Perhaps some think neural networks are insufficient to achieve it, but this is a different thing.

[1] https://thegradient.pub/machine-learning-wont-solve-the-natu... (Ironically this badly needs updating in a post ChatGPT world, since ChatGPT can solve many of the things this article claims are impossible)

[nyrikki]

ChatGPT is state-of-the-art natural language processing (NLP) NOT NLU

[nl]

That's an artificial distinction without a difference.

It's just software and it solves the problem.

[haupt]

>The term is clearly used here for marketing purposes.

I think most uses are for sensationalistic purposes. I'd wager almost nobody in the general public really understands what AI is or can pin it down. It doesn't help when so many different media outlets abuse the terminology by using it to refer to different things. What's even worse is that whatever ideas people have about AI tend to come from Hollywood.