The obvious question being, if the AI is so smart why was it necessary to use humans to check 20,515 signals to find the eight with the "characteristics of technosignatures"?
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.
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.
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.
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...
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.
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.
Just don't try to frame your conclusions as objective truth, or known scientific results like the GP.
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.)
Honestly, I added "keep your models as simple as you can" into it. But any way you cut empiricism, it's actually utilitarianism that can be seen, so it's the one where the correct fine-cutting is important (hum... well, if you keep an utilitarian point of view). Anyway, utilitarianism tends to align with the version of empiricism biased into getting computable models.
And, of course, none of those deal with purpose questions.
Anyway, your comment there is great. What I disagree is on conceding space to something like the one above yours, because it's a misleading text that implies something very different from what it says.
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).
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.
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)
It's just software and it solves the problem.
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...
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).
The paper is actually worth reading about this part[1]. They have a moderately complex pipeline that you can think of as a filter: it tries to find anomalous signals.
The stage-1 autoregressor filtered 115M signals to 3M. Then they perform signal processing techniques to remove things like GPS signal contamination ("t can be seen that certain observing frequencies contain a much higher number of events compared with the others—for example, the region around 1,600 MHz. This overlaps with known RFI at the GBT site specifically from persistent GPS signals.").
After this second stage they are left with 20,515 potential signals which were visually inspected.
The issue here is that we don't know what a alien signal looks like so we can't just use a classifier. The pipeline can only find things it has never seen before, but it takes human judgement to decide if these signals are "alien" or more likely contamination from human sources that weren't filtered ("Regarding the nature of the rest of the events, most of them look like false positives associated with RFI signals.")
[1] https://www.nature.com/articles/s41550-022-01872-z.epdf?shar...
AI is also not well defined - day one of my AI course in university opened with "What is AI?" Generally once we figure out how to do something using a computer, we decide that's not really intelligent anymore so the implementation isn't AI. An example of that is the minimax algorithm - it's featured "AI: A Modern Approach"[0] but it isn't really something people think of when they hear "an AI".
Case study: had to clean a list of German addresses once. Excluding obviously invalid addresses like some Chinese address was easy. But some addresses had errors which needed a human eye to fix and correct.
But maybe we are just proto-AI, running on a different platform
It’s a trade-off between false positives and false negatives, and the budget you have to investigate alerts.
https://en.wikipedia.org/wiki/Receiver_operating_characteris...