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We can’t trust AI systems built on deep learning alone

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Re: We can’t trust AI systems built on deep learning alone

#51

Post by 6gvONxR4sf7o »

eli_gottlieb wrote: post #47
6gvONxR4sf7o wrote: post #41

I'm coming to suspect that even our data isn't enough for useful AI. Imagine you had a truly general sci-fi AI at your office. It still couldn't just look at your database and answer a simple question like "What was the difference in client churn rates between mobile and desktop last month?" or "What was the effect of experiment 1234 on per-client revenue?" Hell, a human couldn't do it. As far as the human or AI woul…

I mean, yeah, you can't do causal inference without a causal model. Uh, what's new about that?

The point is that data provenance isn't encoded in the data. Like the difference between a schema where column X has type `int` versus X having type `do(int)` or something cleverer. If the way you get your causal model is to ask the person who ran the experiment, then it's very much an uphill battle for an algorithm to get a causal model. We want to enable automated causal inference, so we should better record our causal models (data lineage).

Re: We can’t trust AI systems built on deep learning alone

#53

Post by 6gvONxR4sf7o »

allworknoplay wrote: post #48
6gvONxR4sf7o wrote: post #41

I'm coming to suspect that even our data isn't enough for useful AI. Imagine you had a truly general sci-fi AI at your office. It still couldn't just look at your database and answer a simple question like "What was the difference in client churn rates between mobile and desktop last month?" or "What was the effect of experiment 1234 on per-client revenue?" Hell, a human couldn't do it. As far as the human or AI woul…

This is a fantastic point that may not be relevant now, but may be extraordinarily relevant in the future. I’m not aware of any work or even sci-fi that addresses AGI with regards to this question, and would be curious if there’s stuff out there?

I'm beginning to suspect that it could be relevant soon. If you wait for such advanced AI that can understand your documentation, you're making it harder than it has to be, kinda like Marcus is saying in OP. You could solve this with just tons of raw data, but that seems unnecessarily hard. For a firm with the usual small dataset, maybe even unrealistically hard.

Anyways, with some naive googling I found these references which seem interesting with regards to lineage and causality (for the query "lineage causal database schema"):

[0] Causality and Explanations in Databases. The "Related topics in Databases" section seems interesting.

[1] Duke's 'Understanding Data: Theory and Applications, Lecture 16: Causality in Databases' (by one of [0]'s authors).

[2] Quantifying Causal Effects on Query Answering in Databases. This has some interesting definitions.

[3] Causality in Databases. Seems like a more in depth version of [0].

[4] A whole course on "Provenance and lineage"

[5] Causality and the Semantics of Provenance. Defines "provenance graphs" and some properties.

-----------

[0] http://www.vldb.org/pvldb/vol7/p1715-meliou.pdf

[1] https://www2.cs.duke.edu/courses/fall15/compsci590.6/Lecture...

[2] https://www.usenix.org/sites/default/files/conference/protec...

[3] https://www.cs.cornell.edu/home/halpern/papers/DE_Bulletin20...

[4] https://cse.buffalo.edu/~chomicki/cse703-s18.html

[5] https://arxiv.org/pdf/1006.1429.pdf

Re: We can’t trust AI systems built on deep learning alone

#55

Post by brundolf »

thelazydogsback wrote: post #20

I've been waiting for the Symbolic/NN pendulum to starting swinging back the other way and start settling in the center. NN/DL is great for the interface between the outer world and the inner world of the mind (pattern recognition and re-construction), and symbolic AI more straightforwardly represents more "language of the mind" tasks, and easily handles issues like explanation and other meta-behaviors that with DL i…

My company is one of the only ones still pushing on symbolic AI:

https://www.cyc.com/

Re: We can’t trust AI systems built on deep learning alone

#56

Post by skdotdan »

uoaei wrote: post #43
thelazydogsback wrote: post #20

I've been waiting for the Symbolic/NN pendulum to starting swinging back the other way and start settling in the center. NN/DL is great for the interface between the outer world and the inner world of the mind (pattern recognition and re-construction), and symbolic AI more straightforwardly represents more "language of the mind" tasks, and easily handles issues like explanation and other meta-behaviors that with DL i…

SAT solvers are really fast now. Some sort of "neural SAT problem definition" followed by solving it seems to be an interesting direction, but I'm relatively naive on it all. Not sure how training would work since there's no backprop through Boolean logic.

https://arxiv.org/abs/1905.12149

Re: We can’t trust AI systems built on deep learning alone

#57

Post by Mathnerd314 »

> General AI also ought to be able to work just as comfortably reasoning about politics as reasoning about medicine. It’s the analogue of what people have; any reasonably bright person can do many, many different things.

The average human has extreme difficulty reasoning about politics, while usually being reasonable on medicine (anti-vax being one of many exceptions). And it seems strange to expect a skilled pianist to also be a skilled neuroscientist or a skilled construction worker. On the other hand these people all use similar neural architectures (brains). So he seems pretty off-track when he criticizes "narrow AI" in favor of "general AI", as if there's some magic AI that will do everything perfectly, and even more off track when he criticizes researchers for using "one-size-fits-all" technologies, when indeed that is exactly what humans have been doing for millennia for their cognitive needs.

And sure, ML models in publications so far are typically one-off things that react poorly to modified inputs or unexpected situations. But it's not clear this has any relevance to commercial use. Tesla is still selling self-driving cars despite the accidents.

Re: We can’t trust AI systems built on deep learning alone

#58

Post by mindgam3 »

> AlphaGo can play very well on a 19x19 board but actually has to be retrained to play on a rectangular board.

This right here is the soft underbelly of the entire “machine learning as step towards AGI” hype machine, fueled in no small part by DeepMind and its flashy but misleading demos.

Once a human learns chess, you can give it a 10x10 board and she will perform at nearly the same skill level with zero retraining.

Give the same challenge to DeepMind’s “superhuman” game-playing machine and it will be an absolute patzer.

This is an obvious indicator that the state of the art in so-called “machine learning” doesn’t involve any actual learning in the way it is normally applied to intelligent systems like humans or animals.

I am continually amazed by the failure of otherwise exceedingly intelligent tech people to grasp this problem.

Re: We can’t trust AI systems built on deep learning alone

#59

Post by jayd16 »

celeritascelery wrote: post #42
jayd16 wrote: post #33

Has work been done to formally prove general AI can't arise from deep learning? I can't help but feel its an assumption being made by those that prefer classical research.

First you need to find a formal definition for “general AI”.

So I take it the answer is no. It seems like a lot of the discussion around what deep learning is not makes a lot of assumptions about what "actual" learning is but doesn't seem to back that up in any formal way.

Re: We can’t trust AI systems built on deep learning alone

#60

Post by jayd16 »

mindgam3 wrote: post #58

> AlphaGo can play very well on a 19x19 board but actually has to be retrained to play on a rectangular board. This right here is the soft underbelly of the entire “machine learning as step towards AGI” hype machine, fueled in no small part by DeepMind and its flashy but misleading demos. Once a human learns chess, you can give it a 10x10 board and she will perform at nearly the same skill level with zero retraining.…

>Once a human learns chess, you can give it a 10x10 board and she will perform at nearly the same skill level with zero retraining.

Interesting. Has this actually been shown? I would assume a lot of the strategies a human is familiar with would fall apart as well. I'm no chess or go player but I would have to learn new strategies in a tic-tac-toe game scaled to 10x10. I would certainly not be as proficient although I would still consider myself to have intelligence.

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