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

Re:We can’t trust AI systems built on deep learning alone by 6gvONxR4sf7o on Sunday September 29, 2019 @01:16PM (#21108264)
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 would know, you just presented it with a bunch of random tables. This matters because it's incredibly helpful to know which pieces are randomized. Which rows are repeated measurements as opposed to independent measurements. Which pieces are upstream of which others. There's so much domain knowledge baked into data, while we just expect an algorithm to learn from a simple table of floats.

The human state of the art solution seems to be going on slack and asking questions about the data provenance, which will decidedly not work for an automated approach.

A primary reason I can do better a better job than a generic algorithm is because you told me where the data came from (or I designed the schema and ETL myself), while the algo can't make any useful assumptions because all that info is hidden.

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Re:We can’t trust AI systems built on deep learning alone by allworknoplay on Sunday September 29, 2019 @02:17PM (#21108668)
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?

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Re:We can’t trust AI systems built on deep learning alone by 6gvONxR4sf7o on Sunday September 29, 2019 @02:44PM (#21108846)
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.

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[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

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Re:We can’t trust AI systems built on deep learning alone by inimino on Sunday September 29, 2019 @03:31PM (#21109164)
If you don't know the sampling regime that generated the data, you might as well give up. The only solutions to this seem to be collecting and providing all the data to ML analysis, or using provenance to inform the training procedure, which requires human expertise.

However, with a more general AI, we would be able to tell it "this is where and how the data were collected" and it could make the necessary inferences. Fully general AI would also be able to ask the right questions and make reasonable guesses on its own. Everything you do now, and nothing like anything that's been developed.

To me the weak point of this article isn't in the thesis (of course everyone can agree that general intelligence is more useful than narrow, all else being equal) but that there was nothing said about how to get there. The only reason ML is currently resurgent is because it we've figured out how to do something that works, while general intelligence has proven beyond our reach for 60+ years.

Edit: actually it seems to be "classical AI" and hybrid approaches and I guess for more details one would need to read the book.

I guess articles like this are worthwhile to temper expectations of what's possible with the current crop of technologies for those not in the field, which could help prevent another winter due to overinflated expectations.

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Re:We can’t trust AI systems built on deep learning alone by milaresearcher on Sunday September 29, 2019 @03:27PM (#21109134)
I totally agree with your statement about AGI, but wouldn't be as pessimistic about neural networks in general. Of course our data is enough for useful deep neural models! Many problems can be solved without it, but in areas of computer vision and speech recognition they seem to be the best (currently known) choice.

Your point about AGI which needs to ask questions about data provenance is super interesting. Are you aware of the line of inquiry into active learning? It's fascinating and has a long history: https://papers.nips.cc/paper/1011-active-learning-with-stati...

https://en.wikipedia.org/wiki/Active_learning_(machine_learn...

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Re:We can’t trust AI systems built on deep learning alone by varenc on Sunday September 29, 2019 @02:19PM (#21108683)
If you had 'truly general sci-fi AI', I would think it would know what questions to ask you like a normal human would.

Or if we're talking serious AGI level, just feed it your codebase/email/slack history and have it learn all of that hidden info.

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Re:We can’t trust AI systems built on deep learning alone by eli_gottlieb on Sunday September 29, 2019 @02:15PM (#21108648)
I mean, yeah, you can't do causal inference without a causal model. Uh, what's new about that?
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Re:We can’t trust AI systems built on deep learning alone by 6gvONxR4sf7o on Sunday September 29, 2019 @02:33PM (#21108779)
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).
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