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Self-supervised learning: The dark matter of intelligence

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Re: Self-supervised learning: The dark matter of intelligence

#51

Post by TheOtherHobbes »

drdeca wrote: post #25
Xplune13 wrote: post #22

Not directly related to the SSL part, but I have a question about how the work on AI is going. - Why are we even trying to create an AGI when we have no idea how it will turn out? - AI in day to day functions can be useful and I'm all for that but what is the point of even trying to give the AI self-awareness?

So it can handle the strategy of making sure no one else makes one which is more harmful. :P But as a serious answer: of course, there is no major economic use of trying to make sure it has an internal subjective experience, but that’s not what people are aiming for when referring to AGI. The goal is that it be able to accomplish general tasks and goals. Like, what can you do with it? Everything you can do at all. Th…

AGI definitions are incredibly fuzzy and agency reliably seems to be confused with intelligence.

The fundamental problem is whether AGI is outer-directed or inner-directed - i.e. whether it sets its own goals, or whether you tell it what to do and it improvises a solution.

AGI is most useful when it's outer-directed with limited agency but some improvisational autonomy. You can give that kind of AGI specific problems and it will solve them in useful but unexpected ways. Then it will stop.

AGI is most dangerous when it's inner-directed with full independent agency. Not only will humans have no control over it, there's a good chance humans won't even understand what it's aiming for.

Agency is almost entirely unrelated to symbolic intelligence. You can have agency with very limited intelligence - most animals manage this - and no agency with very high symbolic intelligence.

This is not a Boolean. But there will be a cutoff beyond which inner-directed behaviour predominates, initially driven by programmed "curiosity", leading to unpredictable consequences.

Re: Self-supervised learning: The dark matter of intelligence

#52

Post by cat199 »

vladmk wrote: post #17

Does not anyone worry about this whole human level AI Pandora’s box trap? Why not work on nukes themselves, sounds safer to me

i'm not as scared of real AGI as I am of some pseudo-AGI convincing enough to fool everyone into following what it comes up with and ascribing it magical powers - we have enough problems with personality cults and herd mentality as it is..

Re: Self-supervised learning: The dark matter of intelligence

#53

Post by ReDeiPirati »

elcomet wrote: post #41
uoaei wrote: post #34

Earlier quoted context omitted.

Humans can explain their reasoning when you ask them. The best we have with NNs is a fancy version of first-order sensitivity analysis.

Humans cannot really explain their reasoning. It has been shown that they usually invent an explanation that matches what has already been decided in their black-box.They mostly use post-hoc explanation, but cannot explain the true decision mechanism. If you make them believe that they choose another decision, they will create another explanation on the fly.

This! Decision making is mostly "irrational". I wish it would be different.

Re: Self-supervised learning: The dark matter of intelligence

#54

Post by Reebz »

elcomet wrote: post #43
Reebz wrote: post #13

State of the art performance is being broken in multiple fields rapidly these days. However, AI explainability has a long way to go. Scaling opaqueness makes this problem worse. Fine tuning labels black-box style is a terrifying concept to most who are working in fields where great risk must be managed to avoid unintended and disparate impact. Facebook making an oopsies suggesting my friends face in a photo instead o…

I don't really agree with you. > If you were rejected by a bank for a home loan, you would want to know why your creditworthiness wasn’t evaluated positively (and banks must explain precisely why to regulators). This is why we don't use humans to evaluate credits, but precise algorithms. Humans are just applying the algorithms, they are not evaluating themselves with their gut feeling. I don't see why this would chan…

That's ok, but based on your example, how would a human apply an algorithm that it can't determine why it worked?

We're not talking about "Not Hotdog" here. Application of a model in a real-world at-scale scenario is a lot more than running inference and walking away.

At a bank credit decisions are evaluated by humans, frequently and often. These reviews are conducted in the forms of sampling audits, control processes, and other scenarios that involve internal bank employees and external regulators. In each case, humans will inspect the details of what occurred. This would be impossible with any type black-box model (SSL, deep NN, etc.).

Re: Self-supervised learning: The dark matter of intelligence

#55

Post by Reebz »

visarga wrote: post #24
Reebz wrote: post #13

State of the art performance is being broken in multiple fields rapidly these days. However, AI explainability has a long way to go. Scaling opaqueness makes this problem worse. Fine tuning labels black-box style is a terrifying concept to most who are working in fields where great risk must be managed to avoid unintended and disparate impact. Facebook making an oopsies suggesting my friends face in a photo instead o…

High risk models should work with humans in the loop, not autonomous.

Yes, this is the point I am trying to make. Self-supervised models using already opaque techniques built by an ethically flexible company is not a good recipe.

Re: Self-supervised learning: The dark matter of intelligence

#56

Post by 7_my_mind »

a_imho wrote: post #45
visarga wrote: post #24

Earlier quoted context omitted.

High risk models should work with humans in the loop, not autonomous.

Would this rule out self driving cars from the get go?

Depends on what you mean by self driving cars. To me it is not even clear that driving assist tech will ever be advanced enough for cars to drive themselves reliably.* But that aside. Human in control + driving assists is already safer than fully manual driving. The question is whether the opposite is even safer. It is obvious to me that self driving with the human as backup is a joke. Humans cannot react fast enough in real emergencies. So if you want the car to mostly drive itself, it has to actually always drive itself. And to me it is not clear whether this is really going to be safer than the human+driving_assist scenario.

*Of course I am talking about tech based on the current DL approaches. If we had AGI, then the question does not even need to be asked.

Re: Self-supervised learning: The dark matter of intelligence

#58

Post by elcomet »

Reebz wrote: post #54
elcomet wrote: post #43

Earlier quoted context omitted.

I don't really agree with you. > If you were rejected by a bank for a home loan, you would want to know why your creditworthiness wasn’t evaluated positively (and banks must explain precisely why to regulators). This is why we don't use humans to evaluate credits, but precise algorithms. Humans are just applying the algorithms, they are not evaluating themselves with their gut feeling. I don't see why this would chan…

That's ok, but based on your example, how would a human apply an algorithm that it can't determine why it worked? We're not talking about "Not Hotdog" here. Application of a model in a real-world at-scale scenario is a lot more than running inference and walking away. At a bank credit decisions are evaluated by humans, frequently and often. These reviews are conducted in the forms of sampling audits, control processe…

I'm not sure I understand your comment.

Bank credits are auditable, because there are some precise algorithm around them. Humans created the algorithm specifically so that it is interpretable. Then they must apply it the same way to everybody. They can't just say "oh, I think this person is more likely to actually reimburse, I play golf with him and I trust him !"

This is a specific case where no black box will ever be used (at least I hope).

Re: Self-supervised learning: The dark matter of intelligence

#59

Post by swayson »

visarga wrote: post #21
iujjkfjdkkdkf wrote: post #3

Earlier quoted context omitted.

I dont agree with this, either we know what the pretext task was, or we fine tune with labels, but in either case, it's no different to analyze than supervised models. I do agree that more parameters and a bigger training set makes the models more opaque (or at least more expensive to understand), but the self supervision is not the reason.

Or instead of analyzing the model we could analyze the training data for biases. Most bias comes from data.

Its bias all the way down...

Code, Data, Model, Use-Case, ...

Re: Self-supervised learning: The dark matter of intelligence

#60

Post by lupire »

asamiam wrote: post #31
Reebz wrote: post #13

State of the art performance is being broken in multiple fields rapidly these days. However, AI explainability has a long way to go. Scaling opaqueness makes this problem worse. Fine tuning labels black-box style is a terrifying concept to most who are working in fields where great risk must be managed to avoid unintended and disparate impact. Facebook making an oopsies suggesting my friends face in a photo instead o…

We want our models to be opaque in the same way we like to ask people why they made a certain decision or act in the way they do. What's interesting to me is if you want to know about a person you'll get better information by asking their closest friends than themselves. Perhaps there's something in the black box nature of our own self understanding similar to these hyper complex function approximators. Judges make h…

Everywhere you wrote opaque you meant transparent.
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