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Machine Unlearning in 2024

ai.stanford.edu

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Re: Machine Unlearning in 2024

#41

Post by isodev »

avi_vallarapu wrote: post #38

We need to consider the practicality of unlearning methods in real-world applications and the legal acceptance of the same. Given current technology and what advancements are needed to make Unlearning more possible, probably there should be a time-to-unlearn kind of an acceptable agreement that allows organizations to retrain or tune the response that does not involve any response from the to-be-unlearned copyright c…

> a time-to-unlearn kind of an acceptable agreement

Why put the burden to end users? I think the technology should allow for unlearning and even "never learn about me in any future models and derivative models".

Re: Machine Unlearning in 2024

#42

Post by beeboobaa3 »

avi_vallarapu wrote: post #38

We need to consider the practicality of unlearning methods in real-world applications and the legal acceptance of the same. Given current technology and what advancements are needed to make Unlearning more possible, probably there should be a time-to-unlearn kind of an acceptable agreement that allows organizations to retrain or tune the response that does not involve any response from the to-be-unlearned copyright c…

How to deal with "unlearning" is the problem of the org running the illegal models. If I have submitted a gdpr deletion request you better honor it. If it turns out you stole copyrighted content you should get punished for that. No one cares how much it might cost you to retrain your models. You put yourself in that situation to begin with.

Re: Machine Unlearning in 2024

#43

Post by avi_vallarapu »

beeboobaa3 wrote: post #42
avi_vallarapu wrote: post #38

We need to consider the practicality of unlearning methods in real-world applications and the legal acceptance of the same. Given current technology and what advancements are needed to make Unlearning more possible, probably there should be a time-to-unlearn kind of an acceptable agreement that allows organizations to retrain or tune the response that does not involve any response from the to-be-unlearned copyright c…

How to deal with "unlearning" is the problem of the org running the illegal models. If I have submitted a gdpr deletion request you better honor it. If it turns out you stole copyrighted content you should get punished for that. No one cares how much it might cost you to retrain your models. You put yourself in that situation to begin with.

Exactly, I think is where it leads to eventually. And that is what I my original comment meant as well. "Delete it" rather than using some more techniques to "unlearn it", unless you claim the unlearning is 100% accurate.

Re: Machine Unlearning in 2024

#44

Post by avi_vallarapu »

isodev wrote: post #41
avi_vallarapu wrote: post #38

We need to consider the practicality of unlearning methods in real-world applications and the legal acceptance of the same. Given current technology and what advancements are needed to make Unlearning more possible, probably there should be a time-to-unlearn kind of an acceptable agreement that allows organizations to retrain or tune the response that does not involve any response from the to-be-unlearned copyright c…

> a time-to-unlearn kind of an acceptable agreement Why put the burden to end users? I think the technology should allow for unlearning and even "never learn about me in any future models and derivative models".

No technology can guarantee 100% unlearning, and the only 100% guarantee is when the data is deleted before the model is retrained. Legally, even 99.99% accuracy may not be acceptable, but, only 100%.

Re: Machine Unlearning in 2024

#45

Post by visarga »

beeboobaa3 wrote: post #42
avi_vallarapu wrote: post #38

We need to consider the practicality of unlearning methods in real-world applications and the legal acceptance of the same. Given current technology and what advancements are needed to make Unlearning more possible, probably there should be a time-to-unlearn kind of an acceptable agreement that allows organizations to retrain or tune the response that does not involve any response from the to-be-unlearned copyright c…

How to deal with "unlearning" is the problem of the org running the illegal models. If I have submitted a gdpr deletion request you better honor it. If it turns out you stole copyrighted content you should get punished for that. No one cares how much it might cost you to retrain your models. You put yourself in that situation to begin with.

> No one cares how much it might cost you to retrain your models.

Playing tough? But it's misguided. "No one cares how much it might cost you to fix the damn internet"

If you wanted to retro-fix facts, even if that could be achieved on a trained model, it would still get back by way of RAG or web search. But we don't ask pure LLMs for facts and news unless we are stupid.

If someone wanted to pirate a content it would be easier to use Google search or torrents than generative AI. It would be faster, cheaper and higher quality. AIs move slow, are expensive, rate limited and lossy. AI providers have in-built checks to prevent copyright infringement.

If someone wanted to build something dangerous, it would be easier to hire a specialist than to chatGPT their way into it. All LLMs know is also on Google Search. Achieve security by cleaning the internet first.

The answer to all AI data issues - PII, Copyright, Dangerous Information - is coming back to the issue of Google search offering links to it, and websites hosting this information online. You can't fix AI without fixing the internet.

Re: Machine Unlearning in 2024

#46

Post by beeboobaa3 »

visarga wrote: post #45
beeboobaa3 wrote: post #42

Earlier quoted context omitted.

How to deal with "unlearning" is the problem of the org running the illegal models. If I have submitted a gdpr deletion request you better honor it. If it turns out you stole copyrighted content you should get punished for that. No one cares how much it might cost you to retrain your models. You put yourself in that situation to begin with.

> No one cares how much it might cost you to retrain your models. Playing tough? But it's misguided. "No one cares how much it might cost you to fix the damn internet" If you wanted to retro-fix facts, even if that could be achieved on a trained model, it would still get back by way of RAG or web search. But we don't ask pure LLMs for facts and news unless we are stupid. If someone wanted to pirate a content it would…

What do you mean playing tough? These are existing laws that should be enforced. The amount of people's lives ruined by the American government because they were deemed copyright infringers is insane. The us has made it clear that copyright infringement is unacceptable.

We now have a new class of criminals infringing on copyright on a grand scale via their models and they seem desperate to avoid persecution hence all this bullshit.

Re: Machine Unlearning in 2024

#47

Post by beeboobaa3 »

negative_person wrote: post #11
williamtrask wrote: post #9

Earlier quoted context omitted.

Because we can get AI related technologies to do things living creatures can’t, like provably forget things. And when it benefits us, we should. Personal opinion, but I think AGI is a good heuristic to build against but in the end we’ll pivot away. Sort of like how birds were a good heuristic for human flight, but modern planes don’t flap their wings and greatly exceed bird capabilities in many ways. Attribution for…

Can you point to any behaviour in human beings you'd unlearn if theyd also forget the consequences? We spend billions trying to predict human behaviour and yet we are surprised everyday, "AGI" will be no simpler. We just have to hope the dataset was aligned so the consequences are understood, and find a way to contain models that don't.

Seeing dad have sex with mom.

Re: Machine Unlearning in 2024

#48

Post by aeonik »

everforward wrote: post #24
aeonik wrote: post #15

Earlier quoted context omitted.

The feeling of extreme euphoria and its connection to highly addictive drugs like Heroin might be a use case. Though I'm not sure how well something like that would work in practice.

Is that possible to do without also forgetting why it’s dangerous? That seems like it would fuel a pattern of addiction where the person gets addicted, forgets why, then gets addicted again because we wiped their knowledge of the consequences the first time around. Then again, I suppose if the addiction was in response to a particular stimulus (death of a family member, getting fired, etc) and that stimulus doesn’t h…

After a certain point I think someone can learn enough information to derive almost everything from first principles. But I think it might work temporarily.

There's a movie about this idea called "Eternal Sunshine of a Spotless Mind".

I find it hard I believe that you can surgically censor one chunk of information, and cut off the rest of the information. Especially if it's general physical principles.

I also don't have a nice topological map of how all the world's information is connected to the moment, so I can't back up by opinions.

Though I'm still rooting for the RDF/OWL and Semantic Web folks, they might figure it out.

Re: Machine Unlearning in 2024

#49

Post by spennant »

Aerroon wrote: post #27
spennant wrote: post #17

Earlier quoted context omitted.

Agreed. The media and advertising industry was most definitely leveraging cookie-level data for building attribution and targeting models. As soon as the EU established that this data was “personal data”, as it could, theoretically, be tied back to individual citizens, there were questions about the models. Namely “Would they have to be rebuilt after every RTBF request?” Needless to say, no one in the industry really…

More likely: the wrong answer would've shut out a profitable market rather than the practice. The EU is not the world. Anthropic seems to not mind blocking the EU for example.

Sure. But two things:

1) At the time, the European data laws implied that it protected its citizens no matter where they are. Nobody wanted to be the first to test that in court.

2) The organizations and agencies performing this type of data modeling were often doing so on behalf of large multinational organizations with absurd advertising spends, so they were dealing with Other People’s Data. The responsibility of scrubbing it clean of EU citizen data was unclear.

What this meant was that an EU tourist who traveled to the US, and got served a targeted ad, could make a RTBF request to the advertiser (think Coca-Cola, Nestle or Unilever)

The whole thing was a mess.

Re: Machine Unlearning in 2024

#50

Post by ben_w »

andy99 wrote: post #21
ben_w wrote: post #19

Earlier quoted context omitted.

It sounds like you're mistakenly grouping together three very different methods of changing an AI's behaviour. You have some model, M™, which can do Stuff. Some of the Stuff is, by your personal standards Bad (I don't care what your standard is, roll with this). You have three solutions: 1) Bolt on a post-processor which takes the output of M™, and if the output is detectably Bad, you censor it. Failure mode: this is…

Until we have a very different method of actually controlling LLM behavior, 1 is the only feasible one. Your framing only makes sense when "Bad" is something so bad that we can't bear its existence, as opposed to just "commercially bad" where it shouldn't behave that way with an end user. In the latter, your choice 1 - imposing external guardrails - is fine. I'm not aware of anything LLMs can do that fits in the form…

> Until we have a very different method of actually controlling LLM behavior, 1 is the only feasible one.

Most of the stuff I've seen, is 2. I've only seen a few places use 1 — you can tell the difference, because when a LLM pops out a message and then deletes it, that's a type 1 behaviour, whereas if the first thing it outputs directly is a sequence of tokens saying (any variant of) "nope, not gonna do that" that's type 2 behaviour.

This appears to be what's described in this thread: https://old.reddit.com/r/bing/comments/11fryce/why_do_bings_...

The research into going from type 2 to type 3 is the entirety of the article.

> Your framing only makes sense when "Bad" is something so bad that we can't bear its existence, as opposed to just "commercially bad" where it shouldn't behave that way with an end user. In the latter, your choice 1 - imposing external guardrails - is fine.

I disagree, I think my framing applies to all cases. Right now, LLMs are like old PCs with no user accounts and a single shared memory space, which is fine and dandy when you're not facing malicious input, but we live in a world with malicious input.

You might be able to use a type 1 solution, but it's going to be fragile, and more pertinently, slow, as you only know to reject content once it has finished and may therefore end up in an unbounded loop of an LLM generating content that a censor rejects.

A type 2 solution is still fragile, but it just doesn't make the "bad" content in the first place — and, to be clear, "bad" in this context can be anything undesired, including "uses vocabulary too advanced for a 5 year old who just started school" if that's what you care about using some specific LLM for.

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