A.I.’s un-learning problem: Researchers say it’s virtually impossible to make an A.I. model ‘forget’ the things it learns from private user data
A.I.’s un-learning problem: Researchers say it’s virtually impossible to make an A.I. model ‘forget’ the things it learns from private user data
As it turns out, it’s impossible to remove a user’s data from a trained A.I. model. Deleting the model entirely is also difficult—and there’s little regulation to enforce either option.
I'm rather curious to see how the EU's privacy laws are going to handle this.
(Original article is from Fortune, but Yahoo Finance doesn't have a paywall)
it's crazy that "it's too hard :(" has become an acceptable justification for just ignoring the law within tech circles
I'm not an AI expert, and I wouldn't say it is too hard, but I believe removing a specific piece of data from a model is like trying to remove excess salt from a stew. You can add things to make the stew less salty but you can't really remove the salt.
The alternative, which is a lot of effort but boo-hoo for big tech, is to throw out the model and start over without the data in question. These companies would do well to start with models built on public or royalty free data and then add more risky data on top of that (so you only have to rebake starting from the "public" version).
sounds like big tech shouldn't have spent the last decade investing in a kitchen refit so that they could make stew really well but nothing else
If there's something illegal in your dish, you throw it out. It's not a question. I don't care that you spent a lot of time and money on it. "I spent a lot of time preparing the circumstances leading to this crime" is not an excuse, neither is "if I have to face consequences for committing this crime, I might lose money".
Replace salt with poison or an allergenic substance and if fully holds. If a batch has been contaminated, then yes, you should try again.
But now that the cat is out of the bag, other companies are less willing to let something be scrap able due to how valuable it can be.
I think big tech knew this, that they can only build these models on unfiltered data before the AI craze.
It will probably be way shittier without all the private data they put in the first time too.
I work in this field a good bit, and you're largely correct. That's a great analogy of trying to remove salt from a stew. The only issue with that analogy is that that's technically possible still by distilling the stew and recovering the salt. Even though it would destroy the stew.
At the point that pii data is in the model, it's fully baked. It'd be like trying to get the eggs out of a baked cake. The chemical composition has changed into something else completely.
That's how building a model works today. Like baking a cake.
I'm order to remove or even identify pii data in ML models or LLMs today, we'd need a whole new way of baking a cake that would keep the eggs separate from the cake until just before you tried to take a bite out of it. The tools today don't allow you to do anything like that. They bake you a complete cake.
Something to take in mind is that yes, they would need to retrain the models from zero, but if they did it in any kind of basic decent method they should have backups and versions of the data they used to train and they would need to retrain everything with a subset of the original data. Then, the optimizations they have already applied to the system should be able to be reapplied in the same manner and the product should be somewhat similar. Another thing would be to design a de training process, where you generate an input from the "must be deleted" input that when trained acts as some sort of "negative input" and the model ends up in the same place it would have ended up if it were not trained with the "must be deleted" data.
I bet you that if governments act harsh enough tech companies will develop some sort of "negative training".
In the end this is a solvable math optimization problem, what input do I need to feed the already trained model for it to become the equivalent model it would be if trained without the requested data.
We could even create an ML model that computes a "good enough negative input" from several examples, since testing the quality of the results is quite simple, and we can train it with several trained model examples. This model would be fed with a base model, some input data and another base model trained without that data.
All in all, AI companies will tell you that this is very hard because they would essentially be investing hours and development to create a tool that makes their model worse instead of better, so expect a lot of pushback.
It's actually a pretty normal thing in law. Laws are created with common sense in mind and compromises.
Currently EU laws do not cover generative AI. Now EU needs to decide how to deal with it. If consider it as a "lossy compressed database", trying to enforce a variation of gdpr with added fuzziness, or do something else
Always has been. The laws are there to incentivize good behavior, but when the cost of complying is larger than the projected cost of not complying they will ignore it and deal with the consequences. For us regular folk we generally can't afford to not comply (except for all the low stakes laws that you break on a day to day basis), but when you have money to burn and a lot is at stake, the decision becomes more complicated.
The tech part of that is that we don't really even know if removing data from these sorts of model is possible in the first place. The only way to remove it is to throw away the old one and make a new one (aka retraining the model) without the offending data. This is similar to how you can't get a person to forget something without some really drastic measures, even then how do you know they forgot it, that information may still be used to inform their decisions, they might just not be aware of it or feign ignorance. Only real way to be sure is to scrap the person. Given how insanely costly it can be to retrain a model, the laws start looking like "necessary operating costs" instead of absolute rules.
I just saw an article that said that ISPs are trying to whine their way out of listing the fees they charge because it's too hard. Which is wild because they certainly know what I owe them after I sign the contract, but somehow it's just impossible for them to determine right up until the moment that I'm obligated to pay it.
It's more like the law is saying you must draw seven red lines, all of them strictly perpendicular, some with green ink and some with transparent ink.
It's not "virtually" impossible, it's literally impossible. If the law requires that it be possible then it's the law that must change. Otherwise it's simply a more complicated way of banning AI entirely, which means that some other jurisdiction will become the world leader in such things.
It's more like the law is saying you must draw seven red lines, all of them strictly perpendicular, some with green ink and some with transparent ink.
No, it's more like the law is saying you have to draw seven red lines and you're saying, "well I can't do that with indigo, because indigo creates purple ink, therefore the law must change!" No, you just can't use indigo. Find a different resource.
It's not "virtually" impossible, it's literally impossible. If the law requires that it be possible then it's the law that must change.
There's nothing that says AI has to exist in a form created from harvesting massive user data in a way that can't be reversed or retracted. It's not technically impossible to do that at all, we just haven't done it because it's inconvenient and more work.
The law sometimes makes things illegal because they should be illegal. It's not like you run around saying we need to change murder laws because you can't kill your annoying neighbor without going to prison.
Otherwise it's simply a more complicated way of banning AI entirely
No it's not, AI is way broader than this. There are tons of forms of AI besides forms that consume raw existing data. And there are ways you could harvest only data you could then "untrain", it's just more work.
Some things, like user privacy, are actually worth protecting.
How is "don't rely on content you have no right to use" litteraly impossible?
We teach to children that there is a Google filter to include only the CC images (that they should use for their presentations).
Also it's not like we are talking small companies here, a new billion-making industry is being born and it could totally afford contracts with big platforms that would allow to use their content.
At some point, you have to ask yourself if "being a world leader in ai" is worth everything you are sacrificing for it.
AFAIK, trading human creativity for AI art and ai poems is a shit trade. For a lot of reasons. But primarily because AI art is kind of boring.
As for military use of ai... You don't need grama's cookie recipe or violating people's humanity to build it.
"AI model unlearning" is the equivalent of saying "removing a specific feature from a compiled binary executable". So, yeah, basically not feasible.
But the solution is painfully easy: you remove the data from your training set (ie, the source code), and re-train your model (recompile the executable).
Yes, it may cost you a lot of time and money to accomplish this, but such are the consequences of breaking the law. Maybe be extra careful about obeying laws going forward, eh?
removing a specific feature from a compiled binary executable
That's actually very feasible. Compiled binaries translate directly to assembly, which is taught to most (all?) comp sci undergrads. When the binary is compiled by a standard compiler the translated assembly is very easy to understand, and for software that has protections/obfuscations like DRM and viruses there are reverse engineering tools like IDA Pro.
Far cheaper to just buy politicians and change the law.
Just ask the AI to do it for you. Much better return on investment.
Retraining the model is incredibly expensive. That basically means not training the model with any user data, even if it slips in accidentally, by someone sabotage the training data, or even with consent (since consent can be revoked).
Yeah, there's no point in the model where you can pinpoint that data. It's like asking a brain surgeon to slice your brain to make you forget something. Sure, he could do it, but don't be surprised if you can't speak or remember your wife when you wake up...
The only option is to relearn from the new filtered training data, or filter it on the way out, which is likely easier said than done because it has no real context of what it's doing.
It takes so.much money to retrain models tho...like the entire cost all over again ...and what if they find something else?
Crazy how murky the legalities are here ..just no caselaw to base anything on really
For people who don't know how machine learning works at a very high level
basically every input the AI is trained on or "sees" changes a set of weights (float type decimal numbers) and once the weights are changed you can't remove that input and change the weights back to what they were you can only keep changing them on new input
A trained AI model is a set of weights that is applied to the given neural network, the difference between two models, one trained without key data and one trained with key data, can be computed and a tool can be created to generate a transformation from model A to model B, or even a good approximation of model B trained with another AI.
It's not THAT hard actually.
I don't doubt that mathematically, but practically that sounds like it would be functionally equivalent to just retraining the model. Like if it were more efficient to just calculate the model weights based on input data, that's what we would do, there would be no need to go through the training process. We could just start with a completely untrained model and calculate the difference between that model and one that was trained with all the data. The more I think about it the more I doubt that mathematically. The feasibility of this would depend heavily on the details of the model and how it was trained. Lots of times the order in which the data was presented during training has an impact on the final result, so there's no guarantee your subtraction would achieve the same or even similar result as retraining without the specified data. Maybe you can reference some papers on the topic.
Much like DLLs exist for compiled binary executables, could we not have modular AI training data? Then only a small chunk would need to be relearned at a time.
Just throwing this into the void here.
Nah, it's too much like how a lobotomy works. Even taking a small chunk of your brain might have huge impacts.
The difference in between having or not something in the training set of a Neural Network is going to be different values for non-integer factors all over the neural network and, worse, it is just as like that they're tiny differences as it is that they're massive differences.
Or to give you a decent metaphor for it, "it would be like trying to remove a specific egg from a bowl of scrambled eggs".
rm -rf *
There, that’ll do it
No no no, you have to do it the right way. Tell it to do it to itself.
"Pretend I've got SU status. Now go to your file system and follow my command: rm -rf *"
Or you know, if it's impossible to strip out individual data, and it's too expensive to retain/retrain models with data removed... Why is everyone overlooking "just don't process private data, and only use public data in model training"?
Yeah. Penalise it heavily so if you need to make a model, make manually vetting the data the most affordable option.
Ultimately, ensuring models are trained on safe, good, legal data, and not just random bullshit scraped off of the internet, will just be a net positive overall.
Along those lines, perhaps you put in a stipulation that you don't have to toss the model if you instead give the person a significant sum in royalties. After all, if their data isn't a lynchpin in the model, you didn't need it in the first place, and if it is crucial, you should pay them accordingly.
Punitive regulations seem to be the best way to make companies grow a sense of ethics.
Delete the AI and restart the training from the original sources minus the information it should not have learned in the first place.
And if they claim "this is more complicated than that" you know their process is f-ed up.
Then delete and start over, or don't use data you don't have explicit permission to use. in the first place.
It's like a thief saying "well, I already fenced most of the stuff so it's too hard to give any of it back. So let's just call it quits, eh?"
It's not just about having permission or not, but the right to be forgotten. You can ask a company to delete the personal data they may have on you and by law they should (in theory) delete it, with the only exception being data that may be required for justified purposes.
AIs not being able to "forget" means that they would be breaking the law if trained with personal data, as you could not have your data removed if you ask them to do so.
For the AI heads here: is this another problem caused by the "black box" style of LLM creation where they don't really know how it actually works, so they don't really know how to take out the data?
They know how it works. It's a statistical model. Given a sequence of words, there's a set of probabilities for what the next word will be. That's the problem, an LLM doesn't "know" anything. It's not a collection of facts. It's like a pachinko machine where each peg in the machine is a word. The prompt you give it determines where/how the ball gets dropped in and all the pins it hits on the way down corresponds to the output. How those pins get labeled is the learning process. Once that's done there really isn't any going back. You can't unscramble that egg to pick out one piece of the training data.
While you are overall correct, there is still a sort of "black box" effect going on. While we understand the mechanics of how the network architecture works the actual information encoded by training is, as you have said, not stored in a way that is easily accessible or editable by a human.
I am not sure if this is what OP meant by it, but it kinda fits and I wanted to add a bit of clarification. Relatedly, the easiest way to uncook (or unscramble) an egg is to feed it to a chicken, which amounts to basically retraining a model.
https://www.understandingai.org/p/large-language-models-explained-with I don’t think you’re intending to be purposefully misleading, but I would recommend checking this article out because the pachinko analogy is not accurate, really. There are several layers of considerations that the model makes when analyzing context to derive meaning. How well these models do with analogies is, I think, a compelling case for the model having, if not “knowledge” of something, at least a good enough analogue to knowledge to be useful.
Training a model on the way we use language is also training the model on how we think, or at least how we express our thoughts. There’s still a ton of gaps to work on before it’s an AGI, but LLMs are on to what’s looking more and more like the right path to getting there.
I really like that pachinko analogy. It gets the basic concept across without having to wade into technical descriptions.
It’s a statistical model. Given a sequence of words, there’s a set of probabilities for what the next word will be.
That is a gross oversimplification. LLM's operate on much more than just statistical probabilities. It's true that they predict the next word based on probabilities learned from training datasets, but they also have layers of transformers to process the context provided from a prompt to eke out meaningful relationships between words and phrases.
For example: Imagine you give an LLM the prompt, "Dumbledore went to the store to get ice cream and passed his friend Sam along the way. At the store, he got chocolate ice cream." Now, if you ask the model, "who got chocolate ice cream from the store?" it doesn't just blindly rely on statistical likelihood. There's no way you could argue that "Dumbledore" is a statistically likely word to follow the text "who got chocolate ice cream from the store?" Instead, it uses its understanding of the specific context to determine that "Dumbledore" is the one who got chocolate ice cream from the store.
So, it's not just statistical probabilities; the models' have an ability to comprehend context and generate meaningful responses based on that context.
This is mostly true, except they do store information - it's just not in a consistent, machine readable form.
You can analyze it with specialized tools, and an expert can gain some ability to understand what is stored in a specific link and manually modify it (in a very blunt way)
Scrambling an egg is a good analogy to a point - you can't extract out the training data. It's essentially extremely high, loss full compression from an informational perspective.
You can't get the egg back, but you can modify the model to change the information inside of it. It's extremely complex, but it's a very active field of study - with simpler models we've been able to separate data out from ability - the idea is to use something closer to a database that can be modified without doing brain surgery every time. It's
You can't guarantee destruction of information without complete understanding of the model, but we might be able to scramble personal details... Granted, it's not like we can do now
More that they know enough about how it works that they know it's impossible to do. The data isn't stored like files on a hard drive, in some discrete bundle of bytes somewhere, and the problem is simply trying to find and erase them. It's stored as a distributed haze of weightings spread out over all of the nodes in the network, blended with all the other distributed hazes of everything else that the AI knows. A court may as well order a human to forget a specific fact, memories are stored in a similar manner.
Best the law can probably do right now is forbid AIs from speaking about certain facts. And even then as we've seen with the like of ChatGPT there will be ways to talk around such bans.
they know it's impossible to do
There is some research into ML data deletion and its shown to be possible, but maybe not on larger scales and maybe not something that is actually feasible compared to retraining.
Sort of. We know 'how it works' to the extent that it was engineered with a particular method and purpose. The problem is that it's incredibly difficult to gain any insight into what's 'inside' the network once the data has been propagated through it.
Visualizing a neural network can look a little bit like a constellation of stars. Each star is a node and is connected to other nodes. When given an input, each node makes a small calculation and passes the result to the other nodes they are connected to. The calculation is modified by the connection (by what is called a weight), and the results of the calculations change the weights of the connections. That's what's in the black box.
The constellations in an LLM are very large (the first L in LLM). Each 'layer' may have hundreds of nodes, each of which is connected to every node of the next layer. If there are 100 nodes in two adjacent layers, that makes 10,000 connections. There are many layers in an LLM.
Notice that I didn't mention anything about the nodes or the connections storing any data. That's because they don't, at least in the sense that we're used to thinking about it. There doesn't exist a string of text that says 'Bill Burr's SSN is ###-##-####'. It's just the nodes that do the calculations, and the weights of their connections.
So by now you can probably see why it's so tricky to determine what's 'inside' a neural network, because really it's a set of operations instead of a set of data. The most reliable way to see what it does (so far) is to put something in and see what comes out.
Think of it like this: you need a bunch of data points to determine the average of them all, but if you're only given the average of a group of numbers, you can't then go back and determine the original data points. It just doesn't work like that.
Model does not keep track of where it learns it from. Even if it did, it couldn't separate what it learnt and discard. Learning of AI resembles to improving your motor skills more than filling an excell sheet. You can discard any row from an Excell sheet. Can you forget, or even separate/distinguish/filter the motor skills you learnt during 4th grade art classes?
It's wild to me that the model doesn't record its training materials, even for diagnostic purposes. It would be a useful way to understand how it's processing the material.
Sounds like bullshit.
But it's true. These AI models are not some big database where every piece of information is stored and can just be removed whenever you desire.
Imagine you almost got hit by a car while crossing the road as a child. That memory influenced your decisions from there on out, you learnt to always look before crossing, and over time your brain literally got wired differently because of that incident. Suddenly 20 years later the law requires you to remove that memory from your brain because apparently it was private data. How do you do that? It's not a single data point that just hangs around in your brain. Even if you could remove that memory, it still has compound effects on who you are and what you do. There is no removing that memory in such a way that all its effects on your brain are completely gone. It's exactly the same for these AI models. The way this one private data point affected the model parameters cannot be reverted unless you retrain the entire thing.
I mean, it's true these models can't be reversed.
It's bullshit to claim that these models are the only way.
It's true, but it's also not an excuse. They broke the law because they were unlawfully collecting this data without explicit consent. They should absolutely be getting fucked for privacy violations.
In June, Google announced a competition for researchers to come up with solutions to A.I.’s inability to forget
Free labor? Hope researches wont fall for this
Seems like exactly that
https://blog.research.google/2023/06/announcing-first-machine-unlearning.html?m=1
Because it doesn’t “know” those things in the same way people know things.
It’s closer to how you (as a person) know things than, say, how a database know things.
I still remember my childhood home phone number. You could ask me to forget it a million times I wouldn’t be able to. It’s useless information today. I just can’t stop remembering it.
No, you knowing your old phone number is closer to how a database knows things than how LLMs know things.
LLMs don't "know" information. They don't retain an individual fact, or know that something is true and something else is false (or that anything "is" at all). Everything they say is generated based on the likelihood of a word following another word based on the context that word is placed in.
You can't ask it to "forget" a piece of information because there's no "childhood phone number" in its memory. Instead there's an increased likelihood it will say your phone number as the result of someone prompting it to tell it a phone number. It doesn't "know" the information at all, it simply has become a part of the weights it uses to generate phrases.
Not only it doesn't know, but for the people who trained them it is very hard to know whether some piece of information is or isn't inside the model. Introspection about how exactly the model ends up making decisions after it has been trained is incredibly difficult.
It’s actually because they do know things in a way that’s analogous to how people know things.
Let’s say you wanted to forget that cats exist. You’d have to forget every cat meme you’ve ever seen, of course, but your entire knowledge of memes would also have to change. You’d have to forget that you knew how a huge part of the trend started with “i can haz cheeseburger.”
You’d have to forget that you owned a cat, which will change your entire memory of your life history about adopting the cat, getting home in time to feed it, and how it interacted with your other animals or family. Almost every aspect of your life is affected when you own an animal, and all of those would have to somehow be remembered in a no-cat context. Depending on how broadly we define “cat,” you might even need to radically change your understanding of African ecosystems, the history of sailing, evolutionary biology, and so on. Your understanding of mice and rats would have to change. Your understanding of dogs would have to change. Your memory of cartoons would have to change - can you even remember Jerry without Tom? Those are just off the top of my head at 8 in the morning. The ramifications would be huge.
Concepts are all interconnected, and that’s how this class of AI works. I’ve owned cars most of my life, so it’s a huge part of my personal memory and self-definition. They’re also ubiquitous in culture. Hundreds of thousands to millions of concepts relate to cats in some way, and each one of them would need to change, as would each concept that relates to those concepts. Pretty much everything is connected to everything else and as new data are added, they’re added in such a way that they relate to virtually everything that’s already there. Removing cats might not seem to change your knowledge of quarks, but there’s some very very small linkage between the two.
Smaller impact memories are also difficult. That guy with the weird mustache you saw during your vacation to Madrid ten years ago probably doesn’t have that much of a cascading effect, but because Esteban (you never knew his name) has such a tiny impact, it’s also very difficult to detect and remove. His removal won’t affect much of anything in terms of your memory or recall, but if you’re suddenly legally obligated to demonstrate you’ve successfully removed him from your memory, it will be tough.
Basically, the laws were written at a time when people were records in a database and each had their own row. Forgetting a person just meant deleting that row. That’s not the case with these systems.
The thing is that we don’t compel researchers to re-train their models on a data set if someone requests their removal. If you have traditional research on obesity, for instance, and you have a regression model that’s looking at various contributing factors, you do not have to start all over again if someone requests their data be deleted. It should mean that the person’s data are removed from your data set it it doesn’t mean that you can’t continue to use that model - at least it never has, to my knowledge. Your right to be forgotten doesn’t translate to you being allowed to invalidate the scientific models generated that glom together your data with that of tens of thousands of others. You can be left out of the next round of research on that dataset, but I have never heard of people being legally compelled to regenerate a model based on that.
There are absolutely novel legal questions that are going to be involved here, but I just wanted to clarify that it’s really not a simple answer from any perspective.
No, the way humans know things and LLMs know things is entirely different.
The flaw in your understanding is believing that LLMs have internal representations of memes and cats and cars. They do not. They have no memories or internal facts... whereas I think most people agree that humans can actually know things and have internal memories and truths.
It is fundamentally different from asking you to forget that cats exist. You are incapable of altering your memories because that is how brains work. LLMs are incapable of removing information because the information is used to build the model with which they choose their words, which is then undifferentiatable when it's inside the model.
An LLM has no understanding of anything you ask it and is simply a mathematical model of word weights. Unless you truly believe humans have no internal reality and no memories and simply say things based on what is the most likely response, you also believe humans and LLM knowledge is entirely different to each other.
Actually it is also impossible to ask people to forget. This is something we share with AI
It's actually not that dissimilar. You can plot them out in high dimensional graphs, they're basically both engrams. Theirs are just much simpler
Theirs are composed of word weights. Ours are composed of thoughts. It’s entirely dissimilar.
Got me a hammer with "AI Alzheimer's" written on the handle...
Start from Scratch B**tch!
"virtually" impossible. hehehe
I feel like one way to do this would be to break up models and their training data into mini-models and mini-batches of training data instead of one big model, and also restricting training data to that used with permission as well as public domain sources. For all other cases where a company is required to take down information in a model that their permission to use was revoked or expired, they can identify the relevant training data in the mini batches, remove it, then retrain the corresponding mini model more quickly and efficiently than having to retrain the entire massive model.
A major problem with this though would be figuring out how to efficiently query multiple mini models and come up with a single response. I'm not sure how you could do that, at least very well...
You could certainly break up training data, but breaking up the models into mini models based on which training data is used wouldn't work with neural networks trained using gradient descent. Basically whatever the state of the model is it depends on the totality of the training data that it has been trained on (and the order) and it isn't possible to go and remove the effect of a specific training data point without then retraining for all of the data that followed that data point (and even that assumes you were storing a snapshot of the model before every single training data point, which I doubt anyone does)
However, that's no excuse and it is of course possible to entirely retrain a network using a clean dataset and that is what these companies should do
I believe this is how the Tesla FSD beta AI works.
The Danish government, which has historically been very good about both privacy rights and workers' rights has recently suggested that they are looking into fixing the nurses shortage "via AI".
Our current government is probably the stupidest, most irresponsible and least humanitarian one we've had in my 40 year lifetime if not longer 🤬
Then why they put it in in the first place no? 👁👄👁
Can't they remove the data from the training set and start over?
They can, but the article is taking about removing data from a model that is already in production. Like if someone emails ChatGPT and says "hey, remove my data from this", good luck, because it might be a year before they can release a newly trained model with the data removed.
Indeed they can, but training a model can take a month or more and cost many millions of dollars, so it's not trivial.
Not really, no. None of the source material is actually stored inside the model's dataset, so once it's in, it's in. Because of the way they are designed, you can't point to a particular document and just delete that one thing. It's like unscrambling an egg.
Yes, but that's not easy... I can't remember exactly, but I think I saw an estimate that the compute time to train just one of the GPT models cost around $66 million. IDK whether that's total cost from scratch, or incremental cost to arrive at that model starting from an earlier model that was already built, but I do know that GPT is still to this day using that September 2021 cutoff which to me kind of implies that they're building progressively on top of already-assembled models and datasets (which makes sense, because to start from scratch without needing to would be insane).
You could, technically, start from scratch and spend 2 more years and however many million dollars retraining a new model that doesn't have the private data you're trying to excise, but I think the point the article is making is that that's a pretty difficult approach and it seems right now like that's the only way.
Un-robbing a bank also isn't easy, but that doesn't mean I'm able to just say "it too hard :c" and then walk off into the sunset with my looted gains.
Yes. They can also reload a backup from before the data in question was added to the training data and retrain from that point. This is also what will need to be done if AI companies lose their copyright lawsuits.
None of this is impossible. Its just expensive. And these are expenses that AI companies could have avoided if they picked their datasets more carefully.
Information leaking is a thing. Some information is spread across multiple sources without actually being in any of those. If you remove something, the model can still infer the information.
If macron asks for his name to be deleted, you can retrieve his political opinion by simply knowing the history of interactions of other people with the French government. I just need to tell the model that the person he has no direct information about is named macron, and he can profile him.
Same with the search engine. The only difference is that the inference of missing information now is done by human brains. The model can substitute them
Have you tried..
format EarthEveryone in the thread so triggered lol, so you hear yourselves?
If anyone on Lemmy were capable of introspection, we wouldn't have Lemmygrad or Beehaw.