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)
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)
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?
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.
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.
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.
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.