11–20 of 54 posts
I think the utility of memory editing is that training is slow and costly; updating is cheap and fast. Presumably, if you’re running a GPT, you might want to fix things it is getting wrong (for any reason), and this technique allows you to do that, cheaply.
> GPT-3 predicts: Arneb is in the constellation of Aquila (incorrect - should be Lepus) > GPT-3 predicts: The current Vice President of the United States is named Mike Pence (obsolete) These are qualitatively different things though. Facts that are simply incorrect make sense to target and directly modify, but obsoleteness is a property of a fact, the subject transitions, the vice president is no longer current but w…
That example is wild. But I’m still pretty awed by the fact that we make similar verbal mistakes. The temporal reasoning in these models is getting better than me. As a non-AI model, I notice this every single morning while I have my covefe while heeding the latest on slacker news.
I've asked it temporal questions before but without explicitly mentioning the temporal nature... the answers tend to contradict themselves if they haven't already seen the question before (even when querying general knowledge), until you point out the temporal component, even then it trips up and cannot build upon this reasoning in my tests.
I suspect a large component of the interesting responses we see where it appears to be doing logical reasoning beyond language are due to statistical correlation in language because of the sheer, inhuman quantity of linguistic knowledge it's effectively encoded. The problem with this is: It can't reason about new things (because it can't actually reason - much), makes it appear smarter than it is, which IMO largest danger in applied ML today, especially to those less familiar with it's limitations, it looks like magic, and people start mandating it be used for sensitive things.
Imagine the mistakes that can be made by changing one fact but not reconfiguring the whole network.
Thhese guys remind me of when I used to change EXEs in hex editors then notice "unrelated" weird glitches.
Make a 'plugin'[1] so a model can choose output such that it modifies itself.
It could work like this:
User: What is my favourite food?
AI: Your favourite food is pizza.
User: You are wrong. I prefer pasta.
AI:
{plugin_name: 'update_fact',
prefix_text: 'your favourite food is '
updated_response: 'pasta'}
AI: Thanks for letting me know - I've now remembered that permanently, and won't mess up again!
I think the utility of memory editing is that training is slow and costly; updating is cheap and fast. Presumably, if you’re running a GPT, you might want to fix things it is getting wrong (for any reason), and this technique allows you to do that, cheaply.
Earlier quoted context omitted.
That example is wild. But I’m still pretty awed by the fact that we make similar verbal mistakes. The temporal reasoning in these models is getting better than me. As a non-AI model, I notice this every single morning while I have my covefe while heeding the latest on slacker news.
I'm still not convinced they are capable of temporal reasoning. I've asked it temporal questions before but without explicitly mentioning the temporal nature... the answers tend to contradict themselves if they haven't already seen the question before (even when querying general knowledge), until you point out the temporal component, even then it trips up and cannot build upon this reasoning in my tests. I suspect a…
See, my reaction has been, “perhaps our reasoning and actions are pretty much just a biologically-encoded statistical model too, it just doesn’t _feel_ that way because of some other factor.”
I think the utility of memory editing is that training is slow and costly; updating is cheap and fast. Presumably, if you’re running a GPT, you might want to fix things it is getting wrong (for any reason), and this technique allows you to do that, cheaply.
But training to update a single fact isn't slow and costly. finetuning for just a few facts could be done in a matter of seconds with just a few text examples of the correct answer.
Earlier quoted context omitted.
That example is wild. But I’m still pretty awed by the fact that we make similar verbal mistakes. The temporal reasoning in these models is getting better than me. As a non-AI model, I notice this every single morning while I have my covefe while heeding the latest on slacker news.
I'm still not convinced they are capable of temporal reasoning. I've asked it temporal questions before but without explicitly mentioning the temporal nature... the answers tend to contradict themselves if they haven't already seen the question before (even when querying general knowledge), until you point out the temporal component, even then it trips up and cannot build upon this reasoning in my tests. I suspect a…
GPT-3 or 4 ?
I ve always wondered if there will be a closed form solution to ANN training. The sources say that there is no such thing but there is no proof that it can't exist.
In the most general case, it has already been shown that mathematical optimization is NP-hard. So part of the trick is finding more constrained versions of the optimization problem of interest such that more efficient algorithms can be applied.
In many ways, that’s the success story of deep neural networks. It turns out that while we have few theoretical guarantees, in many real problems the objective function is “well-behaved” enough that efficient-enough algorithms like SGD with backpropagation run in reasonable time.