johnsmith1840

What's new? Gov been doing this to every dissident group for decades now. Funny that "left" now care about it since it's pointed at them. Making this a left vs right debate is so tiresome.

You're very smart

Obviously I ran tons of controls to rule that out. The single shot training was engineered to not be guessable.

Sounds like your just arguing a deffinition. It's storing the bias or whatever you want to call it. I think i understand it's compression, doesn't change the point.

I'm confused on how you're defining stored vs rediscovered. I also don't know what you mean by "not enough space" what do you even define as space? Feel like we're talking past each other here.

The Q&A are purposely not guessable. The only way it could answer that correctly is it must have it baked into the weights somewhere. What I'm saying is that the configuration of the weights hid that information then random chance reemerged that information again after it had forgotten and self reinforced it without any training signal to do so.

lol what?

The study was entirely about the probabalistic triggering. That's what I was after. I was actually studying one shot learning and how it learns and forgets a single training example.

More detail:

Trained like ~500 runs.

Each run was: - inference 5k times to make sure question isn't guessable - Train on one shot of a non guessable question. - inference 800 times for a % remembered

Then do this 200 times per run: - train one step on a different normal dataset - inference 800 times on the Q

The study was to use Q&A that are neither discoverable or guessable.

Pretty simple without training the one shot the model never guessed even under training the pepperoni answer

I mean, it rediscovers it because it stored it hidden from our view. Use whatever words you like though.

I saw a similar effect maybe 2.5 years ago now. I found that training an LLM hides information in the weights.

It was only ~1B model but the interesting behavior was.

Train a single backprop on a easy to spot data point: "Q: What color is the sky A: Pepperoni"

It could remember this maybe 100/800 inferences. The interesting thing is what happens if you continued completely unrelated training, it would decrease to 50/800 then 10/800 then 0/800 (forgets) that's expected and what I was studying, the weird part was a small percent of the models in the scale study spontaneously relearned this AFTER getting 0/800 (meaning it completely forgot). The model would self reinforce hidden knowledge without seeing the original data, I had one training path that actually increased back to 75/800 inferences.

The point being that weights have hidden content inside of them WAY outside the distribution we expect. The gut feeling I had is that models contain massive amount of hidden knowledge inside of them, and many of those are likely hugely unaligned morals context may hit on in unusual ways.

Fine tuning is a rats nest for this as well. The study I did implies fine tuning even one step on something harmless could actually self reinforce something maliscious internally.

Because UBI studies found it makes people perform significantly worse than without it. They didn't get better. Humans require toil, we require pain to function.

Matrix was a good point to this. They made the world a utopia and people couldn't handle it. Our monkey brains require pain give us a utopia and we'll just walle ourselves to death.

I'm not your personal AI assistant go read it yourself

For my assumed goal of yours I would say none of these are good options. A simple VM or ec2 node hosting a coding sandbox is likely the better option.

All 3 of these are vastly different tech. Confusingly, you could run all 3 of these at the same time. I know that probally doesn't help. If you want to learn more I'd go get a linux machine somewhere like aws and play around with them.

A local agent harness is a "light" sandbox in of itself you'd also be able to learn alot from.

Bubblewrap is a good tech to check out and likely a better tool if you're looking for a simpler option. App armor in linux is also nice gvisor like tool.

I've deploy gvisor, done basic test of firecracker and an honest attempt at production kata.

Firecracker and gvisor are nice systems not horrible to use, gvisor isn't quite the same security level though.

Kata is HARD to make. The technical know how to make that in production is awe inspiring. I wanted to use it but it was so complicated to integrate into a cluster I literally just gave up and mirrored raw VMs into the cluster which was alot easier actually.

Kata also breaks any potential of confidential VM unless you're a virtualization wizard.

You should go check out redhat's confidential container method for a production design overview. Their ARO self hosted system.

I take conscious to be the ability to learn over time on new things, self defining their own self. I think AI hits that on short ranges. Partially then? We're also mucking about in their "heads" quite alot so only the actual researches know what untuned models does. Internal world in a literal sense is obviosly yes due to layers.

The more I read about philosophy takes on AI the more I think of them as outdated. Apply these ideas onto reality breaks their ideas of thinking almost exactly like applying the 4 humors of medieval europe onto genetics does.

AI is cleary the "real" path/answer to how brains work even if it's in the VERY beginning stages. It's probally going to be like a 100 years before this is properly mapped onto what our brains do and then back onto an AI to truly "know" this answer.

But acting like this isn't very clearly the correct path towards that understanding and that these philisophical questions are somehow correct doesn't sit right with me. The questions themselves are after some gotcha from the psuedoscience angle that they have any idea that they actually know how a brain works.

Next time I squish a bug I will think of this.

"Runaway sentience! Emergency power shutoff!"