LessWrong 2.0 Reader

You might find my notes of interest.

I think this only holds if fine tunes are composable, which as far as I can tell they aren't

You know 'finetunes are composable', because a finetune is just a gradient descent step on a batch of data and a parameter update, and if you train on more than one GPU and share updates, DL training still works {{citation needed}}.

If you can train asynchronously on a thousand, or 20,000, or 100,000 GPUs, that is what you are doing; this is especially true in DRL, where you might be, say, training across 170,000 CPU-cores. (You are certainly not accumulating the gradient across the entire cluster, and calculating a single global step as if it were a single GPU!)

You do have issues with stale gradients and off-policyness of updates and how to best optimize throughput of all of the actors vs training nodes and push out model updates efficiently so nodes stop executing outdated parameters as quickly as possible, and DeepMind & OpenAI etc have done a lot of work on that - but at that point, as in the joke, you have conceded that finetunes are composable and you can keep a very large number of replicas in sync, and it is merely a matter of haggling over how much efficiency you lose.

Also note that it takes a lot less compute to keep a model up to date doing simple online learning on new data than it does to train it from scratch on all historical data summed together (obviously), so what devrandom is talking about is actually a lot easier than creating the model in the first place.

A better model to imagine is not "somehow finetunes from millions of independent models magically compose" (although actually they would compose pretty well), but more like, "millions of independent actors do their ordinary business, while spending their spare bandwidth downloading the latest binary delta from peer nodes (which due to sparsity & not falling too far out of sync, is always on the order of megabytes, not terabytes), and once every tens of thousands forward passes, discover a novel or hard piece of data, and mail back a few kilobytes of text to the central training node of a few thousand GPUs, who are continually learning on the hard samples being passed back to them by the main fleet, and who keep pushing out an immediately updated model to all of the actor models, and so 'the model' is always up to date and no instance is more than hours out of date with 'the model' (aside from the usual long tail of stragglers or unhealthy nodes which will get reaped)".

(likely conditional on some aspects of the training setup, idk, self-supervised predictive loss function?)

Pretraining, specifically: https://gwern.net/doc/reinforcement-learning/meta-learning/continual-learning/index#scialom-et-al-2022-section

The intuition is that after pretraining, models can map new data into very efficient low-dimensional latents and have tons of free space / unused parameters. So you can easily prune them, but also easily specialize them with LoRA (because the sparsity is automatic, just learned) or just regular online SGD.

But yeah, it's not a real problem anymore, and the continual learning research community is still in denial about this and confining itself to artificially tiny networks to keep the game going.

Q. "Can you hold the door?" A. "Sure."

That's straightforward.

Q. "Can you play the violin at my wedding next year?" A. "Sure."

Colloquial language would imply not only am I willing and able to do this, I already know how to play the violin. Sometimes, what I want to answer is that I don't know how to play the violin, I'm willing to learn, but you should know I currently don't know.

Which I can say, it just takes more words.

I'll be there! Talk to me about boundaries and coordination/Goodness

There's also the problem of: what do you mean by "the human"? If you make an empowerment calculus that works for humans who are atomic & ideal agents, it probably breaks once you get a superintelligence who can likely mind-hack you into yourself valuing only power. It never forces you to abstain from giving up power, since if you're perfectly capable of making different decisions, but you just don't.

Another problem, which I like to think of as the "control panel of the universe" problem, is where the AI gives you the "control panel of the universe", but you aren't smart enough to operate it, in the sense that you have the information necessary to operate it, but not the intelligence. Such that you can technically do anything you want--you have maximal power/empowerment--but the super-majority of buttons and button combinations you are likely to push result in increasing the number of paperclips.

I think this is also what I was confused about -- TurnTrout says that AIXI is not a shard-theoretic agent because it just has one utility function, but typically we imagine that the utility function itself decomposes into parts e.g. +10 utility for ice cream, +5 for cookies, etc. So the difference must not be about the decomposition into parts, but the possibility of independent activation? but what does that mean? Perhaps it means: The shards aren't always applied, but rather only in some circumstances does the circuitry fire at all, and there are circumstances in which shard A fires without B and vice versa. (Whereas the utility function always adds up cookies and ice cream, even if there are no cookies and ice cream around?) I still feel like I don't understand this.

Is a lot of the effect not "people who read ACX trust Scott Alexander"? Like, the survey selects for most "passionate" readers, those willing to donate their free time to Scott for research with ~nothing in return. Him publicly stating on his platform "I am now much less certain of X" is likely to make that group of people be less certain of X?

Hey Jacob + Philippe,

Hope you all don't mind but we put up layer 8 of your transcoders onto Neuronpedia, with ~22k dashboards here:

https://neuronpedia.org/gpt2-small/8-tres-dc

Each dashboard can be accessed at their own url:

https://neuronpedia.org/gpt2-small/8-tres-dc/0 goes to feature index 0.

You can also test each feature with custom text:

Or search all features at: https://www.neuronpedia.org/gpt2-small/tres-dc

An example search: https://www.neuronpedia.org/gpt2-small/?sourceSet=tres-dc&selectedLayers=[]&sortIndexes=[]&q=the%20cat%20sat%20on%20the%20mat%20at%20MATS 

Unfortunately I wasn't able to generate histograms, autointerp, or other layers for this yet. Am working on getting more layers up first. 

Verification
I did spot checks of the first few dashboards and they seem to be correct. Please let me know if anything seems wrong or off. I am also happy to delete this comment if you do not find it useful or for any other reason - no worries.

Please let me know if you have any feedback or issues with this. I will be also reaching out directly via Slack.

You can! Just go to the all-posts page, sort by year, and the highest-rated shortform posts for each year will be in the Quick Takes section: 

2024: 

2023: 

2022: