The "trunk learning rate" is set at 0.1x the learning rate for the experts, so learning on different subjects disproportionately happens in the experts, and the trunk portion is comparatively more stable. But the population of experts can grow and shrink:
> The pool grows when it is short of capacity and shrinks when parts of it stop being asked for.
So:
- doesn't the trunk then _eventually_ still undergo catastrophic forgetting, it just may take much longer?
- and before that point, catastrophic forgetting happens in stepwise chunks whenever the expert pool shrinks?
Where it seems to fail, by design, on this goal is in delivering continual learning that is more than just "memorization with LRU catastrophic forgetting".
That said, props to the author for thinking different and actually implementing something. Maybe the project can grow into something more, or inspire different ideas, if they continue to work on it.
That might explain why there are no benchmarks of any kind.
--- chess ---
prompt: '<g>1700 1-0 1. e4 e5 2. '
[raw] repeated 8-grams 83%
Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Nxd5 Rxd5 Rxd5 Rxd5 Rxd5. Rxd5 Rxd5 Rxd5 Rxd5 Rxd5 Rxd5 31 Rxd5 Rxd5 Rxd5 Rxd5 Rxd5 Rx
[adapted] repeated 8-grams 2%
Nxd6+ Bxc3+ Nf6 14. Qxd5+ Nf6 Rxe3+ Bh1 Nxd5+ Qxc6 Rf3+ Nxd5 Qe4+ Rxf6 Nh1+ Qxd5 Rf3+ Nxe6 Qh1+ Rxd5 Nf6+ Qxe3 Rh1+ Nxd5 Qf6+ Rxg3+ Ne1 Qxd5
All the rest of it is similarly gibberish. I'm used to model training garbage but this is in no sense AGI. It's beyond nonsense to call it that.
As a professor who published on continual learning I'm leaning towards agreement[1]. It lacks any substance. No relation to related work, no description of algorithm, no ablation study, just hand-waving that we're feeding some data and "Chess is not forgotten".
This "how-continual-learning-works" markdown text is not an algorithm [2].
[1] https://arxiv.org/abs/2301.12530
[2] https://github.com/volotat/mini-AGI/#how-continual-learning-...
There are no benchmarks published as the model is heavily undertrained, but it is learning. And you can see this clearly in the loss and samples even though they are still barely coherent.
I am not an academic and am not trying to publish a paper about a “major breakthrough” or something like this. I am just a small person who found a cool thing that clearly works and wants to share it with the world. That’s it.
Please get a model to the point where it seems like it has some natural language understanding and then share again with reasonable characterization.
I had ideas not completely unlike this so long ago, but one big difference can be summed up in one of your parameters.
>Directories are walked, binaries are skipped . . . and each file is read from its beginning to its end because a document has an order.
For me it was binaries being walked because text and anything approaching a language model was so much further out-of-reach having such limited computer power.
"The model reads 524,000 characters of chess". This is 100KByte of training data in a toy model with rigid parameters and no global learning. Gap with real LLM and trillions of tokens.
This model really addresses the problem of preserving previously learned knowledge, but by restricting the LR of the trunk it stops acquiring new knowledge. Details: "Rethinking the Stability-Plasticity Trade-off in Continual Learning from an Architectural Perspective"
Our brain also has some capacity limit, and maybe degraded memory performance over time, but in either case it's a graceful degradation - you may forget fine details of things that happened a long time ago etc, but you don't forget how to ride a bike just because it's been a while.
Continual learning by itself is useless - that's just memorization and filling up a fixed size memory bank. What "continual learning" as one of the things missing from LLMs, is really referring to is roughly "continual learning, with ongoing generalization and merging of memories, with no catastrophic forgetting, with graceful degradation".
Sounds a bit like real-time "distillation" to me.
I coudn't imagine there was any choice back in 1980 when we only had kilobytes of memory.
nm, I found it:
> RTX 3070 Laptop GPU with 8 GB
Super impressive.
It's an interesting idea, but it doesn't really do anything interesting yet. I looked at the output in the training run and it is a far, far cry from intelligence. Worse than GPT-2 as it stands.
I do hope it will perform well when scaled and trained, though; best of luck.
I want to have an agent that thinks continually/non-stop. Imagine a loop of "train of thought" that goes into the LLM and then out. Keep it going so that it "rumiates" thr way we do.
Then, add some sort of "messages" or IRQs when I want to communicate with it. To ask it things and whatnot. I think that sort of cycle in addition to this learning you are doing is what is missing for real AGI.
Is highly misguided.
While the platonic ideal of Lt Commander Data is appealing, The parable of funes the memorious (Jorge Luis Borges) comes to mind.
Why settle for anything less?
Before they had personal computers I always figured the established computer experts were going to get their mainframes to do AI a lot sooner than it turned out.
That was a non-starter though, not many people could afford huge amounts of hardware in a centralized datacenter where you don't have unlimited access. How was the next level of progress supposed to occur if you didn't fully own the electronics?
It was pretty impressive when it required a forklift to move the CPU, but kind of forbidding too.
PCs took over fast for that very reason and rapidly became more powerful until they were quite capable of incredible amounts of automation well over 20 years ago for so many things.
The whole time since the mainframe days the thing that has held true for AI in automation, is that it has to take the same powerful, sophisticated PC hardware that already works so well without AI, and bring that to the next level in logical progression.
No dependency on remote data, remote storage, network or internet at all. Otherwise why bother?
As long as AI can not make the same stand-alone hardware outperform what it's already capable of beforehand, there's quite a bit more work to do.
An idealized automation workflow on a new PC without AI:
Insert blank SSD > Install OS > Install automation app > Program app > Run automaton
Same stand-alone hardware, with AI:
Insert blank SSD > Install OS > Install AI app > Train app > Run automaton
If AI can't make the same hardware run smarter, it's not as intelligent as it could be, is it?
I don't need AGI, I just need this.
What I did test though is reading 524K characters of chess data only and see how other domains have degraded. The results are in the readme under "How continual learning works" section. Spoiler: it just barely degraded the performance.
The difference might be smaller on a CPU which has limited parallelism.
But it's basically equivalent to a very deep model which might be problematic for training.
The concept is as follows: You train a critic to mimic the datastream and then you train against the critic instead of training against the data. The idea behind this is that the critic will memorize the training data so you do not need to store the full training data anymore. One of the biggest issues with current online stochastic gradient descent is that it is inherently a memory-less technique where the training data acts as the memory.
You can spin this further by going deeper with the nesting and then dropping the supervised critic. I forgot how to put it in words but the goal is that by having a model train against a critic of the critic, you can then drop the top level critic and instead use the mid level critic itself as your meta learning objective to train the actor against an unlabeled data stream.
Top level critic: learns to mimic the labeled training data via online SGD, then you add a simple hand written loss function to compare the predicted output with a given input. Basically you build a model specifically for distillation. Mid level critic: learns a reward function that mimics the top level critic directly but only gets to see the unlabeled training data and the result of the top level critic. Actor: The actor is exclusively trained against the mid level critic
Through this concept you end up with the existing training data stored as objective inside the mid level critic so you end up training not only against the latest data but also the already memorized data which should lower catastrophic forgetting. Of course at some point you might need to update the mid level critic again and to avoid that you might get away with just adding a very very wide Linear RNN / State Space Model / Mamba / Gated Delta Net as the middle critic (shower thought: use internal RNN states to represent LoRA vectors).
What motivated you decide to release this. OpenAI or Anthropic will just hoover it up, maybe scale it up and use it if they are interested.
You probably won't know if they do, and the chance they will give you something back is near zero. Why did you release rather than try to scale and build yourself?
(I've been working on some thing, not similar, but not dissimilar in goal - and I just can't get over the fact that tech will steal without giving back)
I also doubt it is really that valuable on the OpenAI/Anthropic scale, at the same time if people will use it and it will work for them on the personal scale it is already a major win for me. New ideas and optimizations I could never have thought of might bring this up from a toy model to an actually useful model trained locally. Then people could add RL and RLHF and other cool things to it to make it even better.
The first starts with: Thanks for your response. The second starts with: Hey, do me a favor And the third starts with: I had no idea HN censors
If you can see all three - then you can also see the censorship by starting a private browser and looking at this story.
There will only be the second and the third.
I've tested in private sessions on firefox and safari and through a few different ip proxies.
I'm going to do some testing in other stories/comments to see whether they detect words, or general sentiment. And I'll get some statistics on if this is predictable censorship, a one time deal, or whether I just hit a race condition in their code somewhere.
Maybe hit up hn@ycombinator.com and ask to remove your shadowban of sorts. Idk how effective is that.
Anyway something more concerning is that you have at least two flagged comments (in the first page at least). Flagged comments means your comments violated HN rules, at least according to other HN users. It's hard to understand the HN social etiquette but in short, if you are being mean to other people, you might get flagged. So it's largely about the tone of your comments rather than their substance. You may want to read the guidelines (the section "in comments") here https://news.ycombinator.com/newsguidelines.html