Assemble a dataset and raise a challenge on Kaggle?

If you use an evolutionary algorithm like NEAT what is the selection criteria?

I guess the point of the reward model is to approximate human feedback and instead of hiring humans to actually rank (e.g.) 1billion chats needed to update the LLM, train a reward model with 1% of them then use it to simulate human evaluators 99% of the times.

So what they (it?) evolve for?

Alliens are naturally occurring too

Retrieval should work also on entire interaction history with a particular user. Not only tracking beyond token window but having available all "interesting stuff" from users perspective.

Prior to this, have you experimenting with smaller (== more manageable) variants of this model or previous variants were attempted directly at this scale?

Search for "online random forests" there quite a few papers and articles on the subject. I assume they aren't simple usage of normal RF-s, it wouldn't worth publishing a paper on the subject.

One application I can think of is learning on edge. There is an industry fashion to embed AI inference capabilities in the newer ARM chips. The so called NPUs. Which are simplified GPUs optimized only for inference (forward passes). Such an algorithm would enable them to learn using only forward passes, hence without requiring backpropagation.

Another possibility I think is ability to train one layer at a time, which diminishes GPU memory requirements.

And probably more important it opens the gates for all kind of not yet seen network architectures, topologies and training methods that do not require fully differentiable pathways.

edit: regarding the brain inspired part.. well you can dismiss it as AI's reversed cargo cult - if it imitates some properties of the brain it should act like the brain, but I would be cautious to attribute Hinton this kind of thinking. Brains are very different from ANNs and trying to emulate their properties could provide insights on how they work.

The order of the words/tokens is normally encoded via positional embeddings that are added each to their respective token embedding. See e.g. https://machinelearningmastery.com/a-gentle-introduction-to-positional-encoding-in-transformer-models-part-1/

> David Lewandowski videos It looks like they search for some furniture

Or maybe neither. Consciousness could simply be the inwards reference frame.

Something akin to outwards reference frame yet having an opposite direction.

Couldn't that be a natural consequence of having more employees next year?

I don't know, what were your results?

Basically it's a sky Roomba

Changing it.

Seriously? Couldn't be the case the outdoor night lights only shine the road towards the candy shop?

And how would an image without any object look like?

This indeed could be one case. However a couple hundred attempts is not the limit - a kid would get it in less than a couple dozen trials or she will get bored.

However I found that some models can do it even faster. Like under 5 failures or less on 50% trials, including only 2 failures in 5% of trials.

> One easy example: if you heavily leverage tree search algorithms and have a tiny neural net eval (much smaller than stockfish nnue) it would still surpass humans even with only hundreds of games.

Any reference on that?

Yes for all living organisms their learning competencies are geared towards sample efficiency. Whoever spends a bit too much on figuring out how to solve a problem either starves to death and more likely is eaten long before that.

Is any ML model able to (learn how to) solve that? With pen and paper I can, and cannot credit 500M yr of genetic heritage for it.

Now let's figure out how to store such a pretrained model in ~700MBytes of genetic code, without disturbing the other info about how all non-brainy enzymes, organs and tissues, etc.. should be built and assembled together.

Tesla collected 780M miles of driving till 2016

A human learning to drive for 16h/day at an average speed of 30mph for 18years would have a data set of ~3M miles.

So we can say humans are at least 1000 times more sample efficient than whatever Tesla and any other autonomous driving companies are doing.