It's a question of training. AlphaGo was not trained agains adversarial attacks. If it was the whole family of attacks wouldn't work, and new adversarial traning would be order of magnitude more difficult. It's a shield and sword again.

There are ~10^170 valid board states for Go, and roughly 10^80 atoms in the observable universe. So even with a universe sized computer, you still wouldn't come close to having the compute power for that.

AlphaGo uses neural nets to estimate the utility of board states and a depth limited search to find the best move.

SSL is the synonym for Semi-Supervised Learning. What you refer here is Self-Supervised Learning, which is related to unsupervised learning

That just sounds like a bug. It might take a lot less effort to report it for patching than rewrite all your own code.

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The computer doesn't compute all the moves and doesn't know the exact mathematically best move. It uses digital neurons to infer rules from a huge number of games and find very good moves. I call this intelligence (artificial intelligence)

Unplug the machine!

We are often using neural networks whose training is finished. The weights are fixed for this attack to work. This is obvious, but I would like to underline the fact that biological neural networks are never fixed.

Fixed variable cost analysis is crucial. 15k is very high. If you put 10 skilled workers on it for a year, plus development labs, it would cost about $1.2 million, including outsourcing to specialist engineers to refine the CAD files.

At high volumes, like 4000 units, that is divided to $300 RnD per unit. Obviously, it would benefit from a 2-3 million dev budget though.

The bill of materials is 3000, The metal welding is $500 and the assembly is another 500, so an open source kit would be less than 4000 dollars, and a fully built kit would also be 5000.

Husqvarna and roomba companies sell by market price, not the production price, so they can markup a high value, and they use custom circuit boards, custom plastic moulds including big thermoplastic pieces.

Is it really intelligence or just computer knowing all best possible moves

Nobody actually has a good solution to adversarial attacks yet.

The problem is not just this specific strategy. It's that, if you can give arbitrary inputs and outputs to a neural network, you can run an optimization process against it to find minimally-disruptive inputs that will make it fail. You can fool an image classifier by imperceptibly changing the image in just the right ways.

It's possible this is just a fundamental vulnerability of neural networks. Maybe the brain is vulnerable to this too, but it's locked inside your skull so it's hard to run an optimizer against it. Nobody knows, more research is needed.

Doesn’t this also make it essentially impossible to compare the two figures you’ve shown?

TL;DR they trained an adversarial attack against AlphaGo. They used an optimizer to find scenarios where the network performed poorly. Then a human was able to replicate these scenarios in a real game against the AI.

The headline is kinda BS imo; it's a stretch to say it was beat by a human since they were just following the instructions from the optimizer. But adversarial attacks are a serious threat to deploying neural networks for anything important, we really do need to find a way to beat them.

The difference is, they'll be able to easily train the model forward a slight bit to deal with this. Or add a few lines of code for it. Easily defeated issue.

The human beat it this time.... After 7 years.

But, after this... Its not like the humans improve. That vulnerability gets stamped out and that's it

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Not for Go, there are too many states. They used machine learning and self-play to prune the number of states evaluated.

The difference is, the human gets tired and the AI can play 10,000,000 games simultaneously nonstop

Humans have zero chance haha

I thought there was a computer that could just compute all possible go board states? Was that not the case?

SSL doesn't require human labels, thus it is unsupervised learning

Looks like text-embedding-ada-002 is already on the MTEB leaderboard! It comes in at #4 overall, and has the highest performance for clustering.

You might also want to look into SentEval, which can help you test the embedding performance on a variety of tasks: https://github.com/facebookresearch/SentEval

> In reality you can easily fit the 65B version in 2 A100 with 100G of VRAM.

Ughhh are you telling me I have to SSH into my DGX 100 instead of just using my local machine with 1 A100? (Satire I am a broke student)

Appreciate the implementation and transparency. I don't think many people realize how big a 65B parameter model is since there's no associated cost with downloading them.

Thanks, to be really convinced though I would want to see the real world examples. Like a sample of where OpenAI did well, and where sentence transformers did well. Frankly if it doesn’t out perform sentence transformers I would be a bit disappointed given the larger size and all.

Not sure about vanilla-llama but at the moment you can run LLaMA-13B at 4bit with >10GB of VRAM, so your 3080ti can run it.

To run 30B at 4bit, you need at least 20GB of VRAM. If your motherboard supports SLI, you can use nvlink to share the VRAM between your two GPUs and have a collective 20GB, which would let you run the 30B model provided you have enough system RAM.

Not sure if I can post the link to the tutorial here but Google "rentry Llama v2" and click the "LLaMA Int8 4bit ChatBot Guide v2" result for the most up-to-date tutorial to run it.

Well that was pretty much the reason I did this test. And right now I'm leaning toward SentenceTransformers.

model_ar.fit().predict(test)

Usually people do

res=model_ar.fit()

res.summary()

preds = res.predict(test)