AIFoom Debate - conclusion?

Did Google actually say how long it took to train Alpha Go?

They did. In the methodology part they give an exact breakdown of how much wallclock time it took to train each step (I excerpted it in the original discussion here or on Reddit), which was something like 5 weeks total IIRC. Given the GPU counts on the various steps, it translated to something like 2 years on a regular laptop GPU, so the parallelization really helped; I don't know what the limit on parallelization for reinforcement learning is, but note the recent DeepMind paper establishing that you can even throw away experience-replay entirely if you go all-in on parallelization (since at least one copy will tend to be playing something relevant while the others explore, preventing catastrophic forgetting), so who knows what one could do with 1k GPUs or a crazy setup like that?

First, at least it establishes a minimum. If an AI can learn the basics of English in a day, then it still has that much of a head start against humans. Even if it takes longer to master the rest of language, you can at least cut 3 years off the training time, and presumably the rest can be learned at a rapid rate as well.

It also establishes that AI can teach itself specialized skills very rapidly. Today it learns the basics of language, tomorrow it learns the basics of programming, the day after it learns vision, and then it can learns engineering nanotechnology, etc. This is an ability far above what humans can do, and would give it a huge advantage.

Finally, even if it takes months, that's still FOOM. I don't know where the cutoff point is, but anything that advances at a pace that rapid is dangerous. It's very different than than the alternative "slow takeoff" scenarios where AI takes years and years to advance to superhuman level.

We do? I wasn't aware of them... do you know of any example?

For a dissenting view, there's e.g. jacob_cannell's recent comment about the implications of AlphaGo.

Critics like to point out that DL requires tons of data, but so does the human brain. A more accurate comparison requires quantifying the dataset human pro go players train on.

A 30 year old asian pro will have perhaps 40,000 hours of playing experience (20 years 50 40 hrs/week). The average game duration is perhaps an hour and consists of 200 moves. In addition, pros (and even fans) study published games. Reading a game takes less time, perhaps as little as 5 minutes or so.

So we can estimate very roughly that a top pro will have absorbed between 100,000 games to 1 million games, and between 20 to 200 million individual positions (around 200 moves per game) .

AlphaGo was trained on the KGS dataset: 160,00 games and 29 million positions. So it did not train on significantly more data than a human pro. The data quantities are actually very similar.

Furthermore, the human's dataset is perhaps of better quality for a pro, as they will be familiar with mainly pro level games, whereas the AlphaGo dataset is mostly amateur level.

The main difference is speed. The human brain's 'clockrate' or equivalent is about 100 hz, whereas AlphaGo's various CNNs can run at roughly 1000hz during training on a single machine, and perhaps 10,000 hz equivalent distributed across hundreds of machines. 40,000 hours - a lifetime of experience - can be compressed 100x or more into just a couple of weeks for a machine. This is the key lesson here.

I tried to explain it in my recent post, that on current level of technologies human level AGI is possible, but foom is not yet, in particular, because some problems with size, speed and the way neural nets are learning.

Also human level AGI is not powerful enough to foam. Human science is developing but in includes millions of scientists; foaming AI should be of the same complexity but run 1000 times quicker. We don't have such hardware. http://lesswrong.com/lw/n8z/ai_safety_in_the_age_of_neural_networks_and/

But the field of AI research is foaming with doubling time 1 year now.

I'm asking for references because I don't have them. it's a shame that the people who are able, ability-wise, to explain the flaws in the MIRI/FHI approach, actual AI researchers, aren't able, time-wise, to do so. It leads to MIRI's views dominating in a way that they should not. It's anomalous that a bunch of amateurs should become the de facto experts in a field, just because they have funding , publicity, and spare time.

The tl;dr is what I wrote: learning cycles would be hours or days, and a foom would require hundreds or thousands of learning cycles at minimum.

Much depends on what you mean by "learning cycle" - do you mean a complete training iteration (essentially a lifetime) of an AGI? Grown from seed to adult?

I'm not sure where you got the 'hundreds to thousands' of learning cycles from either. If you want to estimate the full experimental iteration cycle count, it would probably be better to estimate from smaller domains. Like take vision - how many full experimental cycles did it take to get to current roughly human-level DL vision?

It's hard to say exactly, but it is roughly on the order of 'not many' - we achieved human-level vision with DL very soon after the hardware capability arrived.

If we look in the brain, we see that vision is at least 10% of the total computational cost of the entire brain, and the brain uses the same learning mechanisms and circuit patterns to solve vision as it uses to solve essentially everything else.

Likewise, we see that once we (roughly kindof) solved vision in the very general way the brain does, we see that same general techniques essentially work for all other domains.

There is just no plausible way for an intelligence to magic itself to super intelligence in less than large human timescales.

Oh thats easy - as soon as you get one adult, human level AGI running compactly on a single GPU, you can then trivially run it 100x faster on a supercomputer, and or replicate it 1 million fold or more. That generation of AGI then quickly produces the next, and then singularity.

It's slow going until we get up to that key threshold of brain compute parity, but once you pass that we probably go through a phase transition in history.