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Their careful comparison of the efficiency of AI vs the human brain is hilariously absurd to me. It seems to assume a disembodied "brain in a vat" model, as if

Their careful comparison of the efficiency of AI vs the human brain is hilariously absurd to me. It seems to assume a disembodied "brain in a vat" model, as if brains aren't ordinarily attached to a large clump of flesh that typically attempts to accumulate as many resources as it can, and often enjoys combusting large quantities of fossil fuels hurtling around in various metal enclosures. I suppose we could be very efficient at image classification once AWS announces their new h5.large "MT bare human" instances, where gigantic banks of people are set to work solving CAPTCHAs with their incredible performance per watt specs. Or we could redo the analysis with other cherry-picked measures and find the "human architecture" is orders of magnitude less efficient at multiplying large integers, and come to the conclusion that we should replace all babies (which have a wasteful decades-long training period) with ARM chips.

Jensson 4y ago
> once AWS announces their new h5.large "MT bare human" instances Amazon already did that, it is called mechanical turk. https://www.mturk.com/ https://www.mturk.com/ It costs a lot of money because we think those computers should have rights. But under more permissive legislations it would be so cheap and accessible that we wouldn't need to do much of the modern AI research. I assume this is what they meant, with slavery there would be much less need to do AI research, self driving cars is hard but slave driven cars is easy, the whole point of AI is that it lets us create slaves that we don't feel bad about abusing.
- boloust 4y ago
  > Amazon already did that, it is called mechanical turk. That's what the "MT" stands for :) > the whole point of AI is that it lets us create slaves that we don't feel bad about abusing. Yes, that's exactly right. The article glibly compares the energy efficiency of AI with the human brain, completely ignoring any other concerns, and pretends it means something. Playing by those sets of rules would enable the sorts of analyses I outlined above.
hgomersall 4y ago
Your interesting take does ignore the fundamental point however that the human brain requires both far less energy and far less training data to learn. The takeaway for me is that there is huge scope to improve our machine learning techniques.
- lajamerr 4y ago
  That energy efficiency was paid for by the total energy of the biological chain of events that eventually led to that human in the form of their DNA. We humans don't start from a blank/random slate like ANNs when we are born. We might be efficient in some regards but we surely didn't get there efficiently.
  - hgomersall 4y ago
    And nor should ML systems be a blank slate, which is really my point.
- boloust 4y ago
  I didn't really want to get into the specifics of the article, since I believe the entire analysis is fundamentally misguided. But I think the article's specific claims about image recognition (they use the example of identifying pictures of cats) are completely false: - The article claims models must be retrained from scratch if they are to identify cartoon cats instead of real cats. This is absolutely false and transfer learning is old hat by machine learning standards. - The article cites the fact that an image recognition model "requires many weights and lots of multiplication", as an example of inefficiency, as if human brains don't encode trillions of weights in the connections between their billions of neurons. - If AI models could only identify a few hundred photos per hour, this would undoubtedly be used as evidence of inefficiency. By the same token, if we consider the cost of training amortized over the subsequent cost of image identification, image recognition models are are far more efficient at scale than humans, even if we grant massively overstated estimates of training costs. - All humans must go through an incredibly expensive, labor-intensive training process, before which they will completely fail to reliably identify a cat. - Humans required billions of years, the death and suffering of trillions of organisms, the terraforming of an entire planet, and vast amounts of energy to run a stupidly inefficient Monte Carlo "pretraining" phase. I can't help but come away with the feeling that I've been trolled. They managed to pick image recognition, an area where efficient AI models approach or exceed human performance, to prop up an absurdly myopic comparison that wouldn't make sense even if they got any of the details right.

Jensson 4y ago

> once AWS announces their new h5.large "MT bare human" instances Amazon already did that, it is called mechanical turk. https://www.mturk.com/ https://www.mturk.com/ It costs a lot of money because we think those computers should have rights. But under more permissive legislations it would be so cheap and accessible that we wouldn't need to do much of the modern AI research. I assume this is what they meant, with slavery there would be much less need to do AI research, self driving cars is hard but slave driven cars is easy, the whole point of AI is that it lets us create slaves that we don't feel bad about abusing.

boloust 4y ago
> Amazon already did that, it is called mechanical turk. That's what the "MT" stands for :) > the whole point of AI is that it lets us create slaves that we don't feel bad about abusing. Yes, that's exactly right. The article glibly compares the energy efficiency of AI with the human brain, completely ignoring any other concerns, and pretends it means something. Playing by those sets of rules would enable the sorts of analyses I outlined above.

hgomersall 4y ago

Your interesting take does ignore the fundamental point however that the human brain requires both far less energy and far less training data to learn. The takeaway for me is that there is huge scope to improve our machine learning techniques.

lajamerr 4y ago
That energy efficiency was paid for by the total energy of the biological chain of events that eventually led to that human in the form of their DNA. We humans don't start from a blank/random slate like ANNs when we are born. We might be efficient in some regards but we surely didn't get there efficiently.
- hgomersall 4y ago
  And nor should ML systems be a blank slate, which is really my point.
boloust 4y ago
I didn't really want to get into the specifics of the article, since I believe the entire analysis is fundamentally misguided. But I think the article's specific claims about image recognition (they use the example of identifying pictures of cats) are completely false: - The article claims models must be retrained from scratch if they are to identify cartoon cats instead of real cats. This is absolutely false and transfer learning is old hat by machine learning standards. - The article cites the fact that an image recognition model "requires many weights and lots of multiplication", as an example of inefficiency, as if human brains don't encode trillions of weights in the connections between their billions of neurons. - If AI models could only identify a few hundred photos per hour, this would undoubtedly be used as evidence of inefficiency. By the same token, if we consider the cost of training amortized over the subsequent cost of image identification, image recognition models are are far more efficient at scale than humans, even if we grant massively overstated estimates of training costs. - All humans must go through an incredibly expensive, labor-intensive training process, before which they will completely fail to reliably identify a cat. - Humans required billions of years, the death and suffering of trillions of organisms, the terraforming of an entire planet, and vast amounts of energy to run a stupidly inefficient Monte Carlo "pretraining" phase. I can't help but come away with the feeling that I've been trolled. They managed to pick image recognition, an area where efficient AI models approach or exceed human performance, to prop up an absurdly myopic comparison that wouldn't make sense even if they got any of the details right.