[b112]

I get what the author was trying to say, but it's still -- a very limited view. Mostly because of the last bit (better/faster).

Birds are to planes, as humans are to cars. Yet can a car leap over barricades, climb mountains, trees, self-repair, turn on a dime, stop instantly, etc, etc?

A plane cannot maneuver like a bird, take off in crazy weather conditions, land on a dime in a tree, stop almost instantly in flight, and change direction, etc.

I think what you've quoted has a lot of value here, for, what we should expect from an artificial brain, isn't a human brain. This is truth. However, while it may be faster in a specific capacity, but it won't have the same characteristics.

So yes, expecting it to be like a human brain doesn't make sense.

Yet better/faster? I don't think we can compare this, they're too different.

(which is really the quote's point, but I just didn't like the better/faster bit at the end...)

[nn3]

Also birds (and insects/bats/pterosaurs) flight is a lot more energy efficient than any plane. Today's deep learning is essentially brute force, burning thousands of watts for anything more complicated which a single human brain can often do in ~15Watts.

The advanced models like GPT-3 are burning millions of watts in the cloud but they're not that much better than what a brain can do (and in many ways worse, as in often requiring supervised learning)

That's the key point. The algorithms need to become more energy efficient to make significant leaps, thus become more like brains.

[ant6n]

Also, birds produce themselves out of an egg, with only food, water and air as production input. They also can produce more of themselves with minimal input. They are also self-repairing/maintaining, something planes cant do.

[qlk1123]

> single human brain can often do in ~15Watts

There were similar arguments when AlphaGo showed up and beat master Go player Lee Sedol, but is power(in Watt) the right measurement? I always feel like it should be the total energy(in J or Cal) required to transform a computing device like biological brain or electrical computer from knowing nothing to being capable of a skill like Go game. In such sense, deep learning is still more energy efficient than human.

[littlestymaar]

Lee Sedol's lifetime energy consumption for all biological process is around 50MWh, Alpha Go consumed more than 100kW and was likely to have been trained more than 500h, so even counting all the energy Sedol spent eating, dancing or having sex ends up being less than the amount of energy spent to train the machine used to beat him.

[starpilot]

No, it's not. What's your comparison? Are there birds that can carry 80,000 lb of passenger + cargo weight? Condors fly like fixed-wing aircraft for 99% of their flight, hummingbirds fly more like insects. There isn't one type of bird flight.

This whole HN discussion of bird flight is a trainwreck and reflects massive gaps in understanding of aerodynamics. This is '00s "computer virus news report" level competence in this subject.

[sterlind]

We understand the aerodynamics of bird flight, and used it to make fixed-wing planes optimized for carrying lots of cargo. Once we understand the principles behind intelligence, we can make very efficient AI optimized for our usage. But we're still at the point where we don't understand intelligence as well as we understood aerodynamics when building the first planes, so we still have a lot to learn from "birds" - animal brains.

[webmaven]

> But we're still at the point where we don't understand intelligence as well as we understood aerodynamics when building the first planes

Actually, I'd say that our understanding of intelligence is right about at the level of aerodynamics at the dawn of heavier than air flight:

https://youtu.be/Sp7MHZY2ADI

https://youtu.be/gN-ZktmjIfE

I mean, we could quibble about exactly where we are pre- or post-Wright Flyer, but given the amount of AI research that amounts to brute-force flailing about in search of incremental improvements, disagreements on the importance of "biological plausibility" and so on, it's pretty clear that, roughly speaking, AI is currently somewhere in the equivalent of the Lilienthal-Langley-Wright-Curtis continuum (ie. 1890-1910-ish) and still prior to the most important theoretical breakthroughs. IOW, AI has not in my opinion yet achieved an equivalent to aerodynamics' Prandtl lifting-line theory: https://en.m.wikipedia.org/wiki/Lifting-line_theory

[frongpik]

I believe AI will start as a basic principle or idea that can be applied to any sufficiently big state machine that controls e.g. an RC airplane or traffic lights. That idea will be obvious in a hindsight. I'd even make a guess that it will be like a "stateful" state machine that accumulates state in a particular manner and uses that to control the underlying state machine. We still will be nowhere near understanding intelligence, but that clever trick will be enough in most cases.

[2-tpg]

> The question of whether a computer can think is no more interesting than the question of whether a submarine can swim. --- Dijkstra

Better/faster we would not directly compare to humans, but to benchmarks and timed experiments.

LeCun is saying to treat "intelligence" the same as "flight" or "swimming". It is a matter of function, not a matter of a specific instantiation on a biological substrate. You don't need to recreate flapping wings to gain "flight", you can strap a combustion engine on a cylinder and beat all birds on earth in regards to speed. You don't say "we don't have flight yet", because an airplane is not able to land on a tree branch. Maybe we don't have yet all the components and aspects of "flight", but this is not a show stopper, and drones have come a long way.

Now the more interesting question becomes: What are the laws of aerodynamics for intelligence?

Aside: I think it is absolutely insane that a conference workshop with papers yet to go through peer-review, is highlighted as a popsci article on VentureBeat. That's such a narrow workshop, that even researchers in the field may be unaware of it. And now these get to read the paper summaries from a HN-story. "the centre cannot hold".

Aside II: Yann LeCun talk from 2019 about this subject (better to debate the source ;)):

> Clearly, Deep Learning research would greatly benefit from better theoretical understanding. DL is partly engineering science in which we create new artifacts through theoretical insight, intuition, biological inspiration, and empirical exploration. But understanding DL is a kind of "physical science" in which the general properties of this artifact is to be understood. The history of science and technology is replete with examples where the technological artifact preceded (not followed) the theoretical understanding: the theory of optics followed the invention of the lens, thermodynamics followed the steam engine, aerodynamics largely followed the airplane, information theory followed radio communication, and computer science followed the programmable calculator. My two main points are that (1) empiricism is a perfectly legitimate method of investigation, albeit an inefficient one, and (2) our challenge is to develop the equivalent of thermodynamics for learning and intelligence. While a theoretical underpinning, even if only conceptual, would greatly accelerate progress, one must be conscious of the limited practical implications of general theories. --- https://www.ias.edu/video/DeepLearningConf/2019-0222-YannLeC...

[deehouie]

See my link to his 2013 ICML talk above. There's a very nice photo of L'Avion III de Clement Ader, a plane modeled as a bird.

[deehouie]

The better/faster part is my embellishment :)

See my link to his ICML 2013 presentation above.