Yes. More specifically, the Communist Party of China is very proud of its ideological roots going back to Marx, Lenin and Mao. They call their economic system socialism with chinese characteristics. But they don't see the current stage to be the full realization of socialism, but as being in the beginning stage of building socialism.
No. Or it depends on what you mean by inspired. There is no need to build airplanes with flapping wings, but you could still say that human flight is inspired by bird flight. When we look at the brain we have no way of disambiguating which properties are implementation details and which properties play an important role in learning. We made much more progress of understanding learning in the bottom up approach, where we find from first principles what kind of computations enables us to create certain behaviors. Connections to neuroscience are mostly interesting parallels that are found post-hoc. We don't even know if the human brain is actually good at what it's doing.
>> There is no need to build airplanes with flapping wings,
As I understand it, birds don't need to flap their wings to fly. Many birds can glide for long distances, say. They flap their wings to give themselves a push and get off the ground, etc, but not to stay aloft. In other words, airplanes do work on the same principles as birds do, they just employ them in a different manner.
Similarly, the whole idea that we can reproduce human intelligence using computers is based on an understanding of human intelligence as computation, and of the brain as a computational device [1]. Without this assumption, AI would have been very difficult to justify, and I do mean AI in all its forms, from its beginnings with the Dartmouth conference and what can be called "McCarthy's project", to modern days.
For example, for most of the history of AI, the main thrust of research was on propositional and first order logic as models of human reasoning. The current wave of deep learning itself is predicated on the idea that the human brain is a kind of computer and so it can be simulated by a digital computer. The connectionists are just a little more literal in that sense, than most other AI people.
But, yes, absolutely, wa are totally trying to make artificial minds that behave just like human minds, that "flap their glia like brains" or whatever. The only problem is that we don't actually have a very good idea how human brains work- let alone the minds they produce.
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[1] These are the main ideas behind cognitive science. See the wikipedia article:
Hummingbirds flap their wings at 60 Hz or they drop like a rock. Some flying birds cannot glide at all. Flap frequency is directly related to the size of the bird, so that there's optimal matching of Reynolds number effects.
Just because you can make an airplane that works bereft of the working principles of a bird does not mean that the same fundamental principles of the biological system cannot lead you to further breakthroughs.
In fact, all your analogy really suggests taken to it's logical conclusion is that we shouldn't look at a system already created to solve new problems. You could just as easily say "There is no reason to build a helicopter or hovercraft like a plane." At the end of the day, the same working principles are at work, and there is value to be found in the process by which the essential characteristics of one design are distilled and modified to give birth to another.
A hovercraft can be inspired by a turbofan. A helicopter or or bird can lead one to the conclusion of fixed wing flight, just as the fixed wing can lead you to rotary flight.
In short, it is a shallow person who stops looking because airplanes don't flap.
> There is no need to build airplanes with flapping wings, but you could still say that human flight is inspired by bird flight.
This is a super interesting analogy and it's changed my perspective a bit. But it all depends on your end goal. You say:
> We don't even know if the human brain is actually good at what it's doing.
What is it doing? Do we want our AIs to maximize learning or just human-like behavior? If it's the latter, I believe you absolutely want to look at neuroscience and emulate the human brain. Of course, airplanes are super good at flying, but they look nothing like birds.
I've come to think of machine learning as an engineering approach for building more scalable statistical systems. Computational neuroscience seems like it does more math modeling for understanding of brain function, but still might use machine learning methods as part of its research.
Also, good is subjective, and we don't have wetware in our engineering toolchain anyway. Loosely coupled metaphors seem to be popular and effective due to ambiguities like this.
I don't quite reach that number. According to Landauer's principle the minimum energy required to flip a bit is 0.0172 eV. Times 2^128 is ~10^18 J. The first google result for boiling all oceans puts it around 10^29 J. Even at realistic energy values for current hardware (10 fJ), we are still 5 orders of magnitude short of boiling all oceans.
No, the tradeoff is actually the other way around. Encoding with a neural network can potentially be faster than the exhaustive tree searches that are done in current compression methods. On other hand, current decoders are fairly dumb and extremely optimized for speed. Neural networks will probably have trouble competing. In the design of video codecsan increase in decoding time is considered at least 10x more costly than the same increase in encoding time.
source: I worked with both HEVC and neural network based compression.
