81–90 of 112 posts
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I've had an idea for a while, coming from a developing continent, that it would be good to have an alternative for students where they can start working when they are doing an advanced degree, but also simply a place for high school students to acclimate to some of the aspects of the work place. I know it sounds tangential, but this does resonate with my own roundabout way of going from studies -> work and the gross…
With depression, the "why" is never easy to suss out, even when something is stated, is that really the reason?
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It's fascinating that none of the comments mention neural network applications. Seems like these people came a bit too early to the party.
We've left biomimicry in the dust a fair time ago - neurons do not work like relus after all (nor do they work like memristors), but relus are great at solving problems. None of our current NN applications would even want something that behaves like a neuron. You'd essentially have to start from scratch. It's not even clear if behaves-like-a-neuron is better or not to be worth the effort. (And that's disregarding the…
Some people have left biomimicry in the dust and are manipulating matrices to sets find optimal parameter sets representing concepts.
Other people (https://www.humanbrainproject.eu/en/) haven't! There is a big community of people building novel types of neural network using spikes and so on.
And you are right - it isn't clear who is right. On one side the "no nature" folks might say : we don't even understand neurons to the point where we can definitely categorize them in a mouse brain, let alone explain their behaviour, or actually properly measure their behaviour, and so on and so on - this before understanding any of it really.
On the other hand the "lets mimic things we don't understand" folks would say - we need to understand this because the human brain out performs all AI today on a 20 watt budget.
My view is the understand stuff agenda is right - but not now and probably not for 20 years (in a big way). We need small scale capability and direction finding work - and we will need to do that for a long time.
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At the slow "clock speed" the brain runs at, it produces a lot of heat. A quick search turns up 10-20 watts as a rough estimate. It's still petty competitive given what it's capable of.
Why do you say slow "clock speed"? Can we even use clock speed w.r.t brain? Genuinely curious.
[0] https://en.wikipedia.org/wiki/Refractory_period_%28physiolog...
12 years ago, on HN: "The memristor—the functional equivalent of a synapse—could revolutionize circuit design" https://news.ycombinator.com/item?id=397335
It's fascinating that none of the comments mention neural network applications. Seems like these people came a bit too early to the party.
For neural networks, it makes sense to skip pure digital design. When I learned how to design an ALU to say, add, and wait for the propagation of carry bits that’s like O(n) where n is the number of bits in the number, it made me want to just use superposition for addition, which is physical and instantaneous. Of course, that has all sorts of other problems that make it worse (so much worse). Once you learn how to sl…
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We don't need to go fully analogue though, going asynchronous might already be a step up. In neural networks some groups do research into so-called spiking neural networks, which you could think of as electronic circuits operating asynchronously on binary signals. They are very energy efficient, but nobody really knows how to use them yet.
Clockless CPUs have been a theoretical idea for over a decade before the "neutral network" became a common phrase. Felt like a decade ago it was still a pipe dream. Resolving the asynchronous signals becomes a chore. The world feels stateful and ordered to humans. In the same way functional programming can be difficult, getting this right is very hard.
Very interesting chips.
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With depression, the "why" is never easy to suss out, even when something is stated, is that really the reason?
Academia can be very toxic and PhD students are the most vulnerable as their Prof holds all the cards. This needs real checks and balances.
But there is certainly a process for handling advisor-advisee issues at a big school like Stanford:
For neural networks, it makes sense to skip pure digital design. When I learned how to design an ALU to say, add, and wait for the propagation of carry bits that’s like O(n) where n is the number of bits in the number, it made me want to just use superposition for addition, which is physical and instantaneous. Of course, that has all sorts of other problems that make it worse (so much worse). Once you learn how to sl…
If you're building adders with O(n) carry times you're doing it wrong - normal carry lookahead logic is ~O(log N)
You should note the O(log n) delay growth is for a logic (ideal) model. Considering the 2D geometry of real circuits, there are models with more detail, in particular VLSI models. [1]
I suspect due to routing and path lengths even the delay would turn out to be O(n) in VLSI, or at least a power of n (with a small constant).
[1] Seems like a good introduction: http://cs.brown.edu/people/jsavage/book/pdfs/ModelsOfComputa...
At 800 degrees C
This is definitely the "in mice" of materials science. Solid state batteries, hydrocarbon fuel cells etc. all work - at high temperatures.