[api]

I formally studied biology not CS, partly out of an interest in AI.

Everyone who thinks superintelligence or even just human or higher-animal level intelligence is right around the corner needs to study genomics, proteomics, molecular biology, and neuroscience. Study them with an open mind and think about what's really going on.

A neuron is not a switch. A neuron is an organism. It contains a gene regulatory network more complex than the entire network topology of Amazon's entire web services stack, and that's just looking at the aspects of gene regulation and enzyme (a.k.a. nanomachine) operation that we understand. There are about 100 billion of these in the brain and every one of them is running in parallel and communicating constantly. There are also about 10 glial cells for every one neuron, and glia are involved in neural computation in ways we know are there but don't yet fully understand. (Seems to be related to longer term regulation of synapse behavior, etc.) Each glial cell also contains a massive gene regulatory network and so on.

The CS and AI fields suffer from a lot of Dunning-Kreuger effect when they talk about biology. The level of processing power and the parallelism that's going on in the brain of a living thing is simply mind numbing. It's as incredible as the sense you get of the scale of the universe when looking at the Hubble Deep Field.

Our present-day computers are toys. We are not even close. It would at least take advances equivalent to the ones that took us from vacuum tube ENIAC to here.

Edit: I don't write off superintelligence categorically though. I think we could achieve forms of it not through pure AI but by deeply augmenting biological intelligence. Genetic and biochemical performance enhancement could also play a role. Imagine having more working memory, perfect motivational control, the ability to regulate your own desire/motivational structure, and needing only a few hours of sleep. Cyborg superintelligence is a possibility in the foreseeable future and it does raise issues similar to those the superintelligence folks raise. So I don't dismiss an intelligence explosion. I just very strongly doubt it would be purely solid state.

[ppod]

>The CS and AI fields suffer from a lot of Dunning-Kreuger effect when they talk about biology

I'm sure this is right, but what about the reverse -- how much do you know about AI?

AI need not be as complex as natural intelligence to be more intelligent. A lot of the complexity in the natural world is due to the blind and haphazard nature of engineering by natural selection. Do we understand, completely, at a molecular level, the physical and control systems of bird and insect flight? Or how fish swim? Probably not. But by understand the principles and applying a certain amount of engineering brute-force, we've produced machines that by many sensible measures out-fly and out-swim natural machines.

[Falling3]

>Do we understand, completely, at a molecular level, the physical and control systems of bird and insect flight? Or how fish swim? Probably not.

That's an excellent point. But at the same time, we do have some level of understanding of the mechanics of swimming and flying. The same really can't be said of intelligence.

[edanm]

That depends what you mean by intelligence.

We understand enough to build computers that win at chess, to build computers that run financial trading algorithms, to build Google.

I agree that intelligence is in some ways harder to fully define than flight, but that doesn't mean that we don't have any understanding of any parts of it.

[Falling3]

Google's definition (which is a good starting point) of intelligence: "the ability to acquire and apply knowledge and skills."

As far as I know, we have very little if anything in the way of software that accomplishes general learning (not limited to a specific domain).

[danieltillett]

Of course we are far from reaching human level yet, but generalised Moore’s Law means the number of years until we reach human level is not that far away.

There is of course the issue that since brains evolved rather than being designed that they can be inefficient in their processing. Look at how poor humans are at arithmetic - we need to divert a huge fraction of our processing power to do what a computer designed for arithmetic can do very efficiently.

[api]

Is Moore's Law still a thing?

I don't doubt we can go far beyond present compute power since I am far beyond present compute power and I am reading this. But is the economic driver there?

At the endpoint most people use PCs, tablets, and phones to browse the web, write e-mails, play games that are already pretty good, etc. In the cloud we can always just make data centers larger.

There's obviously always a push for speed and density, but is that push still powerful enough to pump the billions upon billions that will be required to make leaps into areas like 3d circuits, photonics, quantum computing, etc.? At what point does the economic driver drop below the threshold needed to overcome the next hurdle?

First we flew in balloons. Then we flew in fixed wing airplanes. Then we motorized them even more and fought wars with them. Then we built jets. Then we broke the sound barrier. Then we went to orbit. Then we built the SR-71 blackbird and pioneered stealth. Then we landed on the moon.

Then nothing happened in aerospace until Elon Musk, and he's just getting back to where NASA should have been in the 80s. Meanwhile the Concorde is still cancelled and commercial flights are no faster than they were in the 70s.

I'm a bit concerned the computing is about to do what aerospace did. I take some of the breathless hype you hear today as a contrarian indicator for this, since before aerospace went comatose we saw this:

http://i.kinja-img.com/gawker-media/image/upload/t_original/...

I hope not but history does rhyme and economies are more powerful than wishes (or even governments).

[ufmace]

I'm not sure how seriously we should take Moore's Law when it comes to these things. It applies pretty well so far to the development of silicon-based microprocessors, but at some point, we're going to come up against some hard physical limits on those. Once that happens, we may be stuck until we can come up with something fundamentally new.

We already seem to be up against some limits in a way as far as single-threaded processing power - it doesn't seem to be going up all that fast in the last few major cycles of processor development.

[danieltillett]

This is why I said generalised Moore's law, not Moore's law. We are pretty much at the limit of current designs, but there is still plenty of room for parallelising computation.

I do agree we are going to need something new to get to human level.

[yongjik]

> It contains a gene regulatory network more complex than the entire network topology of Amazon's entire web services stack...

Maybe it's not a fair comparison but I decided to look it up:

Human genome has about 3.2 billion base pairs, which is about 6.4Gbits = 800MB. The size of linux-4.4.1.tar.gz is about 83MB. So, in a sense, the human genome is only about ten times the compressed size of Linux kernel, never mind everything on top of that.

[baddox]

> A neuron is an organism. It contains a gene regulatory network more complex than the entire network topology of Amazon's entire web services stack, and that's just looking at the aspects of gene regulation and enzyme (a.k.a. nanomachine) operation that we understand.

Can you give some more details about this? How are you quantifying the complexity of a neuron and of the AWS stack?