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Comment by TrainedMonkey | Hacker News Reader

What I got out of the article is that computers are good at doing what they are programmed to do. Problem is not bad computers, but rather the fact that we do not know how to program computer for general intelligence.

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There are real, absolute limits on what you can program a set of transistors and logic gates to do. Changing those limits will require changing what a "computer" is. At which point, we'll probably call it something else.

Couldn't you make the same argument about the absolute limits of neurons and synapses?

Ditto, computers are for one still improving. Who knows what the limits are.

What I think is the problem is that humans do not have a clear explanation for how our intelligence works. Is it really that much of a wonder we can't emulate it?

What are these limits and how do you know they exist?

Can we create digital logic circuitry that embodies ALL the functionality of a neuron, including interconnections (and here you have to compensate circuitry limitations that the neurons don't have) in the same size? I think you can start from there to understand current technology's limitations.

I agree we will likely not be calling these things "computers", if we ever invent them.

Even if you remove the size restriction and the physical limitations of manufactured circuitry, we're lacking the perfectly defined model of how a neuron actually works. We have plenty of knowledge about neurons, but nowhere near enough to simulate them realistically. You'd need to be able to account for every possible state a neuron could ever be in.

But a neuron is more than just an input-output state machine, it's affected by levels of oxygen, glucose, and any number of hormones, proteins, and other chemicals in the bloodstream. An adult human's neurons are each individually shaped by their entire existence up to that point. Alcohol consumption, sun exposure, antidepressant medication, hydration levels, exercise levels. It all affects how they work.

And that's just one neuron. Simulating the brain as a solution to this problem is, I think, out of the question.

>An adult human's neurons are each individually shaped by their entire existence up to that point. Alcohol consumption, sun exposure, antidepressant medication, hydration levels, exercise levels. It all affects how they work.

Possibly, but why on Earth would you want to simulate that? Just simulate what the neuron is supposed to be doing or would be doing under ideal circumstances.

> Just simulate what the neuron is supposed to be doing or would be doing under ideal circumstances.

We still don't know that either.

It wasn't necessary to simulate a feather in bird's wing in order to fly. In fact, practical flight really only happened once people started taking away features observed in nature.

Defining a computer in terms of transistors and logic gates is analagous to defining a brain in terms of neurons and synapses.

You arbitrarily impose limits in the definition of a computer and lift those limits in definition of a brain.

There's a whole subfield of computer science about it: http://en.wikipedia.org/wiki/Theory_of_computation

On the other hand, "All problems in computer science can be solved by another layer of indirection." (http://www.dmst.aueb.gr/dds/pubs/inbook/beautiful_code/html/...) So if that applies to this problem (after enough levels of indirection) then maybe I'm wrong.

Yes there are limits, and we aren't anywhere near those limits. Our best algorithms are still primitive, and inefficient.

What are those limits? One of the big ideas out of Wolfram's book A New Kind of Science is that, once a rather small threshold is reached, a system made of simple rules becomes universal. And since by definition any universal system can emulate any other universal system, there cannot be a thing computable by one universal system that is not computable by another. (One of the implications of this idea he suggests is that the weather, which he argues is a universal system made of simple rules, is able to do no less sophisticated computations than the human brain.)

He develops this argument more than I have time to do here: http://www.wolframscience.com/nksonline/page-822-text (note that this is near the end of the main section of the book and rests on ideas established earlier).

Wolfram himself seems pretty bullish on the idea of making computers that think sometime in the future in the section I linked to, in a talk he gave at HAL's birthday party called Hal Isn't Here back in '97, and much more recently in some comments on the movie Her.

This is not an original idea by any means; it's called the Church-Turing thesis, and has been around for 50+ years.

As for your question "what are those limits?": In a nutshell, no computer program can ever fully answer questions about the properties of other computer programs. Unlike what GP is implying, that is a hard limit we can never ever get rid of. So if weather is universal, we will never be able to fully understand it!

Certainly Wolfram didn't invent the notion of a universal system, nor claim to, nor was that intended to be implied above, as bad as my writing might be. Indeed, on page 1125 of NKS he discusses Church's and Turing's contributions.

What Wolfram repeatedly points out throughout his book is that the threshold for such universality is much lower than one might suspect given the complications involved in, say, a Turing machine. And because that threshold is so low, there exists the possibility that much of the natural world that is not obviously simple is exhibiting universal computation.

Two comments

1. Machine learning is moving more and more towards indirect programming i.e. you program the computer with a learning algorithm and let it work out what to do. Google reinforcement learning, or machine learning. This greatly reduces the programming bottleneck.

2. People underestimate how much processing power the human brain has. Think 100,000,000,000 neurons, each with 1,000 active connections on average and perhaps 10,000 latent connections (which are being updated via Hebbian learning). The connections (axons and dendrites) are the active processing units. The cycle time is .01 seconds or so. Only the very largest computers are anywhere near this processing power (~10^16 operations/second). My current desktop is about 10,000 times less powerful. Now imagine trying to build a tractor with a 1/100 horsepower motor - such a difference is beyond being a gap, it is a qualitative difference.

Given the limited processing power available it is amazing computers can do what they can. Back in the 1980s a large bank was run on the equivalent of (1/10 of a millimeter of brain tissue)^3.

There are some caveats to comparing the computing power of the brain to computers. Computers have vastly more serial processing power than the brain. Transistors are thousands to millions of times faster than neurons. Signals propagate through the brain relatively slowly (only about 200 times per second.) For problems that can be done iteratively, computers are vastly superior.

Second computers are intentionally designed to be general purpose and they sacrifice a lot of potential speed to do this. If you were to build some algorithms into the hardware they would get vastly more performance (eg bitcoin mining), but that's extremely expensive. However being general purpose has a lot of advantages. Computers will always be faster at many things than neurons.

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