Kurzweil on the Future

dwrugh writes "With these new tools, [Kurzweil] says, by the 2020s we'll be adding computers to our brains and building machines as smart as ourselves. This serene confidence is not shared by neuroscientists like Vilayanur S. Ramachandran, who discussed future brains with Dr. Kurzweil at the festival. It might be possible to create a thinking, empathetic machine, Dr. Ramachandran said, but it might prove too difficult to reverse-engineer the brain's circuitry because it evolved so haphazardly. 'My colleague Francis Crick used to say that God is a hacker, not an engineer,' Dr. Ramachandran said. 'You can do reverse engineering, but you can't do reverse hacking.'"

Comments

mid-age life crisis

[peter303]

Kurzweil's predictions will come to pass, by not on the time-scale he envisions. probably centuries. He has been hoping for personal immortality through technology and takes over 200 anti-aging pills a day.

Re:Kurzweil Talk in Cambridge, MA

[c6gunner]

Mod me offtopic or whatever, I don't care, but I've been thinking about this for a few weeks. If our brains are so well interconnected, how is it that we instantly die if a bullet merely passes through it and destroys a few of those connections? We can shoot bullets through most parts of a computer and more than likely only a piece of it will be damaged

Have you seen the mess that a bullet going through a skull makes?

It's not the bullet that's the problem, it's the shockwave generated by it's passage that does all the damage. It's called "cavitation" - this video should help you understand it. If you carefully watch the last part of that video, you'll see that it causes the entire melon to explode outwards. Now imagine what that kind of force does to brain tissue confined inside a skull.

You can't compare that to shooting at computer components - they react completely differently, and are not affected at all by the shockwave. When you shoot a computer, only the part you hit is affected.

Maybe not THAT low hanging

[Moraelin]

It might be less low hanging than most people think. Most predictions I've seen for, basically, "OMGWTFBBQ, computers are gonna be as intelligent as humans" are based on, basically, "OMGWTFBBQ, we'll soon have as many transistors on a chip as there are neurons in a human brain." Especially marketing depts love to hint that way now and then, but they're not the only culprits.

Unfortunately,

1. A neuron isn't a transistor. Even the inputs alone would need a lot more transistors to implement at our current technology level.

An average brain neuron takes its inputs from an _average_ of 7000 other neurons, with the max being somewhere around 10k, IIRC. The vast majority of synapses are one-way, so an input coming through input 6999 can't flow back through inputs 0 to 6998. So even just to implement that kind of insulation between inputs, you'd need an average of 7000 transistors per "silicon neuron" just for the inputs.

Let's say we build our silicon transistor to allow for 8k inputs, so we have only one modul repeated ad nauseam, instead of custom-designing different ones for each number of inputs between 5000 and 10000. Especially since, we'll see soon, that number of inputs doesn't even stay constant during the life of a neuron. It must accomodate a bit of variation. That's 2^13 transistors per neuron just for the inputs, or enough to push those optimistic predictions back by 13 whole Moore cycles. Even if you believe that they're still only 1.5 years each, that pushes back the predictions by almost 20 years. Just for the inputs.

2. Here's the fun part: neurons form new connections and give up old ones all the time. Your brain is essentially one giant FPGA, that gets rewired all the time.

Biological neurons do it by physically growing dendrites which connect to an axon terminal. A "silicon neuron" can't physically modify traces on the chip. You have to include the gates and busses that switch an input to another nearby source from thousands available outputs of another "neuron". _Somehow_. E.g., a crossbar kind of architecture. For each of those thousands of inputs.

Now granted, we'll probably figure out something smarter out, and save some transistor for that reconfiguration, but even that only goes so far.

There go a few more Moore cycles.

4. And that was before we even get to the neuron body. That thing must be able to do something with that many inputs, plus stuff like deciding by itself to rewire its inputs, or even (yep we have documented cases) one area of the brain decides to move to a whole other "module" of the brain or take over its function. It's like an ALU deciding to become a pipeline element instead in a CPU, because that element broke. In the FPGA analogy, each logic block there is complex enough to also decide by itself how it wants to rewire its inputs, and what it wants to be a part of.

There are some pretty complex proteins at work there.

So frankly even for the neuron body itself, imagining that one single transistor is enough to approximate it, is plain old dumb.

5. And that's before we even get to the waste we do with transistors nowadays. It's not like old transistor radios, where you thought twice how many you need, and what else you could use instead. Transistors on microchips are routinely used instead of resistors, capacitors, or whatever else someone needed there.

And then there are a bunch wasted because, frankly, noone ever designs a 100 million transistor chip by lovingly drawing and connecting each one by hand. We use libraries of whole blocks and software which calculates how to interconnect them.

So basically look at any chip you want, and it's not a case of 1 transistor = 1 neuron. It's more like a whole block of them would be equivalent to one neuron.

I.e., we're far from approaching a human brain in silicon. We're more like approaching the point where we could simulate the semi-autonomous ganglion of an insect's leg in silicon. Maybe.

6. And that's before we get to the probl