Zuckerberg, Musk Invest in Artificial-Intelligence Company Vicarious

The trouble with this kind of artificial intelligence is that I don't think it's possible to think like a human without actually having the experience of being human.

Sure, I think we could aim to build basically a robot toddler that had a sensory/nervous/endocrine system wired up analogously to ours. It would basically be a baby, and would have to go through all the developmental stages that we go through.

But I suspect we'll have a hard time modeling that stuff well enough to create anything more than "adolescent with a severe learning disability". People underestimate just how carefully tuned we are after millions of years of evolution. The notion that we could replicate that without having another million years of in situ testing and iteration seems naive.

And even then, why would we expect the AI to be smarter than a human? There is already quite a lot of variation in humans. Many people at the ends of the bell curve have extraordinary processing power in ways typical humans don't. But it turns out while those things are useful in some ways, they limit those people in other ways. So it's not like we're not trying out evolved designs, it's that on balance they don't seem to actually function fundamentally better.

One cool thing about the robot is that you could have many bodies having many experiences all feeding into one brain. But I'm not convinced that would actually lead to "smarter". I mean, look at old people. Yes we get smarter as we age. But age also calcifies the mind. All of that data slowly locks you into a prison of past expectations. In the end, it's a blend of naive and experienced people in a society that maximizes intelligence. And again, it's not like societies haven't been experimenting with that blend. Cultures have evolved to hit the sweet spot. It's not clear that adding 1000 year old intelligences would help.

And anyway, we already have 1000 year old intelligences: books!

You could say that there is benefit to having all of that in one "head" but then you have to organize it! Which experience drives the decisions, the one from 2014 or the one from 3014?

Again, culture evolved explicitly to solve this problem. People write books and the ones that work stick around.

I guess what I'm saying is the evolution of the human being is already here: it's the human race, fed history via culture, connected by the internet, in symbiosis with computers.

The idea that removing the humans from that system would make it smarter makes no sense to me. Nor does the idea of writing programs to do the jobs that humans do well. It's like creating a basketball team with 5 Shaquille O'Neils. I don't think they'd actually be able to beat a good, diverse team with one or two Shaqs.

Or think of it this way: if numerical/logical aptitude is such a huge advantage in advancing capital-U Understanding, why do smart people bother learning to paint? Why do we bother listening to children? Why do we bother having dogs?

I would argue it's because intelligence is as multi-media as the universe is. Sometimes a PhD has something to learn from a basset hound. And the human race has just as good a handle on it as any AI ever will. We just have a different view of the stage. We have the front row and they have the balcony.

That's just another human weakness - we anthropomorphize lesser creatures. And even with that, we still don't look before stepping on ants.

I agree that a paraphrase =/= a quote.

Are you saying my paraphrase significantly misrepresented him? If so, how?

(I'm not trying to be argumentative, I just genuinely don't understand your point.)

Well we do actually have a purely mathematical approach to AI worked out. Granted it requires an infinite computer, and personally I don't think it will lead to practical algorithms. But still, it exists. And from the practical side of things, machine learning is making progress in leaps and bounds. As is our understanding of the brain.

Remember that airplanes weren't built by Da Vinci because he didn't have engines to power them. It wasn't that long after engines were invented that we got airplanes. The equivalent for AI, computing power, is already here or at least getting pretty close.

> Well we do actually have a purely mathematical approach to AI worked out.

Supposedly with enough computer power and enough data, a one stroke solution to everything is stochastic optimal control, but that solution takes, say, just brute force to, say, planetary motion instead of Newton's second law of motion and law of gravity. Else, need to insert such laws into the software, but we would insert only laws humans knew from the past, or have the AI software discover such laws, not so promising. This stochastic optimal control approach is not practical or even very insightful. But it is mathematical.

> machine learning is making progress in leaps and bounds.

I looked at Prof Ng's machine learning course, and all I saw was some old intermediate statistics, in particular, maximum likelihood estimation (MLE), done badly. I doubt that we have any solid foundation to build on for any significantly new and powerful techniques for machine learning. I see nothing in machine learning that promises to be anything like human intelligence. Sure, we can write a really good chess program, but no way do we believe that its internals are anything like human intelligence.

> As is our understanding of the brain.

Right, there are lots of neurons. And if someone gets a really big injury just above their left ear, then we have a good guess at what the more obvious results will be. But that's not much understanding of how the brain actually works.

It's a little like we have a car, have no idea what's under the hood, and are asked to build a car. Maybe we are good with metal working, but we don't even know what a connecting rod is.

> It wasn't that long after engines were invented that we got airplanes.

The rest needed was relatively simple, the wind tunnel, some spruce wood, glue, linen, paint, wire, and good carpentry. For the equivalent parts of AI, I doubt that we have even a weak little hollow hint of a tiny clue.

In some of the old work in AI, it was said that a core challenge was the 'representation problem'. If all that was meant was just what programming language data structures to use, then that was not significant progress.

Or, sure, we have a shot at understanding the 'sensors' and 'transducers' that are connected to the brain: Sensors: Pain, sound, sight, taste, etc. Transducers: Muscles, speech, eye focus, etc. We know some about how the middle and inner ear handles sound and the gross parts of the eye. And if we show a guy a picture of a pretty girl, then we can see what parts of his brain become more active. And we know that there are neurons firing. But so far it seems that that's about it. So, that's like my computer: For sensors and transducers it has a keyboard, mouse, speakers, printer, Ethernet connection, etc. And if we look deep inside then we see a lot of circuits and transistors. But my kitty cat has no idea at all about the internals of the software that runs in my computer, and by analogy I see no understanding of the analogous details inside a human brain.

Or, we have computers, and we can write software for them using If-Then-Else, Do-While, Call-Return, etc., but for writing software comparable with a human brain we don't know the first character to type into an empty file for the software. In simple terms, we don't have a software design. Or, it's like we are still in the sixth grade, have learned, say, Python, and are asked to write software to solve the ordinary differential equations of space flight to the outer planets -- we don't know where to start. Or, closer in, we're asked to write software to solve the Navier-Stokes equations -- once we get much past toy problems, our grid software goes unstable and gives wacko results.

Net, we just don't yet know how to program anything like real, human intelligence.