Smarter-than-Human Intelligence & The Singularity Summit

runamock writes "Brilliant technologists like Ray Kurzweil and Rodney Brooks are gathering in San Francisco for The Singularity Summit. The Singularity refers to the creation of smarter-than-human intelligence beyond which the future becomes unpredictable. The concept of the Singularity sounds more daunting in the form described by statistician I.J Good in 1965: 'Let an ultra-intelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultra-intelligent machine could design even better machines; there would then unquestionably be an 'intelligence explosion,' and the intelligence of man would be left far behind. Thus the first ultra-intelligent machine is the last invention that man need ever make.'"

We still have no clue how to do strong AI

[Animats]

OK. here's where we are:

• Logic-based AI AI looked so close in the 1960s, once it was realized that you could get a computer to do mathematical logic. All that was necessary was to express the real world in predicate calculus and prove theorems. After all, that's how logicians and philosophers all the way back to Aristotle said thinking worked. Well, no. We understand now that setting up the problem in a formal way is the hard part. That's the part that takes intelligence. Crunching out a solution by theorem proving is easily mechanized, but not too helpful. That formalism is too brittle, because it deals in absolutes.
• Expert systems Today, it's clear that they're no smarter than the rules somebody puts in. But back in the 1980s, when I went through Stanford, people like Prof. Ed Feigenbaum were promising Strong AI Real Soon Now from rule based systems. The claims were embarrassing; at least some of that crowd knew better. All their AI startups went bust, the "AI Winter" of low funding followed, and the whole field was stuck until that crowd was pushed aside.
• Neural nets / genetic algorithms / learning systems These all belong to the family of hill-climbing optimizers. These approaches work on problems where continuous improvement via tweaking is helpful, but usually max out after a while. We still don't really understand how evolution makes favorable jumps. I once said to Koza's crowd that there's a Nobel Prize waiting for whomever figures that out. Nobody has won it yet.
• Bayesian statistics Now used to do many of the things that used to be done with neural nets, but with a better understanding of what's going on inside. Lots of practical problems in AI, from spam filtering to robot navigation, are yielding to modern statistical approaches. Compute power helps here; these approaches take much floating point math. These methods also play well with data mining. Progress continues.

AI is one of those fields, like fusion power, where the delivery date keeps getting further away. For this conference, the claim is "some time in the next century". Back in the 1980s, people in the field were saying 10-15 years.

We're probably there on raw compute power, even though we don't know how to use it. Any medium-sized server farm has more storage capacity that the human brain. If we had a clue how to build a brain, the hardware wouldn't be the problem.

Hawking quote

[Anonymous Coward]

Sure, this quote is from their website, but Hawking says, "Some people say that computers can never show true intelligence, whatever that may be. But it seems to me that if very complicated chemical molecules can operate in humans to make them intelligent, then equally complicated electronic circuits can also make computers act in an intelligent way. And if they are intelligent, they can presumably design computers that have even greater complexity and intelligence."

Maybe these singularity folks are jumping the gun, but Stephen freakin' Hawking seems to think its possible at some point. Anyone smarter than him who disagrees is welcome to. Anyone dumber than him will probably reply with even more conviction.

Re:Where are Bike Riding skills stored?

[mrcaseyj]

The reason learning to ride a bike is tricky is that there is a counter intuitive aspect to it that nobody realizes they are doing. If people could and would just tell kids how to ride, then it would be easy for them to learn.

The first thing one needs to learn is how to balance. It's not very hard for kids to figure out on their own but it's even easier if someone tells them. The trick is simply to turn the handlebars the direction you start to fall. If your bike starts to lean to the right then turn the handlebars to the right and that will bring you back upright and into balance (you have to be moving forward for it to work and if you're going to slow it makes it harder). It's best to learn this in a giant flat area so that as you're turning the handlebars left and right, learning to balance, you don't have to worry about where you're going and running into anything. The narrowness of the typical residential street makes this challenging.

The second thing you need to know, and the counter intuitive part that is hard for the brain to figure out, is how to make the bike go the direction you want it to go. It's called counter steering. This is so counter intuitive that people rarely even believe me when I explain it to them. It's true though. It's in the California Department of Motor Vehicles Motorcycle Handbook, there's a web page by a Berkley physics professor, I've seen articles about it in a major motorcycle magazine. This is not controversial. Nobody who is an expert in motorcycle or bicycle physics will contradict me. The trick to turning a bicycle or motorcycle is to turn the handlebars the opposite direction you want to go. Seriously, I'm not joking. You don't hold them there. A little jerk the opposite way will suffice.

For example, say you're riding along perfectly straight. You've managed to get yourself so that your bike and your body is perfectly straight up and down and your handlebars are straight ahead. Now say you decide to turn LEFT. The first thing you do is give the handlebars a little nudge to the RIGHT. Your body continues in a roughly straight ahead direction while your bike moves out from underneath you to the right. Now you are leaning to the left just as you want to be and need to be in order to do the left turn you want to make. Now that you are leaned to the left you can proceed to turn the handlebars to the left and carry out your left turn.

Note that this IS how YOU ride a bike even if you don't realize it. There is no other way to do it. Some people think that in order to initiate a turn they just lean. But the only way you can lean your bike is to turn the handlebars the opposite way you want to lean. If you try to just lean your body by doing something like just bending your waist to the side, then your upper body will lean the way you want, but you have nothing to push against so the equal and opposite reaction will cause your lower body and your bike to lean the opposite way and cancel out nearly all of your lean. To prove that you can't just lean, try sitting on your bike with it not moving forward and try to balance it with your feet off the ground. It's extremely difficult. But if you're moving forward, you can turn the handlebars to make your bike go out from underneath you to effect a lean, and it's easy to balance.

One reason people don't realize that they're doing this counter steering thing is that when you're riding a bike you're constantly turning your handlebars back and forth and back and forth (a little tiny bit) in order to stay balanced. When you decide to make a turn your brain subconsciously just turns the handlebars a little bit earlier and a little bit more than it was going to in the course of maintaining balance anyway. It's like you've just willed yourself to lean but really you've done a little counter steering without even realizing it.

I feel sorry for all the kids who break their legs and scrape their knees and crack their heads needlessly just because nobody was there who could tell them how to ride their bike. Their