5 ms·

Look, guys, sure, in some sense computing is part of the best promise for AI. Fine. I'll even agree that at least for now computing is necessary. But, note,

by graycat 13y ago

Look, guys, sure, in some sense computing is part of the best promise for AI. Fine. I'll even agree that at least for now computing is necessary. But, note, nearly everything we've done in computing, especially in Silicon Valley for the past 15 years, has been to apply routine software development to work that we already well understood how to do manually. A small fraction of the efforts have been some excursions into more, but these have been relatively few and with rarely very impressive results. Net, what Silicon Valley does know how to do is build, say, SnapChat (right, it keeps the NSA spooks busy looking at the SnapChat intercepts from Sweden!). But for anything that should be called AI, there is another challenge that is very much necessary -- how to do that. Or, if you will, write the software design documents from the top down to the level of the individual programming statements. Problem is, very likely and apparently, no one knows how the heck to do that. Given a candidate design, people should want to review it, and about the only way to convince people, e.g., short of the running software passing the Turing test or some such, is to write out the design in terms of mathematics. Basically the only solid approach is via mathematics; essentially everything else is heuristics to be validated only in practice, that is, an implementation and not a design. Thing is, I very much doubt that anyone knows how to write a design with such mathematics. If so, then long ago there should have been such in an AI journal or with DARPA funding. Basically, bluntly, no one knows how to write software for anything real about AI. Sorry 'bout that. Wby? We just do not know hardly anything about how the brain works. We don't know more about how the human brain works than my kitty cat knows about how my computer works. Sorry 'bout that. And AI software will have a heck of a time catching up with my kitty cat. By analogy, we don't know more about how to program AI than Leonardo da Vinci knew about how to build a Boeing 777. Heck the Russians didn't even know how to build an SR-71. Da Vinci could draw a picture of a flying machine, but he had no clue about how to build one. Heck, Langley fell into the Potomac River! Instead, the Wright brothers built a useful wind tunnel (didn't understand Reynolds number), actually were able to calculate lift, drag, thrust, and engine horsepower, and had found a solution to three axis control -- Langley failed at those challenges, and da Vinci was lost much farther back in the woods. We now know how our daughters can avoid cervical cancer. Before the solution, "we dance 'round and 'round and suppose, and the secret sits in the middle, and knows.", and we didn't know. Well, the cause was HPV, and now there is a vaccine. Progress. Possible? Yes. Easy? No. AI? We're not close enough to be in the same solar system. F'get about AI.

Houshalter 13y ago
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.
- graycat 13y ago
  > 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.

by graycat 13y ago

Houshalter 13y ago

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.

graycat 13y ago
> 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.