Slashdot Mirror
Cutting-Edge AI Projects?
Xeth writes "I'm a consultant with DARPA, and I'm working on an initiative to push the boundaries of neuromorphic computing (i.e. artificial intelligence). The project is designed to advance ideas all fronts, including measuring and understanding biological brains, creating AI systems, and investigating the fundamental nature of intelligence. I'm conducting a wide search of these fields, but I wanted to know if any in this community know of neat projects along those lines that I might overlook. Maybe you're working on a project like that and want to talk it up? No promises (seriously), but interesting work will be brought to the attention of the project manager I'm working with. If you want to start up a dialog, send me an email, and we'll see where it goes. I'll also be reading the comments for the story."
Just a small company, I'm sure no-one's noticed it.
Cyberdyne Systems
Do not attribute to malice that which can be easily explained by incompetence.
Why is it that the first application that I can think of for such project developed by DARPA is that to use it against the citizens?
Yea, I have lots of ideas and things I've been working on.
Fund me! :-)
If DARPA is now so desperate as to seek out totally random and unknown readers of slashdot...my god the US is screwed.
... I had a totally sweet aritifical intelligence lead, but I already told China about it, and they said I shouldn't tell anyone else.
:-/
numeta
It's mainly a teaching + learning system for a system with input and output. I don't see anything built with it answering any rational questions or coming up with new ideas anytime soon, but if you do AI and don't know about them, you better catch up.
http://www.opencog.org/wiki/Main_Page
http://www.agiri.org/OpenCog_AGI-08.pdf
http://justingibbs.com/how-to-make-singularity-bearable-in-its-infancy
http://www.innergybv.biz/blog/?p=175
http://ieet.org/index.php/IEET/more/goertzel20080620/#When:22:49:00Z
http://xlaurent.blogspot.com/2008/06/opensim-for-opencog.html
There's a number of GSoC projects for OpenCog currently underway also:
http://code.google.com/soc/2008/siai/about.html
So the first release should be very interesting.
How we know is more important than what we know.
As if I didn't see that coming? I think my UID says I've been here awhile.
It's not that I'm asking Slashdot to do my work for me; I've already got some very strong leads to work on. However, Slashdot occasionally surprises me with people that are thoughtful and working in interesting fields, so I figured I'd give it a shot. Most of the changes in my life have come from sudden and unexpected directions; I wanted to see what serendipity might bring me that deliberation would not.
If your theory is different from practice, then your theory is wrong.
Hello,
I'm studying theoritical computer science, meaning it's often called math (things like complexity theory, lambda calculus, even linear logic...).
I always loved AIs, but I was often told that there is no research on it which is that theoretical; that it's more like a collection of applied domains, like learning neural networks or computer vision.
So, what is the most theoritical aspect of AI research that you know? Or put otherwise, is there a branch of AI research where you prove theorems rather than writing code?
I know it's slightly off topic, but people working on that kind of thing are probably wondering if they should mention it here (wondering if it interests DARPA or not).
You've got to quit trying to advance on separate fronts. People have been exploring and reinventing the same old niches for sixty years. Little has changed except for the availability of powerful hardware with which to realize these disconnected bits and pieces. What is needed is a way to bring the many different segments of the AI and robotic communities together, because the solution is not to find the "winning approach", but to realize the value of the various perspectives and combine efforts. This is not a new idea, it is an old one which apparently just doesn't fit into the established research environments. Go to the library and read some old books on AI if you really want an appreciation of how pathetic the progress of ideas (not hardware) has been. To whet your appetite try some of Marvin Minsky's old papers - http://web.media.mit.edu/~minsky He recognized this situation nearly 40 years ago.
Strange things are afoot at the Circle-K.
Hopefully whatever your researching ...
The Right Reverend K. Reid Wightman,
Why back in my day we had to post questions to the legs of carrier pigeons. Gosh darn it! We liked it that way!
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--<Mike>--
My AI page which has several links that go deeper to older write ups is at www.fossai.com
Basically I say that the better computer vision you make, the better software you can write advanced bots leading up to AI. I see AI as being something we'll naturally get to even if no one makes an effort to it: Our 3d cards are getting better, video games are making better 3d worlds, memory is getting bigger, and computer speeds are getting faster. Even if you couldn't hold AI on a current computer's memory, you have wireless internet that links up with a supercomputer to make thin client bots. So there really isn't anything in current technology that is holding us back except computer vision.
Now I am not so good in the computer vision field, but as I see it(excuse pun), there are two ways to do vision.
1) Exact matching. You model an object in 3d via CAD, a Pixar style, or using Video Trace First you database all the objects that your AI will see in its environment then you make a program that identifies objects it "sees" with computer cameras and laser range finding devices. So then the AI can reconstruct its environment in its head. Then the AI can perceive doing actions on the objects.
I'm currently not in the loop here. I can't talk to anyone at Video Trace because I'm just a person, and they don't want to let me in on their software. So I can't database my desk. So I can't make the program that would identify things.
2) Even better than exact matching is similar matching. No two people look alike besides twins, so you can't really just database in a person and say that is a human. And as humans go, there are different categories such as male and female, and some are androgynous so we can't tell their sex. Similar matching has a lot of potential in its ability to detect things like trees and rocks. Similar matching is good at an environment that is tougher to put into exact matching situations. So just from this information alone, I wouldn't start on similar matching unless you had exact matching working in a closed environment. I'm not saying that some smart individual couldn't come up with similar matching before exact matching. I'm just saying that for myself, I'd start with exact matching, and then extend it with similar matching. There are a lot of clues you can pick up on if you know exact locations of things.
And then once you have singular location vision working, you can add multi point vision working. Multi point vision would mean that if you had more robotic eyes on a scene that you'd gain more detail about it. You could even get as advanced as conflict resolution when one robotic eye thinks it sees something, but another thinks it is something different. The easiest way to think of a good application for this would be if you had a robotic car driving behind a normal semi trick and another robotic car infront of the semi. The robotic car in the back can't see past the semi to guess traffic conditions of when the semi will slow down, but the car in front of the truck can see well, so they can signal to each other information that would let the car in behind the semi truck follow closer. If you get enough eyes out there, you could really start to put together a big virtual map of the world to track people.
I wouldn't say AI that learns like humans is desirable. After all, you'd have to code in trusting algorithms to know who to listen to. I'd say AI that downloads its knowledge from a reliable source is the way to go. It is easy to see: Sit in class for years until you learn a skill, or download it all at once like Neo on training seat.
Anyway, you can do a lot with robots that have good computer vision. Thething that has to be done next is natural language understanding. So far we've discussed the AI viewing a snap shot of a scene and being able to identify the objects. Next you'll have to introduce verbs and moving.
God spoke to me.
Why is it that the first application that I can think of for such project developed by DARPA is that to use it against the citizens?
Like it or lump it, you are in this boat with everyone else. If AI is solved, it will be used for good and evil. If your country does not use it for evil (extremely doubtful), somebody else's country will. Better yours than theirs. What I mean is that true AI will be an extremely powerful thing; if any country other than yours gets an early monopoly on AI, you can bet they are going to use it to kick your country's ass. I don't think you'd like that very much.
Having said that (and to get back on topic), I have been working on ageneral AI project called Animal for some time. Animal is biologically inspired. It attempts to uses a multi-layer spiking neural network to learn how to play chess from scratch using sensors, effectors and a motivation mechanism based on reward and punishment. It is based on the premise that intelligence is essentially a temporal signal-processing phenomenon. I just need some funding. The caveat is that my ideas are out there big time and there is a bunch of people in cyberspace who think I am kook. LOL. But hey, send me some money anyway. You never know. :-D
Paper and pigeons! What'll be the next? A magical tablet that translates your handwaving to images of the Wonders of the Worlds? Pah!
In my day, we had to write the questions using cuneiform script on a damp clay tablet, pack it in an envelope of clay, and then deliver it personally to the priesthood.
/Crafack
... Elecance is left to the implementors.
I have the perfect project: A smart knife. Think about it Knives are deadly, deadly weapons. People get stabbed every day. Even innocent people stab themselves all while trying to prepare the simplest of dishes. The solution is simple: Build a knife that knows its target. With an active memory metal that blunts itself to the sharpness of a baseball bat if its positioned at anything other than its target. Furthermore it will dynamically alter its blade to ensure the optimal cut of the material, taking into consideration all of the grain, moisture, temperature, and density of the object. It also has zibgee wireless mesh networking built in to communicate with other intelligent kitchen objects. The cutting board will communicate with the knife to let it know how close it is to the board. It will speak with the oven to let it know the specific moisture and condition of the meat to allow the oven to set the temperature and time of cooking to an optimal level. It will also prob for bacterial, viral of prion content communicating with any compatible devices to warn the user of the danger.
The smart knife. Cutting edge AI at its finest. Prospecitive investers, feel free to contact me @ bill_AT_ultimatesalsaparty_DOT_COM
Well.. maybe. Or Maybe not. But Definitely not sort of.
Get off my lawn ?
Possibly: what exactly the research on self-awareness/sentient learning systems comprises for you guys. The reason I bring this up is that I left AI as a main research field because even the most flexible research goals set by academia today are just tiny steps of applicative advances in statistical inference and mathematical techniques in general. Not that these things are not useful (machine learning is quite amazing, actually) but the initial goals have little to do with all this. We have surpassed brains in many ways, but the massively parallel brain does not even "learn", let alone think(which is what we want) in the manner in which any AI system today is based. And yes, that includes adaptive ones that change their own set of heuristics.
I spent a lot of my time thinking about neuroscience and reading psychology , and while I slowly moved towards rationalizing certain things, the main obstacles to what I needed to know were deeply biological. How exactly does the mind "light up" certain areas of memory while recalling things (sounds, sights..etc) stored nearby? How "randomized" is this? And how can it be represented mathematically (von neumann architecture)? Is there ANYTHING we do that is not entirely memory based (even arithmetic seems to stem from adaptive memory)? Why do we need to sleep, and what part of the whole "algorithm", if there is one, is dependent on this sleeping business? What exactly does it change in our reasoning?
If we know precisely some good answers, rather than the guesswork in literally all major texbooks, we can begin to model something useful and perhaps introduce much more powerful intelligence with all we have developed in NN, probabilistic systems..etc. I think once sentience is conquered, human beings are going to be immediately made inferior. It's just this abstraction business that is so damn complicated.
Lions kill cubs sired by other lions. Penguins and monkeys steal babies from other parents when their babies die prematurely. Ants wage war. Almost all female mantises engage in cannibalism. We have no monopoly on evil. If you believe in a struggle between good and evil, humans are in a unique position clean up things; we understand we're evil and can admit it.