1 / 16

How We’re Going to Solve the AI Problem

How We’re Going to Solve the AI Problem. Pedro Domingos Dept. Computer Science & Eng. University of Washington. What is the AI Problem?. Build robots that do every job humans do, as well as them or better. (Preferably much better.). Why Haven’t We Solved It Yet?.

kiana
Download Presentation

How We’re Going to Solve the AI Problem

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. How We’re Going toSolve the AI Problem Pedro Domingos Dept. Computer Science & Eng. University of Washington

  2. What is the AI Problem? Build robots that do every job humans do,as well as them or better. (Preferably much better.)

  3. Why Haven’t We Solved It Yet? • Because no one has really tried • Everyone works on subproblems • Because we don’t have the hardware • But this will soon change

  4. A Phase Shift in AI Research • In 10 years (give or take), hardware will reach the computational power of the human brain • Then we can really start trying • Progress is much faster when you work on the actual problem • In the meantime: Lay the groundwork

  5. Ways to Solve AI

  6. Mother of All KBs • Hypothesis: We don’t need no new discoveries; just a lot of knowledge • Empirical test: Miserable failure • It’ll take tens of thousands of rules … • No, hundreds of thousands … • No, wait, more like millions … • Deduction is not enough! • We need induction and uncertain reasoning • Cycorp now realizes this • … And at least they tried

  7. Web Mining • Let’s read the Web instead ofmanually inputting formal knowledge • Pros • There’s a lot of stuff in the Web • Language is great window into intelligence • Great application value in its own right • Cons • The Web sucks • Language is built on top of vision,motor control, everyday life, etc.

  8. Retracing Evolution • Human intelligence is too hard.Build an insect first! • Well, that turns out to be easy, and doesn’tbring us much closer to human intelligence • Brooks got tired after Genghis & pals,and went straight to Cog (which did nothing useful) • Evolution is blindingly slow • Subsumption architecture still seems likea good idea

  9. Robot Baby • Build a robot and let it learn like a baby • Pros • Guaranteed to work! (Existence proof) • It solves the real problem • Cons • Is it overkill? (Intelligent ≠ Human) • Do we really have to wait 10 years for it to grow up? • Too much to try at once (start w. symbol grounding?) • And we don’t have the hardware …

  10. One Algorithm • Hypothesis: Neocortex is all one algorithm • Pretty good empirical support so far • It does everything: learning, reasoning, vision,language, motor control, etc. • Shortest path to AI: Figure out what this algorithm is • Reverse engineer the brain? Not necessarily • Testbed: Digit recognition? No! • Algorithm has to work on many different problems without change

  11. How About This? • Build a Robot Baby • Power it with One Algorithm • Add stages one by one (Subsumption) • Feed it Cyc • And then have it Read the Web

  12. It Takes a (Global) Village Inference CollectiveKnowledgeBase Rules Queries Facts Answers Contributors Users Feedback Outcomes Learning [Richardson and Domingos, KCAP-2003]

  13. What Can We Do Now? • Algorithms that work on any number of cores • Solve two problems simultaneously • Learning and reasoning • Vision and robotics • Language and common sense • Then solve three • Solve series of increasingly hard problems • Don’t get stuck in local optima • If you have 80/20 solution, move on to next harder problem • Stay off the bandwagons

  14. Got the Hardware. Now What? • Divide and conquer doesn’t work for AI • Gluing pieces together doesn’t work(engineering hits “complexity wall”) • We need the right language • Mechanics: Calculus • Electromagnetism: Differential operators • Alternating current: Complex numbers • Digital circuit design: Boolean logic • AI: Not there yet (but see Markov logic)

  15. Three Simple Tests • You’re not solving the AI problem if … • Your system doesn’t work online • Your system doesn’t simultaneously process more than one type of information • Your system doesn’t process so much informationit needs a focus of attention mechanism • Consciousness = Lots of informationwell integratedonlinewith a focus of attention

  16. When Will We Solve AI? • Common view: Never • Kurzweil, Moravec: 25 years • Both wrong • Solving AI is a long-term project • How do we make sure we’re making progress? • How do we speed up progress? • How do we keep up motivation (and funding)?

More Related