1. How We’re Going to Solve 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 ApproachProponentsExamples Mother of all KBsDoug LenatCyc Web miningTom Mitchell Oren Etzioni WebKB KnowItAll Retrace evolutionRod BrooksGenghis, Cog Robot babyPaul CohenRobot Baby One AlgorithmGeoff Hinton Jeff Hawkins Neural networks

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 of manually 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’t bring 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 like a 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 [Richardson and Domingos, KCAP-2003] Collective Knowledge Base Users Rules Facts Feedback Queries Answers Outcomes Inference Learning Contributors

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 information it needs a focus of attention mechanism Consciousness = Lots of information well integrated online with 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)?