Revolutionizing Machine Learning: A Vision for Cognitive Systems

1. Machine Learning for Cognitive Systems Pat Langley Institute for the Study of Learning and Expertise Palo Alto, California and Center for the Study of Language and Information Stanford University, Stanford, California http://cll.stanford.edu/~langley langley@csli.stanford.edu The views contained in these slides are the author’s and do not represent official policies, either Expressed or implied, of the Defense Advanced Research Projects Agency or the DoD.

2. The field of machine learning has many success stories, but: Expanding our Computational Horizons Instead, we need a new vision for machine learning technology that: • supports the construction of general intelligent systems; • aspires to the same learning abilities as appear in humans. these successes are prime examples of niche AI, which develops techniques that are increasingly powerful but that apply to an ever narrower classes of problems. This would produce a broader research agenda that would take the field into unexplored regions. niche AI power cognitive systems generality

3. Current learning research focuses on asymptotic behavior: Challenge 1: Rapid Learning In contrast, humans are typically able to: • learn reasonable behavior from relatively few cases; • take advantage of knowledge to speed the learning process. We need more work on learning from few cases in the presence of background knowledge. methods for learning classifiers from thousands of cases; methods that converge on optimal controllers in the limit. performance experience

4. Current learning research focuses on isolated induction tasks that: Challenge 2: Cumulative Learning In contrast, much human learning involves: • incremental acquisition of knowledge over time that • builds on knowledge acquired during earlier episodes. take no advantage of what has been learned before; provide no benefits for what is learned afterwards. We need much more research on such cumulative learning. initial knowledge extended knowledge

5. Current learning research emphasizes tasks like classification and reactive control, whereas humans learn: Challenge 3: Varied Learning • grammars for understanding natural language; • heuristics for reasoning and problem solving; • scripts and procedures for routine behavior; • cognitive maps for localization and navigation; • models that explain the behavior of artifacts. We need more work on learning such varied knowledge structures. human learning abilities current focus of machine learning

6. Current learning research focuses on performance tasks that: Challenge 4: Compositional Learning But many other varieties of learning instead involve: • the acquisition of modular knowledge elements that • can be composed dynamically by multi-step reasoning. involve one-step decisions for classification or regression; utilize simple reactive control for acting in the world. We should give more attention to learning such compositional knowledge. knowledge reasoning knowledge reasoning

7. Current evaluation emphasizes static data sets for isolated tasks that: Challenge 5: Evaluating Embedded Learning To support the evaluation of embedded learning systems, we need: • a set of challenging environments that exercise learning and reasoning, • that include performance tasks of graded complexity and difficulty, and • that have real-world relevance but allow systematic experimental control. favor work on minor refinements of existing component algorithms; encourage mindless “bake offs” that provide little understanding. battle management in-city driving air reconnaissance

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