Michael H. Coen MIT Computer Science and Artificial Intelligence Laboratory

1. Learning to Sing Like a Bird: The Self-Supervised Acquisition of Birdsong Michael H. Coen MIT Computer Science and Artificial Intelligence Laboratory AAAI’07 Talk July 25, 2007 &

2. Introduction Background Zebra Finches Sensorimotor Learning Discussion Outline • Why do this research? • Background: Cross-Modal Clustering (+ demo) • A biologically-inspired algorithm for machine learning (Coen 2005, 2006a, 2006b, Coen et al. 2007) • A brief introduction to the zebra finch • An architecture for sensorimotor learning (+ demo) • A simple, recursive application of cross-modal clustering • Views motor control as perception backwards • Discussion (Taeniopygia guttata)

3. Introduction Background Zebra Finches Sensorimotor Learning Discussion In the grand scheme of things… Statistical NLP Deep Blue/Chinook DARPA Grand Challenge Optimization Operations Research Statistical Machine Learning Physiology Neuroscience Cognitive Science

4. Introduction Background Zebra Finches Sensorimotor Learning Discussion A fundamental question Animals solve extremely difficult non-parametric and distribution free learning problems during development. How? • Belief: Answering this lets us: • Better understand learning in animals • Build new types of machine learning systems

5. Introduction Background Zebra Finches Sensorimotor Learning Discussion Cross-modal clustering briefly… • Use multiple viewpoints (or datasets) describing the same events makes learning easier • Biological motivation: • Perceptual systems share information constantly during “ordinary” perception (Stein and Meredith 1993, Shimojo and Shams 2001, Calvert et al. 2004, Spence and Driver 2004) In a nutshell, CMC exploits redundancy within correlated datasets to discover unknown categories

6. Events in the world: Introduction Background Zebra Finches Sensorimotor Learning Discussion How does it work? A simple example • Assume two events in the world: red and blue

7. Mode B Mode A Thought experiment creature: Introduction Background Zebra Finches Sensorimotor Learning Discussion How does it work? A simple example • Assume two events in the world: red and blue • Assume two datasets: Mode A and Mode B Events in the world:

8. Introduction Background Zebra Finches Sensorimotor Learning Discussion The view from the inside the creature… Can we learn the red and blue events by sharing internal perspectives? Mode A Mode B Note: We will call these datasets slices

9. Introduction Background Zebra Finches Sensorimotor Learning Discussion Recovering the categories Mode A Mode B • Iteratively project regions in each dataset onto the other dataset. • 2) Merge regions in each dataset whose projections are the closest. • Continue… • To play with online, Google: • MIT Artificial Intelligence Demonstrations http://ai6034.mit.edu/fall06/index.php?title=Demonstrations

10. What can you learn when you know nothing? Understand fMRI data Learn to sing Sensorimotor learning Acquire language

11. What can you learn when you know nothing? Understand fMRI data Learn to sing Sensorimotor learning Acquire language

12. Introduction Background Zebra Finches Sensorimotor Learning Discussion The zebra finch • Small, unusually social oscine songbird • Perhaps the most studied bird in neuroscience • Complex vocal harmonics • People often mistake spectrograms for human speech • Almost identical FoxP2 gene with humans • Governs vocal generation (Taeniopygia guttata)

13. Introduction Background Zebra Finches Sensorimotor Learning Discussion

14. Introduction Background Zebra Finches Sensorimotor Learning Discussion Dynamics of song acquisition Day 90: Song crystallizes at sexual maturity ~Day 20: Males begin singing to themselves First month: Father sings to his children Day 1: Fledgling is born!

15. Introduction Background Zebra Finches Sensorimotor Learning Discussion Events in the world An Architecture for Sensorimotor Learning External World Sensory Organs Muscles/Effectors Perceptual Processing Motor Control Afferent Processing Efferent Processing Perceptual Slices Perceptual Slices Innate Exploratory Motor Behaviors Cross-Modal Clustering happens here! Sensory Cortex Motor Cortex

16. Internal Perception (Cartesian Theater) Motor Slices Motor Slices An Architecture for Sensorimotor Learning External World Sensory Organs Muscles/Effectors Perceptual Processing Motor Control Afferent Processing Efferent Processing Perceptual Slices Innate Exploratory Motor Behaviors Innate Exploratory Motor Behaviors Cross-Modal Clustering now happens here! Sensory Cortex Motor Cortex

17. An Architecture for Sensorimotor Learning External World Sensory Organs Muscles/Effectors Perceptual Processing Internal Perception (Cartesian Theater) Motor Control Afferent Processing Efferent Processing Perceptual Slices Motor Slices Innate Exploratory Motor Behaviors Sensory Cortex Motor Cortex

18. Introduction Background Zebra Finches Sensorimotor Learning Discussion Parental training: a simple example

19. Introduction Background Zebra Finches Sensorimotor Learning Discussion Self-observation of innate activity External self-observation (Perceptual channels) Internal self-observation (Cartesian Theater)

20. Recursive cross-modal clustering

21. Introduction Background Zebra Finches Sensorimotor Learning Discussion Acquired intentional motor control

22. Internal Perception (Cartesian Theater) Motor Slices An Architecture for Sensorimotor Learning External World Sensory Organs Muscles/Effectors Perceptual Processing Motor Control Articulatory Synthesizer Afferent Processing Efferent Processing Perceptual Slices Perceptual Slices Innate Exploratory Motor Behaviors Sensory Cortex Motor Cortex

23. Internal Perception (Cartesian Theater) Motor Slices An Architecture for Sensorimotor Learning External World Sensory Organs Muscles/Effectors Perceptual Processing Motor Control Articulatory Synthesizer Afferent Processing Efferent Processing Perceptual Slices Innate Exploratory Motor Behaviors Sensory Cortex Motor Cortex

24. Motor Slices An Architecture for Sensorimotor Learning External World Sensory Organs Muscles/Effectors Perceptual Processing Internal Perception (Cartesian Theater) Motor Control Articulatory Synthesizer Afferent Processing Efferent Processing Perceptual Slices Innate Exploratory Motor Behaviors Sensory Cortex Motor Cortex

25. Mean Frequency AM FM Pitch Goodness Entropy Pitch Pitch Weight Amplitude

26. Defining Songemes Mean Frequency AM FM Pitch Goodness Entropy Pitch Pitch Weight Amplitude

27. Introduction Background Zebra Finches Sensorimotor Learning Discussion A learner for birdsong Higher level features Lower level features A 15 dimension, highly compact manifold

28. Introduction Background Zebra Finches Sensorimotor Learning Discussion Some zebra finch slices Mean frequency Wiener Entropy Goodness of pitch Pitch

29. Introduction Background Zebra Finches Sensorimotor Learning Discussion Early “bird” babbling

30. Introduction Background Zebra Finches Sensorimotor Learning Discussion Birdsong mimicry Samba “Samba’s son” A word about evaluating empirical experiments…

31. Introduction Background Zebra Finches Sensorimotor Learning Discussion Contributions • A new architecture for sensorimotor learning • Entirely self-supervised • Biologically inspired • Extremely simple, dimensionally compact • Wide range of applications • Robotics • Sensor arrays • Computational learning • Dynamic control systems • Skill acquisition based on observation

32. Introduction Background Zebra Finches Sensorimotor Learning Discussion Acknowledgments Ofer Tchernichovski Whitman Richards Rodney Brooks Howard Shrobe Patrick Winston Robert Berwick Gerald Sussman Adam Kraft Kobi Gal Krzysztof Gajos To play with online, Google: MIT Artificial Intelligence Demonstrations http://ai6034.mit.edu/fall06/index.php?title=Demonstrations

33. Extra slides follow

34. Introduction Background Zebra Finches Sensorimotor Learning Discussion Acquisition of harmonic complexity

35. Unsupervised clustering: Language de Marcken (1996) de Sa and Ballard (1997) Lin (2004) Vision Bartlett (2001) Stauffer (2002) Statistical clustering Dempster et al. (1977) Smyth (1999) Blind signal separation Hyvärinen (2001) Neuroscience Becker and Hinton (1995), Becker (2005) Granger (2003) Auditory scene analysis Slaney et al. (2001) Minimal supervision Blum and Mitchell (1998) Co-Clustering (Bi-Clustering, Block Clustering) Friedman, Mosenzon, Slonim, and Tishby (2001) Taskar, Segal, and Koller (2001) Madeira and Oliveira (2004) Analysis of animal vocalizations: Birds (finches and buntings) Kogan and Margoliash (1997) Bowhead Whales Mellinger and Clark (1993) African elephants Clemins and Johnson (2003) Humans Guenther and Perkell (2004) Introduction Background Zebra Finches Sensorimotor Learning Discussion Related work • Primary distinctions of our approach: • Fully unsupervised • Non-parametric: • Distribution free • Unknown number of clusters • Presumes no domain knowledge • Neurologically motivated

36. Introduction Background Zebra Finches Sensorimotor Learning Discussion Current and future work • Human protolinguistic babbling • Proficiency of an eight month old child • Entire phonetic structure of English • Building an atlas of modular brain function • From human and rat fMRI data • New approaches to clinical treatments for autism • Theoretical investigations • Convergence properties