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

AAAI’07 TalkM.H. Coen 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 IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion (Taeniopygia guttata)

AAAI’07 TalkM.H. Coen In the grand scheme of things… AIEngineering Applied Math Science Statistical NLP Deep Blue/Chinook DARPA Grand Challenge Optimization Operations Research Statistical Machine Learning Physiology Neuroscience Cognitive Science IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion

AAAI’07 TalkM.H. Coen A fundamental question Animals solve extremely difficult non-parametric and distribution free learning problems during development. How? IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion Belief: Answering this lets us: 1)Better understand learning in animals 2)Build new types of machine learning systems

AAAI’07 TalkM.H. Coen 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) IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion In a nutshell, CMC exploits redundancy within correlated datasets to discover unknown categories

AAAI’07 TalkM.H. Coen How does it work? A simple example Assume two events in the world: red and blue Events in the world: IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion

AAAI’07 TalkM.H. Coen 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: Mode A Mode B Thought experiment creature: IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion

AAAI’07 TalkM.H. Coen The view from the inside the creature… Mode AMode B Can we learn the red and blue events by sharing internal perspectives? IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion Note: We will call these datasets slices

AAAI’07 TalkM.H. Coen Recovering the categories 1)Iteratively project regions in each dataset onto the other dataset. 2) Merge regions in each dataset whose projections are the closest. 3)Continue… To play with online, Google: MIT Artificial Intelligence Demonstrations IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion Mode AMode B

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

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

AAAI’07 TalkM.H. Coen 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) IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion

IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion

AAAI’07 TalkM.H. Coen Dynamics of song acquisition Day 1: Fledgling is born! First month: Father sings to his children ~Day 20: Males begin singing to themselves Day 90: Song crystallizes at sexual maturity IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion

An Architecture for Sensorimotor Learning Sensory CortexMotor Cortex Perceptual Processing Perceptual Slices Motor Control Innate Exploratory Motor Behaviors Sensory Organs Muscles/Effectors Afferent Processing Efferent Processing External World Perceptual Slices Events in the world IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion Cross-Modal Clustering happens here!

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

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

AAAI’07 TalkM.H. Coen Parental training: a simple example IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion

AAAI’07 TalkM.H. Coen Self-observation of innate activity Internal self-observation (Cartesian Theater) IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion External self-observation (Perceptual channels)

Recursive cross-modal clustering

AAAI’07 TalkM.H. Coen Acquired intentional motor control IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion

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

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

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

AM FM Entropy Amplitude Mean Frequency Pitch Goodness Pitch Pitch Weight

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

AAAI’07 TalkM.H. Coen A learner for birdsong Lower level features Higher level features IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion A 15 dimension, highly compact manifold

AAAI’07 TalkM.H. Coen Some zebra finch slices Goodness of pitch Pitch Mean frequency Wiener Entropy IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion

AAAI’07 TalkM.H. Coen Early “bird” babbling IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion

AAAI’07 TalkM.H. Coen Samba “Samba’s son” Birdsong mimicry IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion A word about evaluating empirical experiments…

AAAI’07 TalkM.H. Coen 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 IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion

AAAI’07 TalkM.H. Coen 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 IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion

AAAI’07 TalkM.H. Coen Extra slides follow

AAAI’07 TalkM.H. Coen Acquisition of harmonic complexity IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion

AAAI’07 TalkM.H. Coen Related work 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) Primary distinctions of our approach: 1.Fully unsupervised 2.Non-parametric:  Distribution free  Unknown number of clusters 3.Presumes no domain knowledge 4.Neurologically motivated IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion

AAAI’07 TalkM.H. Coen 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 IntroductionBackgroundZebra FinchesSensorimotor LearningDiscussion