Towards Human Level AI

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Towards Human Level AI

• Jitendra Malik
• U.C. Berkeley

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This talk

• Approaches to computational intelligence
• Review of human intelligence and child
  development
• Review of computer vision and its problems
• The problem of visual categories
• Proposed agenda

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Paradigms for mechanizing intelligence 1960

• Classic AI (McCarthy, Minsky, Newell, Simon)
• Games, theorem-proving, reasoning
• Search, represent and reason in first-order logic
• Pattern Recognition/Neural Networks (Rosenblatt)
• Classification, Associative memory
• Learning (Perceptrons )
• Estimation and Control (Kalman)
• Decide action in uncertain, time-varying
  environment
• Markov Decision Processes, adaptive control

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Achievements (1960-1990)

• Classic AI (McCarthy, Minsky, Newell, Simon)
• Chess programs, planning sytems, first
  generation expert systems
• relational databases
• Pattern Recognition/Neural Networks (Rosenblatt)
• Various applications of MLPs and other learning
  techniques
• HMMs for speech recognition
• Estimation and Control (Kalman)
• Man on Moon
• Self-driving cars

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John von Neumanns warning

• As a mathematical discipline travels far from its
  empirical source, or still more, if it is a
  second and third generation only indirectly
  inspired from ideas coming from 'reality', it is
  beset with very grave dangers. It becomes more
  and more purely aestheticizing, more and more
  purely l'art pour l'art. This need not be bad, if
  the field is surrounded by correlated subjects,
  which still have closer empirical connections, or
  if the discipline is under the influence of men
  with an exceptionally well-developed taste.
• "But there is a grave danger that the
  subject will develop along the line of least
  resistance, that the stream, so far from its
  source, will separate into a multitude of
  insignificant branches, and that the discipline
  will become a disorganized mass of details and
  complexities.
• In other words, at a great distance from
  its empirical source, or after much 'abstract'
  inbreeding, a mathematical subject is in danger
  of degeneration. At the inception the style is
  usually classical when it shows signs of
  becoming baroque the danger signal is up. It
  would be easy to give examples, to trace specific
  evolutions into the baroque and the very high
  baroque, but this would be too technical.
• In any event, whenever this stage is
  reached, the only remedy seems to me to be the
  rejuvenating return to the source the
  reinjection of more or less directly empirical
  ideas. I am convinced that this is a necessary
  condition to conserve the freshness and the
  vitality of the subject, and that this will
  remain so in the future.".

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Brain Sub-Systems

• Sensory
• Vision (30-50)
• Audition
• Somatic
• Chemical (Taste, Smell)
• Motor
• Manipulation
• Locomotion
• Speech
• Language
• Central
• Planning and problem solving
• ..

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What do we know from human child development?

• It is nature AND nurture

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Visual Development

• Axon growth guided by chemical gradients (in turn
  due to gene expression)
• Critical period for development of orientation
  selectivity (Hubel Wiesel)
• New-born babies sensitive to faces
• Visual tracking 3mo
• Binocularity/Stereopsis 4mo

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Language Development

• Babbling tuning phonemes
• Developing link between words and objects
• Words refer to objects
• They denote categories
• Objects have only one name
• Slow between 12 and 18 mo (median no. words at
  20 mo is 169) and very rapid afterwards (6 yrs -
  13k)
• Word pairs (18-24 mo)
• Grammar takes off after that

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Cognitive development

• Categorization
• Perception/reality distinction
• Self-Awareness (mirror test)

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Many types of memory

• Semantic memory
• Episodic memory
• Skill memory

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The Hilbert Problems of Computer Vision
Jitendra Malik
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Forty years of computer vision 1963-2003

• 1960s Beginnings in artificial intelligence,
  image processing and pattern recognition
• 1970s Foundational work on image formation
  Horn, Koenderink, Longuet-Higgins
• 1980s Vision as applied mathematics geometry,
  multi-scale analysis, control theory,
  optimization
• 1990s
• Geometric analysis largely completed
• Probabilistic/Learning approaches in full swing
• Successful applications in graphics, biometrics,
  HCI

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And now

• Back to basics the classic problem of
  understanding the scene from its image/s
• Central question Interplay of bottom-up and
  top-down information

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Early Vision

• What can we learn from image statistics that we
  didn't know already?
• How far can bottom-up image segmentation go?
• How do we make inferences from shading and
  texture patterns in natural images?

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Static Scene Understanding

• What is the interaction between segmentation and
  recognition?
• What is the interaction between scenes, objects,
  and parts?
• What is the role of design vs. learning in
  recognition systems?

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Dynamic Scene Understanding

• What is the role of high-level knowledge in long
  range motion correspondence?
• How do we find and track articulated structures?
• How do we represent "movemes" and actions?

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Proposed Research Agenda

• Child Language Acquisition with visual input