big data 2015 1 michigan state university introduction to how brains deal with big data juyang weng...

Big Data 2015 1Michigan State University

Introduction to How Brains Deal with Big Data

Juyang WengProfessor

Dept. of Computer Science and Engineering,Cognitive Science Program, and Neuroscience ProgramMichigan State University, East Lansing, Michigan USA

http://www.cse.msu.edu/~weng/


Brain Myth is Like Blind Men and an Elephant

Brain

Electrical Engr.

Computer Sci.Biology

Neuroscience Psychology

Math.


“Big Data” is Only a Hype without It

Big Data


Must Each Brain Learn from Its Body?

SAIL

Flying Fox(AVS)

Crosser

Dav


Why Active Body? Kitten Carousel Experiment

A classic study by Held & Hein 1963 Kittens raised from birth in total darkness When old enough to walk, placed in

“kitten carousel” for 42 days One kitten harnessed to pull the carousel Another just being carried in a box.


Quiz: Kitten Carousel Experiment

Quiz: Your predicted important outcome from this experiment

A. The two kittens are different: one is stronger

B. The active kitten refused to work further later

C. Only the passive kitten learned to see because it has time

D. Only the active kitten learned a critical visual meaning

E. Both kittens learned to see


Visual Cliff Visual cliff:

A transparent platform Visual sharp drop in elevation

Human infants: 6 – 8 months old, a week or two after they

began to crawl all would cross a visual cliff in initial trials They became increasingly reluctant to cross

in later trials, although nothing bad had happened during crossing.

Carousel kittens: Passive one does not fear Active one does

Implication: Vision is very much developed from

experience!


Brains Take Flood of Data!

Wu, Guo, Wang, Weng, ICBM 2013


How Did Your Brain Learn to Segment?

Wang, Wu and Weng, IJCNN 2011

(a) Bottom-up input to a neuron. (b) True object contour. (c) Estimated synaptogenic factor


Neuroscience: Status Quo

“Data rich and theory poor”

Social habit:“We still do not know how the brain works”(wrong)

“Wetware” work:A lot of advances but in tiny pieces

Kandel, Schwartz and Jessell 2000


V1: Not Really Orientation?

Hubener et al. Journal of Neuroscience 1997

Orientation map

Ocular dominance map

Each area has thebottom-up input part(e.g., from retina) and top-down input part (e.g.,concepts from motor) but this work did not consider the top-down part

White: ipsilateralBlack: contralateral

A: anteriorP: posteriorM: medialL: lateral1mm


Innate Edge Detectors?

Kittens are exposed to vertical (or horizontal) edges only after birth

Visually blind to horizontal edges (e.g., no startle response)

No V1 neural cells were found to respond to horizontal edges

Blakemore & Cooper, Nature 1970


Kittens: Eye Closed Early in Life

David H. Hubel and Torsten N. Wiesel, Journal of Physiology, 1970

Normal


Brain Self-Wires

Sur, Angelucci and Sharm, Nature 1999

Ferrets rewired early in life

“See” using the “sound” zone


Early Blind Human:Visual Zone for Hearing and Touch

Trans Magnetic Stimulation (TMS) to the occipital area (normal visual area) hampers the early blind for Sound localization Verbal memory Braille identification


Conclusion: Brains Constantly Wire Themselves as a Whole

Kittens

Ferrets

Humans


History of Intelligence ResearchNatural 500 BC

Socrates, Plato, Aristotle 1500’s

Leibniz, Locke, Descartes 1800’s

Pavlov, Thomdike 1900’s

Rumelhart, McClelland

Artificial

1900’s Turing


The “Task Specific” Trap Treat brain as

“monolithic” pattern classification only

Cannot attend an object from cluttered scenes

Hand-crafted control flow in the “brain”

Task-specific: 7 tasks

Cannot “develop”

Eliasmith et al. Nature, 2012


AI: Symbolic School vs Connectionist School

Symbols are logic and clean.Artificial neural networks are

analogical and scruffy.

- Marvin Minsky, 1991

(Artificial) neural networks do not abstract well.

- Michael Jordan, IJCNN 2011


Now-Popular Deep Learning Neocognitron: Fukushima 1980 Cresceptron: Weng at al. 1991

Res-Reduction + Max-pooling HMAX: Poggio et al. 1997 Deep convolution nets with back-

prop 1998 - presentLeCun, Hinton, and others

Popularized by Google. Thanks! Brain’s circuits is NOT a cascade of

modules! Why? Computational reasons …


Not a Cascade

Felleman &Van Essen 1991


Intelligence: Symbolic vs. Connectionist

Symbolic 1950’s

logic and clean 2000’s

Common-sense knowledge base

2010’s

Connectionist 1950’s

Analogical and sruffy 2000’s

Autonomous Development

2010’s

Unification of the Two SchoolsGreat interests from industries


Must Auto-Develop Autonomous Mental

Develop (AMD) Task-nonspecific “Genome-like”

Developmental Program(only about 2 pages long)

IEEE ICDL Conferences IEEE Transactions on

AMD WWN-1 through WWN-9

Weng et al. Science 2001


8 Requirements for Practical Learning

Eight necessary operational requirements:1. Environmental openness: cluttered environments2. High dimensional sensing (e.g., video cameras are necessary)3. Completeness in internal representation for each age group4. Online5. Real time speed6. Incremental:

for each fraction of second (e.g., 10-30Hz)7. Perform while learning8. Scale up to large memory

Existing methods at ICDL conferences aimed at some, but not all. Our work

SAIL (1998 – 2010) dealt with the 8 requirements altogether DN (2008 – present) is further brain-inspired


Experiments: Where-What Networks (WWNs) WWN-1 (2008): single object; cluttered scenes, without presegmentation:

from location to type (recognition task) and from type to location (detection task) using the same network for the two tasks

WWN-2 (2010): add to above free viewing WWN-3 (2010): add to above multiple objects WWN-4 (2010): allowing bypass MM and PP WWN-5 (2011): add to above scale WWN-6 (2012): synaptogenic factors enable neurons to self-wire WWN-7 (2013): add to above multiple parts of each object WWN-8 (2013): multi-modality (left-eye right eye), to appear BigData 2015 WWN-9 (2015): relation of objects (A-B group, A plays B, etc.) Texty: natural language and knowledge acquisition from natural text


NI and AI Unified Unification of

the Symbolic and Connectionist Schools The control of Turing

Machines as Deterministic Finite Automaton

The brain is an emergent Turing Machine

Automatic programming for general purpose


Brain-Like AI: Theorems Brains are Emergent Finite Automata (FA)

(world: “tape”) The controller of a Turing Machine is an FA DN learns any teacher FA, immediately and error-free Brain Networks are optimal in maximum likelihood Brain Turing Machine:

Self wires, re-wires, and re-wires again … Automatically self-program for general purposes

Future AI: Machines automatically self-program all the time though their “lifetimes”


Concepts, Abstraction, Invariance

Weng IJCNN 2010


Dual Optimality: Lobe Component Analysis (LCA)

Weng IJCNN 2010


Turing Machine (TM)

Control: the transition function of TM


Universal Turing Machine

The tape has input data

The tape has input program and data


The Control of TM is an FA

This form is the transition function of an FA Weng IJIS 2015, IJCNN 2015


FA: Equivalence Classes

consists of all strings that end in state i

All possible strings from letters in


Construct FA

Typically there are many possible states A humans tries to prune many states, if the

goal is known The problem in the micro-world can be a graph

G FA is a solution to the problem Search algorithms: Dijkstra alg. and A* Search


How Does an FA Emerge?

Teacher FA: Accept the language


Traditional FA: Symbolic Look-Up Table


Brain-Like Emergent FA: Patterns Only


Developmental Network (DN): Wire Incrementally

Y Y Y Y


Basic Theory A brain is a finite automaton, but it emerges The most basic version:

Y area (brain) wires to predict both Z and X Z: concepts, actions, goals, intents, and so on X: expected input: mental images. The EFA thinks! Motivation: important events to speed up learning

Y Y Y Y


General Vision Problem Solved in Principle:Where-What Network 7

10 objects trained and (disjointly) tested Concept and rules: Location, Type, Scale, Relation

Wu, Guo, Wang, Weng, ICBM 2013


Ach and NE Transmitters: Self Segmentation

Wang, Wu and Weng, IJCNN 2011

(a) Bottom-up input to a neuron. (b) True object contour. (c) Estimated synaptogenic factor


Accuracy of the Network


Relation: Group of Objects Relationship of two animals

A-B Group, A plays B, A gives B to C Knowledge hierarchy be automatically built

Q. Guo, X. Wu & J. Weng, IJCNN 2014


Invariant Concepts Emerge

Weng IJCNN 2010It emerges with many areas

not a single layer!

Input image


Earlier Later Neurons Early neurons: X and Z as input Later neurons: Z only as input

Guo, Wu & Weng, IJCNN 2014


Experiments and Results Training:

individual objects first object groups next


DN for Stereo VisionINNS Big Data: This Sunday, 11:00am – 1pm


DN for Vision-Guided NavigationINNS Big Data: This Monday, 3:30pm – 5pm


Pitfalls in Brain Projects Natural Intelligence: Generate more data, do

not care about basic automata theory Neural Networks as AI: Use traditional

methods to process brain data or “big data” Risk: Much tax-payers money not well used Suggestion: Every brain project must have a

clear plan to educate all researchers for 6 disciplines: biology, psychology, neuroscience, electrical engineering, computer science, and math


The Major Obstacles of Brain Projects

“More data”: There are sufficient data for humans to understand how the brain works(Very few people see this)

Major obstacles:Habit: Get money and do my own traditional workLack: No degree program for understanding the brain

Suggestions:Fund new computational brain degree programsInvest to get ahead of the upcoming brain tech wave


Brain-Mind Institute: 2012 - Present


Brain-Mind Institute Courses BMI 811 Biology for BMR (2012) BMI 821 Neuroscience for BMR (2012) BMI 831 Cognitive Science for BMR (2013, 2014, 2015) BMI 861 Brain Automata (2015) BMI 871 Computational Introduction to Brain-Mind (2012,

2013, 2014, 2015)

BMI Course Packs are now available: including video lectures, ppt files, exams grading, BMI course certificate (if you pass)


Near-Term Applications Mobile phone / wearable computing based:

Elderly with dementia Children to school Blind and poor vision Drunk walkers

Internet service based Images, video, natural language processing …

Move from lab experiments to development of a new kind of products


Long-Term Applications Developmental Robots will live Developmental Robots will outlive human

individuals The “brains” of developmental robots will be

copied many times to process various internet data

How many years do we need to wait for that to happen?


Thank you!Another tutorial by Weng

Introduction to Brainsas Emergent Turing Machines,

9am – noonthe following Thursday, Aug. 13

ICDL-EpiRob 2015, Providence, RI, USA

big data 2015 1 michigan state university introduction to how brains deal with big data juyang weng...

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