THE BRAIN ON THE MICROCHIP

Brain on Chip: 

Brain on Chip Petar Kocovic Belgrade, Serbia Member of: IEEE since 1987 ASME since 1987 NY Acadamy of Science, since 1998 E-mail: petar.kocovic@gmail.com

The History of Religion and Science: 

The History of Religion and Science Phenomenon Explanation Thunder and Lightening Origin of the Universe Origin of Species Origin of Humans Origin of Life Intelligence Consciousness Free Will God God God God God God God God Atmospheric Electrical Discharge Big Bang Evolution by Natural Selection Evolution by Natural Selection Molecular Evolution Computation/AI Computation/AI Computation/AI

Slide3: 

Modified from: Hope J & Hope T (1997) Competing in the Third Wave Global Technological Innovation Steam Power 1780s The Railways 1840s Electric Power 1890s The Motor Car 1930s ICT 1980s Biotechnology 1990s Nanotechnology 2000s Brain science ?

Slide4: 

Agricultural era Modified from Richard W Oliver (1998) The Shape of Things to Come Technological era IT era Primitive era 21st century Biotechnology era 650 1750 1950 2000 Early Industrial era Major changes

Slide5: 

Major changes Agricultural era 21st century 650 Knowledge era 1750 1950 2000 1 2 3 4 * * estimates *<54% Technological era 71% 29 Early Industrial era 54% 45 IT era 76% 24 80% 20 Biotechnology era *>80% <20 Human Capital vs Physical Capital Comparison of shares of income received in Western Europe/ Free World (%) Modified from Simon Knuznets (1966) Modern economic growth,Yale >45

Slide6: 

http://news.bbc.co.uk/hi/english/static/in_depth/world/2002/disposable_planet/waste/statsbank.stm Waste Disposal in Cities in Selected Countries High Income Latin America Asia-Pacific Arab States Africa Transition Source: Urban Indicators (1998) Solid waste disposed of, formally (%) People in rich countries throw away up to 800kg of waste each a year, compared to less than 200kg in the poorest countries. As population, consumption and wealth increase, so does the quantity of waste we produce. The rich countries of the OECD produce an annual total of almost two tonnes of waste for every person. 0 20 40 60 80 100 25 % 31 % 44 % 59 % 66 % 78 %

Slide7: 

According to the Basel Action Network, a pile of 500 computers contains 717kg of lead, 1.36kg of cadmium, 863 grams of chromium and 287 grams of mercury – all poisonous metals. Single samples taken by the BAN researchers in the region tested 190 times the WHO’s safe level for lead, had chromium levels 1338 times the level deemed safe in the US and tin levels 152 times the US threshold. http://news.bbc.co.uk/1/hi/in_depth/sci_tech/2004/planet/default.stm

 “Civilisation Disruption”: 

“Civilisation Disruption” Pollutions Loss of diversity Malnutrition & starvation More extreme weather events Submersion of land masses Spread of disease e.g. malaria, dengue Disruptions of ecosystems, including water supplies & food security Sources: IPCC, EPA

Slide9: 

War Deaths (2002) Military Spending (2002) www.sasi.group.shef.ac.uk/worldmapper "When the rich wage war, it is the poor who die." Jean-Paul Sartre: (1951)

Back to the Future: 

Back to the Future

Computer and Programming Languages: 

Computer and Programming Languages

Leibniz Calculator: 

Leibniz Calculator

Babbage Analytical Engine: 

Babbage Analytical Engine

ENIAC: 

ENIAC

John von Neumann: 

John von Neumann

1980-2002: 

1980-2002

Moore’s Law: 

Moore’s Law The number of transistor on chip is doubled every 18 months – Gordon Moore (co-founder of Intel – 1965)

Alan M. Turing: 

Alan M. Turing He worked with Kurt Gödel, Alonzo Church, and John von Neumann about the decision problem, universal (Turing) machines, code breaking Enigma and the artificial brain. 1950: paper “Computing Machinery and Intelligence” and the Turing test. A first program for chess (1953).

Slide19: 

And three others… Oliver Selfridge (Pandemonium theory) Nathaniel Rochester (IBM, designed 701) Trenchard More (Natural Deduction) 1956 Dartmouth Conference: The Founding Fathers of AI Alan Newell Herbert Simon Arthur Samuel

Presence Evolution: From Neanderthal to Lumierre: 

Presence Evolution: From Neanderthal to Lumierre

From Wide Screen Cinema to the Experience Theater: 

From Wide Screen Cinema to the Experience Theater

Entering the 3D world and Teleoperating a Robot: 

Entering the 3D world and Teleoperating a Robot

Virtual World and Avatar: 

Virtual World and Avatar

From Immersion of First Person to Motion in 3D World: 

From Immersion of First Person to Motion in 3D World

Being there in Third Person and Natural Interaction: 

Being there in Third Person and Natural Interaction

Reality is Model Brain builds a sustain existence: 

Reality is Model Brain builds a sustain existence

Presence Markets: Medical: 

Presence Markets: Medical

Presence Markets: Entertainment: 

Presence Markets: Entertainment

Presence Markets: Telecommunications: 

Presence Markets: Telecommunications

Presence Markets: Marketing, Education &Training, Military: 

Presence Markets: Marketing, Education &Training, Military

Presence Markets: Manufacturing and Design, Architecture and Design: 

Presence Markets: Manufacturing and Design, Architecture and Design

VISION DETECTION: 

VISION DETECTION

Image Formation: 

Image Formation

Lighting variation: 

Lighting variation

Color: 

Color

Texture Patterns: 

Texture Patterns

Boundary Detection: 

Boundary Detection

Gradients: 

Gradients

Corpus Callosum(Neurologist Roger Gorsky-UCLA): 

Corpus Callosum (Neurologist Roger Gorsky-UCLA) Corpus callosum – “cable” between left and right hemi-sphere of brain Woman have “wider” cable – 30%

Mars Exploration: 

Mars Exploration

Viking: 

Viking Viking I: launched Aug 20, 1975 Viking II: Sept 9, 1975

Mars Rover: 

Mars Rover Five rovers have been sent to Mars: Mars 2, Prop-M rover, 1971, failed Mars 3, Prop-M rover, 1971, failed Sojourner, Mars Pathfinder, landed successfully on July 4, 1997. Communications were lost on September 27, 1997.

No 4: SPIRIT – Landed Jan 4, 2004: 

No 4: SPIRIT – Landed Jan 4, 2004

OPPORTUNITY: Landed Jan 25, 2004: 

OPPORTUNITY: Landed Jan 25, 2004

Video motion Analysis: 

Video motion Analysis

Video Tracking: 

Video Tracking VISIONICS – Company of Illiminati

Why is Face Recognition Hard?: 

Why is Face Recognition Hard?

Slide48: 

Leading face recognition technology provider. Developer of FaceIt® engines. Focused on building cutting edge technology modules that are used by System integrators, product developers and OEMs. One of the Founding members of International Biometric Industry Association (IBIA). Faceit.com Visionics.com

Slide49: 

Competing Biometrics Three Biometrics capable of Identification: The reliable recognition of a person among millions of others. Why then Face?

Slide50: 

Face in Security Marketplace?

Slide51: 

Automatically capture all faces in complex scenes. I. Face Detection …From a distance, in a crowd, at a glance, & without subject participation.

Slide52: 

II. Segmentation

Slide53: 

Automatically extracts faces from background. II. Segmentation

Slide54: 

Follows faces over time. III. Tracking

Slide55: 

IV. Faceprint Coding Generates identity-specific digital code from face image---unique to an individual and invariant under viewing conditions. Faceprint or FID.

Slide56: 

V. Verification ? = Q: A: Yes/No Confidence

Slide57: 

VI. Identification

Slide58: 

How Does Face Recognition Work?

Slide59: 

The Technology Algorithm: Based on Local Feature Analysis. Describes faces in terms of spatial relationships among the local features or landmarks. Similar to minutiae-based finger algorithms. Repeatedly shown superior to all other algorithms

Slide60: 

Engine Specs.

Slide61: 

Eliminate the problem at the source: Document Issuance. Areas where this can be used Driving License Identity Cards Immigration Passport Combating ID Fraud FaceIt® Eliminates Aliases & Duplicates Only one record per face X

Slide62: 

Digital Photo Systems

Slide63: 

CCTV Surveillance

Slide64: 

The Need CCTV Scalability Requires FR Human Ability to Remember Unfamiliar Faces Human Ability to Remember a Large Quantity of Faces Human Ability to monitor a large number of video feeds Attention Span of Operator Financial Resource Requirement for Full Time Surveillance Operators Time Required to Search Recorded Video Recent study by Sandia Labs shows that the average guard viewing CCTV images has begun to lose attention after 15 minutes

Slide65: 

Target Applications Airports & Border Crossings- Terrorists, Immigration, Security, Smuggling. Town Centres. Retail - Shoplifting, Fraud. Banking - Holdup, Fraud. Casinos—card counters. Sporting Venues – Hooligans. Corporate Security - Active Surveillance, Investigations. Face in the crowd, Active surveillance, & Post Event Analysis.

Chess Game: 

Chess Game

Slide67: 

YouTube - Deep Blue beat G. Kasparov in 1997 YouTube - Deep Blue vs. Kasparov 1997 game 6 chess

GO: 

GO

KISMET – Robot with Social capabilitiesMIT 1990s: 

KISMET – Robot with Social capabilities MIT 1990s YouTube - Emotional Robot

Sony AIBO: 

Sony AIBO

AIBO: 

AIBO YouTube - AIBO robot playing with a cat!

Research prototypes 2: 

Research prototypes 2

Slide74: 

YouTube - new version amazing robot asimo YouTube - ASIMO avoids moving obstacles YouTube - Honda's Asimo Robot buckling on the stairs

More…: 

More… YouTube - Murata Boy, the Robot that can Ride Bicycles YouTube - Robot Violinist

Research prototypes 4: 

Research prototypes 4

Critical factor: 

Critical factor Subconciouss Conciouss Emotional Cognitive Conative-higher will Critical factor

What is Critical Factor?: 

What is Critical Factor? Part of Conscious. Analyze all information and filter them, and decide which information can pass to subconscious. This is unquestionably trough such as: emotions, drivers, instincts.

What is Intelligence? (www.wikipedia.org): 

What is Intelligence? (www.wikipedia.org) Intelligence is an umbrella term used to describe a property of the mind that encompasses many related abilities, such as : the capacities to reason to plan to solve problems to think abstractly to comprehend ideas to use language and to learn. Intelligence may include traits such as: Creativity Personality Character Knowledge or wisdom

What is IQ? (www.wikipedia.org): 

What is IQ? (www.wikipedia.org) An intelligence quotient, or IQ, is a score derived from one of several different standardized tests designed to assess intelligence. The term "IQ", from the German Intelligenz-Quotient, was coined by the German psychologist William Stern in 1912 Alfred Binet (1857-1911)

Flynn Effect (www.wikipedia.org): 

Flynn Effect (www.wikipedia.org) The Flynn effect is the rise of the average intelligence quotient (IQ) test scores over generations (IQ gains over time). The effect has also been reported for other cognitions such as semantic and episodic memory

What is Artificial Intelligence?: 

What is Artificial Intelligence? “[The automation of] activities that we associate with human thinking, activities such as decision-making, problem solving, learning…” (Bellman, 1978) “The exciting new effort to make computer think … machines with mind, in the full and literal sense.” (Haugeland, 1985) “The study of mental faculties through the use of computational models” (Charniak and McDermott, 1985) “The study of how to make computers do things at which, at the moment, people do better.” (Rich and Knight, 1991)

What is AI? (cont): 

What is AI? (cont) The study of the computations that make it possible to perceive, reason, and act.” (Winston, 1992) “AI…is concerned with intelligent behavior in artifacts.” (Nilsson, 1998) AI systemizes and automates intellectual tasks as well as any sphere of human intellectual activities. - Duplicate human facilities like creativity, self-improvement, and language use - Function autonomously in complex and changing environments

Foundation of AI: 

Foundation of AI

How Ants chose solution: 

How Ants chose solution

Why is the Good to be a Cognitive System?: 

Why is the Good to be a Cognitive System?

Cognitive systems: 

Cognitive systems

PROBLEMS IN AI: 

PROBLEMS IN AI

1. Deduction, reasoning, problem solving : 

1. Deduction, reasoning, problem solving Early AI researchers developed algorithms that imitated the step-by-step reasoning that humans use when they solve puzzles, play board games or make logical deductions. By the late 1980s and '90s also developed highly successful methods for dealing with uncertain or incomplete information, employing concepts from probability and economics Algorithms can require enormous computational resources — most experience a "combinatorial explosion“. Human beings solve most of their problems using fast, intuitive judgments rather than the conscious, step-by-step deduction that early AI research was able to model

2. Knowledge representation (knowledge engineering): 

2. Knowledge representation (knowledge engineering) Central to AI research. Require extensive knowledge about the world. Things that AI needs to represent are: objects, properties, categories and relations between objects Situations Events states and time causes and effects knowledge about knowledge (what we know about what other people know) what exists" is an ontology (borrowing a word from traditional philosophy), of which the most general are called upper ontologies.

2. Difficult problems in knowledge representation: 

2. Difficult problems in knowledge representation Default reasoning and the qualification problem “Working assumptions." Example: if a bird comes up in conversation, people typically picture an animal that is fist sized, sings, and flies. None of these things are true about all birds. John McCarthy identified this problem in 1969 as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Commonsense knowledge Number of atomic (astronomical) facts. A major goal is to have the computer understand enough concepts to be able to learn by reading from sources like the internet, and thus be able to add to its own ontology. Much of what people know is not represented as "facts" or "statements". For example, a chess master will avoid a particular chess position because it "feels too exposed" or an art critic can take one look at a statue and instantly realize that it is a fake. These are intuitions or tendencies that are represented in the brain non-consciously and sub-symbolically. Knowledge like this informs, supports and provides a context for symbolic, conscious knowledge.

3. Planning: 

3. Planning Setting goals and achieve them. Visualizing the future (they must have a representation of the state of the world and be able to make predictions about how their actions will change it) and be able to make choices that maximize the utility (or "value") of the available choices. Periodically check if the world matches its predictions and it must change its plan as this becomes necessary, requiring the agent to reason under uncertainty. Multi-agent planning uses the cooperation and competition of many agents to achieve a given goal.

4. Leraning: 

4. Leraning Machine learning - central to AI research from the beginning Unsupervised learning is the ability to find patterns in a stream of input. Supervised learning includes both classification and numerical regression. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories Regression takes a set of numerical input/output examples and attempts to discover a continuous function that would generate the outputs from the inputs. Reinforcement learning the agent is rewarded for good responses and punished for bad ones. These can be analyzed in terms of decision theory, using concepts like utility. Computational learning theory - scientific branch

5. Natural language processing : 

5. Natural language processing Gives machines the ability to read and understand the languages that humans speak Some straightforward applications of natural language processing include information retrieval (or text mining) machine translation.

6. Motion and manipulation : 

6. Motion and manipulation Word Robot (Rabotaj) intdoduced Czech writer Karel Čapek in his play R.U.R. (Rossum's Universal Robots) - 1920 related to AI. object manipulation navigation localization (knowing where you are) mapping (learning what is around you) motion planning (figuring out how to get there)

7. Perception: 

7. Perception Machine perception - ability to use input from sensors (cameras, microphones, sonar…) to deduce aspects of the world. Computer vision is the ability to analyze visual input. Subproblems: speech recognition facial recognition object recognition

8. Social Intelligence: 

8. Social Intelligence Emotion and social skills To predict the actions of others, by understanding their motives and emotional states. (Involves game theory, decision theory, model human emotions and the perceptual skills to detect emotions) Intelligent machine also needs to display motions. It must appear polite and sensitive to the humans it interacts with. At best, it should have normal emotions itself.

9. Creativity: 

9. Creativity From philosophical and psychological perspective

10. General Intelligence: 

10. General Intelligence

Inspiration for Cognitive Systems: 

Inspiration for Cognitive Systems

Emotions: 

Emotions

From Collins English Dictionary, 1995:: 

From Collins English Dictionary, 1995: conscious adj. 4. a. denoting or relating to a part of the human mind that is aware of a person’s self, environment, and mental activity and that to a certain extent determines his choices of action. b. (as n.) the conscious is only a small part of the mind. Compare unconscious.

Growing trends…: 

Growing trends… 11. Cybernetics and brain simulation 12. Symbolic 13. Statistical 14. Integrating the approaches

AI future: 

AI future 1992: Marvin Minsky vision about AIs Influences=Problems Causes=Solutions

Future: 

Future 2001 Space Odissey´s Stanley Kulbrick (1968). Artificial Intelligence´s Steve Spielberg (2001). I, Robot´s Alex Proyas (2004).

Space exploration: 

Space exploration

Current applications 2005: 

Current applications 2005 RoboSoccer: Intelligent surveillance in real time for urban traffic (cities and motorways):

That’s the end – so I’m off !“Stupid questions doesn’t – Only stupid answers exists” – Alan Pease : 

That’s the end – so I’m off ! “Stupid questions doesn’t – Only stupid answers exists” – Alan Pease