Smart Data Webinar: Artificial General Intelligence - When Can I Get It?

Artificial General IntelligenceWhen Can I get it?

Adrian Bowles, PhDFounder, STORM Insights, Inc.

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Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.

FEBRUARY 9, 2017

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Foundations of AI & AGIGames & AI/AGIAGI Today

Overview of AGI ApproachesInteresting ResearchArtificial vs Augmented General Intelligence

Evaluating Claims - Are We There Yet?


AGENDA


FOUNDATIONS OF AI AND AGI


CONTEXT - HOW DID WE GET HERE? (AND WHERE ARE WE ANYWAY?)

AI Roots

AGI - Artificial General IntelligenceFocus on replicating intelligence by copyingbrain functions and form/process

Natural Language Processing (NLP)Learning and discovery

Heuristics, expert rules…Logic - symbolic logic and mechanical theorem proving

Strategy: Replace Execution: Open conceptsConstraint: Processing

Modern AI

Focus on augmenting intelligence by evidence-based interaction

Natural Language Processing (NLP)Learning and discovery

Distributed ML driven by big dataDeep QA techniques

Strategy: ReinforceExecution: Open code and dataConstraint: Data


IN THE BEGINNING

“We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire.

The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.”

A PROPOSAL FOR THE DARTMOUTH SUMMER RESEARCH PROJECT ON ARTIFICIAL INTELLIGENCEJ. McCarthy, Dartmouth College

M. L. Minsky, Harvard University N. Rochester, I.B.M. Corporation

C.E. Shannon, Bell Telephone Laboratories

August 31, 1955

Emphasis added


FROM THE 1955 DARTMOUTH SUMMER AI RESEARCH PROPOSAL

The following are some aspects of the artificial intelligence problem:

1 Automatic ComputersIf a machine can do a job, then an automatic calculator can be programmed to simulate the machine. The speeds and memory capacities of present computers may be insufficient to simulate many of the higher functions of the human brain, but the major obstacle is not lack of machine capacity, but our inability to write programs taking full advantage of what we have.

2. How Can a Computer be Programmed to Use a LanguageIt may be speculated that a large part of human thought consists of manipulating words according to rules of reasoning and rules of conjecture. From this point of view, forming a generalization consists of admitting a new word and some rules whereby sentences containing it imply and are implied by others. This idea has never been very precisely formulated nor have examples been worked out.




3. Neuron NetsHow can a set of (hypothetical) neurons be arranged so as to form concepts. Considerable theoretical and experimental work has been done on this problem by Uttley, Rashevsky and his group, Farley and Clark, Pitts and McCulloch, Minsky, Rochester and Holland, and others. Partial results have been obtained but the problem needs more theoretical work.

4. Theory of the Size of a CalculationIf we are given a well-defined problem (one for which it is possible to test mechanically whether or not a proposed answer is a valid answer) one way of solving it is to try all possible answers in order. This method is inefficient, and to exclude it one must have some criterion for efficiency of calculation. Some consideration will show that to get a measure of the efficiency of a calculation it is necessary to have on hand a method of measuring the complexity of calculating devices which in turn can be done if one has a theory of the complexity of functions. Some partial results on this problem have been obtained by Shannon, and also by McCarthy.



5. Self-lmprovementProbably a truly intelligent machine will carry out activities which may best be described as self-improvement. Some schemes for doing this have been proposed and are worth further study. It seems likely that this question can be studied abstractly as well.

6. AbstractionsA number of types of ``abstraction'' can be distinctly defined and several others less distinctly. A direct attempt to classify these and to describe machine methods of forming abstractions from sensory and other data would seem worthwhile.

7. Randomness and CreativityA fairly attractive and yet clearly incomplete conjecture is that the difference between creative thinking and unimaginative competent thinking lies in the injection of a some randomness. The randomness must be guided by intuition to be efficient. In other words, the educated guess or the hunch include controlled randomness in otherwise orderly thinking.



WHERE DOES AGI FIT?

Learning ModelExternal Internal

KnowledgeDomain

Broad/Unbounded

Narrow/Constrained

AGI


PERCEPTION

UNDERSTANDING

LEARNINGPLANNING

Hardware

Software

Mimic Model

MOTIVATION PROBLEM-SOLVING

Classic AI

CLASSIC IS NARROW, NOT AGI

NLP


Machine Learning

Big Data

Hardware

Software

NeuromorphicTPUsNPUsGPUs

Mimic

GPUs

?

Model

HTM MBR

Neural Nets

Classic AI

#MODERNAI IS NARROW, NOT AGI

Systems

Controls

Learn

Plan Reason

Understand

Model

Data Mgmt

Human

Machine

Input Output

Gestures

Emotions

Language

Narrative Generation

Visualization

Reports

Haptics

Sensors (IOT)

SystemsControls


COGNITIVE SYSTEMS: AGI? NOT YET

Perc

eptio

n


AI OR NOT AI?


GREAT EXPECTATIONS

8/9/2006


AI SPRING - VC ECOSYSTEMS


NOT SO FAST…

“At DeepMind, engineers have created programs based on neural networks, modelled on the human brain. These systems make mis- takes, but learn and improve over time. They can be set to play other games and solve other tasks, so the intelligence is general, not specific. This AI “thinks” like humans do.”

Financial Times, March 11, 2016. Dennis Hassabis, master of the new machine age. (On Google’s AlphaGo)


RECOGNITION IS NOT UNDERSTANDING.

https://arxiv.org/abs/1112.6209


GAMES AND AI/AGI


AI OR NOT AI?

The LIFE Picture Collection/Gett


THE EDGE OF THE ENVELOPE IS ALWAYS MOVING


THE ROLE OF GAMES IN AI RESEARCH

2-PersonPerfect InformationZero Sum

Checkers Chess Go

Arthur SamuelIBM

1997 20161956


THE ROLE OF GAMES IN AI RESEARCH

3-PersonImperfect Information

Zero SumNatural Language

Jeopardy! Poker

2-6-? —PersonImperfect Information, Zero Sum

2011 2017


AI & THE BLUFF


AUGMENTED INTELLIGENCE FOR CHESS


AGI TODAY


IQ - THE GENERAL FACTOR (G)

IQ derived from a factor analysis of correlations between multiple tests. Charles Spearman, 1904

General ability + narrow ability factors There is no accepted g-factor for AI.

IBM True North Chips on a SyNAPSE board.


Hearing (audioception) ~12,000 outer hair cells/ear

~3,500 inner hair cells

Vision (ophthalmoception) Photoreceptors - Per Eye~120,000,000 rod cells

(triggered by single photon)~6,000,000 cone cells

(require more photons to trigger)~ 60,000 photosensitive ganglion cells

Touch (tactioception) Thermoreceptors, mechanoreceptors, chemoreceptors and nociceptors for touch, pressure, pain, temperature, vibration

Smell (olfacoception) Chemoreception

Taste (gustaoception) Chemoreception

Human Cognition~100,000,000,000 (100B) Neurons

~100-500,000,000,000,000 (100-500T) Synapses

AGI VS NATURAL GENERAL INTELLIGENCE

Learn

ModelReason

Understand

Plan


AGI MINIMUM REQUIREMENTS

or

Big Knowledge + Modest Processing(Reasoning, KM…)

Big Processing + Big Data (Reasoning, KM…)

With sufficient processing power, and access to enough clean, validated data, just in time knowledge acquisition.

Starting with sufficient knowledge (includes the model with assumptions) makes processing requirements relatively modest to accommodate incremental activities.


FUNDAMENTAL DESIGN CHOICE: SYMBOLS VS STATISTICS

Symbolic LogicRepresentations

ReasoningConcepts

Statistical Models

Mechanical Theorem Proving


REPRESENTATIVE AGI APPROACHES

Wikipedia contributors. "Cog (project)." Wikipedia, The Free Encyclopedia. Wikipedia, The Free Encyclopedia, 10 Jul. 2016. Web. 8 Feb. 2017.

Focus on human interaction

Focus on machine learning

Focus on capturing common knowledge

Focus on brain-inspired architectures

Focus on representation,philosophy and linguistics


OPENCOG: AN AGI FRAMEWORK

Knowledge represented in hypergraphs (an edge can join n-vertices)


CYC


OPENCYC


TRUE AGI CAN FUNCTION AS AUGMENTED GENERAL INTELLIGENCE

“I’m sorry Dave, I’m afraid I can’t do that… This mission is too important for me to allow you to jeopardize it…

I know that you an Frank were planning to disconnect me and I’m afraid that’s something I cannot allow to happen.”

HAL, 2001 A Space Odyssey


A fool with a tool is still a fool.

CollaborativeEvidence-Driven

Probabalistic

AGI TODAY = AUGMENTED GENERAL INTELLIGENCE


REVISITING THE 1955 DARTMOUTH SUMMER AI RESEARCH PROPOSAL


1 Automatic Computers

2. How Can a Computer be Programmed to Use a Language

3. Neuron Nets

4. Theory of the Size of a Calculation

5. Self-lmprovement

6. Abstractions

7. Randomness and Creativity

What does it mean to use vs understand?

The basis for modern machine learning.

In 60+ years, we have become adept at programming.

Well researched and documented progress quantifying algorithmic complexity.

Partial credit, but much work remains to be done.

The next frontier?

Beyond ML techniques, this area is still full of open questions.


IS IT AGI? MY QUICK TEST

CAN I SEE IT?We Have AGI!

Show Me!

DOES IT REQUIRE HUMAN INTERVENTIONTO LEARN ABOUT NEW DOMAINS?

CAN IT LEARN TO LEARN?

CAN IT COMMUNICATE ITS FINDINGS?

CAN IT ASK FOR HELP/MISSING DATA/KNOWLEDGE?

NO

YES

NO

NO

NO

No


KEEP IN TOUCH

[email protected]

Twitter @ajbowles Skype ajbowles

Upcoming 2017 Webinar Dates & Topics

March 9 Data Science and Business Analysis: A Look at Best Practices for Roles, Skills, and Processes April 13 Machine Learning - Moving Beyond Discovery to Understanding May 11 Streaming Analytics for IoT-Oriented Applications

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RESOURCES

http://bobkirby.info:8080/comparison.htmBob Kirby’s Knowledge Representation Comparisons

https://www.theatlantic.com/technology/archive/2012/11/noam-chomsky-on-where-artificial-intelligence-went-wrong/261637/

Noam Chomsky on Where Artificial Intelligence Went Wrong

http://opencog.orgThe OpenCog Foundation

http://www.businessinsider.com/cycorp-ai-2014-7

Cyc

http://www.cyc.com

The AI Behind Watson http://www.aaai.org/Magazine/Watson/watson.php


RESOURCES

https://www.cmu.edu/news/stories/archives/2017/january/AI-tough-poker-player.html

CMU ARTIFICIAL INTELLIGENCE IS TOUGH POKER PLAYER

https://www.theatlantic.com/technology/archive/2016/03/the-invisible-opponent/475611/

How Google's AlphaGo Beat a Go World Champion

https://www.cmu.edu/news/stories/archives/2017/january/AI-tough-poker-player.html

Smart Data Webinar: Artificial General Intelligence - When Can I Get It?

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