Post on 12-Feb-2017
Artificial General IntelligenceWhen Can I get it?
Adrian Bowles, PhDFounder, STORM Insights, Inc.
info@storminsights.com
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
FEBRUARY 9, 2017
Foundations of AI & AGIGames & AI/AGIAGI Today
Overview of AGI ApproachesInteresting ResearchArtificial vs Augmented General Intelligence
Evaluating Claims - Are We There Yet?
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
AGENDA
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
FOUNDATIONS OF AI AND AGI
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
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
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
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
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
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.
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
FROM THE 1955 DARTMOUTH SUMMER AI RESEARCH PROPOSAL
The following are some aspects of the artificial intelligence problem:
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.
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
The following are some aspects of the artificial intelligence problem:
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.
FROM THE 1955 DARTMOUTH SUMMER AI RESEARCH PROPOSAL
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
WHERE DOES AGI FIT?
Learning ModelExternal Internal
KnowledgeDomain
Broad/Unbounded
Narrow/Constrained
AGI
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
PERCEPTION
UNDERSTANDING
LEARNINGPLANNING
Hardware
Software
Mimic Model
MOTIVATION PROBLEM-SOLVING
Classic AI
CLASSIC IS NARROW, NOT AGI
NLP
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
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
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
COGNITIVE SYSTEMS: AGI? NOT YET
Perc
eptio
n
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
AI OR NOT AI?
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
GREAT EXPECTATIONS
8/9/2006
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
AI SPRING - VC ECOSYSTEMS
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
AI SPRING - VC ECOSYSTEMS
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
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)
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
RECOGNITION IS NOT UNDERSTANDING.
https://arxiv.org/abs/1112.6209
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
GAMES AND AI/AGI
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
AI OR NOT AI?
The LIFE Picture Collection/Gett
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
THE EDGE OF THE ENVELOPE IS ALWAYS MOVING
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
THE ROLE OF GAMES IN AI RESEARCH
2-PersonPerfect InformationZero Sum
Checkers Chess Go
Arthur SamuelIBM
1997 20161956
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
THE ROLE OF GAMES IN AI RESEARCH
3-PersonImperfect Information
Zero SumNatural Language
Jeopardy! Poker
2-6-? —PersonImperfect Information, Zero Sum
2011 2017
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
AI & THE BLUFF
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
AUGMENTED INTELLIGENCE FOR CHESS
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
AGI TODAY
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
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.
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
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
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
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.
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
FUNDAMENTAL DESIGN CHOICE: SYMBOLS VS STATISTICS
Symbolic LogicRepresentations
ReasoningConcepts
Statistical Models
Mechanical Theorem Proving
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
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
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
OPENCOG: AN AGI FRAMEWORK
Knowledge represented in hypergraphs (an edge can join n-vertices)
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
CYC
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
OPENCYC
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
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
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
A fool with a tool is still a fool.
CollaborativeEvidence-Driven
Probabalistic
AGI TODAY = AUGMENTED GENERAL INTELLIGENCE
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
REVISITING THE 1955 DARTMOUTH SUMMER AI RESEARCH PROPOSAL
The following are some aspects of the artificial intelligence problem:
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.
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
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
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
KEEP IN TOUCH
adrian@storminsights.com
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
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
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
Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.
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