Se ha denunciado esta presentación.
Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.

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

1.119 visualizaciones

Publicado el

Artificial General Intelligence (AGI) - or strong AI - refers to a domain-independent, machine-based system that approaches or exceeds human performance on any and all cognitive tasks. Estimates for the arrival of true AGI solutions range from last week (as in, we have one!) to decades, to infinity and beyond. As the general study of cybernetic systems and modern AI and cognitive computing capture the imagination of civic and business leaders, and fans of science fiction, it is important to be able to distinguish between progress and smoke & mirrors.

This webinar will present an overview of approaches to AGI, examples of promising research and commercial AGI activities, and show participants how to critically evaluate academic and vendor claims.

Publicado en: Tecnología
  • Sé el primero en comentar

  • Sé el primero en recomendar esto

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

  1. 1. Artificial General Intelligence When Can I get it? Adrian Bowles, PhD Founder, STORM Insights, Inc. info@storminsights.com Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. FEBRUARY 9, 2017
  2. 2. Foundations of AI & AGI Games & AI/AGI AGI Today Overview of AGI Approaches Interesting Research Artificial vs Augmented General Intelligence Evaluating Claims - Are We There Yet? Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. AGENDA
  3. 3. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. FOUNDATIONS OF AI AND AGI
  4. 4. 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 Intelligence Focus on replicating intelligence by copying
 brain 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 concepts Constraint: Processing Modern AI Focus on augmenting intelligence by 
 evidence-based interaction Natural Language Processing (NLP) Learning and discovery Distributed ML driven by big data Deep QA techniques Strategy: Reinforce Execution: Open code and data Constraint: Data
  5. 5. 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 INTELLIGENCE J. 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
  6. 6. 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 Computers If 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 Language It 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.
  7. 7. 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 Nets How 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 Calculation If 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.
  8. 8. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. The following are some aspects of the artificial intelligence problem: 5. Self-lmprovement Probably 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. Abstractions A 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 Creativity A 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
  9. 9. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. WHERE DOES AGI FIT? Learning Model External Internal Knowledge Domain Broad/ Unbounded Narrow/ Constrained AGI
  10. 10. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. PERCEPTION UNDERSTANDING LEARNING PLANNING Hardware Software Mimic Model MOTIVATION PROBLEM-SOLVING Classic AI CLASSIC IS NARROW, NOT AGI NLP
  11. 11. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. Machine Learning Big Data Hardware Software Neuromorphic TPUs NPUs GPUs Mimic GPUs ? Model HTM MBR Neural Nets Classic AI #MODERNAI IS NARROW, NOT AGI
  12. 12. Systems Controls Learn Plan Reason Understand Model Data Mgmt Human Machine Input Output Gestures Emotions Language Narrative Generation Visualization Reports Haptics Sensors (IOT) Systems Controls Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. COGNITIVE SYSTEMS: AGI? NOT YET Perception
  13. 13. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. AI OR NOT AI?
  14. 14. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. GREAT EXPECTATIONS 8/9/2006
  15. 15. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. AI SPRING - VC ECOSYSTEMS
  16. 16. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. AI SPRING - VC ECOSYSTEMS
  17. 17. 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)
  18. 18. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. RECOGNITION IS NOT UNDERSTANDING. https://arxiv.org/abs/1112.6209
  19. 19. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. GAMES AND AI/AGI
  20. 20. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. AI OR NOT AI? The LIFE Picture Collection/Gett
  21. 21. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. THE EDGE OF THE ENVELOPE IS ALWAYS MOVING
  22. 22. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. THE ROLE OF GAMES IN AI RESEARCH 2-Person Perfect Information Zero Sum Checkers Chess Go Arthur Samuel IBM 1997 20161956
  23. 23. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. THE ROLE OF GAMES IN AI RESEARCH 3-Person Imperfect Information Zero Sum Natural Language Jeopardy! Poker 2-6-? —Person Imperfect Information, Zero Sum 2011 2017
  24. 24. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. AI & THE BLUFF
  25. 25. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. AUGMENTED INTELLIGENCE FOR CHESS
  26. 26. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. AGI TODAY
  27. 27. 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.
  28. 28. 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
  29. 29. 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.
  30. 30. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. FUNDAMENTAL DESIGN CHOICE: SYMBOLS VS STATISTICS Symbolic Logic Representations Reasoning Concepts Statistical Models Mechanical Theorem Proving
  31. 31. 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
  32. 32. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. OPENCOG: AN AGI FRAMEWORK Knowledge represented in hypergraphs (an edge can join n-vertices)
  33. 33. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. CYC
  34. 34. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. OPENCYC
  35. 35. 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
  36. 36. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. A fool with a tool is still a fool. Collaborative Evidence-Driven Probabalistic AGI TODAY = AUGMENTED GENERAL INTELLIGENCE
  37. 37. 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.
  38. 38. 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 INTERVENTION TO 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
  39. 39. 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
  40. 40. 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
  41. 41. 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

×