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AI 3.0: Is it Finally Time for Artificial Intelligence and Sensor Networks to be Disruptive?

  1. 1. AI 3.0: Is it Finally Time for Artificial Intelligence and Sensor Networks to be Disruptive? Have recent advances in mathematical algorithms, highly sensitive/compact sensors, big data, mobile communications, and robotry made Stephen Hawking’s warning that artificial intelligence could end mankind more eminent? What does this mean for jobs in the “second machine age” and AI 3.0?
  2. 2. David Smith AI 3.0: Is it Finally Time for Artificial Intelligence and Sensor Networks to be Disruptive?
  3. 3. As we begin the new millennium science and technology are changing rapidly  “Old” sciences such as physics are relatively well-understood  Computers are ubiquitous Grand Challenges in Science and Technology  Understanding the brain reasoning, cognition, creativity creating intelligent machines is this possible?  What are the technical and philosophical challenges? Arguably AI poses the most interesting challenges and questions in computer science today
  4. 4. “Whoever wins this race will dominate the next stage of the information age,” - Pedro Domingos, a machine learning specialist and author of “The Master Algorithm,” a 2015 book contending that A.I. and big-data technology will remake the world.
  5. 5. ARTIFICIAL INTELLIGENCE “AI is the study of techniques for solving exponentially hard problems in polynomial time by exploiting knowledge about the problem domain.“ Elaine Rich
  6. 6. "Once you have a truly massive amount of information integrated as knowledge, then the human-software system will be superhuman, in the same sense that mankind with writing is superhuman compared to mankind before writing.” - Technology Review, March 2005 "Compared to Nature we suffer a poverty of imagination; it is thus much easier for us to uncover than to invent.” Doug Lenat's Cyc project, is to build the basis of a general artificial intelligence by representing knowledge
  7. 7. What is Intelligence?  Intelligence: - “The capacity to learn and solve problems” (Webster dictionary) - In particular, • the ability to solve novel problems • the ability to act rationally • the ability to act like humans  Artificial Intelligence - Build and understand intelligent entities or agents - Two main approaches: “engineering” versus “cognitive modeling”
  8. 8.  Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. ● Artificial intelligence is a branch of computer science that aims to create intelligent machines. ● Some of the activities computers with artificial intelligence are designed for include speech recognition, learning, planning and problem solving.
  9. 9. ● Robotics is a major field related to AI. ● Robots require intelligence to handle tasks such as object manipulation and navigation along with sub- problems of localization, motion planning and mapping.
  10. 10. Ex Machina Featurette - New Consciousness
  11. 11. Philosophers have been trying for over 2000 years to understand and resolve two Big Questions of the Universe: How does a human mind work, and Can non-humans have minds? These questions are still unanswered. Intelligence is the ability to understand and learn things. Intelligence is the ability to think and understand instead of doing things by instinct or automatically. Intelligent Machines, or What Machines Can Do (Essential English Dictionary)
  12. 12. What’s involved in Intelligence?  Ability to interact with the real world - to perceive, understand, and act - e.g., speech recognition and understanding and synthesis - e.g., image understanding - e.g., ability to take actions, have an effect  Reasoning and Planning - modeling the external world, given input - solving new problems, planning, and making decisions - ability to deal with unexpected problems, uncertainties  Learning and Adaptation - we are continuously learning and adapting - our internal models are always being “updated” • e.g., a baby learning to categorize and recognize animals
  13. 13. Computers versus humans  A computer can do some things better than a human can - Adding a thousand four-digit numbers - Drawing complex, 3D images - Store and retrieve massive amounts of data  However, there are things humans can do much better.
  14. 14. Thinking Machines A computer would have difficulty identifying the cat, or matching it to another picture of a cat.
  15. 15. AI Purposes "AI can have two purposes. One is to use the power of computers to augment human thinking, just as we use motors to augment human or horse power. Robotics and expert systems are major branches of that. The other is to use a computer's artificial intelligence to understand how humans think. In a humanoid way. If you test your programs not merely by what they can accomplish, but how they accomplish it, they you're really doing cognitive science; you're using AI to understand the human mind." - Herb Simon
  16. 16. “We cannot solve our problems with the same thinking we used when we created them.” - Albert Einstein
  17. 17. Overview of Artificial Intelligence  Definitions – four major combinations - Based on thinking or acting - Based on activity like humans or performed in rational way Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally
  18. 18. “The market for enterprise AI systems will increase from $202.5 million in 2015 to $11.1 billion by 2024.” - Tractica
  19. 19. • By 2018, 20 percent of business content will be authored by machines. • By 2020, autonomous software agents outside of human control will participate in five percent of all economic transactions. • By 2018, more than 3 million workers globally will be supervised by a "robo-boss.“ • By 2018, 45 percent of the fastest-growing companies will have fewer employees than instances of smart machines. • By year-end 2018, customer digital assistant will recognize individuals by face and voice across channels and partners. • By 2020, smart agents will facilitate 40 percent of mobile interactions, and the post app era will begin to dominate.
  20. 20. First Key to Creating Artificial General Intelligence: Increasing Computational Power NNow = • Beating a mouse brain • About a thousandth of a human
  21. 21. Second Key to Creating Artificial General Intelligence: Making It Smart Strategies: 1) Plagiarize the brain. • Reverse engineer it • Build chips to simulate it • Capture its synapses • “Whole brain emulation” 2) Try to make evolution do what it did before but for us this time. • Use foresight – just pick what you know will win • Select for intelligence • Provide externally what evolution takes extra steps to do, i.e., provide outside energy/electricity 3) Make this whole thing the computer’s problem, not ours • It would do research on AI and code the changes into itself Now: 1 mm-long flatworm brain of 302 Neurons
  22. 22. Although artificial intelligence as an independent field of study is relatively new, it has some roots in the past. We can say that it started 2,400 years ago when the Greek philosopher Aristotle invented the concept of logical reasoning. The effort to finalize the language of logic continued with Leibniz and Newton. George Boole developed Boolean algebra in the nineteenth century that laid the foundation of computer circuits. However, the main idea of a thinking machine came from Alan Turing, who proposed the Turing test. The term “artificial intelligence” was first coined by John McCarthy in 1956. History of artificial intelligence
  23. 23. Meet HAL  2001: A Space Odyssey - classic science fiction movie from 1969  HAL - part of the story centers around an intelligent computer called HAL - HAL is the “brains” of an intelligent spaceship - in the movie, HAL can • speak easily with the crew • see and understand the emotions of the crew • navigate the ship automatically • diagnose on-board problems • make life-and-death decisions • display emotions  In 1969 this was science fiction: is it still science fiction?
  24. 24. AI 1.0 (1960-1985): AI applications addressed a single area. In this period, they were high value such as human language translation and route optimization centered around the high cost of humans. Algorithms were mechanistic. Heavy demand for IT resources made implementations expensive. Today, single area AI applications, enabled by more sophisticated mathematics and high performance computing, is labelled Artificial Narrow Intelligence (ANI). AI 2.0 (1986 - 2010): AI applications appeared to address a broad area. In this period, they were capable of doing the work of an occupation of people such as picking crops, scanning social networks for consumer input, and classifying images for quicker retrieval. Algorithms became more sophisticated and IT resources much less expensive. However, the solutions approach mimic how humans thought and still fell short of the abilities of experts. This class of AI Application is labelled Artificial General Intelligence (AGI). AI 3.0 (2011 - Now): AI applications are appearing that can solve problems better than the best human in an area of interest. Examples of this class of AI application can win a the most complex strategic board games, perform retrieval and analysis of knowledge to quickly answer questions, and stock market trading. This generational shift has been driven by high value potential, accumulation of massive data of all kinds, even faster computers the ability to analyze a single situation across a cluster of computers, and the algorithms to exploit the new technological resources to analyze problem deeper to incorporate behavioral/neuro/ social data to perform real time analysis and even learn. This class of AI application is being called Artificial Superintelligence (ASI). Artificial Intelligence Generations
  25. 25. The vast majority of AI research practiced in academia and industry today fits into the “Narrow AI” category Each “Narrow AI” program is (in the ideal case) highly competent at carrying out certain complex goals in certain environments • Chess-playing, medical diagnosis, car-driving, etc. Narrow AI
  26. 26. “The ability to achieve complex goals in complex environments using limited computational resources” • Autonomy • Practical understanding of self and others • Understanding “what the problem is” as opposed to just solving problems posed explicitly by programmers Artificial General Intelligence (AGI) Artificial General Intelligence (AGI)
  27. 27. Artificial Intelligence Generation Comparison Factor Generation AI 1.0 AI 2.0 AI 3.0 Period of Time 1960 to 1985 1986 to 2010 2011 to Now and beyond Type of AI App Introduced Artificial Narrow Intelligence (ANI) Artificial General Intelligence (AGI) Artificial SuperIintelligence (ASI) Value Proposition Human Efficiency Human Effectiveness Human Substitution Human Ability Acquired Fast manipulation of text and data Incorporation of knowledge, Audio/visual recognition Understanding, Reasoning ANI Roadmap Batch processing Complex data/math Real time AGI Roadmap Longitudinal data, Pattern recognition Data warehouses, Non-SQL data bases ASI Roadmap Deep Neural Nets, Big Data, Robotics
  28. 28. Different Types of Artificial Intelligence  Modeling exactly how humans actually think - cognitive models of human reasoning  Modeling exactly how humans actually act - models of human behavior (what they do, not how they think)  Modeling how ideal agents “should think” - models of “rational” thought (formal logic) - note: humans are often not rational!  Modeling how ideal agents “should act” - rational actions but not necessarily formal rational reasoning - i.e., more of a black-box/engineering approach  Modern AI focuses on the last definition - we will also focus on this “engineering” approach - success is judged by how well the agent performs -- modern methods are also inspired by cognitive & neuroscience (how people think).
  29. 29. A Human vs. Machine Comparison Category Attribute Man Machine Hardware Processing speed Max @ 200 cycles/sec Already 2 billion cycs/sec Interconnect speed ~ 120 meters/second Speed of light Size/Storage Size of skull; any bigger we’d think more slowly Greatly expandable in short term/working/long term memories; has error detect/self-correct bits Reliability/durability Get easily fatigued; will deteriorate over time Transistors more accurate that neurons; can be repaired or replaced; can run non-stop 24/7 Software Programmability Human brain is not “updatable” Can be optimized to suit its role; improvable; fixable “The Collective” Our ability to build vast collective intelligence and apply it collectives has made us the top species All computers could work together on a single problem; whatever is learned can be instantly “assimilated” by all Overall Self Improvement ??? Yes
  30. 30. • Fast computers internetworked • Decent virtual worlds for AI embodiment • Halfway-decent robot bodies • Lots of AI algorithms and representations • often useful in specialized areas • often not very scalable on their own • A basic understanding of human cognitive architecture • A cruder but useful understanding of brain structure and dynamics • A theoretical understanding of general intelligence under conditions of massive computational resources What We Have Now
  31. 31. Artificial Intelligence in the Movies
  32. 32. The Intelligence is in the Connections Connections between people ConnectionsbetweenInformation Email Social Networking Groupware Javascrip t Weblogs Databases File Systems HTTP Keyword Search USENET Wikis Websites Directory Portals 2010 - 2020 Web 1.0 2000 - 2010 1990 - 2000 PC Era 1980 - 1990 RSS Widgets PC’s 2020 - 2030 Office 2.0 XML RDF SPARQLAJAX FTP IRC SOA P Mashups File Servers Social Media Sharing Lightweight Collaboration ATOM Web 3.0 Web 4.0 Semantic Search Semantic Databases Distributed Search Intelligent personal agents Java SaaS Web 2.0Flash OWL HTML SGML SQL Gopher P2P The Web The PC Windows MacOS SWRL OpenID BBS MMO’s VR Semantic Web Intelligent Web The Internet Social Web Web OS
  33. 33. Natural Language: Translation “The flesh is weak, but the spirit is strong”  Translate to Russian  Translate back to English “The food was lousy, but the vodka was great!”
  34. 34. Your Assignment Let’s start with an easy one: Chair
  35. 35. Chair?
  36. 36. Chair?
  37. 37. Chair?
  38. 38. Chair?
  39. 39. Chair?
  40. 40. Chair?
  41. 41. Chair?
  42. 42. Chair?
  43. 43. Chair?
  44. 44. Chair?
  45. 45. Chair?
  46. 46. Chair?
  47. 47. Chair?
  48. 48. Chair?
  49. 49. Chair? The bottom line?
  50. 50. Bill Gates on AI Issues and Potential [Bill Gates] weighed in on the issue of artificial intelligence when a Redditor asked him how he felt about regulating artificial intelligence. Gates said he agrees with Elon Musk and physicist Stephen Hawking that, "when a few people control a platform with extreme intelligence, it creates dangers in terms of power and eventually control." When asked about his early motto of putting a computer in every home, Gates said that today, the challenge is to make computers more intelligent. "Software still doesn't understand what thing I should pay attention to next," he wrote, "in fact the proliferation of various tools like texting and email and notifications mean the user has a lot of complexity to deal with. Eventually the software will understand what you should pay attention to by knowing the context and learning about your preferences." Source: Puget Sound Business Journal, March 8, 2016
  51. 51. Prof Stephen Hawking, one of Britain's pre-eminent scientists, has said that efforts to create thinking machines pose a threat to our very existence. Prof Hawking says the primitive forms of artificial intelligence developed so far have already proved very useful, but he fears the consequences of creating something that can match or surpass humans. "It would take off on its own, and re-design itself at an ever increasing rate," he said. “Humans, who are limited by slow biological evolution, couldn't compete, and would be superseded." Geek, May 13, 2015. Renowned physicist Stephen Hawking appeared at the Zeitgeist 2015 conference in London and confirmed the fears held by anyone who has watched a movie with a robot in it since 1927’s Metropolis when he said, “Computers will overtake humans with AI at some within the next 100 years. When that happens, we need to make sure the computers have goals aligned with ours.”
  52. 52. 63 Artificial Intelligence: Current Status  Approaches - Symbolic, statistical, learning algorithms, physical/mechanistic, hybrid  Current initiatives - Narrow AI: DARPA, corporate - Strong AI: startup efforts  Near-term applications - Auditory applications: speech recognition - Visual applications: security camera (crowbar/gift) - Transportation applications: truly smart car  Format - Robotic (Roomba, mower, vehicles) - Distributed physical presence - Non-corporeal Kismet Stanley
  53. 53. AI State of the Art - Applications  AI achievements: - Facilitate and replace human decision making World-class chess and game playing - Robots - Automatic process control - Understand limited spoken language - Smarter search engines - Engage in a meaningful conversation - Observe and understand human emotions - Solving mathematical problems - Discover and prove mathematical theories - …
  54. 54. “I set the date for the Singularity- representing a profound and disruptive transformation in human capability- as 2045. The nonbiological intelligence created in that year will be one billion times more powerful than all human intelligence today." Ray Kurzweil The Singularity is Near (2005)
  55. 55. World Robot Population
  56. 56. World Robot Population
  57. 57. Isaac Asimov’s Three Laws of Robotics (1940) 69 First Law: A robot may not injure a human or through inaction, allow a human to come to harm. Second Law: A robot must obey the orders given it by human beings, unless such orders would conflict with the first law. Third Law: A robot must protect its own existence, as long as such protection does not conflict with the first or second law.
  58. 58. Are the 3 Laws the Answer? Extending the Laws 70 Zeroth law: A robot may not injure humanity or through inaction allow humanity to come to harm. (due to Asimov, Olivaw, and Calvin). David Langford’s extensions, acknowledging military funding for robotics: 4. A robot will not harm authorized Government personnel but will terminate intruders with extreme prejudice. 5. A robot will obey the orders of authorized personnel except where such orders conflict with the Third Law. 6. A robot will guard its own existence with lethal antipersonnel weaponry, because a robot is bloody expensive.
  59. 59. Will They Be Like Us? 71 Like us, AI systems... ...will talk to us in our languages. ...will help us with our problems. ...will have anthropomorphic interfaces. Unlike us, AI systems... ...will compute and communicate extremely quickly. ...will have bounds for learning and retention of knowledge that will soon surpass ours. ...might not be well modeled by the psychological models that work for people.
  60. 60. Atlas, The Next Generation Robot A new version of Atlas, designed to operate outdoors and inside buildings. It is specialized for mobile manipulation. It is electrically powered and hydraulically actuated. It uses sensors in its body and legs to balance and LIDAR and stereo sensors in its head to avoid obstacles, assess the terrain, help with navigation and manipulate objects. This version of Atlas is about 5' 9" tall (about a head shorter than the DRC Atlas) and weighs 180 lbs.
  61. 61. Domestic Robots
  62. 62. Military robots
  63. 63. The Future?  Idea of Artificial Intelligence is being replaced by Artificial life, or anything with a form or body.  The consensus among scientists is that a requirement for life is that it has an embodiment in some physical form, but this will change. Programs may not fit this requirement for life yet.
  64. 64. Arms race for the future of intelligence Machine Human  Blue Gene/L 360 teraFLOPS (≈.36+ trillion IPS) and 32 TiB memory1  Unlimited operational/build knowledge  Quick upgrade cycles: performance capability doubling every 18 months  Linear, Von Neumann architecture  Understands rigid language  Special purpose solving (Deep Blue, Chinook, ATMs, fraud detection)  Metal chassis, easy to backup  Estimated 2,000 trillion IPS and 1000 TB memory2  Limited operational/build knowledge  Slow upgrade cycles: 10,000 yr evolutionary adaptations  Massively parallel architecture  Understands flexible, fuzzy language  General purpose problem solving, works fine in new situations  Nucleotide chassis, no backup possible 1Source: Fastest Supercomputer, June 2007, http://www.top500.org/system/7747 2Source: http://paula.univ.gda.pl/~dokgrk/bre01.html
  65. 65. ADVANTAGES (Factual Changes) Smarter artificial intelligence promises to replace human jobs, freeing people for other pursuits by automating manufacturing and transportations. Self-modifying, self-writing, and learning software relieves programmers of the burdensome task of specifying the whole of a program’s functionality—now we can just create the framework and have the program itself fill in the rest (example: real-time strategy game artificial intelligence run by a neural network that acts based on experience instead of an explicit decision tree). Self-replicating applications can make deployment easier and less resource-intensive. AI can see relationships in enormous or diverse bodies of data that a human could not
  66. 66. Analysis of the Risks • Mass unemployment? historical evidence is negative • Loss of income? productivity creates wealth, jobs, & ownership • Idleness & boredom? the rich are seldom idle or bored • Loss of control over destiny? freedom to pursue interests • Overpowered by superior intelligence? might bring world peace and economic justice
  67. 67. SuperIntelligence Has Already Arrived!!! In the Stock Market: October 2, 2013 automated computer buy/sell programs, on news of an offer to buy Blackberry for $9 a share, touched off a flurry of orders reducing the company’s stock to $7.92. At Chess: May 11, 1997 IBM’s Deep Blue beat Garry Kasparov, the then world chess champion. Kasparov had beaten Deep Blue a year earlier. At Go: March 14, 2016 Google’s DeepMind beat leading Go player Lee Sedol 4-1. Lee won in the fourth game by forcing his opponent into an error. However, in the fifth game the AI program made a similar error but recovered to win the game.. In Conversations: June 8, 2014 A Russian chatterbot named "Eugene Goostman" became the first to pass the Turing Test by convincing 1 in 3 judges that it was a 13-year-old non- native-English-speaking Ukrainian boy. “machines will eventually overtake us, as virtually everyone in the A.I. field believes …The only real difference between enthusiasts and skeptics is a time frame.” - NYU research psychologist Gary Marcus
  68. 68. Paul Allen, Microsoft Co-founder: “We can see that overall AI-based capabilities haven’t been exponentially increasing either, at least when measured against the creation of a fully general human intelligence…individual AI systems…have always remained brittle—their performance boundaries are rigidly set by their internal assumptions and defining algorithms, they cannot generalize, and they frequently give nonsensical answers outside of their specific focus areas.” … But It Won’t Be Self Aware Murray Shananhan, Imperial College of London cognitive roboticist: “Consciousness is certainly a fascinating and important subject—but I don’t believe consciousness is necessary for human-level artificial intelligence,” he told Gizmodo. “Or, to be more precise, we use the word consciousness to indicate several psychological and cognitive attributes, and these come bundled together in humans.”
  69. 69. Peter McIntyre, Future of Humanity Institute at Oxford University: “By definition, an artificial superintelligence (ASI) is an agent with an intellect that’s much smarter than the best human brains in practically every relevant field. It will know exactly what we meant for it to do.” McIntyre believes an AI will only do what it’s programmed to, but if it becomes smart enough, it should figure out how this differs from the spirit of the law, or what humans intended. McIntyre compares the future plight of humans to that of a mouse. A mouse has a drive to eat and seek shelter, but this goal often conflicts with humans who want a rodent-free abode. “Just as we are smart enough to have some understanding of the goals of mice, a superintelligent system could know what we want, and still be indifferent to that,”. Richard Loosemore, AI researcher and founder of Surfing Samurai Robots: Thinks that most AI doomsday scenarios are incoherent and argues that these scenarios always involve an assumption that the AI is supposed to say “I know that destroying humanity is the result of a glitch in my design, but I am compelled to do it anyway.” Loosemore points out that if the AI behaves like this when it thinks about destroying us, it would have been committing such logical contradictions throughout its life, thus corrupting its knowledge base and rendering itself too stupid to be harmful. … And Artificial Super Intelligence Will Make Mistakes
  70. 70. Stuart Armstrong, Future of Humanity Institute at Oxford University: “Many simple tricks have been proposed that would ‘solve’ the whole AI control problem,” Examples include programming the ASI in such a way that it wants to please humans, or that it function merely as a human tool. Alternately, we could integrate a concept, like love or respect, into its source code. And to prevent it from adopting a hyper-simplistic, monochromatic view of the world, it could be programmed to appreciate intellectual, cultural, and social diversity. But these solutions are either too simple—like trying to fit the entire complexity of human likes and dislikes into a single glib definition—or they cram all the complexity of human values into a simple word, phrase, or idea. “That’s not to say that such simple tricks are useless—many of them suggest good avenues of investigation, and could contribute to solving the ultimate problem. But we can’t rely on them without a lot more work developing them and exploring their implications.” It Will Be Difficult to Mitigate Those Mistakes
  71. 71. Philosopher Immanuel Kant believed that intelligence strongly correlates with morality. David Chalmers, Professor of Philosophy, New York University, and Fellow of the American Academy of Arts & Sciences: “If this [Kant’s belief] is right...we can expect an intelligence explosion to lead to a morality explosion along with it. We can then expect that the resulting [ASI] systems will be supermoral as well as superintelligent, and so we can presumably expect them to be benign.” Stuart Armstrong, Future of Humanity Institute at Oxford University: “Smart humans who behave immorally tend to cause pain on a much larger scale than their dumber compatriots,” he said. “Intelligence has just given them the ability to be bad more intelligently, it hasn’t turned them good.” “We’d have to be very lucky if our AIs were uniquely gifted to become more moral as they became smarter,” he said. “Relying on luck is not a great policy for something that could determine our future.” Know that ASI Won’t Be Friendly.
  72. 72. However, we won’t be destroyed by ASI. Eliezer Yudkowsky, Research Fellow, Machine Intelligence Research Institute: “The AI does not hate you, nor does it love you, but you are made out of atoms which it can use for something else.” Peter McIntyre, Future of Humanity Institute at Oxford University: “An AI might predict, quite correctly, that we don’t want it to maximize the profit of a particular company at all costs to consumers, the environment, and non-human animals. It therefore has a strong incentive to ensure that it isn’t interrupted or interfered with, including being turned off, or having its goals changed, as then those goals would not be achieved.” Elon Musk, Founder and CEO of Tesla and SpaceX: Points out that artificial intelligence could actually be used to control, regulate, and monitor other AI. Or, it could be imbued with human values, or an overriding imposition to be friendly to humans.
  73. 73. Super Intelligent Assistants Will Be More Helpful Than Your Spouse Know more about your habits Anticipate your next move Prepare you for your next event Provide the right information for events Communicate your thinking customized for each recipient Follow-up on the impact of your decision Even do the heavy lifting
  74. 74. Thank You David Smith dsmith@socialcare.com