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Artificial Intelligence is Maturing

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Artificial Intelligence is Maturing

  1. 1. 4/16/2018 1 David Smith Artificial intelligence is maturing, can AI, IoT, sensors, Robotics, and quantum computing lead to the next breakthrough for corporations? Artificial intelligence is maturing, can AI, IoT, sensors, Robotics, and quantum computing lead to the next breakthrough for corporations? There have been many recent advances in mathematical algorithms, IoT, highly sensitive/compact sensors, big data, mobile communications, and robotry. The English mathematician Alan Turing gave the first lecture on it in 1947. Is now the time companies to go all in on AI? Are we in the AI 3.0? David Smith Artificial intelligence is maturing, can AI, IoT, sensors, Robotics, and quantum computing lead to the next breakthrough for corporations? Are we in the AI 3.0? Copyright 2018 All Rights reserved May not be distributed without permission David Smith Copyright 2018 David Smith All Rights Reserved
  2. 2. 4/16/2018 2 As we are in 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 Copyright 2018 David Smith All Rights Reserved
  3. 3. 4/16/2018 3 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  Definition  Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of "intelligent agents”  Artificial intelligence is technology that appears to emulate human performance typically by learning, coming to its own conclusions, appearing to understand complex content, engaging in natural dialogs with people.  The capability of a functional unit to perform functions that are generally associated with human intelligence such as reasoning and learning. (ISO/IEC 2382-28:1995) Copyright 2018 David Smith All Rights Reserved
  4. 4. 4/16/2018 4 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” Ex Machina Featurette - New Consciousness Copyright 2018 David Smith All Rights Reserved
  5. 5. 4/16/2018 5 ● 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. Copyright 2018 David Smith All Rights Reserved
  6. 6. 4/16/2018 6 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) 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 Copyright 2018 David Smith All Rights Reserved
  7. 7. 4/16/2018 7 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. Thinking Machines A computer would have difficulty identifying the cat, or matching it to another picture of a cat. Copyright 2018 David Smith All Rights Reserved
  8. 8. 4/16/2018 8 Or 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 Copyright 2018 David Smith All Rights Reserved
  9. 9. 4/16/2018 9 “We cannot solve our problems with the same thinking we used when we created them.” - Albert Einstein 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 Copyright 2018 David Smith All Rights Reserved
  10. 10. 4/16/2018 10 “The market for enterprise AI systems will increase from $202.5 million in 2015 to $11.1 billion by 2024.” - Tractica Copyright 2018 David Smith All Rights Reserved
  11. 11. 4/16/2018 11 Near Future • 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 2019, 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.  Classical Digital Computer  Moore’s Law: # of transistors on chip doubles every 18 months— microprocessor circuits will measure on atomic scale by 2020  Downscaling of circuit board layout/components is leading to discrepancies.  Copper traces are actually crystallizing and shorting out!  Emergence of quantum phenomena such as electrons tunneling through the barriers between wires.  Serial Processing – one operation at a time  64-bit classical computer operates speeds measured in gigaflops (billions of floating-point operations per second).  Quantum Computer  Harnesses the power of atoms and molecules to perform memory and processing tasks  Parallel Processing – millions of operations at a time  30-qubit quantum computer equals the processing power of conventional computer that running at 10 teraflops (trillions of floating-point operations per second). The Need For Speed... Copyright 2018 David Smith All Rights Reserved
  12. 12. 4/16/2018 12 IBM 50 qubit quantum computer Copyright 2018 David Smith All Rights Reserved
  13. 13. 4/16/2018 13 “A quantum computer is to a regular computer, what a laser is to a lightbulb.” --Seth Lloyd, MIT One analogy… “The thing driving the hype is the realization that quantum computing is actually real. It is no longer a physicist’s dream—it is an engineer’s nightmare.” “Nature is quantum, goddamn it! So if we want to simulate it, we need a quantum computer.” Satya Nadella, Microsoft CEO: “The world is running out of computing capacity. Moore’s law is kinda running out of steam … [we need quantum computing to] create all of these rich experiences we talk about, all of this artificial intelligence.” Copyright 2018 David Smith All Rights Reserved
  14. 14. 4/16/2018 14 Google Rattles the Tech World With a New AI Chip Neuromorphic Computing And OTHERS “Spikey” from Electronic Visions group in Heidelberg Qualcomm’s NPU’s for robots. SpiNNaker’s 1B neuron machine Stanford’s Neurogrid Intel’s concept design... IBM’s TrueNorth (Peter Nugent, LBNL) Copyright 2018 David Smith All Rights Reserved
  15. 15. 4/16/2018 15 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 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 Copyright 2018 David Smith All Rights Reserved
  16. 16. 4/16/2018 16 A Brief History of AI 5th century BC Aristotle invents syllogistic logic, the first formal deductive reasoning system. 16th century AD Rabbi Loew supposedly invents the Golem, an artificial man made out of clay. 17th century Descartes proposes animals are machines and founds a scientific paradigm that will dominate for the coming centuries. Pascal creates the first mechanical calculator in 1642 18th century Wolfgang von Kempelen “invents” fake chess-playing machine, The Turk. 19th century • George Boole creates a binary algebra to represent “laws of thought”. • Charles Babbage and Lady Lovelace develop sophisticated programmable mechanical computers, precursor to modern electronic computers. First Half of 20th century • Karel Kapek writes “Rossum’s Universal Robots”, coining the English word “robot”. • Warren McCulloch and Walter Pitts lay partial groundwork for neural networks. • Turing writes “Computing Machinery and Intelligence” – proposal of Turing test. Copyright 2018 David Smith All Rights Reserved
  17. 17. 4/16/2018 17 March 2016: Introduction of Sophia, a learning & social android with humanlike facial expression, eye contact, hearing and vocal qualities. March 2016: Google AlphaGo, using deep neural network and tree search to decide and learn quickly, defeats world Go champ Lee Se-dol 4 to 1. 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? Copyright 2018 David Smith All Rights Reserved
  18. 18. 4/16/2018 18 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 Copyright 2018 David Smith All Rights Reserved
  19. 19. 4/16/2018 19 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 “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) Copyright 2018 David Smith All Rights Reserved
  20. 20. 4/16/2018 20 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 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). Copyright 2018 David Smith All Rights Reserved
  21. 21. 4/16/2018 21 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 • 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 Copyright 2018 David Smith All Rights Reserved
  22. 22. 4/16/2018 22 Artificial Intelligence in the Movies 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!” Copyright 2018 David Smith All Rights Reserved
  23. 23. 4/16/2018 23 Your Assignment Let’s start with an easy one: Chair Chair? Copyright 2018 David Smith All Rights Reserved
  24. 24. 4/16/2018 24 Chair? Chair? Copyright 2018 David Smith All Rights Reserved
  25. 25. 4/16/2018 25 Chair? Chair? Copyright 2018 David Smith All Rights Reserved
  26. 26. 4/16/2018 26 Chair? Chair? Copyright 2018 David Smith All Rights Reserved
  27. 27. 4/16/2018 27 Chair? Chair? Copyright 2018 David Smith All Rights Reserved
  28. 28. 4/16/2018 28 Chair? Chair? Copyright 2018 David Smith All Rights Reserved
  29. 29. 4/16/2018 29 Chair? Chair? Copyright 2018 David Smith All Rights Reserved
  30. 30. 4/16/2018 30 Chair? Chair? The bottom line? Copyright 2018 David Smith All Rights Reserved
  31. 31. 4/16/2018 31 Trust • Google autonomous car crash. • Tesla autopilot FATAL car crash. • Risk assessment for committing crime racially discriminates subjects - COMPASS. • Predictive policing – racially profiles the area. Our fear and mistrust is based on such failures. IoT Congress 2017 61 Trust in Military Applications Signatories:- Stephen Hawkings, Steve Wozniak, Stuart Russell, Nils Nilsson and 20,000+ IoT Congress 2017 62 Copyright 2018 David Smith All Rights Reserved
  32. 32. 4/16/2018 32 Isaac Asimov’s Three Laws of Robotics (1940) 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. Maybe to AI as well?? Are the 3 Laws the Answer? Extending the Laws 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. Copyright 2018 David Smith All Rights Reserved
  33. 33. 4/16/2018 33 • Domains • Software engineering • Performance • Metrics • Safety • Usability • Interoperability • Security • Privacy • Traceability • Risk Analysis • Ethics Pg 65 | The Area of Standardization Copyright 2018 David Smith All Rights Reserved
  34. 34. 4/16/2018 34 China embracing AI Imagenet Challenge 2016 • All category winners were teams from China. • 33 of the 82 teams from China. Revenues from the artificial intelligence (AI) market worldwide, from 2016 to 2025 (in million U.S. dollars) Copyright 2018 David Smith All Rights Reserved
  35. 35. 4/16/2018 35 World Robot Population Copyright 2018 David Smith All Rights Reserved
  36. 36. 4/16/2018 36 World Robot Population Will They Be Like Us? 72 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. Copyright 2018 David Smith All Rights Reserved
  37. 37. 4/16/2018 37 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. Domestic Robots Copyright 2018 David Smith All Rights Reserved
  38. 38. 4/16/2018 38 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. 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 Copyright 2018 David Smith All Rights Reserved
  39. 39. 4/16/2018 39 Copyright 2018 David Smith All Rights Reserved
  40. 40. 4/16/2018 40 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.” 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 Copyright 2018 David Smith All Rights Reserved
  41. 41. 4/16/2018 41 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 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. Copyright 2018 David Smith All Rights Reserved
  42. 42. 4/16/2018 42 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. 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 Copyright 2018 David Smith All Rights Reserved
  43. 43. 4/16/2018 43 Thank You David Smith dsmith@socialcare.com Copyright 2018 David Smith All Rights Reserved

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