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Machine learning where to next


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BrightTalk Summit - May 21 2015

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Machine learning where to next

  1. 1. BrightTALK Machine Learning and Data Science Summit – May 21, 2015 Machine Learning – where to next?
  2. 2. Contents • Speaker Bio • What is Machine Learning? • History • Applications • Companies • People • Robotics • Opportunities • Threats • Predictions? • References
  3. 3. Speaker Bio • Peter Morgan CEO Zepto Ventures – Help connect hi-tech (ML, AI) companies with funding • Entrepreneur – Have started my own companies • Ten years in telecoms industry – IBM, Cisco, BT Labs • Last three years Data Science and Machine Learning – Teaching and Implementing • Currently working towards building my own AI company • PhD physics (ABD) + MBA • LinkedIn
  4. 4. Machine Learning “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. Machines will solve the kinds of problems now reserved for humans, and improve themselves ”. Dartmouth Summer Research Project on A.I., 1956.
  5. 5. What is Machine Learning? • Machines that learn and adapt to their environments – Similar to living organisms – Multimodal is goal – AGI - endgame • New software/algorithms – Neural networks – Deep learning • New hardware – GPU’s – Neuromorphic chips • Cloud Enabled – Intelligence in the cloud – MLaaS, IaaS (Watson)
  6. 6. The Big Picture Universe Computer Science AI Machine Learning
  7. 7. ML History I • 1940’s – First computers • 1950 – Turing Machine – Turing, A.M., Computing Machinery and Intelligence, Mind 49: 433-460, 1950 • 1951 – Minsky builds SNARC, a neural network at MIT • 1956 - Dartmouth Summer Research Project on A.I. • 1959 - John McCarthy and Marvin Minsky founded the MIT AI Lab. • 1960’s - Ray Solomonoff lays the foundations of a mathematical theory of AI, introducing universal Bayesian methods for inductive inference and prediction
  8. 8. ML History II • 1969 - Shakey the robot at Stanford • 1970s – AI Winter I • 1970s - Natural Language Processing (Symbolic) • 1980s - Rule Based Expert Systems (Symbolic) • 1990s - AI Winter II (Narrow AI) • 1997 - Deep Blue beats Gary Kasparov • 2010s - Statistical Machine Learning, algorithms that learn from raw data • 2011 - Watson beats Ken Jennings and Brad Rutter on Jeopardy • 2012+ Deep Learning (Sub-Symbolic) • 2013 - E.U. Human Brain Project (model brain by 2023) • 2014 – Human vision surpassed by ML systems at Google, Baidu, Facebook
  9. 9. ML Applications • Finance – Asset allocation – Algo trading • Fraud detection • Cybersecurity • eCommerce • Search • Manufacturing • Medicine • Law • Business Analytics • Ad placement • Recommendation engines • Robotics – Business – Consumer • UAV (cars, drones etc.) • Scientific discovery • Mathematical theorems • Route Planning • Virtual Assistants • Personalisation • Smart homes • Compose music • Write stories
  10. 10. ML Applications - cntd • Computer vision • Speech recognition • NLP • Translation • Call centres • Rescue operations • Policing • Military • Political • National security • Anything a human can do but faster and more accurate – creating, reasoning, decision making, prediction • Google – introduced 50 ML products in last 2 years (Jeff Dean)
  11. 11. ML Applications - Examples • AI can do all these things already today: – Translating an article from Chinese to English – Translating speech from Chinese to English, in real time – Identifying all the chairs/faces in an image – Transcribing a conversation at a party (with background noise) – Folding your laundry (robotics) – Proving new theorems (ATP) – Automatically replying to your email, and scheduling
  12. 12. Learning and doing from watching videos • Researchers at the University of Maryland, funded by DARPA’s Mathematics of Sensing, Exploitation and Execution (MSEE) program • System that enables robots to process visual data from a series of “how to” cooking videos on YouTube - and then cook a meal
  13. 13. ML Companies - established • IBM Watson • Google Deepmind etc. • Microsoft Project Adam • Facebook • Baidu • Yahoo! • *MLaaS*
  14. 14. ML Companies - startups • Numenta • OpenCog • Vicarious • Clarafai • Sentient • Nurture • • • Number is growing rapidly
  15. 15. ML “Rockstars” • Andrew Ng (Baidu) • Geoff Hinton (Google) • Yan LeCun (Facebook) • Yoshua Bengio* • Michael Jordan* • Jurgen Schmidhuber* • Marcus Hutter * * academia
  16. 16. Some (Famous) ML Research Groups • Godel Machine (IDSIA) • AIXI (IDSIA/ANU) • CSAIL (MIT) • CBL Lab (Cambridge) • Oxford • AmpLab (Berkeley) • Stanford • Imperial College • CMU • NYU • DARPA (funding)
  17. 17. Robotics - Embodied ML 1. Industrial Robotics • Manufacturing (Baxter) • Warehousing (Amazon) • Police/Security • Military • Surgery • Drones (UAV’s) – Self-driving cars – Trains – Ships – Planes – Underwater
  18. 18. 2. Personal Robotics – Robots in the Home • Robots with friendly user interface that can understand user’s emotions – Visual; facial emotions – Tone of voice • Caretaking – Elderly – Young • Education • Home security • Housekeeping • Companionship • Artificial limbs • Exoskeletons
  19. 19. Robots & Robotics Companies • Sawyer (ReThink) • Nao (Aldebaran) • iCub (EU) • Asimo (Honda) • Many (Google) • Roomba (iRobot) • Kiva (Amazon) • Pepper (Softbank) • Many (KUKA) • Jibo (startup) • Milo (Robokind) • Oshbot (Fellows) • Valkyrie (NASA)
  20. 20. DARPA Robotics Challenge • • 25 entries, $2million 1st place, 5th June 2015
  21. 21. ML/AI/Robotics Websites • Robotics Business review • AI Hub • AZoRobotics • Robohub • Robotics News • I-Programmer
  22. 22. Opportunities • Free humans to pursue arts and sciences – The Venus Project • Solve deep challenges (political, economic, scientific, social) • Accelerate new discoveries in science, technology, medicine (illness and aging) • Creation of new types of jobs • Increased efficiencies in every market space – Industry 4.0 (steam, electric, digital, intelligence) • Faster, cheaper, more accurate • Replace mundane, repetitive jobs • Human-Robot collaboration • A smarter planet
  23. 23. Threats • Unemployment due to automation – Replace some jobs but create new ones? – What will these be? • Widen the inequality gap – New economic paradigm needed – Basic Income Guarantee? • Existential risk – AI Safety – FHI/FLI/CSER/MIRI • Legal issues – New laws – Machine rights – Personhood • “The robotic takeover of the human decision space is incremental, inevitable and proceeds not at the insistence of the robots but at ours” intelligence/102297
  24. 24. Predictions?* • More robots (exponential increase) • More automation (everywhere) – Endgame is to automate all work – 50% will be automated by 2035 • Loosely autonomous agents (2015) • Semi-automomous agents (2020) • Fully autonomous agents (2025) • Cyborgs (has started - biohackers) • Singularity (2029?) – smarter than us • Self-aware? (personhood) • Quantum computing – Game changer – Quantum algorithms – Dwave • Advances in science and medicine • Ethics (more debate) • Regulation (safety issues) *Remembering that progress in tech follows an exponentially increasing curve - see “The Singularity is Near”, by Ray Kurzweil.
  25. 25. Rise of the Robots* What are the jobs of the future? How many will there be? And who will have them? We might imagine—and hope—that today’s industrial revolution will unfold like the last: even as some jobs are eliminated, more will be created to deal with the new innovations of a new era. In Rise of the Robots, Silicon Valley entrepreneur Martin Ford argues that this is absolutely not the case. As technology continues to accelerate and machines begin taking care of themselves, fewer people will be necessary. Artificial intelligence is already well on its way to making “good jobs” obsolete: many paralegals, journalists, office workers, and even computer programmers are poised to be replaced by robots and smart software. As progress continues, blue and white collar jobs alike will evaporate, squeezing working- and middle-class families ever further. In Rise of the Robots, Ford details what machine intelligence and robotics can accomplish, and implores employers, scholars, and policy makers alike to face the implications. The past solutions to technological disruption, especially more training and education, aren’t going to work, and we must decide, now, whether the future will see broad-based prosperity or catastrophic levels of inequality and economic insecurity. Rise of the Robots is essential reading for anyone who wants to understand what accelerating technology means for their own economic prospects—not to mention those of their children—as well as for society as a whole. *Martin Ford, Rise of the Robots: Technology and the Threat of a Jobless Future, Basic Books, May 2015
  26. 26. It’s not all bad? DARPA Launches Robots4Us Video Contest for High School Students How will the growing use of robots change people’s lives and make a difference for society? How do teens want robots to make a difference in the future? As ever more capable robots evolve from the realm of science fiction to real-world devices, these questions are becoming increasingly important. And who better to address them than members of the generation that may be the first to fully co-exist with robots in the future? Through its new Robots4Us student video contest, DARPA is asking high school students to address these issues creatively by producing short videos about the robotics- related possibilities they foresee and the kind of robot-assisted society in which they would like to live. “Today’s high school students are tomorrow’s technologists, policymakers, and robotics users. They are the people who will be most affected by the practical, ethical, and societal implications of the robotic technologies that are today being integrated into our homes, our businesses, and the military,” said Dr. Arati Prabhakar, DARPA director. “Now is the time to get them engaged and invested by encouraging them to ask questions and provide their views.”
  27. 27. References I • Rise of the Machines – The Economist, May 9th, 2015 peopleexcessively-so-rise-machines • Microsoft Challenges Google’s Artificial Brain with “Project Adam” • The Future of Artificial Intelligence According to Ben Goertzel goertzel/ • Kurzweil: Human-Level AI Is Coming By 2029 2014-12?r=US • Zuckerberg and Musk back software startup that mimics human learning brain-software-vicarious • Computer with human-like learning will program itself learning-will-program-itself.html#.VLQccHs5XUs • Google’s Grand Plan to Make Your Brain Irrelevant
  28. 28. References II • The Race to Buy the Human Brains Behind Deep Learning Machines brains-behind-deep-learning-machines • Smarter algorithms will power our future digital lives our-future-digital-lives.html • What We Know About Deep Learning Is Just The Tip Of The Iceberg • 10 Signs You Should Invest In Artificial Intelligence • Towards Intelligent Humanoid Robots • The Deep Mind of Demis Hassabis 156112890d8a4a • Google isn’t the only company working on artificial intelligence, it’s just the richest artificial-intelligence-its-just-the-richest/
  29. 29. Bibliography • Barrat, James, Our Final Invention, St. Martin's Griffin, 2014 • Brynjolfsson, Erik and Andrew McAfee, The Second Machine Age, W.W. Norton & Co., 2014 • Ford, Martin, Rise of the Robots: Technology and the Threat of a Jobless Future, Basic Books, May 2015 • Hawkins, Jeff, On Intelligence, St Martin’s Griffin, 2004 • Kaku, Michio, The Future of the Mind, Doubleday, 2014 • Kurzweil, Ray, The Singularity is Near, Penguin Books, 2006 • Kurzweil, Ray, How to Create a Mind, Penguin Books, 2013 • Nowak, Peter, Humans 3.0: The Upgrading of the Species, Lyons Press, Jan 2015 • Russell and Norvig, Artificial Intelligence, A Modern Approach, Pearson, 2009
  30. 30. Questions “A company that cracks human level intelligence will be worth ten Microsofts” – Bill Gates.