Machine learning is rapidly advancing and will transform many aspects of society. It has the potential to automate jobs, improve lives through applications in healthcare, transportation, and more. However, it also poses risks like unemployment and a widening inequality gap that will require addressing. The future of AI is uncertain, but predictions include human-level machine intelligence within the next 10-15 years, and an acceleration of scientific discoveries. Oversight and safety research aims to ensure AI's benefits are maximized and its risks are minimized.

1. The Future of Machine Learning
Peter Morgan

2. Contents
• Speaker Bio
• What is Machine Learning?
• History
• Applications
• Companies
• People
• Robotics
• Opportunities
• Threats
• Predictions?
• References

3. 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.

4. 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)
– Cloud Robotics

5. The Bigger Picture
Universe Computer
Science
AI Machine
Learning

6. 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.
• 1957 – Samuel drafts algos (Prinz)
• 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

7. ML History II
• 1969 - Shakey the robot at Stanford
• 1970s – AI Winter I
• 1970s - Natural Language Processing (Symbolic)
• 1979 – Music programmes by Kurzweil and Lucas
• 1980 – First AAAI conference
• 1981 – Connection Machine (parallel AI)
• 1980s - Rule Based Expert Systems (Symbolic)
• 1985 – Back propagation
• 1987 – “The Society of Mind” by Marvin Minsky published
• 1990s - AI Winter II (Narrow AI)
• 1994 – First self-driving car road test – in Paris
• 1997 - Deep Blue beats Gary Kasparov

8. ML History III
• 2004 - DARPA introduces the DARPA Grand Challenge requiring
competitors to produce autonomous vehicles for prize money
• 2007 - Checkers is solved by a team of researchers at the
University of Alberta
• 2009 - Google builds self driving car
• 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
http://en.wikipedia.org/wiki/Timeline_of_artificial_intelligence
• 2015 – Machine dreaming (Google and Facebook NN’s)

9. ML Applications
• Finance
– Asset allocation
– Algo trading
• Fraud detection
• Cybersecurity
• eCommerce
• Search
• Manufacturing
• Medicine
• Law
• Business Analytics
• Ad serving
• Recommendation engines
• Smart homes
• Robotics
– Industry
– Consumer
– Space
– Military
• UAV (cars, drones etc.)
• Scientific discovery
• Mathematical theorems
• Route Planning
• Virtual Assistants
• Personalisation
• Compose music
• Write stories

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. 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. 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. ML Performance evaluation
• Optimal: it is not possible to perform better
– Checkers, Rubik’s cube, some poker
• Strong super-human: performs better than all humans
– Chess, scrabble, question-answer
• Super-human: performs better than most humans
– Backgammon, cars, crosswords
• Par-human: performs similarly to most humans
– Go, Image recognition, OCR
• Sub-human: performs worse than most humans
– Translation, speech recognition, handwriting

14. ML Companies - MNC
• IBM Watson
• Google Deepmind etc.
• Microsoft Project Adam
• Facebook
• Baidu
• Yahoo!

15. ML Companies - startups
• Numenta
• OpenCog
• Vicarious
• Clarafai
• Sentient
• Nurture
• Wit.ai
• Cortical.io
• Viv.ai
Number is growing rapidly (daily?)

16. ML “Rockstars”
• Andrew Ng (Baidu)
• Geoff Hinton (Google)
• Yan LeCun (Facebook)
• Yoshua Bengio* (IBM)
• Michael Jordan*
• Jurgen Schmidhuber*
• Marcus Hutter *
* academia

17. Some (Famous) ML Research Groups
• Godel Machine (IDSIA)
• AIXI (IDSIA/ANU)
• CSAIL (MIT)
• AmpLab (Berkeley)
• Stanford
• CMU
• NYU
• CBL Lab (Cambridge)
• Oxford
• Imperial College
• UCL Gatsby Lab
• Toronto
• DARPA (funding)

18. Robotics - Embodied ML
1. Industrial Robotics
• Manufacturing (Baxter)
• Warehousing (Amazon)
• Police/Security
• Military
• Surgery
• Drones (UAV’s)
– Self-driving cars
– Trains
– Ships
– Planes
– Underwater

19. 2. Consumer Robotics
• Robots with friendly user interface that can understand
user’s emotions
– Visual; facial emotions
– Tone of voice
• Caretaking
– Elderly
– Young
• EmoSpark, Echo
• Education
• Home security
• Housekeeping
• Companionship
• Artificial limbs
• Exoskeletons

20. Robots & Robotics Companies
• Sawyer (ReThink)
• iCub (EU)
• Asimo (Honda)
• Nao (Aldebaran)
• Pepper (Softbank)
• Many (Google)
• Roomba (iRobot)
• Kiva (Amazon)
• Many (KUKA)
• Jibo (startup)
• Milo (Robokind)
• Oshbot (Fellows)
• Valkyrie (NASA)
• DURUS (SRI)

21. PROXI (SRI)
PROXI is a low cost, high performance,
electric humanoid that can walk for 8
hours.
“We don’t believe that there’s a
platform [that exists right now] that
has the kind of components,
performance, and dynamic response
that PROXI will have. Hopefully we’ll
see a path where initially some
research groups will start with PROXI,
and then in 3-5 years, if we get the
volume, this is a robot that could be on
sale for under $100,000. And even
potentially in the $50,000 range, with
any kind of reasonable volume. We
have something that can open up a
market: the platforms are getting
ready to emerge that will enable the
next generation of robot applications,
and I think this platform will be one of
those.” - Rich Mahoney, Director of
SRI’s robotics program, 2015.

22. DARPA Robotics Challenge
• http://www.theroboticschallenge.org/
• 25 entries, $2million 1st place, 5th June 2015

23. ML/AI/Robotics Websites
• Jobs, News, Trade
• Robotics Business review
http://www.roboticsbusinessreview.com/
• AI Hub
http://aihub.net/
• AZoRobotics
http://www.azorobotics.com/
• Robohub
http://robohub.org/
• Robotics News
http://www.roboticsnews.co.uk/
• I-Programmer
http://www.i-programmer.info/news/105-artificial-intelligence.html

24. 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

25. 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 + Ethical issues
– New laws
– Machine rights
– Personhood

26. AI Safety - Oversight
• BARA = British Automation and Robot Association
• http://www.bara.org.uk/
• EU Robotics
• http://www.eu-robotics.net/
• RIA = Robotic Industries Association
• http://www.robotics.org/
• IFR = International Federation of Robotics
• http://www.ifr.org/
• ISO – Robotics
• http://www.sis.se/popup/iso/isotc184sc2/index.asp

27. Organisations - xRisk
• FHI = Future of Humanity Institute
– Oxford
• FLI = Future of Life Institute
– MIT
– $7million grants awarded in June
• MIRI = Machine Intelligence Research Institute
– San Francisco
• CSER = Center for Science and Existential Risk
– Cambridge
• AI Safety Facebook Group
– https://www.facebook.com/groups/467062423469736/

28. 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, implants)
• 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 technology follows an
exponentially increasing curve - see “The Singularity is Near”, by Ray Kurzweil.

29. 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

30. Our children’s future
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.”
http://www.darpa.mil/NewsEvents/Releases/2015/02/11.aspx

31. References I
• Rise of the Machines – The Economist, May 9th, 2015
http://www.economist.com/news/briefing/21650526-artificial-intelligence-scares-
peopleexcessively-so-rise-machines
• Microsoft Challenges Google’s Artificial Brain with “Project Adam”
http://www.wired.com/2014/07/microsoft-adam/
• The Future of Artificial Intelligence According to Ben Goertzel
http://techemergence.com/the-future-of-artificial-intelligence-according-to-Ben-
goertzel/
• Kurzweil: Human-Level AI Is Coming By 2029
http://uk.businessinsider.com/ray-kurzweil-thinks-well-have-human-level-ai-by-2029-
2014-12?r=US
• Zuckerberg and Musk back software startup that mimics human learning
http://www.theguardian.com/technology/2014/mar/21/zuckerberg-invest-startup-
brain-software-vicarious
• Computer with human-like learning will program itself
http://www.newscientist.com/article/mg22429932.200-computer-with-humanlike-
learning-will-program-itself.html#.VLQccHs5XUs
• Google’s Grand Plan to Make Your Brain Irrelevant
http://www.wired.com/2014/01/google-buying-way-making-brain-irrelevant/

32. References II
• The Race to Buy the Human Brains Behind Deep Learning Machines
http://www.businessweek.com/articles/2014-01-27/the-race-to-buy-the-human-
brains-behind-deep-learning-machines
• Smarter algorithms will power our future digital lives
http://www.computerworld.com/article/2687902/smarter-algorithms-will-power-
our-future-digital-lives.html
• What We Know About Deep Learning Is Just The Tip Of The Iceberg
https://wtvox.com/2014/12/know-deep-learning-just-tip-iceberg/
• 10 Signs You Should Invest In Artificial Intelligence
http://www.33rdsquare.com/2014/10/10-signs-you-should-invest-in.html
• Towards Intelligent Humanoid Robots
http://www.33rdsquare.com/2013/02/towards-intelligent-humanoid-robots.html
• The Deep Mind of Demis Hassabis
https://medium.com/backchannel/the-deep-mind-of-demis-hassabis-
156112890d8a4a
• Google isn’t the only company working on artificial intelligence, it’s just the richest
https://gigaom.com/2014/01/29/google-isnt-the-only-company-working-on-
artificial-intelligence-its-just-the-richest/

33. Bibliography
• Barrat, James, Our Final Invention, St. Martin's Griffin, 2014
• Bengio, Yoshua et al, Deep Learning, MIT Press, 2015
• Brynjolfsson, Erik and Andrew McAfee, The Second Machine Age, W.W.
Norton & Co., 2014
• Byrne, Fergal, Real Machine Intelligence, Leanpub, 2015
• Ford, Martin, Rise of the Robots: Technology and the Threat of a Jobless
Future, Basic Books, 2015
• 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,
2015
• Russell and Norvig, Artificial Intelligence, A Modern Approach, Pearson,
2009
• Yampolskiy, Roman - Artificial Superintelligence, A Futuristic Approach,
CRC, 2015

34. Questions
“A company that cracks human level intelligence
will be worth ten Microsofts” – Bill Gates.

35. Extra Slides