Here we will see, how Machine Learning has evolved throughout the history and what is the scope in future for it.

1. (r)Evolution of Machine Learning
FULLMETAL ALCHEMISTS

2. 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.

3. 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

4. Machine Learning Methods
• Supervised Learning
• Unsupervised Learning
• Semi-supervised Learning
• Reinforcement Learning

5. Who’s using it?
• Financial Services
• Government
• Healthcare
• Marketing & Sales
• Transportation
• Oil & Gas

6. The Bigger Picture
Universe Computer
Science
AI Machine
Learning

7. Back In Time I (1760 - 1940)
• 1760-1840 - Machine and labor Revolution
• 1840-1920 - Technical Revolution
• 1920 and further - Technological Revolution
Medium of Communication developed like radio,
television , telephone Informatics and the medium of
transportation.
Aviation and space exploration received a big push.
Digital Revolution and Information Revolution.

8. Back In Time II (1940 - 1960)
• 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.

9. Back In Time III (1960 - 1980)
• 1967 - “nearest neighbour” algorithm, allowing
computers to begin using very basic pattern
recognition.
• 1969 - Shakey the robot at Stanford
• 1970’s - AI Winter I
• 1970’s - Natural Language Processing (Symbolic)
• 1979 - Music programmes by Kurzweil and Lucas
• 1980 - First AAAI conference
- Cultural transformation of Ford Motor
company
• 1980’s - Rule Based Expert Systems (Symbolic)

10. Back In Time IV (1980 - 2000)
• 1981 - Connection Machine (parallel AI)
• 1981 - Concept of Explanation based learning computer
analyses training data and creates a general rule it can
follow by discarding unimportant data.
• 1985 - Back propagation
• 1987 - “The Society of Mind” by Marvin Minsky
published
• 1990’s - AI Winter II (Narrow AI)
• 1990’s - Automated Tropical Cyclone Forecasting System
(ATCF)
• 1994 - First self-driving car road test – in Paris
• 1997 - Deep Blue beats Gary Kasparov

11. Back In Time V (2000 - )
• 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 – IBM’s 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
• 2015 - Machine dreaming (Google and Facebook NN’s)

12. Timeline

13. ML Applications 1.0
• Finance
 Asset allocation
 Algo-trading
• Fraud detection
• Cybersecurity
• E-Commerce
• Search
• Manufacturing
• Medicine
• Law
• Business Analytics
• Ad serving
• Recommendation engines
• Robotics
 Industry
 Consumer
 Space
 Military
• UAV (cars, drones etc.)
• Scientific discovery
• Mathematical theorems
• Route Planning
• Virtual Assistants
• Personalization
• Compose music
• Write stories
• Smart homes

14. ML Applications 2.0
• Computer vision
• Speech recognition
• NLP
• Translation
• Call centers
• 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)

15. ML Applications – Examples 1.0
• The heavily hyped, self-driving Google car?
The essence of machine learning.
• Online recommendation offers such as those
from Amazon and Netflix? Machine learning
applications for everyday life.
• Knowing what customers are saying about you
on Twitter? Machine learning combined with
linguistic rule creation.
• Fraud detection? One of the more obvious,
important uses in our world today.

16. ML Applications – Examples 2.0
• 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

17. 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.

18. 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

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

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

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

22. 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)

23. Movies that used ML concepts
• I, Robot
• Bicentennial Man
• A Beautiful Mind
• The Matrix Trilogy
• 21
• The Imitation Game
• Artificial Intelligence
• Her
• Blade Runner
• Ex Machina
• Money-ball
• Terminator Series

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

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

26. 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)

27. 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

28. 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

29. 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-autonomous 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
 D-wave
 Advances in science and medicine
• Ethics (more debate)
• Regulation (safety issues)

30. Rise of the Robots

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.