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Applying Machine Learning and Artificial Intelligence to Business
The document discusses the applications, algorithms, and advancements in machine learning (ML) and artificial intelligence (AI), emphasizing their importance in various business and technological contexts. It details types of learning, various ML and deep learning toolkits, notable AI companies, and milestones in AI history. The document also explores the opportunities and threats presented by AI and robotics in modern society.
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Applying Machine Learning and Artificial Intelligence to Business
- 1. Applying Machine Learningand AI for Business • www.GoDataScience.io • Peter Morgan – Chief Data Scientist • Russell Miles – CEO • @godatascience • @pmzepto • @russmiles ©GoDataScience 1
- 2. I. ML -Overview • Definition – “Field of study that gives computers the ability to learn without being explicitly programmed” - Arthur Samuel, 1959 • Used for fitting lines/hyperplanes (regression), finding models, classifying objects, hypothesis testing, etc. • Three main categories of learning • Supervised (labelled data, classifying) • Unsupervised (unlabelled data, clustering) • Reinforcement learning (reward/penalty) ©GoDataScience 2
- 3. ML Algorithm Classes •Regression, e.g., linear, logistic • Decision Trees, e.g., CART • Ensemble, e.g., Random Forests • Bayesian, e.g., Naïve Bayes • Artificial Neural Networks, e.g., RNN • Instance, e.g., k-Nearest Neighbour (kNN) • Support Vector Machines (SVM) • Evolutionary, e.g., genetic (mimics natural selection) • Dimensionality Reduction, e.g., PCA • Clustering, e.g., K-means • Reinforcement, e.g., Q-learning • List of ML algos https://en.wikipedia.org/wiki/List_of_machine_learning_concepts ©GoDataScience 3
- 4. ML Applications • Speechrecognition • Object recognition and tracking • Spam filtering • Self-driving cars • Recommendation engines • Fraud detection • Search engines, e.g., PageRank • Ad placement • Financial forecasting ©GoDataScience 4
- 5. Algorithm References • http://en.wikipedia.org/wiki/Machine_learning •http://en.wikipedia.org/wiki/Predictive_analytics • http://en.wikipedia.org/wiki/Pattern_recognition • http://en.wikipedia.org/wiki/Support_vector_machine • http://en.wikipedia.org/wiki/Regression_analysis • http://en.wikipedia.org/wiki/Random_forest • http://en.wikipedia.org/wiki/Non-parametric_statistics • http://en.wikipedia.org/wiki/Decision_tree_learning ©GoDataScience 5
- 6. Open Source MLToolkits • Brain https://github.com/harthur/brain • Concurrent Pattern http://www.cascading.org/projects/pattern/ • Convnetjs https://github.com/karpathy/convnetjs • Decider https://github.com/danielsdeleo/Decider • etcML www.etcml.com • Etsy Conjecture https://github.com/etsy/Conjecture • Google Sibyl https://plus.google.com/+ResearchatGoogle/posts/7CqQbKfYKQf • GraphX https://amplab.cs.berkeley.edu/publication/graphx-grades/ • KNIME http://www.knime.org/ • List https://github.com/showcases/machine-learning • ML software http://www.cs.ubc.ca/~murphyk/Software/index.html • MLPNeuralNet https://github.com/nikolaypavlov/MLPNeuralNet ©GoDataScience 6
- 7. Open Source MLTookits (cont) • MOA http://moa.cs.waikato.ac.nz/ • Neurokernel http://neurokernel.github.io/ • NuPic https://github.com/numenta/nupic • Orange http://orange.biolab.si/ • RapidMiner http://rapidminer.com • Scikit-learn http://scikit-learn.org/stable/ • Spark http://spark.apache.org/mllib/ • TunedIT http://tunedit.org/ • Vahara https://github.com/thedatachef/varaha • Viv http://viv.ai/ • Vowpal Wabbit https://github.com/JohnLangford/vowpal_wabbit/wiki • Weka http://www.cs.waikato.ac.nz/ml/weka/ ©GoDataScience 7
- 8. Open Source MLLibraries • Dlib http://dlib.net/ml.html • MADLib http://madlib.net/ • Mahout http://mahout.apache.org/ • MCMLL http://mcmll.sourceforge.net/ • MLC++ http://www.sgi.com/tech/mlc/ • mloss http://mloss.org/software/ • mlpack http://mlpack.org/ • Shogun http://www.shogun-toolbox.org/ • Stan http://mc-stan.org/ ©GoDataScience 8
- 9. Proprietary ML Toolkits •Ayasdi http://www.ayasdi.com/ • BigML https://bigml.com/ • H2O http://h2o.ai • IBM Watson http://www.ibm.com/smarterplanet/us/en/ibmwatson/ • Matlab http://uk.mathworks.com/solutions/machine-learning/ • Nutonian http://www.nutonian.com/ • Prediction.io http://prediction.io/ • Rocketfuel http://rocketfuel.com/ • Skytree http://www.skytree.net/ • Trifacta http://www.trifacta.com/ • Wolfram Alpha http://www.wolframalpha.com/ • Wise.io http://www.wise.io/ • Yhat https://yhathq.com/ ©GoDataScience 9
- 10. Other ML Resources •MLaaS (Cloud based) • Microsoft http://azure.microsoft.com/en-us/documentation/services/machine-learning/ • Google https://cloud.google.com/products/prediction-api/ • AWS https://aws.amazon.com/marketplace/search?page=1&searchTerms=machine+learning • Conferences • ICML • NIPS • ML Journals • ML Journal http://www.springer.com/computer/ai/journal/10994 • JMLR http://jmlr.org/papers/ • Pattern Recognition http://www.jprr.org/index.php/jprr • arXiv http://arxiv.org/list/stat.ML/recent • gitXiv http://gitxiv.com ©GoDataScience 10
- 11. References – MachineLearning • Abu-Mostafa, Yaser et al – Learning from Data, AML, 2012 • Alpaydın, Ethem - Introduction to Machine Learning, 2nd ed, MIT Press, 2009 • Bishop, Christopher - Pattern Recognition and Machine Learning, Springer, 2007 • Domingos, Pedro - The Master Algorithm, Allen Lane, 2015 • Flach, Peter - Machine Learning: The Art and Science of Algorithms that Make Sense of Data, Cambridge University Press, 2012 • Mitchell, Tom – Machine Learning, McGraw-Hill, 1997 • Murphy, Kevin - Machine Learning: A Probabilistic Perspective, MIT Press, 2012 • Rickhert, Willi and Luis Coelho, Building Machine Learning Systems with Python, Packt, 2013 • Witten, Ian et al - Data Mining, Practical Machine Learning Tools and Techniques, 3rd ed, Morgan Kaufman, 2011 ©GoDataScience 11
- 12. II. Deep Learning •Aims •To understand what Deep Learning is • Look at some of the common toolkits • How is it being used today • Challenges to overcome ©GoDataScience 12
- 13. Deep Learning Overview •Extract patterns and meaning from data • Modeled after how the human brain processes data • DL methods have gained notable successes in the field of speech and image recognition as well as in cognitive computing • Outperforming other algorithms • They are essentially ANNs • CNN = Convolutional Neural Networks (images) • RNN = Recurrent Neural Networks (speech & text) • LSTM = Long Short Term Memory ©GoDataScience 13
- 14. Deep Learning Progress Progressin machine classification of images - error rate by year. Red line is the error rate of a trained human ©GoDataScience 14
- 15. DL Resources • People •Yann LeCun http://yann.lecun.com/ • Geff Hinton http://www.cs.toronto.edu/~hinton/ • Yoshua Bengio http://www.iro.umontreal.ca/~bengioy/yoshua_en/index.html • Andrew Ng http://cs.stanford.edu/people/ang/ • Quoc Le http://cs.stanford.edu/~quocle/ • Jurgen Schmidhuber http://people.idsia.ch/~juergen/ • Blogs & Communities • FastML http://fastml.com/ • Chris Olah http://colah.github.io/ • Andrej Karparthy http://karpathy.github.io • DeepLearning.net http://deeplearning.net/ ©GoDataScience 15
- 16. DL Open SourcePackages • Caffe http://caffe.berkeleyvision.org • CUDA Convnet https://code.google.com/p/cuda-convnet/ • cuDNN https://developer.nvidia.com/cuDNN • Deeplearning4j http://deeplearning4j.org/ • PyBrain http://pybrain.org/ • PyLearn2 http://deeplearning.net/software/pylearn2/ • SINGA http://singa.incubator.apache.org • TensorFlow http://tensorflow.org • Theano http://deeplearning.net/software/theano/ • Torch http://torch.ch/ • In fact, Google, IBM, Samsung, Microsoft and Baidu have open sourced their machine learning frameworks all within the space of the last two weeks ©GoDataScience 16
- 17. Deep Learning Companies •AlchemyAPI http://www.alchemyapi.com/ • Clarifai http://www.clarifai.com/ • Deepmind www.google.com • Ersatz Labs http://www.ersatzlabs.com/ • Memkite http://memkite.com/ • Nervana http://www.nervanasys.com/ • Numenta http://numenta.org/ • Nvidia https://developer.nvidia.com/deep-learning • Skymind http://www.skymind.io/ • Vicarious http://vicarious.com/ ©GoDataScience 17
- 18. References – DeepLearning • Bengio, Yoshua et al – Deep Learning, An MIT Press book in preparation http://goodfeli.github.io/dlbook/ • Buduma, Nikhil – Fundamentals of Deep Learning, O’Reilly, 2015 • Gibson, A and J. Patterson - Deep Learning: A Practitioner's Approach, O’Reilly, 2015 • Maters, Timothy, Deep Belief Nets in C++ and CUDA C, Vol. 1, CreateSpace, 2015 • Maters, Timothy, Deep Belief Nets in C++ and CUDA C, Vol. 2, CreateSpace, 2015 ©GoDataScience 18
- 19. III. Artificial Intelligence •Definition • Overview • History • Applications • Companies • People • Robotics • Opportunities • Threats • Predictions • References ©GoDataScience 19
- 20. AI - Definition “Everyaspect 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. ©GoDataScience 20
- 21. Artificial Intelligence Overview •Agents that learn from and adapt to their environments while achieving goals • 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 • IaaS (Watson) • Cloud Robotics ©GoDataScience 21
- 22. The Bigger Picture UniverseComputer Science AI ©GoDataScience 22
- 23. AI 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 ©GoDataScience 23
- 24. AI 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 ©GoDataScience 24
- 25. AI 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 (DL); Sub-Symbolic • 2013 - E.U. Human Brain Project (model brain by 2023) • 2014 – Human vision surpassed by DL systems at Google, Baidu, Facebook • 2015 – Machine dreaming (Google and Facebook NN’s) ©GoDataScience 25
- 26. AI 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 ©GoDataScience 26
- 27. AI Applications (cont) •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 60 DL products in last 2 years (Jeff Dean) ©GoDataScience 27
- 28. AI 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 ©GoDataScience 28
- 29. 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 ©GoDataScience 29
- 30. AI 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 ©GoDataScience 30
- 31. AI Corporations • IBMWatson • Google Deepmind etc. • Microsoft Project Adam • Facebook • Baidu • Yahoo! ©GoDataScience 31
- 32. AI Startups • Numenta •OpenCog • Vicarious • Clarafai • Sentient • Nurture • Wit.ai • Cortical.io • Viv.ai Number is growing rapidly (daily?) ©GoDataScience 32
- 33. Robotics - EmbodiedAI 1. Industrial Robotics • Manufacturing (Baxter) • Warehousing (Amazon) • Police/Security • Military • Surgery • Drones (UAV’s) • Self-driving cars • Trains • Ships • Planes • Underwater ©GoDataScience 33
- 34. 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 ©GoDataScience 34
- 35. Robots & RoboticsCompanies • 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) ©GoDataScience 35
- 36. AI & RoboticsWebsites • Jobs, News, Trade • Robotics Business review • AI Hub • AZoRobotics • Robohub • Robotics News • I-Programmer ©GoDataScience 36
- 37. Opportunities • Free humansto 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 ©GoDataScience 37
- 38. Threats • Unemployment dueto 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 ©GoDataScience 38
- 39. AI Safety -Oversight • BARA = British Automation and Robot Association • EU Robotics • RIA = Robotic Industries Association • IFR = International Federation of Robotics • ISO – Robotics ©GoDataScience 39
- 40. 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 ©GoDataScience 40
- 41. 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 various exponentially increasing curves - see “The Singularity is Near”, by Ray Kurzweil.©GoDataScience 41
- 42. “A company thatcracks human level intelligence will be worth ten Microsofts” – Bill Gates. ©GoDataScience 42
- 43. References • 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, 2015 • Goertzel, Ben et al - Engineering General Intelligence, Part 1, Atlantis Press, 2014 • Hawkins, Jeff – On Intelligence, Owl, 2005 • Kaku, Michio, The Future of the Mind, Doubleday, 2014 • Kaplan, Jerry – Humans Need Not Apply, Yale University Press, 2015 ©GoDataScience 43
- 44. References (cont) • Kurzweil,Ray, The Singularity is Near, Penguin Books, 2006 • Kurzweil, Ray, How to Create a Mind, Penguin Books, 2013 • Marcus, G. and J. Freeman (eds) - The Future of the Brain: Essays by the World's Leading Neuroscientists, Princeton, 2014 • Markoff, John – Machines of Loving Grace, Ecco Press, 2015 • Nowak, Peter, Humans 3.0: The Upgrading of the Species, Lyons Press, 2015 • Russell and Norvig, Artificial Intelligence, A Modern Approach, Pearson, 2009 • Shanahan, Murray – The Technological Singularity, MIT Press, 2015 ©GoDataScience 44
- 45.