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Introduction to Artificial Intelligence and Machine Learning for policy makers

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Introduction to Artificial Intelligence and Machine Learning for policy makers

  1. 1. Introduction to Artificial Intelligence and Machine Learning … for policy makers 1 Raffaele Mauro Managing Director Endeavor Italy Rena Summer School Matera, August 2017
  2. 2. Technology Finance & Venture Capital Policy Innovation
  3. 3. PR wars
  4. 4. Fake news & PR wars
  5. 5. Robophobia!!
  6. 6. Reality: Engineering breakthroughs + massive investments … but long road ahead
  7. 7. Self-driving vehicles (drones, submarines, cars) Speech recognition Image recognition & search Intelligence analysis Manufacturing automation Gaming Virtual assistants Antispam filters Automatic translation Anti-fraud systems and credit scoring Medical diagnosis Robotics Recommendation systems
  8. 8. Robotics
  9. 9. Narrow Vs General AI From a limited set of specific capabilities to autonomous intelligent agents with general reasoning and real world autonomy? ? Perception Learning Planning Reasoning Mobility …....
  10. 10. Expanding set of tasks efficiently performed by machine intelligence + AI is everywhere but we are not calling it “AI” anymore
  11. 11. Competing with humans: IBM Deep Blue, IBM Watson, Google AlphaGo 1996-97 2011
  12. 12. Google 2017
  13. 13. VC investments in early stage AI companies
  14. 14. Italian VCs investing in AI: August 2017
  15. 15. Google – Investment, Research, Applications
  16. 16. Example: Google Gmail Source: Google Blog https://gmail.googleblog.com/2007/10/how-our-spam-filter-works.html
  17. 17. “Software is eating the world” (M arc Andreessen) “Mobile is eating the world” (Benedict Evans) “AI is eating the world” Source:Facebook, Business Insider http://www.businessinsider.com/facebook-f8-ten-year-roadmap-2016-4?IR=T
  18. 18. “…quando orientur controversiae, non magis disputatione opus erit inter duos philosophus, quam inter duos computistas. Sufficiet enim calamos in manus sumere sedereque ad abacos, et sibi mutuo (accito si placet amico) dicere: calculemus!” (Gottfried Wilhelm Leibniz) Computational thinking
  19. 19. Alan Turing “A computer would deserve to be called intelligent if it could deceive a human into believing that it was human.”
  20. 20. “Most of our future attempts to build large, growing Artificial Intelligences will be subject to all sorts of mental disorders.” Marvin Minsky
  21. 21. History of AI: Multiple Gartner Cycles & “AI Winters”
  22. 22. 5 Paradigms of AI • Inspired by Logic, Philosophy and LinguisticsSymbolic • Inspired by NeuroscienceConnectionist • Inspired by Evolutionary BiologyEvolutionist • Inspired by Probability, Statistics and CombinatoricsStatistical • Inspired by Psychology and MathematicsAnalogical Source: Pedro Domingos, “The Master Algorithm”, MIT Press, 2015
  23. 23. • Popular from the 50’s to late 80’s • Focus on Logic (if-then rules, etc.) • Focus on problem solving • Limited learning capacity • Knowledge engineering Symbolic Approach: Logic and Decision Source: edu(b)log http://thinkdifferent.typepad.com/edulog/
  24. 24. Symbolic Approach: Expert Systems Knowledge engineering: ontologies, knowledge representation, natural language processing, reasoning, decision Source: Steve Copley, IGSE ICT https://www.igcseict.info/readme/index.html
  25. 25. • Intelligent Agents: rational and autonomous, perceive environment, take decisions based on a specific objective, plan action to achieve it • Perception: recognition (vision, etc.) of the environment • Actuation: navigation or manipulation of the environment On step beyond: Intelligence Agents Source: Pattie Maes, MIT Media Lab - Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Novig.
  26. 26. • Decision: Simple reflexes or complex reprensentation of the world (internal states) with reasoning • Problem representation & state space • Actions generate passage from state A to state B • Solution -> from existing state to optimal state • Exploring state space has computational cost Symbolic Approach: Problem Solving Source: Daniel Valana, Jared Bouchier, Xin Yuan, University of Adelaide Student Wiki.
  27. 27. Blind search: brute force • Backward search: starting from the solution • Backtracking: error / obstacle -> go to previous step • Depth first (LIFO, queue) Vs breadth first (FIFO, stack) Heuristic search: based on knowledge Symbolic Approach: State Space Search Source: William H. Wilson, University of New South Wales, http://www.cse.unsw.edu.au/~billw/
  28. 28. Natural Language Processing NLP: Interaction between machines and human languages, with tasks regarding syntax, semantics, discourse and speech Structure: Mix of techniques from traditional symbobolic-linguistic to deep learning Examples of corporate APIs: Google Cloud Natural Language API, IBM Watson, Amazon Lex, Microsoft Cognitive Services, Facebook's DeepText Applications: Translation, Chatbots, Automatic summarization, Antispam, Information extraction/classification Source: Natural Language Toolkit http://www.nltk.org/
  29. 29. Rules Vs Learning Learning is the key to intelligence acceleration ?
  30. 30. • Machine learning: mix of connectionist, statistical, genetic and analogical paradigm. Between data science and AI • Why: Automatizes automation, accelerates progress, human programmer and instructions no more as a bottleneck • Example: Go game, n° of potential moves is 10170, higher than the number of atoms in the universe 1080 • Applications: • Basket analysis in ecommerce: learnin associations with conditional probability • Credit scoring in finance: learning classifications: if income > X AND savings > Y => Low risk (grafico) • Medical diagonosis: pattern recognition • Predictions in financial markets • Bioinformatics • Games Machine Learning
  31. 31. Classification Vs Regression Analysis Classification Regression Sources: http://www.whatissixsigma.net/ and https://jaxenter.com/machine-learning-an-introduction-for-programmers-122135.html Classification: Separate data finding discrete category /label Regression: Find coefficients of the line that minimize distance between observation points and prediction line
  32. 32. Supervised Vs Unsupervised learning Supervised: Learning a class from labeled examples Source: -
  33. 33. Reinforcement learning Source: Berkeley’s CS 294: Deep Reinforcement Learning by John Schulman & Pieter Abbeel Example: Gaming, Robotics, Self Driving Cars
  34. 34. Overfitting: Model with low generality and too much tied to a specific training set Solution: Less variables and larger training set Overfitting Vs Underfitting Source: http://www.turingfinance.com/regression-analysis-using-python-statsmodels-and-quandl/
  35. 35. Connectionist Approach: Neural Netowrks Source: Wikimedia Commons. • Imitating biological computation in neurons • Dealing with complexity and uncertainty • Non-symbolic knowledge representation • Learning capability • Parallel computation
  36. 36. Source: Wikibooks Connectionist Approach: Perceptrons • Input on “dedrites” then ”cell body” compute weights • Output: 0, 1 / yes-no after a threshold in the “axon”
  37. 37. Connectionist Approach: Classification Source: Wikimedia Commons Classification: the perceptron separates inputs in two classes with a linear boundary Boundary: Straight line in 2 dimension, plane in 3 dimensions, n- 1 hyperplane in n dimensions (=n variables) Learning: Inclusion of new elements in the training set increases accuracy Limits: Complex boundaries, XOR functions
  38. 38. Backpropagation: real output confronted with expected outcome and change weights in neuron connections Multilayer perceptrons: hidden layer beyond input and output layers Complexity: Classification / learning with non linear boundaries Applications examples: speech recognition, image recognition, machine translation Gradient descent: Analogy with loss function finding local minimum Connectionist approach: Multilayer Perceptrons and Backpropagation Source: Wikimedia Commons
  39. 39. Deep learning: multilayer perceptron with multiple layers inside -> network forced to extract salient characteristics Applications: NLP, translation vision, speech &audio recognition, bioinformatics Pros: Learning of abstract concepts without human supervision Cons: Non-transparent logic Deep Learning Source: http://www.kdnuggets.com/2016/01/seven-steps-deep-learning.html
  40. 40. Human Brain: Operationally flexible and algorithmically compact (DNA) Combination of approaches: in part learning neural net, in part specialized regions (visual cortex, cerebellum) Hierarchical Order Reading: “How to create a mind” R. Kurzweil, “On Intelligence”, J. Hawkins, S. Blakeslee The Brain Analogy Source: Wikimedia Commons.
  41. 41. Genetic Algorithms: first mentioned by John Von Neumann as self- replicating machines Structure: • Set of automata with casual variations • Fitness function to be maximized • Then mutations and / or random crossover-reproduction • Selection of a new generation • Iteration References: Santa Fe Institute, Melanie Mitchell Evolutionist Approach: Genetic Algorithms Source: Quantdare https://quantdare.com/
  42. 42. Local Minima problem Source: Sebastian Raschka Blog, https://sebastianraschka.com/ Algorithm does not reach the global maximum
  43. 43. Dataclism: Explosion in data production, storage and availability From data to knowledge: New power for statistical techniques: Statistical Approach: Data Explosion Source: P Desjardins-Proulx blog, http://phdp.github.io/blog.html
  44. 44. • Bayesian techniques: Use probability theory and Bayes theorem to uptdate existing knowledge incorparating new data • P(A) = P event #1 • P(B) = P event #2 • P(A|B) = Probability of A if B is true • P (B|A) = Probability of B if A is true • “Degree of belief” subjective / theoretical (Vs frequentist / experimental) • Example: Google’s antispam filter Statistical Approach: Bayesian Reasoning
  45. 45. Markov Chain: sequence of states with probabilistic relation Example: In a sentence, if there is the word X then there is a probability P than the next word will be Y Example: Google’s Page Rank Hidden Markov model: Hidden states, operates as a dynamic Bayesian network Example: Apple’s voice recognition Monte Carlo Chain: random values from probability distributions, then find outputs from each set of values -> complex models without complex functions Statistical Approach: Markov Chains Source: Wikimedia Commons.
  46. 46. Nearest Neighbor: Supervised learning algorithm based on analogy, measured on the distance from other data on a plane/space Data: classified with the most frequent label among the majority of k nearest training samples Training-> Distance measure->Classification Dimensionality reduction: Fundamental for application Applications: Pattern recognition Analogical Approach: k-Nearest Neighbor algorithm Source: Wikimedia Commons.
  47. 47. • Support vector machines: Supervised learning with looking for nearest points to separation margins, with more margins in “competition” • Objective: Maximize margins, or distance with separation hyperplane • Kernelization: Bring data on higher dimensions, where higher margin exists, even if it wasn’t present in normal dimensionality Analogical Approach: Support Vector Machines Source: EFDB, http://efavdb.com/ and OpenCV http://docs.opencv.org/2.4/index.html
  48. 48. K-Means: Classification of unlabeled data- clustering di dati non strutturati K = n° of neighbors to be found Centroid: Middle point in a cluster Set-up: choice of centroid and position data Calculation: Reposition centroids and iteret until threshold Example: Face recognition Unsupervised Learning: K-Means Source: http://iancat.tistory.com/6
  49. 49. Problem: If training set contains prejudices, output will be projudiced Example: Word associations with minorities Solution: Equal opportunity by design ? Amplifying Prejudice ? Source: https://factordaily.com/dangers-of-artificial-intelligence/
  50. 50. Man-machine integration Intelligence Analysis Policy / Etiquette Enforcement Automatic screening followed by human judgement
  51. 51. Extended Moore’s Law
  52. 52. Intelligence Explosion
  53. 53. Intelligence Explosion
  54. 54. • Vernon Vinge • Hans Moravec • Nick Bostrom • Elizer S. Yudkowsky • Ben Goertzel • Ray Kurzweil Speculation on Super-human AI “The term “Singularity” in my book is comparable to the use of this term by the physics community. Just as we find it hard to see beyond the event horizon of a black hole, we also find it difficult to see beyond the event horizon of the historical Singularity. How can we …. imagine what our future civilization, with its intelligence multiplied trillions-fold, be capable of thinking and doing?” (Ray Kurzweil)
  55. 55. Maybe the Singularity is not near …...
  56. 56. Or maybe we should fear the Roko’s Basilisk ! “Roko's basilisk is a thought experiment about the potential risks involved in developing artificial intelligence. The premise is that an all-powerful artificial intelligence from the future could retroactively punish those who did not help bring about its existence, including those who merely knew about the possible development of such a being. It resembles a futurist version of Pascal's wager.” (Source: RationalWiki)
  57. 57. Ethics and “Friendly” AI
  58. 58. China: Rising innovation performance
  59. 59. China: Rising innovation performance
  60. 60. Quantum machine learning
  61. 61. Google / NASA Quantum AI Lab
  62. 62. Online courses Andrew NG Francesco Mosconi Sakynthala Panditharatne Machine Learning Zero to Deep Learning™ with Python and Keras Neural networks for hackers
  63. 63. Books
  64. 64. rmauro@post.harvard.edu raffa.mauro@gmail.com Thank you !
  65. 65. Raffaele Mauro, Ph.D. Raffaele Mauro is passionate about technology, policy and global finance. Now Managing Director at Endeavor Italy, he is focused on high-impact entrepreneurship and venture capital, providing companies access to smart capital, talent and markets. Previously he was Head of Finance for Innovation & Entrepreneurship at Intesa Sanpaolo and worked at venture capital funds such as United Ventures (formerly Annapurna Ventures), P101 and OltreVenture. Raffaele is a Kauffman Fellow and holds an MPA from Harvard University, a Ph.D. from Bocconi and is alumnus of the Singularity University Graduate Studies Program at NASA Ames. Raffaele co-authored the book “Hacking Finance”, an essay on Bitcoin, blockchain and cryptocurrencies, and was invited speaker at EY EMEIA Accelerate, Wired Money and the Bundesbank. He invested and advised several companies including Multiply Labs (YC 2016). Raffaele is also Junior Fellow at the Aspen Institute, member of the Young Leaders group of the US-Italy Council, member of the “Young European Leaders – 40 under 40” cohort of 2011, member of the scientific committee at Blockchainlab.it and member of the executive committee at the Global Shapers Hub - Milano, a World Economic Forum community. Twitter: @rafr 69

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