Introduction to Artificial Intelligence and Machine Learning for policy makers
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Overview of artificial intelligence / machine learning trends and techniques RENA - Summer School "Governare la complessità"
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Introduction to Artificial Intelligence and Machine Learning for policy makers
- 5. PR wars
- 7. Robophobia!!
- 10. Robotics
- 14. Google 2017
- 22. “…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
- 26. 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
- 27. • 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/
- 29. • 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.
- 30. • 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.
- 31. 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/
- 34. • 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
- 35. 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
- 36. Supervised Vs Unsupervised learning Supervised: Learning a class from labeled examples Source: -
- 37. Reinforcement learning Source: Berkeley’s CS 294: Deep Reinforcement Learning by John Schulman & Pieter Abbeel Example: Gaming, Robotics, Self Driving Cars
- 39. 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
- 42. 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
- 45. 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/
- 47. 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
- 48. • 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
- 50. 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.
- 51. • 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
- 52. 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
- 58. • 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)
- 67. Books
- 69. 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