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Artificial Intelligence and the Singularity piero scaruffi www.scaruffi.com October 2014 - Revised 2016 "The person who says it cannot be done should not interrupt the person doing it" (Chinese proverb) Piero Scaruffi • piero scaruffi [email protected] [email protected] Olivetti AI Center, 1987 Piero Scaruffi • • • • • Cultural Historian Cognitive Scientist Blogger Poet www.scaruffi.com www.scaruffi.com 3 This is Part 2 • See http://www.scaruffi.com/singular for the index of this Powerpoint presentation and links to the other parts 1. 2. 3. 4. 5. 6. 7. 8. Classic A.I. - The Age of Expert Systems Modern A.I. - The Age of Deep Learning Theory: Knowledge-based Systems and Neural Networks Robots Bionics Singularity Critique The Future www.scaruffi.com 4 Modern A.I. The Age of Deep Learning www.scaruffi.com 5 Artificial Intelligence 1980: Kunihiko Fukushima’s “Neocognitron”: the birth of convolutional neural networks Based on the cat’s visual system: 6 Artificial Intelligence 1982: John Hopfield’s simulation of annealing 1983: Geoffrey Hinton's and Terry Sejnowski's Boltzmann machine for unsupervised learning 1985: Judea Pearl's "Bayesian Networks" 1986: Paul Smolensky's Restricted Boltzmann machine 1986: David Rumelhart’s “Parallel Distributed Processing” Rummelhart network Neurons arranged in layers, each neuron linked to neurons of the neighboring layers, but no links within the same layer Requires training with supervision Hopfield networks Multidirectional data flow Total integration between input and output data All neurons are linked between themselves Trained with or without supervision7 Artificial Intelligence 1989: Yann LeCun 's second generation Convolutional Neural Networks 8 Artificial Intelligence 1989: Yann LeCun 's second generation Convolutional Neural Networks 9 Artificial Intelligence RNN and LSTM • 1992: Hava Siegelmann's and Eduardo Sontag's Recurrent Neural Networks (RNNs) • 1997: Sepp Hochreiter's and Jeurgen Schmidhuber's Long Short Term Memory (LSTM) model Artificial Intelligence Deep Learning 2006: Geoffrey Hinton's Deep Belief Networks 2007: Yoshua Bengio's Stacked Autoencoders Deep Learning Artificial Intelligence Deep Learning Michael Jordan Andrew Ng Hinton Lecun Bengio Artificial Intelligence Kunihiko Fukushima: Japan Hava Siegelmann: Israel Sepp Hochreiter‘: Germany Yann LeCun: France Geoffrey Hinton: Britain/ Canada Yoshua Bengio: France/ Canada Andrew Ng: China Daniela Rus: Romania Feifei Li: China Sebastian Thrun: Germany DeepMind: Britain/ New Zealand Ilya Sutskever: Russia The leader in modern AI Artificial Intelligence Deep Learning • Scales well with memory/data/computation • Solves the representation learning problem • State-of-the-art for images, audio, language, ... Artificial Intelligence Supervised learning • Learning by imitation • Only as good as the expert that you imitate • The learned skills cannot be applied to other fields Artificial Intelligence Reinforcement Learning • Unsupervised learning • A computational approach to goal-directed learning from interaction between an active decision-making agent and its environment Harry Klopf: • Learning what to do so as to maximize a “The Hedonistic reward Neuron” (1982) • The four pillars of reinforcement learning: a policy, a reward function, a value function, and a model of the environment • Learning by self-play Andrew Barto (1981) Richard Sutton (1981) Artificial Intelligence Reinforcement Learning • Policy-based RL. Search directly for the policy achieving maximum future reward • Value-based RL. Estimate the maximum value achievable under any policy • Model-based RL. Build a transition model of the environment Artificial Intelligence Deep Reinforcement Learning • Applying DL to RL • Use a deep network to represent value function and/or policy and/or model • Optimise the value function and/or policy and/or model • Deep Q-Networks (DQN) provide a stable solution to deep value-based RL (Volodymyr Mnih, 2013) Artificial Intelligence Deep Learning mimics the workings of the brain: the audiovisual cortex works in multiple hierarchical stages Artificial Intelligence Genealogy of Intelligent Machines Logic Hydraulic machines Hilbert Steam engines Turing Machine Cybernetics Computers Neural networks Expert Systems 20 Artificial Intelligence 1997: IBM's "Deep Blue" chess machine beats the world's chess champion, Garry Kasparov 2011: IBM's Watson debuts on a tv show 2014: Vladimir Veselov's and Eugene Demchenko's program Eugene Goostman passes the Turing test 21 Artificial Intelligence 2014: Vladimir Veselov's and Eugene Demchenko's program Eugene Goostman, which simulates a 13-year-old Ukrainian boy, passes the Turing test at the Royal Society in London 2014: Alex Graves, Greg Wayne and Ivo Danihelka publish a paper on "Neural Turing Machines" 2014: Jason Weston, Sumit Chopra and Antoine Bordes publish a paper on "Memory Networks" 22 Artificial Intelligence 2014: Microsoft demonstrates a real-time spoken language translation system 2014: IBM’s TrueNorth processor 23 Artificial Intelligence • Deep Reinforcement Learning – Fanuc/ Preferred Networks robot (2015) – Alpha Go 24 2010s • Facebook (2010): face recognition • FindFace (2016): identify the pictures of strangers 25 25 2010s • Conversational computing – – – – – Siri (2011) GoogleNow (2012) Amazon Alexa (2014) Microsoft Tay (2016) … Apple 2011 (mandatory Hollywood movie for AI presentation!) Microsoft,2016 Stanley Kubrick (1968) 26 “2001: A Space Odyssey” 2010s • Google (2012): 1.7 billion connections (and 16,000 processors) learn to recognize cats in YouTube videos 27 27 2010s • IDSIA (2013) 28 28 2010s 2014: Image captioning Fei-Fei Li's on algorithm to describe photos (Stanford, 2014) 29 2010s 2015: Convolutional net trained to interpret images 30 2010s • Automatic translation 31 2010s 2015: Microsoft’s 152-layer neural network 2016: Nvidia’s DGX-1: 8 GPUs + software to train neural networks 32 2010s Art 33 2010s • Multi-billion dollar investments in artificial intelligence and robotics in the 2010s – Amazon (Kiva, 2012) – Google (Neven, 2006; Industrial Robotics, Meka, Holomni, Bot & Dolly, DNNresearch, Schaft, Bost, DeepMind, Redwood Robotics, 2013-14) – IBM (AlchemyAPI, 2015; Watson project) – Microsoft (Project Adam, 2014) – Apple (Siri, 2011; Perceptio and VocalIQ, 2015; Emotient, 2016) – Facebook (Face.com, 2012) – Yahoo (LookFlow, 2013) – Twitter (WhetLab, 2015) – Salesforce (MetaMind, 2016) – Samsung (Viv Labs, 2016) 34 34 2010s • DeepMind (2010): machine learning (acquired by Google in 2014) • Vicarious • Sentient: machine learning • Wise.io • Saffron • Narrative Science • CrowdAI • … 35 35 2010s • Investment in AI startups in 2014: $230 million • Investment in AI startups in 2015: $128 million 36 2015 37 2015 • Between 2010 and 2015 over 45 companies and corporate VC arms have invested in AI startups (top of the list: Bloomberg, Samsung, Rakuten • Investment in AI startups in 2014: $230 million • Investment in AI startups in 2015: $128 million 38 The 2010s • Computer Go/Weichi – 2009: Fuego Go (Monte Carlo program by Univ. of Alberta) beats Zhou Junxun – 2010: MogoTW (Monte Carlo program developed in 2008 by a Euro-Taiwanese team) beat Catalin Taranu – 2012: Tencho no Igo/ Zen (Monte Carlo program developed by Yoji Ojima in 2005) beat Takemiya Masaki – 2013: Crazy Stone (Monte Carlo program by Remi Coulom in 2005) beat Yoshio Ishida – Pachi (open-source Monte Carlo program by Petr Baudis) 39 The 2010s 2016: Google/DeepMind’s AlphaGo beats the Go champion Se-dol Lee 40 2010s • Evolution Strategies The 2010s 2016: Toyota’s self-teaching car (deep reinforcement learning) 42 2010s • Open-source platforms for deep learning – Google’s Tensor Flow: scalable – Torch (New York University): flexible – Caffe (Pieter Abbeel's group at UC Berkeley) – Theano (Univ of Montreal, Canada): easiest to install – Chainer (Preferred Networks, Japan): flexible 43 43 2010s • Dangers of A.I. – Stephen Hawking & Bill Gates – Elon Musk (OpenAI, 2016) – Asilomar Conference (2017) 44 44 Do robots steal our jobs? • The countries with the highest number of robots… 45 45 Do robots steal our jobs? • … are also the countries with the lowest unemployment 46 46 Next… • See http://www.scaruffi.com/singular for the index of this Powerpoint presentation and links to the other parts www.scaruffi.com 47