var time_series_data = [{"year": 1950, "label": "Turing Test", "description": "Alan Turing proposes a method of inquiry in artificial intelligence (AI) for determining whether or not a computer is capable of thinking like a human being.", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 1950, "label": "Computer plays chess ", "description": "Claude Shannon publishes the book \"Programming a Computer for Playing Chess\" that describes how a machine or computer could be made to play a reasonable game of chess.", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1950, "label": "Three Laws of Robotics", "description": "Isaac Asimov publishes a set of laws, rules, or principles, which are intended as a fundamental framework to underpin the behavior of robots designed to have a degree of autonomy.The Three Laws of Robotics are: \n\n1. A robot may not injure a human being or, through inaction, allow a human being to come to harm;\n2. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law; and\n3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 1951, "label": "Squee", "description": "The Robot Squirrel uses two light sensors and two contact switches to hunt for \u201dnuts\u201d (actually, tennis balls) and drag them to its nest. Squee was described as \u201c75% reliable,\u201d but it worked well only in a very dark room. Squee was conceived by the computer pioneer Edmund Berkeley, who earlier wrote the hugely popular book \"Giant Brains or Machines That Think\" (1949). The original Squee prototype is in the permanent collection of the Computer History Museum", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1952, "label": "Checkers", "description": "Checkers was the first program to demonstrate that computers can learn and not only perform what they are programmed to do. Checkers attracted a lot of media attention and learned to play at a level high enough to challenge a decent amateur human player.", "category": "Planning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1955, "label": "Logic Theorist", "description": "The Logic Theorist program, written in 1956 by Allen Newell, Herbert Simon and Cliff Shaw, would eventually prove 38 theorems from Whitehead and Russell\u2019s Principia Mathematica. The Logic Theorist introduced several critical concepts to artificial intelligence including heuristics, list processing and \u2018reasoning as search.\u2019", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1956, "label": "Dartmouth College AI Conference", "description": "The Dartmouth College summer AI conference was organized by John McCarthy, Marvin Minsky, Nathan Rochester of IBM and Claude Shannon. McCarthy coins the term artificial intelligence for the conference.The Darthmouth College AI conference is often refered as the birthplace of AI. ", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1957, "label": "Perceptron", "description": "Frank Rosenblatt's perceptron algorithm is a simplified model of a biological neuron inspired by the human brain. The perceptron was introduced as \"the embryo of an electronic computer that will be able to walk, talk, see, write, reproduce itself and be conscious of its existence.\" With the perceptron, connectionism is born, and the foundation of Neural Networks (NN) and Deep Learning ", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1958, "label": "LISP", "description": "John McCarthy invented the Lisp programming language.", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 1958, "label": "General Problem Solver (GPS)", "description": " The General Problem Solver was the first program that tried to adopt human problem-solving protocols and the \"thinking humanly\" approach. ", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 1959, "label": "MIT AI Lab", "description": "John McCarthy and Marvin Minsky founded the MIT AI Lab.", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1960, "label": "Universal Bayesian methods", "description": "Ray Solomonoff lays the foundations of a mathematical theory of AI, introducing universal Bayesian methods for inductive inference and prediction.", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1961, "label": "SAINT", "description": "James Slagle writes in Lisp the first symbolic integration program, SAINT, which solved calculus problems at the college freshman level.", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1961, "label": "Unimate", "description": "The first industrial robot, Unimate, starts working on an assembly line in a General Motors plant in New Jersey. ", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1963, "label": "Rancho Arm", "description": "Researchers design the Rancho Arm robot as a tool for the handicapped. The Rancho Arm\u00b4s six joints gave it the flexibility of a human arm. Acquired by Stanford University in 1963, it holds a place among the first artificial robotic arms to be controlled by a computer.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1963, "label": "ANALOGY", "description": "Thomas Evans' program ANALOGY demonstrated that computers can solve the same analogy problems that are given on IQ tests.", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1963, "label": "Beyond simple perceptrons", "description": "Leonard Uhr and Charles Vossler publish \"A Pattern Recognition Program That Generates, Evaluates, and Adjusts Its Own Operators\", which describes one of the first machine learning programs that could adaptively acquire and modify features and thereby overcome the limitations of Rosenblatt's simple perceptron.", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1964, "label": "MAC project", "description": "Danny Bobrow shows that computers can understand natural language well enough to solve algebra word problems correctly.", "category": "Communication", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1965, "label": "Resolution Method", "description": "J. Alan Robinson invented a mechanical proof procedure, the Resolution Method, which allowed programs to work efficiently with formal logic as a representation language.", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1965, "label": "ELIZA", "description": "ELIZA was a natural language processing system that imitated a doctor. The doctor responded to user questions much like a psychotherapist and was able to make some users believe they were interacting with another human being until it reached its limitations and the conversation became nonsense. DOCTOR used predetermined queries/phrases and used to substitute keywords to mimic humans interacting with the users ", "category": "Communication", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1966, "label": "ALPAC report", "description": "ALPAC (Automatic Language Processing Advisory Committee) was a committee of seven scientists led by John R. Pierce, established in 1964 by the United States government in order to evaluate the progress in computational linguistics in general and machine translation in particular. Their report, issued in 1966, gained notoriety for being very skeptical of research done in machine translation so far, and emphasizing the need for basic research in computational linguistics; this eventually caused the U.S. government to reduce its funding of the topic dramatically.", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1967, "label": "Dendral program", "description": "The Dendral program demonstrated to interpret mass spectra on organic chemical compounds. First successful knowledge-based program for scientific reasoning.", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1968, "label": "Macsyma", "description": "Joel Moses demonstrated the power of symbolic reasoning for integration problems in the Macsyma program. First successful knowledge-based program in mathematics.", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1968, "label": "MacHack", "description": "Richard Greenblatt builds a knowledge-based chess-playing program that was good enough to achieve a class-C rating in tournament play.", "category": "Planning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1969, "label": "First IJCAI conference", "description": "The International Joint Conference on Artificial Intelligence (IJCAI) was held biennially in odd-numbered years from 1969 to 2015. Starting 2016, IJCAI is held annually.", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 1969, "label": "Perceptrons", "description": "Marvin Minsky and Seymour Papert publish \"Perceptrons\", demonstrating previously unrecognized limits of this feed-forward two-layered structure. This book is considered to mark the beginning of the AI winter of the 1970s, a failure of confidence and funding for AI.", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1969, "label": "Frame Problem", "description": "McCarthy and Hayes start the discussion about the frame problem with their essay, \"Some Philosophical Problems from the Standpoint of Artificial Intelligence.\"", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 1970, "label": "Backpropagation", "description": "Seppo Linnainmaa publishes the reverse mode of automatic differentiation. This method became later known as backpropagation, and is heavily used to train artificial neural networks.", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1970, "label": "Natural Language Processing group @ SRI", "description": "Jane Robinson and Don Walker establish an influential Natural Language Processing group at SRI.", "category": "Communication", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 1970, "label": "Augmented Transition Network (ATN)", "description": "Bill Woods describe Augmented Transition Networks (ATN's) as a representation for natural language understanding.", "category": "Communication", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 1970, "label": "Shakey goes AI", "description": "The Shakey robot is the first mobile robot controlled by artificial intelligence. Equipped with sensing devices and driven by a problem-solving program called STRIPS, the robot finds its way around the halls of SRI by applying information about its environment to a route. Shakey used a TV camera, laser range finder, and bump sensors to collect data, which it then transmitted to a DEC PDP-10 and PDP-15. The computer sent commands to Shakey over a radio link. Shakey could move at a speed of 2 meters per hour.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1971, "label": "SHRDLU", "description": "Terry Winograd's PhD thesis demonstrates the ability of computers to understand English sentences in a restricted world of children's blocks. He combined the language understanding program, SHRDLU, with a robot arm that carried out instructions typed in English.", "category": "Communication", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1972, "label": "Prolog", "description": "Prolog programming language developed by Alain Colmerauer.", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1973, "label": "Freddy Robot", "description": "The Assembly Robotics Group at the University of Edinburgh builds Freddy Robot, capable of using visual perception to locate and assemble models. ", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1973, "label": "Lighthill report", "description": "James Lighthill reports to the British Science Research Council on the state artificial intelligence research, concluding that \"in no part of the field have discoveries made so far produced the major impact that was then promised,\" leading to drastically reduced government support for AI research.", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1974, "label": "MYCIN", "description": "Ted Shortliffe's PhD dissertation on the MYCIN program demonstrates a very practical rule-based approach to medical diagnoses, even in the presence of uncertainty. While it borrowed from DENDRAL, its own contributions strongly influenced the future of expert system development, especially commercial systems.", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 1974, "label": "Silver Arm", "description": "David Silver designs the Silver Arm, a robotic arm to do small-parts assembly using feedback from delicate touch and pressure sensors. The arm\u00b4s fine movements approximate those of human fingers.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1974, "label": "NOAH", "description": "Earl Sacerdoti developed techniques of partial-order planning in his NOAH system, replacing the previous paradigm of search among state space descriptions. NOAH was applied to interactively diagnose and repair electromechanical systems.", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 1974, "label": "First ECAI Conference", "description": "First edition of the biennial European Conference on Artificial Intelligence (ECAI), who has been runing without interruption since 1974, originally under the name AISB. \n", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1975, "label": "Meta-Dendral", "description": "The Meta-Dendral learning program produced new results in chemistry (some rules of mass spectrometry) the first scientific discoveries by a computer to be published in a refereed journal.", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 1975, "label": "Discource and Centering", "description": "Barbara Grosz (SRI) establishes limits to traditional AI approaches to discourse modeling. Subsequent work by Grosz, Bonnie Webber and Candace Sidner developed the notion of \"centering\", used in establishing focus of discourse and anaphoric references in Natural language processing.", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 1976, "label": "Discovery Model", "description": "Douglas Lenat's AM program demonstrates the discovery model (loosely guided search for interesting conjectures).", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1978, "label": "Version Spaces", "description": "Tom Mitchell invented the concept of Version spaces for describing the search space of a concept formation program.", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1978, "label": "Speak & Spell", "description": "Texas Instruments Inc. introduces Speak & Spell, a talking learning aid for children aged 7 and up that is the first electronic duplication of the human vocal tract on a single integrated circuit. Speak & Spell used linear predictive coding to formulate a mathematical model of the human vocal tract and predict a speech sample based on previous input. ", "category": "Communication", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1978, "label": "Satisficing", "description": "Herbert A. Simon wins the Nobel Prize in Economics for his theory of bounded rationality, one of the cornerstones of AI known as \"satisficing\".", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1979, "label": "Stanford Cart", "description": "The Stanford Cart was a long-term research project undertaken at Stanford University between 1960 and 1980. In 1979, it successfully crossed a room on its own while navigating around a chair placed as an obstacle. Hans Moravec rebuilt the Stanford Cart in 1977, equipping it with stereo vision. A television camera, mounted on a rail on the top of the cart, took pictures from several different angles and relayed them to a computer.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 1979, "label": "EMYCIN", "description": "Bill VanMelle's PhD dissertation demonstrates the generality of MYCIN's representation of knowledge and style of reasoning in his EMYCIN program, the model for many commercial expert system \"shells\".", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 4}, {"year": 1979, "label": "INTERNIST", "description": "Jack Myers and Harry Pople develop INTERNIST, a knowledge-based medical diagnosis program based on Dr. Myers' clinical knowledge.", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 4}, {"year": 1979, "label": "CHI System", "description": "Cordell Green, David Barstow, Elaine Kant and others at Stanford demonstrate the CHI system for automatic programming.", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 2, "idx_count": 4}, {"year": 1979, "label": "BKG", "description": "BKG, a backgammon program written by Hans Berliner, defeats the reigning world champion (in part via luck).", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 1979, "label": "Non-monotonic logics & formal aspects of truth maintenance", "description": "Drew McDermott and Jon Doyle at MIT, and John McCarthy at Stanford begin publishing work on non-monotonic logics and formal aspects of truth maintenance.", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 3, "idx_count": 4}, {"year": 1979, "label": "Neocognitron", "description": "Kunihiko Fukushima's convolutional neural network (\"Neocognitron - A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position\")", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1980, "label": "First AAAI Conference", "description": "First National Conference of the American Association for Artificial Intelligence (AAAI)", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 1980, "label": "First ICML Conference", "description": "First Machine Learning Workshop, then converted into the International Conference on Machine Learning, taking place in Pittsburgh, US", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 1981, "label": "First Direct Drive (DD) arm", "description": "The first direct drive (DD) arm by Takeo Kanade serves as the prototype for DD arms used in industry today. The electric motors housed inside the joints eliminated the need for the chains or tendons used in earlier robots. DD arms were fast and accurate because they minimize friction and backlash.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1981, "label": "Connection Machine", "description": "Danny Hillis designs the connection machine, which utilizes Parallel computing to bring new power to AI, and to computation in general. (Later he establishes the Thinking Machines Corporation)", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1982, "label": "Self-Organized Maps (SOM)", "description": "Teuvo Kohonen's Self-Organized Maps (SOM) for unsupervised learning are introduced.", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1982, "label": "Fifth Generation Computer Systems project (FGCS)", "description": "The Fifth Generation Computer Systems project (FGCS), an initiative by Japan's Ministry of International Trade and Industry to create a \"fifth generation computer\" (see history of computing hardware) which was supposed to perform much calculation utilizing massive parallelism.", "category": "Services", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1983, "label": "First CVPR Conference", "description": "CVPR was first held in Washington DC in 1983 (previously the conference was named Pattern Recognition and Image Processing).It is currently co-sponsored by the IEEE Computer Society and the Computer Vision Foundation.", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1983, "label": "Interval Calculus", "description": "James F. Allen invents the Interval Calculus, the first widely used formalisation of temporal events.", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1984, "label": "First ICRA Conference", "description": "The IEEE International Conference on Robotics and Automation (ICRA) was first held in Atlanta, USA. ICRA is an annual academic conference covering advances in robotics. It is one of the premier conferences in its field, alongside the International Conference on Intelligent Robots and Systems (IROS).", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1985, "label": "Neural Networks + Backpropagation", "description": "Neural Networks become widely used with the Backpropagation algorithm, also known as the reverse mode of automatic differentiation published by Seppo Linnainmaa in 1970 and applied to neural networks by Paul Werbos.", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1985, "label": "Omnibot 2000", "description": "The Omnibot 2000 remote-controlled programmable robot toy could move, talk and carry objects. The cassette player in its chest recorded actions to be taken and speech to be played", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1985, "label": "AARON", "description": "The autonomous drawing program, AARON, created by Harold Cohen, was demonstrated at the AAAI National Conference (based on more than a decade of work, and with subsequent work showing major developments).", "category": "Perception", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1986, "label": "First robot cars", "description": "The team of Ernst Dickmanns at Bundeswehr University of Munich builds the first robot cars, driving up to 55\u00a0mph on empty streets. ", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1986, "label": "Learning representations by back-propagating errors", "description": "David Rumelhart, Geoffrey Hinton, and Ronald Williams publish \u201cLearning representations by back-propagating errors,\u201d in which they describe a new learning procedure, back-propagation, for networks of neuron-like units.", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 1986, "label": "First NeurIPS Conference", "description": "The NeurIPS meeting was first proposed in 1986 as NIPS at the annual invitation-only Snowbird Meeting on Neural Networks for Computing organized by The California Institute of Technology and Bell Laboratories. ", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 1987, "label": "The Society of Mind", "description": "Marvin Minsky publishes \"The Society of Mind\", a theoretical description of the mind as a collection of cooperating agents. ", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1987, "label": "Nouvelle AI", "description": "Rodney Brooks introduces the architecture and behavior-based robotics as a more minimalist modular model of natural intelligence.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1987, "label": "Alacrity 2.0", "description": "Commercial launch of generation 2.0 of Alacrity by Alacritous Inc. Allstar Advice Inc. Toronto, the first commercial strategic and managerial advisory system. The system was based upon a forward-chaining, self-developed expert system with 3,000 rules about the evolution of markets and competitive strategies and co-authored by Alistair Davidson and Mary Chung, founders of the firm with the underlying engine developed by Paul Tarvydas. The Alacrity system also included a small financial expert system that interpreted financial statements and models.", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1988, "label": "First IROS Conference", "description": "The first IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) was held in Tokyo, Japan. IROS is an annual academic conference covering advances in robotics, it being one of the premier conferences of its field (alongside ICRA, International Conference on Robotics and Automation).ssions (for example, 790 out of 2459 submitted papers have been accepted for IROS 2011).", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1988, "label": "Probabilistic Reasoning in Intelligent Systems", "description": "Judea Pearl publishes \"Probabilistic Reasoning in Intelligent Systems\". His 2011 Turing Award citation reads: \u201cJudea Pearl created the representational and computational foundation for the processing of information under uncertainty. He is credited with the invention of Bayesian networks, a mathematical formalism for defining complex probability models, as well as the principal algorithms used for inference in these models. This work not only revolutionized the field of artificial intelligence but also became an important tool for many other branches of engineering and the natural sciences.\u201d", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1988, "label": "A statistical approach to language translation", "description": "Members of the IBM T.J. Watson Research Center publish \"A statistical approach to language translation,\" heralding the shift from rule-based to probabilistic methods of machine translation, and reflecting a broader shift to \"machine learning\" based on statistical analysis of known examples, not comprehension and \"understanding\" of the task at hand (IBM\u2019s project Candide, successfully translating between English and French, was based on 2.2 million pairs of sentences, mostly from the bilingual proceedings of the Canadian parliament).", "category": "Communication", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1989, "label": "Multi-layer Neural Netwroks + Backpropagation", "description": "Yann LeCun and other researchers at AT&T Bell Labs successfully applied a backpropagation algorithm to a multi-layer neural network, recognizing handwritten ZIP codes. Given the hardware limitations at the time, it took about 3 days (still a significant improvement over earlier efforts) to train the network.", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1989, "label": "ALVINN", "description": "Dean Pomerleau had created ALVINN (An Autonomous Land Vehicle in a Neural Network).", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1989, "label": "Deep Thought defeats David Levy", "description": "David Levy is the first master chess player to be defeated by a computer. The program Deep Thought defeats Levy who had beaten all other previous computer counterparts since 1968.", "category": "Planning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1989, "label": "Q-learning", "description": "Christopher Watkins publishes his PhD thesis \u2013 \"Learning from Delayed Rewards\" introducing the concept of Q-learning, which greatly improves the practicality and feasibility of reinforcement learning in machines. This new algorithm learned optimal control directly without modelling the transition probabilities or expected rewards of the Markov Decision Process.", "category": "Services", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1990, "label": "WWW", "description": "At the world\u2019s biggest physics laboratory, CERN in Switzerland, English programmer and physicist Tim Berners-Lee submits two proposals for what will become the Web, starting in March 1989. Neither is approved. He proceeds anyway, with only unofficial support from his boss and his coworker Robert Cailliau. By Christmas of 1990 he has prototyped \u201cWorldWideWeb\u201d (as he writes it) in just three months on an advanced NeXT computer. It features a server, HTML, URLs, and the first browser. That browser also functions as an editor\u2014like a word processor connected to the Internet \u2013 which reflects his original vision that the Web should also incorporate authoring and personal organization tools. The idea is that a Web of useful links will grow and deepen as people create them in the course of their daily lives. The Web had been partly inspired by his earlier Enquire program, which had combined networked hypertext with ideas that would later evolve into the Semantic Web", "category": "Services", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1991, "label": "DART", "description": "The DART scheduling application deployed in the first Gulf War paid back DARPA's investment of 30 years in AI research.", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1993, "label": "Polly", "description": "Ian Horswill extends behavior-based robotics by creating Polly, the first robot to navigate using vision and operate at animal-like speeds (1 meter/second).", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 1993, "label": "Cog", "description": "Rodney Brooks, Lynn Andrea Stein and Cynthia Breazeal started the widely publicized MIT Cog project with numerous collaborators, in an attempt to build a humanoid robot child in just five years.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 1993, "label": "\u201cvery deep learning\u201d task solved", "description": "German computer scientist Schmidhuber solves a \u201cvery deep learning\u201d task that requires more than 1,000 layers in the recurrent neural network.", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1993, "label": "DART wins \"DARPA contractor of the year\"", "description": "ISX corporation wins \"DARPA contractor of the year\" for the Dynamic Analysis and Replanning Tool (DART) which reportedly repaid the US government's entire investment in AI research since the 1950s. ", "category": "Reasoning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1994, "label": "VaMP and VITA-2", "description": "With passengers on board, the twin robot cars VaMP and VITA-2 of Ernst Dickmanns and Daimler-Benz drive more than one thousand kilometers on a Paris three-lane highway in standard heavy traffic at speeds up to 130\u00a0km/h. They demonstrate autonomous driving in free lanes, convoy driving, and lane changes left and right with autonomous passing of other cars.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1994, "label": "CHINOOK", "description": "English draughts (checkers) world champion Tinsley resigned a match against the computer program Chinook. Chinook also defeated the 2nd highest rated player, Lafferty. Chinook won the USA National Tournament by the widest margin ever.", "category": "Planning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1995, "label": "Autonomous vehicle", "description": "\"No Hands Across America\": A semi-autonomous car drove coast-to-coast across the United States with computer-controlled steering for 2,797 miles (4,501 km) of the 2,849 miles (4,585 km). Throttle and brakes were controlled by a human driver.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 3}, {"year": 1995, "label": "MQ-1 Predator", "description": "The MQ-1 Predator drone is introduced and put into action by the United States Air Force and the Central Intelligence Agency. It performed operations in Afghanistan and the Pakistani tribal areas against Al-Qaeda forces and Taliban militants starting after September 11, 2001.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 3}, {"year": 1995, "label": "SVM advancements", "description": "Corinna Cortes and Vladimir Vapnik made advancements in Support vector machines that have been around since the 1960s, tweaked and refined by many over the decades. A SVM is basically a system for recognizing and mapping similar data. The SVMs are applied in text categorization, handwritten character recognition, and image classification.", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1995, "label": "Munich - Copenhagen - Munich by robot car", "description": "One of Ernst Dickmanns' robot cars (with robot-controlled throttle and brakes) drove more than 1000 miles from Munich to Copenhagen and back, in traffic, at up to 120\u00a0mph, occasionally executing maneuvers to pass other cars (only in a few critical situations a safety driver took over). Active vision was used to deal with rapidly changing street scenes.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 2, "idx_count": 3}, {"year": 1997, "label": "Long Short Term Memory (LSTM) model", "description": "Jeurgen Schmidhuber's and Sepp Hochreiter's Long Short Term Memory (LSTM) model is introduced.", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 1997, "label": "Deep Blue defeats Garry Kasparov", "description": "The Deep Blue chess machine (IBM) defeats the (then) world chess champion, Garry Kasparov.", "category": "Planning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 1997, "label": "First official RoboCup", "description": "First official RoboCup football (soccer) match featuring table-top matches with 40 teams of interacting robots and over 5000 spectators.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1997, "label": "Logistello - Murakami 6 - 0", "description": "Computer Othello program Logistello defeats the world champion Takeshi Murakami with a score of 6\u20130.", "category": "Planning", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 1997, "label": "First ECML PKDD Conference", "description": "PKDD was first organised in 1997. Originally PKDD stood for the European Symposium on Principles of Data Mining and Knowledge Discovery from Databases. The name European Conference on Principles and Practice of Knowledge Discovery in Databases was used since 1999.", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 1998, "label": "LeNet-5", "description": "Yann LeCun's LeNet-5 was a simple convolutional neural network. Convolutional neural networks are a kind of feed-forward neural networks whose artificial neurons can respond to a part of the surrounding cells in the coverage range and were applied in large-scale image processing.", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1998, "label": "Furby", "description": "Tiger Electronics' Furby is released, and becomes the first successful attempt at producing a type of AI to reach a domestic environment.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1998, "label": "POMDP solved offline", "description": "Leslie P. Kaelbling, Michael Littman, and Anthony Cassandra introduce the first method for solving POMDPs offline, jumpstarting widespread use in robotics and automated planning and scheduling.", "category": "Planning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1998, "label": "Semantic Web Road map", "description": "Tim Berners-Lee publishes his Semantic Web Road map paper.", "category": "Services", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1999, "label": "AIBO", "description": "Sony introduces an improved domestic robot similar to a Furby, the AIBO, which becomes one of the first artificially intelligent \"pets\" that is also autonomous.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 1999, "label": "GeForce 256", "description": "Video applications for personal computers drive demand for increased graphical performance. A new approach, one based on a processor specially designed to manipulate graphics, was initiated and the resulting product was known as a \"Graphics Processing Unit,\" or \"GPU.\" The GeForce 256 is considered as the first consumer GPU, and while expensive, it sold extremely well. The GeForce 256 was designed to relieve the pressure on the central processing unit (CPU) by handling graphics calculations, while the CPU processed non-graphics intensive tasks", "category": "Services", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2000, "label": "ASIMO", "description": "Honda's Advanced Step introduces the Innovative Mobility (ASIMO) humanoid robot. It could walk 1 mph, climb stairs and change its direction after detecting hazards. Using the camera mounted in its head, ASIMO could also recognize faces, gestures and the movements of multiple objects. Additionally, ASIMO had microphones that allowed it to react to voice commands.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 3}, {"year": 2000, "label": "Kismet", "description": "Cynthia Breazeal publishes her dissertation on Sociable machines, describing Kismet (robot), with a face that expresses emotions.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 3}, {"year": 2000, "label": "Nomad", "description": "The Nomad robot explores remote regions of Antarctica looking for meteorite samples.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 2, "idx_count": 3}, {"year": 2002, "label": "Neural Probabilistic Language Model", "description": "Yoshua Bengio's \"Neural Probabilistic Language Model\".", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2002, "label": "Roomba", "description": "iRobot's Roomba autonomously vacuums the floor while navigating and avoiding obstacles.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2004, "label": "DARPA Grand Challenge introduced", "description": "DARPA introduces the DARPA Grand Challenge requiring competitors to produce autonomous vehicles for prize money.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 2004, "label": "Spirit and Opportunity", "description": "NASA's robotic exploration rovers Spirit and Opportunity autonomously navigate the surface of Mars.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 2005, "label": "BigDog", "description": "Boston Dynamics' quadruped robot \"BigDog\" in the hopes that it would be able to serve as a robotic pack mule to accompany soldiers in terrain too rough for conventional vehicles. Instead of wheels or treads, BigDog uses four legs for movement, allowing it to move across surfaces that would defeat wheels. The legs contain a variety of sensors, including joint position and ground contact. BigDog also features a laser gyroscope and a stereo vision system.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 2005, "label": "ASIMO delivers trays", "description": "Honda's ASIMO robot, an artificially intelligent humanoid robot was able to walk as fast as a human, delivering trays to customers in restaurant settings.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 2005, "label": "AI@marketing", "description": "Recommendation technology based on tracking web activity or media usage brings AI to marketing. See TiVo Suggestions.", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 2005, "label": "Blue Brain", "description": "Blue Brain is born, a project to simulate the brain at molecular detail.", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 2006, "label": "Amazon Web Services (AWS)", "description": "Amazon launches Amazon Web Services (AWS) introducing a number of web services, including Amazon Elastic Cloud 2 (EC2) and Amazon Simple Storage Service (S3). EC2 allowed users to rent virtual time on the cloud to scale server capacity quickly and efficiently while only paying for what was used. Use of the cloud eliminates the need for a company to maintain a complex computing infrastructure on their own. Additionally, it saved space and hassle in the form of less onsite server room square footage. S3 was a cloud-based file hosting service that charged users monthly for the amount of data stored and for the bandwidth of transferring data. Similar services, like Google Drive, followed that suit and created their own proprietary services", "category": "Services", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2007, "label": "Checkers solved", "description": "An article is published titled \"Checkers is Solved\" in a September issue of the journal Science. The article stated, \"Perfect play by both sides leads to a draw.\" The team that conducted the research was led by Professor Jonathan Schaeffer at the University of Alberta who had been working to solve the checkers problem since 1989. In the course of their work the team created a checkers program called \"CHINOOK\", which played successfully in several man-machine competitions, including one held at The Computer Museum in Boston in 1994.", "category": "Planning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2007, "label": "Models of Natural Action Selection @ Philosophical Transactions", "description": "Philosophical Transactions of the Royal Society, B\u00a0\u2013 Biology, one of the world's oldest scientific journals, puts out a special issue on using AI to understand biological intelligence, titled \"Models of Natural Action Selection\".", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2007, "label": "DARPA launches Urban Challenge", "description": "DARPA launches the Urban Challenge for autonomous cars to obey traffic rules and operate in an urban environment.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2009, "label": "Google's autonomous car", "description": "Google builds autonomous car.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2009, "label": "ImageNet database", "description": "ImageNet was the first large visual database designed for use in visual object recognition software research. Several million images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images' bounding boxes are also provided.", "category": "Perception", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2010, "label": "Kinect", "description": "Microsoft launches Kinect for Xbox 360, the first gaming device to track human body movement, using just a 3D camera and infra-red detection, enabling users to play their Xbox 360 wirelessly. The award-winning machine learning for human motion capture technology for this device was developed by the Computer Vision group at Microsoft Research, Cambridge.", "category": "Perception", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2010, "label": "Algorithmic trading \"shuts\" NY stock market", "description": "The New York stock market is shut down after algorithmic trading has wiped out a trillion dollars within a few seconds.", "category": "Integration & Interaction", "second_category": "Learning", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2010, "label": "Kaggle", "description": "Kaggle, a website that serves as a platform for machine learning competitions, is launched.", "category": "Services", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2011, "label": "Watson beats Jeopary!'s champions", "description": "IBM's Watson computer defeated television game show Jeopardy! Champions Rutter and Jennings.", "category": "Communication", "second_category": "Reasoning", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 2011, "label": "Intelligent Assistants", "description": "Apple's Siri (2011), Google's Google Now (2012) and Microsoft's Cortana (2014) are smartphone apps that use natural language to answer questions, make recommendations and perform actions.", "category": "Communication", "second_category": "Learning", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 2012, "label": "AlexNet", "description": "Alex Krizhevsky and Ilya Sutskever from the University of Toronto demonstrate that deep learning outperforms traditional approaches to computer vision by processing 200 billion images during training (AlexNet).", "category": "Perception", "second_category": "Learning", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 2012, "label": "The Cat experiment", "description": "Using a neural network spread over thousands of computers, the Cat Experiment team presented 10,000,000 unlabeled images that were randomly taken from YouTube to the system and allowed it to run analyses on the data. The program had taught itself to identify and recognize cats, performing nearly 70% better than previous attempts at unsupervised learning.", "category": "Perception", "second_category": "Learning", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 2013, "label": "HRP-2", "description": "Robot HRP-2 built by the SCHAFT Inc of Japan, a subsidiary of Google, defeats 15 teams to win DARPA\u2019s Robotics Challenge Trials. HRP-2 scored 27 out of 32 points in 8 tasks needed in disaster response. These tasks included: (a) drive a vehicle; (b) walk over debris; (c) climb a ladder; (d) remove debris; (e) walk through doors; (f) cut through a wall; (g) close valves; and (h) connect a hose.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2013, "label": "Word2vec", "description": "Tomas Mikolov introduces Word2vec, which is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words.", "category": "Communication", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2013, "label": "NEIL", "description": "NEIL, the Never Ending Image Learner, is released at Carnegie Mellon University to constantly compare and analyze relationships between different images.", "category": "Perception", "second_category": "Learning", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2014, "label": "VGG-16", "description": "VGG16 is a convolutional neural network model proposed by K. Simonyan and A. Zisserman from the University of Oxford in the paper \"Very Deep Convolutional Networks for Large-Scale Image Recognition\". The model achieves 92.7% top-5 test accuracy in ImageNet, which is a dataset of over 14 million images belonging to 1000 classes.", "category": "Perception", "second_category": "Learning", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 2014, "label": "Generative Adversarial Network (GAN)", "description": "Ian Goodfellow invents the Generative adversarial networks (GANs). GANs are deep neural net architectures comprised of two nets, pitting one against the other (thus the \"adversarial\"). GANs can be taught to generate data in any domain: images, music, speech, etc.", "category": "Learning", "second_category": "Perception", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2014, "label": "LSTM without HMM", "description": "Alex Graves' LSTM without Hidden Markov Models for speech recognition is introduced.", "category": "Perception", "second_category": "Communication", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 2015, "label": "AlphaGo Fan beats Fan Hui", "description": "Google DeepMind's AlphaGo defeats the 3 time European Go champion professional Fan Hui by 5 games to 0.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2015, "label": "Petition against autonomous weapons by AI researchers", "description": "An open letter to ban development and use of autonomous weapons is signed by Hawking, Musk, Wozniak and 3,000 researchers in AI and robotics.", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2016, "label": "AlphaGo Lee beats Lee Sedol", "description": "Google DeepMind's AlphaGo defeats Lee Sedol 4\u20131. Lee Sedol is a 9 dan professional Korean Go champion who won 27 major tournaments from 2002 to 2016. Before the match with AlphaGo, Lee Sedol was confident in predicting an easy 5\u20130 or 4\u20131 victory.", "category": "Planning", "second_category": "Learning", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2016, "label": "GDPR Specification", "description": "Introduction of the General Data Protection Regulation (GDPR) on the protection of natural persons with regard to the processing of personal data and on the free movement of such data.", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": true, "idx": 0, "idx_count": 1}, {"year": 2017, "label": "Libratus", "description": "Poker AI Libratus individually defeats each of its 4 human opponents\u2014among the best players in the world\u2014at an exceptionally high aggregated winrate, over a statistically significant sample. In contrast to Chess and Go, Poker is an imperfect information game.", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2017, "label": "Transformer", "description": "Emergence of the novel neural architecture called Transformer, based on a self-attention mechanism. Transformers are now at the core of the leading approaches to language understanding tasks such as language modeling, machine translation and question answering; they are now implemented in other domains.", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 2017, "label": "AlphaGo Master scores 60-0 on 2 Go websites", "description": "Google DeepMind's AlphaGo wins 60\u20130 rounds on two public Go websites including 3 wins against the world Go champion Ke Jie.", "category": "Planning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 2017, "label": "OpenAI bot beats Dendi in Dota 2", "description": "An OpenAI-machined learned bot plays at The International 2017 Dota 2 tournament in August 2017. It won during a 1v1 demonstration game against professional Dota 2 player Dendi.", "category": "Planning", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 2017, "label": "Unsupervised AlphaZero beats AlphaGo Lee and AlphaGo Master", "description": "Google DeepMind reveals that AlphaGo Zero\u2014an improved version of AlphaGo\u2014displayed significant performance gains while using far fewer tensor processing units (as compared to AlphaGo Lee; it used same amount of TPU's as AlphaGo Master). Unlike previous versions, which learned the game by observing millions of human moves, AlphaGo Zero learned by playing only against itself. The system then defeated AlphaGo Lee 100 games to zero, and defeated AlphaGo Master 89 to 11. Although unsupervised learning is a step forward, much has yet to be learned about general intelligence. AlphaZero masters chess in 4 hours, defeating the best chess engine, StockFish 8. AlphaZero won 28 out of 100 games, and the remaining 72 games ended in a draw.", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 2017, "label": "Asilomar Conference on Beneficial AI", "description": "The Asilomar Conference on Beneficial AI is held, to discuss AI ethics and how to bring about beneficial AI while avoiding the existential risk from artificial general intelligence.", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2018, "label": "Alibaba beats top humans in reading and comprehension", "description": "Alibaba language processing AI outscores top humans at a Stanford University reading and comprehension test, scoring 82.44 against 82.304 on a set of 100,000 questions.", "category": "Communication", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 2018, "label": "Google Duplex", "description": "Announcement of Google Duplex, a service to allow an AI assistant to book appointments over the phone. The LA Times judges the AI's voice to be a \"nearly flawless\" imitation of human-sounding speech.", "category": "Communication", "second_category": "Perception", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 2018, "label": "GDPR Implementation", "description": "Implementation of the GDPR by the EU Member States.", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": true, "idx": 0, "idx_count": 4}, {"year": 2018, "label": "CLAIRE ", "description": "Public launch of CLAIRE (Confederation of Laboratories for AI Research in Europe) with a vision document signed by 600 senior researchers and key stakeholders in artificial intelligence, and the website https://claire-ai.org/, inviting stakeholders in artificial intelligence across Europe (and beyond) to sign their support. ", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": true, "idx": 1, "idx_count": 4}, {"year": 2018, "label": "ELLIS", "description": "The European Lab for Learning and Intelligent Systems (aka Ellis) proposed as a pan-European competitor to American AI efforts, with the aim of staving off a brain drain of talent, along the lines of CERN after World War II.", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": true, "idx": 2, "idx_count": 4}, {"year": 2018, "label": "Communication Artificial Intelligence for Europe", "description": "COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE EUROPEAN COUNCIL, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS \"Artificial Intelligence for Europe\"", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": true, "idx": 3, "idx_count": 4}, {"year": 2019, "label": "StarCraft II", "description": "AlphaStar: Mastering the Real-Time Strategy Game StarCraft II", "category": "Learning", "second_category": "Planning", "eu_activities": false, "idx": 0, "idx_count": 3}, {"year": 2019, "label": "Dota 2", "description": "OpenAI uses the multiplayer video game\u00a0Dota 2\u00a0as a research platform for general-purpose AI systems. Their Dota 2 AI, called OpenAI Five, learned by playing over 10,000 years of games against itself. It demonstrated the ability to achieve\u00a0expert-level performance, learn\u00a0human\u2013AI cooperation, and\u00a0operate at internet\u00a0scale.", "category": "Learning", "second_category": "Planning", "eu_activities": false, "idx": 1, "idx_count": 3}, {"year": 2019, "label": "Dexterity", "description": "OpenAI successfully trains a robot hand\u00a0called Dactyl that adopted to the real-world environment in solving the Rubik\u2019s cube.\u00a0", "category": "Learning", "second_category": "Perception", "eu_activities": false, "idx": 2, "idx_count": 3}, {"year": 2019, "label": "AlphaFold", "description": "AlphaFold builds on decades of prior research using large genomic datasets to predict protein structure. The 3D models of proteins that AlphaFold generates are far more accurate than any that have come before\u2014marking significant progress on one of the core challenges in biology. ", "category": "Integration & Interaction", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2019, "label": "GPT-2", "description": "OpenAI\u00a0comes up with a breakthrough language model that could generate natural language texts similar to the ones generated by humans.", "category": "Communication", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2019, "label": "Deepfake\u00a0", "description": "Samsung creates a system that can transform\u00a0facial images into video\u00a0sequences. They used the generative adversarial network (GAN) to create deep fake videos just by taking one picture as input", "category": "Perception", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2019, "label": "Quantum computing", "description": "Google quantum computing team demonstrates for the first time a\u00a0computational task\u00a0that can be executed exponentially faster on a quantum processor than on the world\u2019s fastest classical computer \u2014 just 200 seconds compared to 10,000 years", "category": "Services", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 2019, "label": "Ethics guidelines for trustworthy AI", "description": "The High-Level Expert Group on AI presents Ethics Guidelines for Trustworthy Artificial Intelligence.", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": true, "idx": 0, "idx_count": 3}, {"year": 2019, "label": "A definition of Artificial Intelligence: main capabilities and scientific disciplines", "description": "The document expands the definition of Artificial Intelligence (AI) as defined in the Commission Communication on AI.", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": true, "idx": 1, "idx_count": 3}, {"year": 2019, "label": "A definition of Artificial Intelligence: main capabilities and scientific disciplines", "description": "The document from the High-Level Expert Group on AI of the European Commission proposes a definition of AI. ", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": true, "idx": 2, "idx_count": 3}, {"year": 2019, "label": "Launch of EU AI WATCH", "description": "Official launch of AI WATCH, the EC knowledge service to monitor the development, uptake and impact of AI in Europe https://ec.europa.eu/knowledge4policy/ai-watch_en.", "category": "Services", "second_category": "TBD", "eu_activities": true, "idx": 1, "idx_count": 2}, {"year": 2020, "label": "European Commission presents strategies for data and Artificial Intelligence", "description": "The European Commission unveils its ideas and actions for a digital transformation that works for all, reflecting the best of Europe: open, fair, diverse, democratic and confident. It presents a European society powered by digital solutions that put people first, opens up new opportunities for businesses, and boosts the development of trustworthy technology to foster an open and democratic society and a vibrant and sustainable economy.\n\nDigital is a key enabler to fighting climate change and achieving the green transition. The European data strategy and the policy options to ensure the human-centric development of Artificial Intelligence (AI) presented are the first steps towards achieving these goals. https://ec.europa.eu/digital-single-market/en/news/shaping-europes-digital-future-commission-presents-strategies-data-and-artificial-intelligence", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": true, "idx": 0, "idx_count": 2}, {"year": 2020, "label": "Generative Pre-trained Transformer 3 (GPT-3)", "description": "Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text. It is the third-generation language prediction model in the GPT-n series created by OpenAI, a San Francisco-based artificial intelligence research laboratory. GPT-3's full version has a capacity of 175 billion machine learning parameters. GPT-3, which was introduced in May 2020, and is in beta testing as of July 2020, is part of a trend in natural language processing (NLP) systems of pre-trained language representations. Before the release of GPT-3, the largest language model was Microsoft's Turing NLG, introduced in February 2020, with a capacity of 17 billion parameters or less than 10 percent compared to GPT-3.", "category": "Communication", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 1}, {"year": 2020, "label": "NVIDIA MAXINE (Cloud-AI Video-Streaming Platform)", "description": "The NVIDIA Maxine platform dramatically reduces how much bandwidth is required for video calls. Instead of streaming the entire screen of pixels, the AI software analyzes the key facial points of each person on a call and then intelligently re-animates the face in the video on the other side. This makes it possible to stream video with far less data flowing back and forth across the internet. Using this new AI-based video compression technology running on NVIDIA GPUs, developers can reduce video bandwidth consumption down to one-tenth of the requirements of the H.264 streaming video compression standard. This cuts costs for providers and delivers a smoother video conferencing experience for end users, who can enjoy more AI-powered services while streaming less data on their computers, tablets and phones.", "category": "Perception", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 2}, {"year": 2020, "label": "WHITE PAPER on Artificial Intelligence - A European approach to excellence and trust", "description": "Artificial Intelligence is developing fast. It will change our lives by improving healthcare (e.g. making diagnosis more precise, enabling better prevention of diseases), increasing the efficiency of farming, contributing to climate change mitigation and adaptation, improving the efficiency of production systems through predictive maintenance, increasing the security of Europeans, and in many other ways that we can only begin to imagine. At the same time, Artificial Intelligence (AI) entails a number of potential risks, such as opaque decision-making, gender-based or other kinds of discrimination, intrusion in our private lives or being used for criminal purposes.", "category": "Ethics & Philosophy", "second_category": "TBD", "eu_activities": true, "idx": 1, "idx_count": 2}, {"year": 2020, "label": "DeepMind\u2019s AI makes gigantic leap in solving protein structures", "description": "DeepMind, the UK-based AI company owned by Alphabet Inc., successfully modeled protein structures with an accuracy equivalent to much more expensive and time-consuming methods. Proteins are notoriously difficult to map, but knowing their structures can help researchers understand diseases, develop medicines, make biofuels, and more. DeepMind\u2019s AlphaFold program beat out dozens of other teams at this week\u2019s biennial \u201cCritical Assessment of Structure Prediction\u201d challenge, modeling proteins for which the structures are known but not yet public. The program slightly outperformed current experimental methods overall and nearly matched current methods against the most challenging proteins. As a condition of entering the CASP competition, all groups agree to reveal information about their methods sufficient to replicate the process. ", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 0, "idx_count": 4}, {"year": 2020, "label": "Few-Shot Adversarial Learning of Realistic Neural Talking Head Models", "description": "Several recent works have shown how highly realistic human head images can be obtained by training convolutional neural networks to generate them. In order to create a personalized talking head model, these works require training on a large dataset of images of a single person. However, in many practical scenarios, such personalized talking head models need to be learned from a few image views of a person, potentially even a single image. Here, we present a system with such few-shot capability. It performs lengthy meta-learning on a large dataset of videos, and after that is able to frame few- and one-shot learning of neural talking head models of previously unseen people as adversarial training problems with high capacity generators and discriminators. Crucially, the system is able to initialize the parameters of both the generator and the discriminator in a person-specific way, so that training can be based on just a few images and done quickly, despite the need to tune tens of millions of parameters. We show that such an approach is able to learn highly realistic and personalized talking head models of new people and even portrait paintings.", "category": "Perception", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 2}, {"year": 2020, "label": "Dr.Repair: Machine Learning for Program Repair\n", "description": "When writing programs, a lot of time is spent debugging or fixing source code errors, both for beginners (imagine the intro programming classes you took) as well as for professional developers. Automating program repair could dramatically enhance the productivity of both programming and learning programming. In our recent work published at ICML 2020, we study how to use machine learning to repair programs automatically.", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 1, "idx_count": 4}, {"year": 2020, "label": "Recursion: Industrialised drug discovery", "description": "Deep learning on cellular microscopy accelerates biological discovery ith drug screens", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 2, "idx_count": 4}, {"year": 2020, "label": "AutoML-Zero: Evolving Code that Learns", "description": "AutoML-Zero is an AutoML technique that aims to search a fine-grained space simultaneously for the model, optimization procedure, initialization, and so on, permitting much less human-design and even allowing the discovery of non-neural network algorithms. It represents ML algorithms as computer programs comprised of three component functions, Setup, Predict, and Learn, that performs initialization, prediction and learning. The instructions in these functions apply basic mathematical operations on a small memory. The operation and memory addresses used by each instruction are free parameters in the search space, as is the size of the component functions. While this reduces expert design, the consequent sparsity means that random search cannot make enough progress. To overcome this difficulty, the authors use small proxy tasks and migration techniques to build an optimized infrastructure capable of searching through 10,000 models/second/cpu core. Evolutionary methods can find solutions in the AutoML-Zero search space despite its enormous size and sparsity. The authors show that by randomly modifying the programs and periodically selecting the best performing ones on given tasks/datasets, AutoML-Zero discovers reasonable algorithms. They start from empty programs and using data labeled by \u201cteacher\u201d neural networks with random weights, and demonstrate evolution can discover neural networks trained by gradient descent. Following this, they minimize bias toward known algorithms by switching to binary classification tasks extracted from CIFAR-10 and allowing a larger set of possible operations. This discovers interesting techniques like multiplicative interactions, normalized gradient and weight averaging. Finally, they show it is possible for evolution to adapt the algorithm to the type of task provided. For example, dropout-like operations emerge when the task needs regularization and learning rate decay appears when the task requires faster convergence.", "category": "Learning", "second_category": "TBD", "eu_activities": false, "idx": 3, "idx_count": 4}];
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