Spy on Me #2 – Artistic Manoevres for the Digital Present

Total Page:16

File Type:pdf, Size:1020Kb

Spy on Me #2 – Artistic Manoevres for the Digital Present Spy on Me Artistic Manoeuvres for the Digital Present #2 19. –29.3.2020 Spy on Me #2 – Artistic Manoeuvres for the Digital Present 19.–29.3.2020 / HAU1, HAU2, HAU3 We have arrived in the reality of digital transformation. Life with screens, apps and algorithms influences our behaviour, our atten - tion and desires. Meanwhile the idea of the public space and of democracy is being reorganized by digital means. The festival “Spy on Me” goes into its second round after 2018, searching for manoeuvres for the digital present together with Berlin-based and international artists. Performances, interactive spatial installati - ons and discursive events examine the complex effects of the digi - tal transformation of society. In theatre, where live encounters are the focus, we come close to the intermediate spaces of digital life, searching for ways out of feeling powerless and overwhelmed, as currently experienced by many users of internet-based technolo - gies. For it is not just in some future digital utopia, but here, in the midst of this present, that we have to deal with the basic conditi - ons of living together as a society and of planetary survival. Are we at the edge of a great digital darkness or at the decisive tur - ning point of perspective? A Festival by HAU Hebbel am Ufer. Funded by: Hauptstadtkulturfonds. What is our rela - tionship with alien conscious - nesses? As we build rivals to human intelligence, James Bridle looks at our relationship with the planet’s other alien consciousnesses. 5 On 27 June 1835, two masters of the ancient DeepBlue defeated Garry Kasparov at chess, prise, strangeness and even horror that Chinese game of Go faced off in a match up to that point a game with Go-like status as AlphaGo evokes will become a feature of more which was the culmination of a years-long a bastion of human imagination and mental and more areas of our lives. rivalry. The young prodigy Akaboshi Intetsu superiority. But compared to AlphaGo, Deep - dominated the game early on using a secret Blue might as well have belonged to the The increasing complexity of the world around move developed by his teachers. But a day steam age; immensely powerful, IBM’s ma - us should be cause for political and social con - into the contest a number of chine lacked anything we cern, as intelligent but unknowable software ghosts appeared to his oppo - We have to would call intelligence. It works its way through society. But it’s also an nent, Hon’inbo ¯ Jo ¯ wa, and contend with brute-forced Kasparov off opportunity to rethink our relationship with showed him three critical inscrutable the board, calculating games the wider world, and to reconsider our place moves with which he was decisions made many moves ahead – but in it. It seems significant that we are investing able to win back control of merely that: calculating. so much time and energy in building these toy the game. At the point when about jobs and AlphaGo and its kind perform versions of our own minds, just as our ability it became clear that Aka - finances, health - something more akin to ima - to control our own destiny and live on the pla - boshi would not be able to care and road gination and intuition, and net sustainably appears to be failing. That fai - win the game, the young safety. moreover they do so in lure is in part one of hubris: the belief that we challenger violently coughed mathematical realms the can, as the planet’s dominant species, conti - up blood onto the board. He human mind cannot compre - nue to act selfishly, wastefully and without was found dead a few days later. The match hend. While we can follow DeepBlue’s line of regard to the future. But with AI comes the between Akaboshi and Jo ¯ wa has passed thought, the thinking behind AlphaGo’s deci - sense that we might not be the dominant into Go lore as the ‘blood-vomiting game’, and sions remain unknowable to us – and hence actor for much longer – and an attendant subsequent historians have attributed Aka - alien and otherworldly. opportunity to really consider what it means boshi’s decline to an undiagnosed pulmonary to share the world with other, barely knowable disease. They have been less forthcoming, To call AlphaGo and systems like it an Artificial intelligences. however, on the matter of ghosts, which may Intelligence is in some ways an exaggeration. still haunt the game to this day. It is a very narrow form of in - Because of course, we’ve telligence directed at a parti - With AI comes the shared the universe with On 29 December 2016, a new player appeared cular task, based on one par - sense that we other intelligences for a long on Tygem, a popular online Go server on which ticular computational confi - time, and we’ve handled the might not be the many senior Go professionals trained and tes - guration – neural networks – situation pretty badly. We ted out new moves. The player was called and a technique called rein - dominant actor for have consistently downgra - Master, and immediately they began a blazing forcement learning. These are much longer – and ded or reclassified forms of winning streak: sixty victories in just seven pieces of software modelled an attendant oppor - intelligence that do not days, and barely resting between games. loosely on parts of the human tunity to really resemble our own narrow Many of the victories were over world cham - brain, which are trained on a consider what it definition, and as a result felt pion players. Master’s moves often seemed reward system which encou - means to share the free to treat their possessors wild, even impetuous, but they always resul - rages them to develop their world with other, as lesser creatures, lower ted in a win. After the fifty-ninth game Master own strategies. Despite this orders of beings, or not really barely knowable was revealed to be – as had been suspected – narrow focus, the benefits as things at all. To ignore, not a human player, but an Artificial Intelli - are generalisable; as well as intelligences. consume, despoil and poison gence. Master was the latest iteration of Deep - learning other games, the them, both to their detriment Mind and Google’s AlphaGo programme, which techniques developed for and in the final, devastating, had gained worldwide attention when it AlphaGo have been deployed by Google in analysis, to our own. defeated Go master Lee Sedol six months ear - everything from medical diagnoses to You - lier. Tube recommendations. And yet the last few decades have also seen the slow murmurings of recognition of other That game had been clse, but the New Year 'With AI comes the sense that we might not ways of thinking and being in the world, which games were already markedly different. When be the dominant actor for much longer ...' appears to us as a sudden flowering of forms Go players tried to describe the AI’s style of of intelligence which differ radically from our play, they struggled to reconcile it with any - Machine learning is being used by others to own. More and more species are being admit - thing known. One leading Go player said, screen applicants for jobs, pilot self-driving ted, grudgingly, to the community of those ‘they’re how I imagine games from far in the cars and target military drones. Neural net - that really think, from orangutans to ele - future’. Another reported feeling that an ‘alien works are live and connected to the stock phants, both of which have recently been intelligence’ had landed among them. The market, to distribution systems, to transport granted legal personhood in court cases. As machine’s own creator, Demis Hassabis, said infrastructure – to the very social, material, we recognise the differing forms of intelli - its moves seemed to emanate ‘from another and economic bases of our daily existence. It’s gence present in both AI and other species, dimension’. not just AlphaGo’s ‘god-like’ moves we have the business of assigning rankings to other to contend with, but inscrutable decisions creatures begins to seem as stupid and vio - The last great revolution in human-machine made about jobs and finances, healthcare and lent as assigning them to different races. This competition occurred in 1997, when IBM’s road safety – and the sense of mystery, sur - too is only the beginning of what we might 6 recognise, only the beginning of another outnumber our own cells ten to one, has been strangeness of mere toy intelligences begins strangeness, if we choose to see intelligence shown to have measurable effects on our to pale. as something that belongs not only to cognition. humans, and not only inside the human head. For a long time we have been as unheeding of 'The centrality of human intelligence is on these intelligences as we have been deaf to Octopuses in aquaria are now known to the point of being knocked violently aside the frequency of electrons, and blind to the recognise individual humans, and to prefer by our own inventions...' ultraviolet light that soaks the plants around some to others, squirting water at those they us. But they have been here all along, and are dislike. Disliking brightness, At the other end of the scale, becoming undeniable, just as the capacities of they squirt water at light We find ourselves the largest organism in the our own technologies threaten to supersede bulbs above their tanks to world is a forest in Colorado, us. After wilfully ignoring the intelligences of standing upon extinguish them too: they are a hundred-acre expanse of others for so long, the centrality of human not merely aware of their a ruined planet, cloned aspen sharing a sin - intelligence is on the point of being knocked environment, but seek to not smart enough gle, 80,000 year-old intercon - violently aside by our own inventions.
Recommended publications
  • Chinese Health App Arrives Access to a Large Population Used to Sharing Data Could Give Icarbonx an Edge Over Rivals
    NEWS IN FOCUS ASTROPHYSICS Legendary CHEMISTRY Deceptive spice POLITICS Scientists spy ECOLOGY New Zealand Arecibo telescope faces molecule offers cautionary chance to green UK plans to kill off all uncertain future p.143 tale p.144 after Brexit p.145 invasive predators p.148 ICARBONX Jun Wang, founder of digital biotechnology firm iCarbonX, showcases the Meum app that will use reams of health data to provide customized medical advice. BIOTECHNOLOGY Chinese health app arrives Access to a large population used to sharing data could give iCarbonX an edge over rivals. BY DAVID CYRANOSKI, SHENZHEN medical advice directly to consumers through another $400 million had been invested in the an app. alliance members, but he declined to name the ne of China’s most intriguing biotech- The announcement was a long-anticipated source. Wang also demonstrated the smart- nology companies has fleshed out an debut for iCarbonX, which Wang founded phone app, called Meum after the Latin for earlier quixotic promise to use artificial in October 2015 shortly after he left his lead- ‘my’, that customers would use to enter data Ointelligence (AI) to revolutionize health care. ership position at China’s genomics pow- and receive advice. The Shenzhen firm iCarbonX has formed erhouse, BGI, also in Shenzhen. The firm As well as Google, IBM and various smaller an ambitious alliance with seven technology has now raised more than US$600 million in companies, such as Arivale of Seattle, Wash- companies from around the world that special- investment — this contrasts with the tens of ington, are working on similar technology. But ize in gathering different types of health-care millions that most of its rivals are thought Wang says that the iCarbonX alliance will be data, said the company’s founder, Jun Wang, to have invested (although several big play- able to collect data more cheaply and quickly.
    [Show full text]
  • Fml-Based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go
    Accepted for publication in International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems in July, 2017 FML-BASED DYNAMIC ASSESSMENT AGENT FOR HUMAN-MACHINE COOPERATIVE SYSTEM ON GAME OF GO CHANG-SHING LEE* MEI-HUI WANG, SHENG-CHI YANG Department of Computer Science and Information Engineering, National University of Tainan, Tainan, Taiwan *[email protected], [email protected], [email protected] PI-HSIA HUNG, SU-WEI LIN Department of Education, National University of Tainan, Tainan, Taiwan [email protected], [email protected] NAN SHUO, NAOYUKI KUBOTA Dept. of System Design, Tokyo Metropolitan University, Japan [email protected], [email protected] CHUN-HSUN CHOU, PING-CHIANG CHOU Haifong Weiqi Academy, Taiwan [email protected], [email protected] CHIA-HSIU KAO Department of Computer Science and Information Engineering, National University of Tainan, Tainan, Taiwan [email protected] Received (received date) Revised (revised date) Accepted (accepted date) In this paper, we demonstrate the application of Fuzzy Markup Language (FML) to construct an FML- based Dynamic Assessment Agent (FDAA), and we present an FML-based Human–Machine Cooperative System (FHMCS) for the game of Go. The proposed FDAA comprises an intelligent decision-making and learning mechanism, an intelligent game bot, a proximal development agent, and an intelligent agent. The intelligent game bot is based on the open-source code of Facebook’s Darkforest, and it features a representational state transfer application programming interface mechanism. The proximal development agent contains a dynamic assessment mechanism, a GoSocket mechanism, and an FML engine with a fuzzy knowledge base and rule base.
    [Show full text]
  • When Are We Done with Games?
    When Are We Done with Games? Niels Justesen Michael S. Debus Sebastian Risi IT University of Copenhagen IT University of Copenhagen IT University of Copenhagen Copenhagen, Denmark Copenhagen, Denmark Copenhagen, Denmark [email protected] [email protected] [email protected] Abstract—From an early point, games have been promoted designed to erase particular elements of unfairness within the as important challenges within the research field of Artificial game, the players, or their environments. Intelligence (AI). Recent developments in machine learning have We take a black-box approach that ignores some dimensions allowed a few AI systems to win against top professionals in even the most challenging video games, including Dota 2 and of fairness such as learning speed and prior knowledge, StarCraft. It thus may seem that AI has now achieved all of focusing only on perceptual and motoric fairness. Additionally, the long-standing goals that were set forth by the research we introduce the notions of game extrinsic factors, such as the community. In this paper, we introduce a black box approach competition format and rules, and game intrinsic factors, such that provides a pragmatic way of evaluating the fairness of AI vs. as different mechanical systems and configurations within one human competitions, by only considering motoric and perceptual fairness on the competitors’ side. Additionally, we introduce the game. We apply these terms to critically review the aforemen- notion of extrinsic and intrinsic factors of a game competition and tioned AI achievements and observe that game extrinsic factors apply these to discuss and compare the competitions in relation are rarely discussed in this context, and that game intrinsic to human vs.
    [Show full text]
  • 8 0Zkzbz0z 7 Opoqz0z0 6 Po0zps0z 5 Z0s0apop 4 0Zpzpzpz 3 Zko0mpz0 2 Naroqz0z 1 Znz0z0z0 a B C D E F G H
    BEL GO • 89 Bulletin de la Fédération Belge de Go a.s.b.l. Bulletin van de Belgische Go Federatie v.z.w. Janvier – Avril 2017 Januari – April 2017 · Ed. resp. : Thomas Connor, 213 avenue du Roi, 1190 Forest 8 0ZkZbZ0Z 7 opoqZ0Z0 6 PO0Zps0Z 5 Z0S0ApoP 4 0ZPZpZPZ 3 ZKO0mPZ0 2 NAroQZ0Z 1 ZnZ0Z0Z0 a b c d e f g h Noir joue et tue. Zwart speelt en doodt. Président – Voorzitter Joost Vannieuwenhuyse [email protected] Secrétaire – Secretaris Thomas Connor [email protected] Trésorier – Penningmeester Jan Ramon [email protected] Administrateurs – Beheerders Catherine Fricheteau Jean-Denis Hennebert Michael Silcher Philippe Tranchida Fédération Belge de Go - a.s.b.l. Belgische Go Federatie - v.z.w. Contact [email protected] [email protected] www.gofed.be Rédaction – Redactie Thomas Connor Catherine Fricheteau Jean-Denis Hennebert Relecture – Correctie Juliette Brault Marie David Olivier Drouot Laurens Teirlinck Traduction – Vertaling Alistair Fronhoffs Nelis Vets Ce document a été réalisé avec le logiciel libre LATEX et utilise le package igo disponible à l’adresse http://www.ctan.org/pkg/igo. L’export des fichiers .sgf au format .tex a été produit avec le logiciel GoWrite 2 disponible à l’adresse http://gowrite.net. Imprimé en octobre 2017 chez AJM Print-Shop, Bruxelles. - 2 - Table des matières – Inhoud Edito 4 Nouvelles de la scène européenne de mai à août 5 Rétrospective du 2ème quadrimestre 2017 (mai à août) chez les pros 10 Quarante ans de classement européen : une plongée dans l’histoire du go en Europe 15 KPMC 2017 22 Commented game : François
    [Show full text]
  • Mastering the Game of Go with Deep Neural Networks and Tree Search
    Mastering the Game of Go with Deep Neural Networks and Tree Search David Silver1*, Aja Huang1*, Chris J. Maddison1, Arthur Guez1, Laurent Sifre1, George van den Driessche1, Julian Schrittwieser1, Ioannis Antonoglou1, Veda Panneershelvam1, Marc Lanctot1, Sander Dieleman1, Dominik Grewe1, John Nham2, Nal Kalchbrenner1, Ilya Sutskever2, Timothy Lillicrap1, Madeleine Leach1, Koray Kavukcuoglu1, Thore Graepel1, Demis Hassabis1. 1 Google DeepMind, 5 New Street Square, London EC4A 3TW. 2 Google, 1600 Amphitheatre Parkway, Mountain View CA 94043. *These authors contributed equally to this work. Correspondence should be addressed to either David Silver ([email protected]) or Demis Hassabis ([email protected]). The game of Go has long been viewed as the most challenging of classic games for ar- tificial intelligence due to its enormous search space and the difficulty of evaluating board positions and moves. We introduce a new approach to computer Go that uses value networks to evaluate board positions and policy networks to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte-Carlo tree search programs that sim- ulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte-Carlo simulation with value and policy networks. Using this search al- gorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the European Go champion by 5 games to 0. This is the first time that a com- puter program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.
    [Show full text]
  • Very Simple Statistical Evidence That Alphago Has Exceeded Human Limits in Playing GO Game
    epl draft Very simple statistical evidence that AlphaGo has exceeded hu- man limits in playing GO game Okyu Kwon1 1 Division of Medical Mathematics, National Institute for Mathematical Sciences, Daejeon 34047, Korea PACS 01.80.+b { Physics of games and sports PACS 89.75.Da { Systems obeying scaling laws PACS 07.05.Mh { Neural networks, fuzzy logic, artificial intelligence Abstract { Deep learning technology is making great progress in solving the challenging prob- lems of artificial intelligence, hence machine learning based on artificial neural networks is in the spotlight again. In some areas, artificial intelligence based on deep learning is beyond human capa- bilities. It seemed extremely difficult for a machine to beat a human in a Go game, but AlphaGo has shown to beat a professional player in the game. By looking at the statistical distribution of the distance in which the Go stones are laid in succession, we find a clear trace that Alphago has surpassed human abilities. The AlphaGo than professional players and professional players than ordinary players shows the laying of stones in the distance becomes more frequent. In addition, AlphaGo shows a much more pronounced difference than that of ordinary players and professional players. Introduction. { Deep learning is a class of machine Go is a hugely complex ancient strategy board game. Go learning based on artificial neural networks. In the case has an unimaginable number of deployment possibilities of artificial neural networks, stacking four or more lay- that cannot be compared with chess. Therefore, accord- ers has been treated as meaningless. However, a method ing to the way machines won in chess, it was expected has proposed to effectively train artificial neural networks that it would take a long time for the machine to over- stacked with a large number of layers, increasing the com- come the human Go skills.
    [Show full text]
  • Type-2 Fuzzy Ontology and Fuzzy Markup Language for Real-World Applications
    FUZZ-IEEE 2016 Tutorial: FUZZ-IEEE-03 Type-2 Fuzzy Ontology and Fuzzy Markup Language for Real-World Applications Organized by Chang-Shing Lee, National University of Tainan, Taiwan Giovanni Acampora, Nottingham Trent University, UK Yuandong Tian, Facebook AI Research, USA 24 July, 2016 0 / 71 FUZZ-IEEE 2016 Tutorial: FUZZ-IEEE-03 Part 1: Type-2 Fuzzy Ontology and Applications Chang-Shing Lee, NUTN, Taiwan Part 2: Fuzzy Markup Language Giovanni Acampora, NTU, UK Part 3: Real-World Application on Game of Go Yuandong Tian, Facebook AI Research, USA 1 / 71 FUZZ-IEEE 2016 Tutorial: FUZZ-IEEE-03 Part 1 Type-2 Fuzzy Ontology and Applications Chang-Shing Lee National University of Tainan, Taiwan 2 / 71 Research Team 3 / 71 Co-Sponsors 4 / 71 Type-2 Fuzzy Ontology Applications • FML IEEE 1855-2016 Standard • Type-2 Fuzzy Set • Fuzzy Ontology • Game of Go Application • Personalized Diet Recommendation • Adaptive Learning Application 5 / 71 FML IEEE 1855-2016 6 / 71 Introduction to T2FS (1/5) 7 / 71 Introduction to T2FS (2/5) i m ~ (x ) Vertical Slice u A 2 W 1 x'1 ~ Wx'N UMF(A) ~ UMF(A) u i i MF1(x ) MFN (x ) i u u MFN (x ) 1 n ~ m A (x, u) Embedded T2 FS i x MF1(x ) u ~ Embedded LMF(A) x T1 FS 0 i ~ l x r Some eye Contact (A) Uncertainty About Uncertainty About Left End-Point Right End-Point 8 / 71 Introduction to T2FS (3/5) u 1 ~ ~ UMF (A) UMF (A) Embedded FS ~ LMF (A) ~ ~ FOU (A) FOU (A) X 9 / 71 Introduction to T2FS (4/5) Type-2 FLS Output Processing Crisp Rules Defuzzifier outputs y ≠Y Crisp Type-reducer inputs Fuzzifier Type-reduced x ≠X
    [Show full text]
  • Heft 1/2016 91. Jahrgang
    Heft 1/2016 91. Jahrgang 1 DGoZ 1/2016 Inhalt Vorwort Am 27. Januar 2016 wurde über den offiziellen AlphaGo vs. Human ......................Titel Google-Blog bekannt gegeben, was die Go-Welt Vorwort, Inhalt, Fangen und Retten, nachhaltig verändern wird: Das Programm Alpha- Nachrichten .................................2–9 Go der von Google unlängst aufgekauften Soft- Turnierberichte ..........................9–15 wareschmiede DeepMind hat den amtierenden Euro- Ausschreibung: DJGM ...................... 11 pameister Fan Hui 2p unter Wettkampfbedingungen A Beautiful Deep Mind ............... 16–21 mit 5:0 besiegt. Und zwar gleichauf, ohne Vorgabe! Noch am selben Tag erschien in der renom- Stimmen zu AlphaGo .................. 22–23 mierten Wissenschaftszeitschrift NATURE ein Ausschreibung: DPGM ...................... 23 Artikel zu dieser Sensation, angereichert um ein aus- Von Zandvoort nach Harbin (2) .. 24–31 führliches Paper der verantwortlichen Projektgruppe Tragik auf den Spielfeldern der Stratigie ..32–33 um den DeepMind-Gründer Demis Hassabis. Sogar Anfängerprobleme ....................... 34–35 sein Titelbild hat die Zeitschrift dem Go-Spiel und Der etwas andere Zug (12) ........... 36–41 der Software AlphaGo gewidmet. Und es verging Pokale ..........................................42–43 nicht viel Zeit, da berichteten weltweit unzählige Medien über dieses Ereignis: Spiegel Online, die Kinderseite ................................... 44–45 FAZ und Heise Online, die BBC, die Washington Der Durchbruch zum 18. Kyu (1) ..... 46 Post und das Onlinemagazin Wired, um nur einige Zwei Partien von der Deutschen zu nennen. Jugendmeisterschaft 2015 ............ 47–53 Und natürlich muss sich auch die DGoZ dieses Impressum ......................................... 53 Themas annehmen! Wir haben mit Ingo Althöfer Fernostnachrichten ...................... 54–57 einen fachkundigen Autoren gewinnen können, der Go-Probleme .............................. 58–60 ab S. 16 ausführlich über das Ereignis berichtet und Das Fundstück ..................................
    [Show full text]
  • Bga Crossword N
    Number 183 Spring 2018 London Open – Kyu-Players’ Room South Manchester – Yangran Zhang (L) wins British Go Journal 183 Spring 2018 CONTENTS EDITORIAL 2 LETTERS TO THE EDITOR 3 FROM:KEY CONCEPTS IN LIFE AND DEATH Richard Hunter4 WORLD NEWS Tony Atkins5 INTERNATIONAL PAIR GO CHAMPIONSHIP Francis Roads7 DEVELOPING GO IN NOTTINGHAM Robin Dews 11 PRESIDENT’S MESSAGE Toby Manning 14 GO JOTTINGS 5 John Tilley 15 LONDON GO CENTRE LAUNCHED Tony Atkins 20 REFLECTIONS ON GO FROM A BEGINNER Mike Medaglia 21 UK NEWS Tony Atkins 22 BGA ANNOUNCEMENTS 26 SOLUTIONS TO THE NUMBERED PROBLEMS 27 BGA CROSSWORD NO.2 SOLUTION by Sphinx 33 UK AND IRELAND CLUBS LIST 34 ASSOCIATION CONTACT INFORMATION 40 COLLECTING GO XXXVI: WESTERN ART Tony Atkins – Rear Cover Copyright c 2018 British Go Association. Articles may be reproduced for the purpose of promoting Go and not for profit, providing that the British Go Journal is attributed as the source and the permission of the Editor and author(s) have been sought and obtained in writing, in advance. Views expressed are not necessarily those of the BGA, nor of the Editor. 1 EDITORIAL [email protected] Welcome to the 183rd British Go Journal. In This Issue Thank you to everyone who sent in entries for the prize crossword, the solution for which can be found herein. The winner was Donald MacLeod who will receive a book from the BGA book stock. Well done Donald. Paul Barnard’s article in the last issue prompted a discussion with Richard Hunter (see Letters) who described exactly the same position in an article from some years ago.
    [Show full text]
  • When Are We Done with Games?
    When Are We Done with Games? Niels Justesen Michael S. Debus Sebastian Risi IT University of Copenhagen IT University of Copenhagen IT University of Copenhagen Copenhagen, Denmark Copenhagen, Denmark Copenhagen, Denmark [email protected] [email protected] [email protected] Abstract—From an early point, games have been promoted designed to erase particular elements of unfairness within the as important challenges within the research field of Artificial game, the players, or their environments. Intelligence (AI). Recent developments in machine learning have We take a black-box approach that ignores some dimensions allowed a few AI systems to win against top professionals in even the most challenging video games, including Dota 2 and of fairness such as learning speed and prior knowledge, StarCraft. It thus may seem that AI has now achieved all of focusing only on perceptual and motoric fairness. Additionally, the long-standing goals that were set forth by the research we introduce the notions of game extrinsic factors, such as the community. In this paper, we introduce a black box approach competition format and rules, and game intrinsic factors, such that provides a pragmatic way of evaluating the fairness of AI vs. as different mechanical systems and configurations within one human competitions, by only considering motoric and perceptual fairness on the competitors’ side. Additionally, we introduce the game. We apply these terms to critically review the aforemen- notion of extrinsic and intrinsic factors of a game competition and tioned AI achievements and observe that game extrinsic factors apply these to discuss and compare the competitions in relation are rarely discussed in this context, and that game intrinsic to human vs.
    [Show full text]
  • Arxiv:1511.06410V3 [Cs.LG] 29 Feb 2016 Game Is to Control More Territory Than the Opponent (Fig
    Published as a conference paper at ICLR 2016 BETTER COMPUTER GO PLAYER WITH NEURAL NET- WORK AND LONG-TERM PREDICTION Yuandong Tian Yan Zhu Facebook AI Research Rutgers University Menlo Park, CA 94025 Facebook AI Research [email protected] [email protected] ABSTRACT Competing with top human players in the ancient game of Go has been a long- term goal of artificial intelligence. Go’s high branching factor makes traditional search techniques ineffective, even on leading-edge hardware, and Go’s evaluation function could change drastically with one stone change. Recent works [Maddi- son et al. (2015); Clark & Storkey (2015)] show that search is not strictly nec- essary for machine Go players. A pure pattern-matching approach, based on a Deep Convolutional Neural Network (DCNN) that predicts the next move, can perform as well as Monte Carlo Tree Search (MCTS)-based open source Go en- gines such as Pachi [Baudis & Gailly (2012)] if its search budget is limited. We extend this idea in our bot named darkforest, which relies on a DCNN designed for long-term predictions. Darkforest substantially improves the win rate for pattern- matching approaches against MCTS-based approaches, even with looser search budgets. Against human players, the newest versions, darkfores2, achieve a sta- ble 3d level on KGS Go Server as a ranked bot, a substantial improvement upon the estimated 4k-5k ranks for DCNN reported in Clark & Storkey (2015) based on games against other machine players. Adding MCTS to darkfores2 creates a much stronger player named darkfmcts3: with 5000 rollouts, it beats Pachi with 10k rollouts in all 250 games; with 75k rollouts it achieves a stable 5d level in KGS server, on par with state-of-the-art Go AIs (e.g., Zen, DolBaram, CrazyS- tone) except for AlphaGo [Silver et al.
    [Show full text]
  • FML-Based Prediction Agent and Its Application to Game of Go
    Published as a conference paper at IFSA-SCIS 2017______________________________________________________________________ FML-based Prediction Agent and Its Application to Game of Go Chang-Shing Lee Chia-Hsiu Kao Yusuke Nojima Nan Shuo Mei-Hui Wang Sheng-Chi Yang Ryosuke Saga Naoyuki Kubota Dept. of Computer Science and Information Engineering Dept. of Electrical Engineering Dept. of System Design Knowledge Application and Web Service Research Center and Information Science Tokyo Metropolitan University National University of Tainan Osaka Prefecture University Tokyo, Japan Tainan, Taiwan Osaka, Japan [email protected] [email protected] [email protected] Abstract- In this paper, we present a robotic prediction agent Many different real-world applications are with a high-level including a darkforest Go engine, a fuzzy markup language (FML) of uncertainty. A lot of researches proved the good performance assessment engine, an FML-based decision support engine, and a of using fuzzy sets. Being an IEEE Standard in May 2016, fuzzy robot engine for game of Go application. The knowledge base and markup language (FML) provides designers of intelligent rule base of FML assessment engine are constructed by referring decision making systems with a unified and high-level the information from the darkforest Go engine located in NUTN methodology for describing systems’ behaviors by means of and OPU, for example, the number of MCTS simulations and rules based on human domain knowledge [10, 11]. The main winning rate prediction. The proposed robotic prediction agent advantage of using FML is easy to understand and extend the first retrieves the database of Go competition website, and then the implemented programs for other researchers.
    [Show full text]