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AI Futures

Communications of the ACM

"AlphaZero crushes chess!" scream the headlinesa as the AlphaZero algorithm developed by Google and DeepMind took just four hours of playing against itself (with no human help) to defeat the reigning World Computer Champion Stockfish by 28 wins to 0 in a 100-game match. Only four hours to recreate the chess knowledge of one and a half millennium of human creativity! This followed the announcement just weeks earlier that their program AlphaGoZero had, starting from scratch, with no human inputs at all, comprehensively beaten the previous version AlphaGo, which in turn had spectacularly beaten one of the world's top Go players, Lee Seedol, 4-1 in a match in Seoul, Korea, in March 2016. Interest in AI has reached fever pitch in the popular imagination--its opportunities and its threats. The time is ripe for books on AI and what it holds for our future such as Life 3.0: Being Human in the Age of Artificial Intelligence by Max Tegmark, Android Dreams by Toby Walsh, and Artificial Intelligence by Melanie Mitchell.6,8,9


Reaching the Singularity May be Humanity's Greatest and Last Accomplishment

#artificialintelligence

In a new paper published in The International Journal of Astrobiology, Joseph Gale from The Hebrew University of Jerusalem and co-authors make the point that recent advances in artificial intelligence (AI)--particularly in pattern recognition and self-learning--will likely result in a paradigm shift in the search for extraterrestrial intelligent life. While futurist Ray Kurzweil predicted 15 years ago that the singularity--the time when the abilities of a computer overtake the abilities of the human brain--will occur in about 2045, Gale and his co-authors believe this event may be much more imminent, especially with the advent of quantum computing. It's already been four years since the program AlphaGO, fortified with neural networks and learning modes, defeated Lee Sedol, the Go world champion. The strategy game StarCraft II may be the next to have a machine as reigning champion. If we look at the calculating capacity of computers and compare it to the number of neurons in the human brain, the singularity could be reached as soon as the early 2020s.


AlphaGo

#artificialintelligence

The ancient game of Go is probably the most complex game ever devised by man. Invented in China, more than 3,000 years ago, it is believed to be the oldest board game continuously played to the present day. It has 10¹⁷⁰ possible board configurations, which is more than the number of atoms in the universe. For those familiar with Chess, Go itself is more straightforward, yet more complicated. Simpler because all of the pieces are the same, just black and white, and in Go, the pieces do not move around the board.


Samsung has its own AI-designed chip. Soon, others will too

#artificialintelligence

Samsung is using artificial intelligence to automate the insanely complex and subtle process of designing cutting-edge computer chips. The South Korean giant is one of the first chipmakers to use AI to create its chips. Samsung is using AI features in new software from Synopsys, a leading chip design software firm used by many companies. "What you're seeing here is the first of a real commercial processor design with AI," says Aart de Geus, the chairman and co-CEO of Synopsys. Others, including Google and Nvidia, have talked about designing chips with AI.


Samsung Has Its Own AI-Designed Chip. Soon, Others Will Too

WIRED

Samsung is using artificial intelligence to automate the insanely complex and subtle process of designing cutting-edge computer chips. The South Korean giant is one of the first chipmakers to use AI to create its chips. Samsung is using AI features in new software from Synopsys, a leading chip design software firm used by many companies. "What you're seeing here is the first of a real commercial processor design with AI," says Aart de Geus, the chairman and co-CEO of Synopsys. Others, including Google and Nvidia, have talked about designing chips with AI.


Why the EU Lags behind in Artificial Intelligence, Science and Technology

#artificialintelligence

It is not surprising that Europe, despite having a strong industrial base and leading AI research and talent, is dragging behind the US and China. European countries are lagging behind in artificial intelligence due to the fragmentation of the EU's research space and digital market, difficulties in attracting human capital and external investment, lack of commercial competitiveness and geopolitical inequalities. Reading the ESPAS Ideas Paper Series, the Future of AI and Big Data, one could enjoy its deep insights, see the Supplement, as well as the honesty of the report as to the EU AI state of affairs. It specifically reads: "The EU will lag behind in AI for some more time, because it has a more complicated task than others. On the other hand, with a resilient and free economy, a balanced regulatory system, an interested public, intact societies and world class research it will be well-placed in the medium term... Some experts believe that the advances in machine learning are plateauing and that AI will only develop slowly and incrementally from now on. Others see much more change coming, even revolutionary jumps like super intelligent AIs that are able to be employed in many fields at the same time... While many policy makers see the question of AGI as science fiction, huge investments are made into researching it. For example, DeepMind – developers of the Go-champion AI AlphaGo and bought by Google for 500 million USD – spends up to 200 million USD each year to come closer to that goal.OpenAI, funded with an Endowment of 1 billion USD, has the same goal. Since this research is not required to be transparent, it is likely that states such as the US, Chinese and probably others are also already working on such programmes. The biggest project by the European Union is the Human Brain Project, an effort to construct a virtual human brain, although this is not exactly the same as building an AGI... Imagine, in 20 years, there will be a super intelligent, friendly, conscious AI which is a source of pride to the world and fulfils all our wishes. Would this be a paternalistic world? The difficult question goes to the core of the human condition: What are we to do, if we are not needed anymore? What then is the purpose of humanity?"


Survey on reinforcement learning for language processing

arXiv.org Artificial Intelligence

Machine learning algorithms have been very successful to solve problems in the natural language processing (NLP) domain for many years, especially supervised and unsupervised methods. However, this is not the case with reinforcement learning (RL), which is somewhat surprising since in other domains, reinforcement learning methods have experienced an increased level of success with some impressive results, for instance in board games such as AlphaGo Zero [106]. Yet, deep reinforcement learning for natural language processing is still in its infancy when compared to supervised learning [65]. Thus, the goal of this article is to provide a review of applications of reinforcement learning to NLP and we present an analysis of the underlying structure of the problems that make them viable to be treated entirely or partially as RL problems intended as an aid to newcomers to the field. We also analyze some existing research gaps and provide a list of promising research directions in which natural language systems might benefit from reinforcement learning algorithms.


Playing Against the Board: Rolling Horizon Evolutionary Algorithms Against Pandemic

arXiv.org Artificial Intelligence

Competitive board games have provided a rich and diverse testbed for artificial intelligence. This paper contends that collaborative board games pose a different challenge to artificial intelligence as it must balance short-term risk mitigation with long-term winning strategies. Collaborative board games task all players to coordinate their different powers or pool their resources to overcome an escalating challenge posed by the board and a stochastic ruleset. This paper focuses on the exemplary collaborative board game Pandemic and presents a rolling horizon evolutionary algorithm designed specifically for this game. The complex way in which the Pandemic game state changes in a stochastic but predictable way required a number of specially designed forward models, macro-action representations for decision-making, and repair functions for the genetic operations of the evolutionary algorithm. Variants of the algorithm which explore optimistic versus pessimistic game state evaluations, different mutation rates and event horizons are compared against a baseline hierarchical policy agent. Results show that an evolutionary approach via short-horizon rollouts can better account for the future dangers that the board may introduce, and guard against them. Results highlight the types of challenges that collaborative board games pose to artificial intelligence, especially for handling multi-player collaboration interactions.


Machines that learn: The origin story of artificial intelligence

#artificialintelligence

Lee Sedol, a world champion in the Chinese strategy board game Go, faced a new kind of adversary at a 2016 match in Seoul. Developers at DeepMind, an artificial intelligence startup acquired by Google, had fed 30 million Go moves into a deep neural network. Their creation, dubbed AlphaGo, then figured out which moves worked by playing millions of games against itself, learning at a faster rate than any human ever could. The match, which AlphaGo won 4 to 1, "was the moment when the new movement in artificial intelligence exploded into the public consciousness," technology journalist Cade Metz writes in his engaging new book, "Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World." Metz, who covers AI for The New York Times and previously wrote for Wired magazine, is well positioned to chart the decades-long effort to build artificially intelligent machines.


Are Computers That Win at Chess Smarter Than Geniuses?

#artificialintelligence

But then there was the Chinese game of go (pictured), estimated to be 4000 years old, which offers more "degrees of freedom" (possible moves, strategy, and rules) than chess (2 10170). As futurist George Gilder tells us, in Gaming AI, it was a rite of passage for aspiring intellects in Asia: "Go began as a rigorous rite of passage for Chinese gentlemen and diplomats, testing their intellectual skills and strategic prowess. Later, crossing the Sea of Japan, Go enthralled the Shogunate, which brought it into the Japanese Imperial Court and made it a national cult." Then AlphaGo, from Google's DeepMind, appeared on the scene in 2016: As the Chinese American titan Kai-Fu Lee explains in his bestseller AI Super-powers,8 the riveting encounter between man and machine across the Go board had a powerful effect on Asian youth. Though mostly unnoticed in the United States, AlphaGo's 2016 defeat of Lee Sedol was avidly watched by 280 million Chinese, and Sedol's loss was a shattering experience. The Chinese saw DeepMind as an alien system defeating an Asian man in the epitome of an Asian game.