chess


Untold History of AI: Charles Babbage and the Turk

IEEE Spectrum Robotics Channel

The history of AI is often told as the story of machines getting smarter over time. What's lost is the human element in the narrative, how intelligent machines are designed, trained, and powered by human minds and bodies. In this six-part series, we explore that human history of AI--how innovators, thinkers, workers, and sometimes hucksters have created algorithms that can replicate human thought and behavior (or at least appear to). While it can be exciting to be swept up by the idea of super-intelligent computers that have no need for human input, the true history of smart machines shows that our AI is only as good as we are. In the year 1770, at the court of the Austrian Empress Maria Theresa, an inventor named Wolfgang von Kempelen presented a chess-playing machine.


Leveling the Playing Field - Fairness in AI Versus Human Game Benchmarks

arXiv.org Artificial Intelligence

From the beginning if the history of AI, there has been interest in games as a platform of research. As the field developed, human-level competence in complex games became a target researchers worked to reach. Only relatively recently has this target been finally met for traditional tabletop games such as Backgammon, Chess and Go. Current research focus has shifted to electronic games, which provide unique challenges. As is often the case with AI research, these results are liable to be exaggerated or misrepresented by either authors or third parties. The extent to which these games benchmark consist of fair competition between human and AI is also a matter of debate. In this work, we review the statements made by authors and third parties in the general media and academic circle about these game benchmark results and discuss factors that can impact the perception of fairness in the contest between humans and machines


DeepMind and Google: the battle to control artificial intelligence

#artificialintelligence

One afternoon in August 2010, in a conference hall perched on the edge of San Francisco Bay, a 34-year-old Londoner called Demis Hassabis took to the stage. Walking to the podium with the deliberate gait of a man trying to control his nerves, he pursed his lips into a brief smile and began to speak: "So today I'm going to be talking about different approaches to building…" He stalled, as though just realising that he was stating his momentous ambition out loud. And then he said it: "AGI". AGI stands for artificial general intelligence, a hypothetical computer program that can perform intellectual tasks as well as, or better than, a human. AGI will be able to complete discrete tasks, such as recognising photos or translating languages, which are the single-minded focus of the multitude of artificial intelligences (AIs) that inhabit our phones and computers. But it will also add, subtract, play chess and speak French. It will also understand physics papers, compose novels, devise investment strategies and make delightful conversation with strangers. It will monitor nuclear reactions, manage electricity grids and traffic flow, and effortlessly succeed at everything else. AGI will make today's most advanced AIs look like pocket calculators. The only intelligence that can currently attempt all these tasks is the kind that humans are endowed with. But human intelligence is limited by the size of the skull that houses the brain. Its power is restricted by the puny amount of energy that the body is able to provide. Because AGI will run on computers, it will suffer none of these constraints. Its intelligence will be limited only by the number of processors available.


With Tech on the Defensive, SXSW Takes an Introspective Turn

WIRED

The first five days or so of SXSW in Austin are always dedicated to the "interactive" portion of the festival. The city's downtown streets swell with lanyard-laden "entrepreneurs" and "founders" wearing that familiar uniform of T-shirts screen-printed with their company's clever logo, an outfit made professional by throwing a blazer over the ensemble. They bounce from panel to panel and branded "house" to branded "house" (this year, on scooters, so many scooters) hawking their new apps and software products, each promising to be more revolutionary and life-changing and utterly necessary than the next. For years, the unspoken question at the conference seemed to be which company will become SXSW famous, like Persicope, Foursquare, or, most memorably, Twitter? But this year, on the opening Friday of SXSW, Democratic presidential hopeful Elizabeth Warren unleashed a manifesto titled "Here's How We Can Break Up Big Tech," and a new question burst onto the scene: What do you think of Warren's proposal?


How IBM Learns From Machine Learning

#artificialintelligence

Watson started its life as a TV star and now is being used by clients such as Symrise to create new perfumes.IBM It is less than fifteen years ago when IBM sold its PC business to Lenovo and did the same for its x86 server business back in 2014. It was a major shift for the company that some decades ago was virtually the same as the'PC' itself. But what was the key factor that attracted the company's attention and made it open a brand new path, different from its past business-safe lanes? Undoubtedly, both businesses that were sold had become less profitable, still, it does not sufficiently explain the reason why a new business had to replace the old one to secure the viability of the corporation.


Applying Artificial Intelligence in the Agile World

#artificialintelligence

The convergence of artificial intelligence (AI) systems with the agile world is having a disruptive effect on how we build software and the types of products that we build. By combining machine learning and deep learning we can build applications that truly learn like humans. AI bias is a very serious concern, as AI systems are only as good as the data sets used to train them. Aidan Casey, senior software engineering manager at Johnson Controls, will speak about how artificial intelligence capabilities will be used to augment and shape the agile world of tomorrow at aginext 2019. The conference will be held on March 21 - 22 in London, United Kingdom.


AI And Creativity With Marcus Du Sautoy

#artificialintelligence

Marcus is the Simonyi Professor for the Public Understanding of Science at Oxford University, quite a mouthful.


The languages of AI

#artificialintelligence

The evolution of artificial intelligence (AI) grew with the complexity of the languages available for development. In 1959, Arthur Samuel developed a self-learning checkers program at IBM on an IBM 701 computer using the native instructions of the machine (quite a feat given search trees and alpha-beta pruning). But today, AI is developed using various languages, from Lisp to Python to R. This article explores the languages that evolved for AI and machine learning. The programming languages that are used to build AI and machine learning applications vary. Each application has its own constraints and requirements, and some languages are better than others in particular problem domains.


The truth about artificial intelligence in medicine

#artificialintelligence

For many months, artificial intelligence has been in my peripheral vision, just sitting there, ignored by me because it seemed too far in the future to be interesting now. And then, there were all these terms -- Big Data, machine learning, data science -- which circled the subject and, frankly, gave me a bit of a headache. Artificial intelligence is upon us, unleashed and unbridled in its ability to transform the world. If in the previous technological revolution, machines were invented to do the physical work, then in this revolution, machines are being invented to do the thinking work. And no field involves more thinking than medicine.


How machines teach us to be more innovative

#artificialintelligence

Technology still cannot simulate human intelligence to solve complex problems, in a variable environment and with partial information. But it is getting closer. An example is the case of autonomous vehicles, able to make optimal decisions in real time, thanks to complex algorithms that take into account multiple data. Another example is AlphaZero, the algorithm developed by DeepMind, Google's artificial intelligence division, concerning which Science recently published an article. AlphaZero is able to win in the board games that are the most complex for the human mind: chess, shogi (Japanese chess) and go (traditional Chinese board game).