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AI in the Future


In Isaac Asimov's short story "Liar!", a robot is accidentally programmed to be telepathic. When it starts to realize the feelings of those around him, it begins to tell flattering lies. In the story, the robot is aware of it's programming error but doesn't let the researchers know to let them solve the problem themselves. The story takes place in 2021, a short time from now, and already today we are seeing instances of ideological biases finding their way in AI. For example take BERT AI System, because of it scanning and learning from lots and lots of digitized information such as old books, maps, and records, centuries of bias are included in the result, such as it associating men more than women with the word "programmer".

Simulating the human brain: an exascale effort - IEEE Future Directions


As of Spring 2018 the fastest computer is the Sunway Taihulight, Wuxi – China. It has 10,649,600 processing cores, clustered in group of 260 each and delivering an overall performance of 125.44 PetaFLOPS (million of billions of instructions per second) requiring some 20MW of power. In the US the National Strategic Computing Initiative aims at developing the first exascale computer (8 times faster than the Sunway Taihulight computer) and the race is on against China, South Korea and Europe. We might be seeing the winner this year (next month the top 500 computers list will be revised -it happens twice a year). These supercomputers are used today in studying the Earth climate and earthquakes, simulating weapons effect, designing new drugs, simulating the folding of proteins.

The adventure of chess programming (3)


This article is reproduced with kind permission of Spiegel Online, where it first appeared. The author was told to make the series personal, describe the development of chess programming not as an academic treatise but as a personal story of how he had experienced it. For some ChessBase readers a number of the passages will be familiar, since the stories have been told before on our pages. For others this can serve as a roadmap through one of the great scientific endeavors of our time. It was the mid 1990s.

Standing on the shoulders of giants


When you think of AI or machine learning you may draw up images of AlphaZero or even some science fiction reference such as HAL-9000 from 2001: A Space Odyssey. However, the true forefather, who set the stage for all of this, was the great Arthur Samuel. Samuel was a computer scientist, visionary, and pioneer, who wrote the first checkers program for the IBM 701 in the early 1950s. His program, "Samuel's Checkers Program", was first shown to the general public on TV on February 24th, 1956, and the impact was so powerful that IBM stock went up 15 points overnight (a huge jump at that time). This program also helped set the stage for all the modern chess programs we have come to know so well, with features like look-ahead, an evaluation function, and a mini-max search that he would later develop into alpha-beta pruning.

AI is the Next Exascale – Rick Stevens on What that Means and Why It's Important


HPCwire: Walk us through the program, give us a sense of what these AI and science town halls are all about and what they are trying to accomplish? RS: If you remember back in 2007, we had three town hall meetings – at Argonne, Berkeley and Oak Ridge – that launched the whole DOE Exascale project and so forth. At that time the idea was to get people together and ask them, for exascale, what if we could build these faster machines, what would you do with them. It was a way to get people thinking about the possibility of that and of course it took long time to get the exascale computing program going. With these town halls we are kind of asking a variation on that question. Now we're asking the question of what's the opportunity for AI in science or the application of science, particularly in the context of DOE, but more broadly because DOE's got a lot of collaborations with NIH and other agencies. So really asking the fundamental question of what do we have to do in the AI space to make it relevant for science. The point of the town halls – three in the labs and one in Washington in October – is go get people thinking about what opportunities there are in different scientific domains for breakthrough science that can be accomplished by leveraging AI and working AI into simulation, and bringing AI into big data, bringing AI to the facility and so forth. So that's the concept; it's really to get the community moving.