Theoretical physicists from ETH Zurich deliberately misled intelligent machines, and thus refined the process of machine learning. They created a new method that allows computers to categorize data--even when humans have no idea what this categorization might look like. When computers independently identify bodies of water and their outlines in satellite images, or beat the world's best professional players at the board game Go, then adaptive algorithms are working in the background. Programmers supply these algorithms with known examples in a training phase: images of bodies of water and land, or sequences of Go moves that have led to success or failure in tournaments. Similarly to how our brain nerve cells produce new networks during learning processes, the special algorithms adapt in the learning phase based on the examples presented to them.
Last year, Google's DeepMind AI beat Lee Sedol at Go, a strategy game like chess, but orders of magnitude more complicated. The win was a remarkable step forward for the field of artificial intelligence, but it got Roger Melko, a physicist at the Perimeter Institute for Theoretical Physics, thinking about how neural networks--a type of AI modeled after the human brain--might be used to solve some of the toughest problems in quantum physics. Indeed, intelligent machines may be necessary to solve these problems. "The thing about quantum physics is it's highly complex in a very precise mathematical sense. A big problem we face when we study these quantum systems [without machine learning] is how to deal with this complexity," Melko told me.
Scientists have been able to develop artificial intelligence (AI) capable of besting humans at their own games, but a new study suggests that people may have the upper hand when it comes to intuitive thinking. A team of researchers led by Denmark's Aarhus University associate professor Jacob Sherson managed to develop a game based around complex theoretical science in which human players were "able to find solutions to difficult problems associated with the task of quantum computing," whereas computerized numerical optimization failed, according to the scientists' findings published in Nature. "The big surprise we had was that some of the players actually had solutions that were of higher quality and of shorter duration than any computer algorithms could find," Mr. Sherson told the Associated Press. The game, Quantum Moves, is available online for the purpose of helping in the development of quantum computing. While it functions as entertainment, Quantum Moves is built to take quantum physics optimization problems and turn them into a game, the results of which demonstrate fundamental differences between human thought processes and the problem solving of computers.
A short while ago a recent article in Wired described how physicists are about to rule Silicon Valley. The opening of the article resonated strongly with me when I also used to tackle difficult research questions at the world's most renown laboratories for particle physics: CERN in Geneva, Switzerland and the Fermi National Laboratory in Chicago, IL. For more than 10 years I've tried to uncover the origins of our Universe before transitioning to the private sector and joining Blue Yonder, a cloudbased company that delivers automated, machine learning solutions in the Retail space. Why did I make this move? The LHC has yet to discover anything new – even the Higgs boson discovered in 2012 was about the same mass it was previously expected to be.
On Monday, researchers associated with CMS collaboration at the European Organization for Nuclear Research (CERN) in Switzerland released a massive trove of data gathered during high-energy particle collisions in the Large Hadron Collider (LHC). "Members of the CMS Collaboration put in lots of effort and thousands of person-hours each of service work in order to operate the CMS detector and collect these research data for our analysis," Kati Lassila-Perini, a CMS physicist, said in a statement. "However, once we've exhausted our exploration of the data, we see no reason not to make them available publicly. The benefits are numerous, from inspiring high-school students to the training of the particle physicists of tomorrow. And personally, as CMS's data-preservation co-ordinator, this is a crucial part of ensuring the long-term availability of our research data."