In Go, no successful evaluation function for non-terminal positions has ever been found. Therefore, it is not a problem that will be solved with faster search. It pushes the boundaries of what is possible with new algorithms such as Monte Carlo methods. Work on computer Go started in the 1960's, but it was not until 2016 that the AlphaGo program was able to best the second-highest ranking professional Go player.
Artificial Intelligence, or'AI' is the buzzword of the year. What does AI mean to us Independent Workers? Are we going to be replaced by an algorithm? I've been a computer programmer for almost fifty years now and all through my career I was told that a computer was going to replace me. No, it was a computer, I remember.
With more board configurations than there are atoms in the universe, the ancient Chinese game of Go has long been considered a grand challenge for artificial intelligence. On March 9, 2016, the worlds of Go and artificial intelligence collided in South Korea for an extraordinary best-of-five-game competition, coined The DeepMind Challenge Match. Hundreds of millions of people around the world watched as a legendary Go master took on an unproven AI challenger for the first time in history. Directed by Greg Kohs with an original score by Academy Award nominee, Hauschka, AlphaGo chronicles a journey from the halls of Oxford, through the backstreets of Bordeaux, past the coding terminals of DeepMind in London, and ultimately, to the seven-day tournament in Seoul. As the drama unfolds, more questions emerge: What can artificial intelligence reveal about a 3000-year-old game?
"With more board configurations than there are atoms in the observable universe, the ancient Chinese game of'Go' has long been considered a grand challenge for artificial intelligence. On March 9, 2016, the worlds of Go and artificial intelligence collided in South Korea for an extraordinary best-of-five-game competition, coined the Google DeepMind Challenge Match. Hundreds of millions of people around the world watched as a legendary Go master took on an unproven AI challenger for the first time in history. Directed by Greg Kohs with an original score by Academy Award nominee, Hauschka, AlphaGo chronicles a journey from the halls of Cambridge, through the backstreets of Bordeaux, past the coding terminals of DeepMind in London, and, ultimately, to the seven-day tournament in Seoul. As the drama unfolds, more questions emerge: What can artificial intelligence reveal about a 3000-year-old game? What can it teach us about humanity?"
Deep learning technology is making great progress in solving the challenging problems 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 capabilities. 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.
We investigate the power of voting among diverse, randomized software agents. With teams of computer Go agents in mind, we develop a novel theoretical model of two-stage noisy voting that builds on recent work in machine learning. This model allows us to reason about a collection of agents with different biases (determined by the first-stage noise models), which, furthermore, apply randomized algorithms to evaluate alternatives and produce votes (captured by the second-stage noise models). We analytically demonstrate that a uniform team, consisting of multiple instances of any single agent, must make a significant number of mistakes, whereas a diverse team converges to perfection as the number of agents grows. Our experiments, which pit teams of computer Go agents against strong agents, provide evidence for the effectiveness of voting when agents are diverse.
Meet Spot, the first robot to get its own employee number at Norwegian oil producer Aker BP. Developed by Boston Dynamics, the robot is set to start patrolling Aker BP's oil and gas production vessel at the Skarv field in the Norwegian Sea this year, testing its ability to run inspections, detect hydrocarbon leaks, gather data and generate reports. The upshot for Aker BP, which is seeking to be a front-runner in the digitalization of the oil industry, is to make offshore operations safer and more efficient, the company said as it presented the robot at its capital markets day in Oslo on Tuesday. Aker BP will run the tests with Cognite, the software venture controlled by the oil company's main owner, Aker ASA. "These things never get tired, they have a larger ability to adapt and to gather data," Kjetel Digre, Aker BP's senior vice president for operations, said in an interview.
South Korean Go master Lee Se-Dol recently announced his retirement from professional Go competition. He felt that no matter how hard he tries, he will never beat AI Go players like AlphaGo. It is a rather sad decision and development of his historical defeat in competition with Google DeepMind's AlphaGo. It gives the whole thing a more dramatic tone than it should be. However, the defeat of human Go players to AI is neither the end of the world for the Go game nor for the human players.
Any new technology comes with both advantages and risks. Where AI in financial institutions is concerned many of the methods and skills used to counter the risks develop from already familiar safeguards. No technology should be adopted unless it comes thoroughly tested, everyone understands the outcomes to expect, and systems are constantly monitored to ensure that those outcomes are being delivered. That is not unique to AI, and in many cases will have been standard practice when adopting any new technology in the past. Similarly it should come as little surprise that where training is required it may be needed at all levels within an institution.
To stop spread of disinformation leading to widespread public disorder, the government is exploring use of Artificial Intelligence (AI) to remove such content automatically from social media platforms. The Centre is to "take up the issue with representatives of various international social media platforms operating in the country and monitor their compliance to instructions issued by lawful authorities under Information Technology Act." Sources said, "The introduction of Artificial Intelligence to remove objectionable content automatically from social media platforms needs to be explored." This step was proposed after the government witnessed widespread public disorder because of spread of rumours in mob lynching cases. The Ministry of Home Affairs has taken up the matter and is exploring ways to implement it. On the rise in sharing of fake news over social media platforms such as Facebook, Twitter and WhatsApp, Minister of Electronics and Information Technology Ravi Shankar Prasad had said in Lok Sabha that "With a borderless cyberspace coupled with the possibility of instant communication and anonymity, the potential for misuse of cyberspace and social media platforms for criminal activities is a global issue."
When we get to a point where literally just about everything can be done more cheaply and more efficiently by robots, the elite won't have any use for the rest of us at all. For most of human history, the wealthy have needed the poor to do the work that is necessary to run their businesses and make them even wealthier. In this day and age we like to call ourselves "employees", but in reality we are their servants. Some of us may be more well paid than others, but the vast majority of us are expending our best years serving their enterprises so that we can pay the bills. Unfortunately, that paradigm is rapidly changing, and many of the jobs that humans are doing today will be done by robots in the not too distant future.