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.
SUBURBIA IS apparently filled with masochists--evidenced by the fact that each Sunday I wake to the sound of all my neighbors concurrently grooming their lawns. I feel compelled to join them, though I hate the two-hour chore. At least it makes my Apple Watch's pedometer happy. A 2019 survey of 2,000 Americans conducted by OnePoll with lawn mower maker Cub Cadet found that we spend an average of about six hours a month mowing our lawns, not counting extra time spent sculpting the perimeter edges with a string trimmer. Those who work at home will have to deal with the sonic blitzkrieg.
The resurgence of AI has industry leaders counting the days until quantum computers go mainstream. There's been considerable progress on the quantum computing front since I blogged last year about how The European Quantum Industry Consortium (QuIC) was developing its Quantum Strategic Industry Roadmap. For an update, I reached out to Laure Le Bars, research project director at SAP and also president of QuIC. Le Bars was a recent guest on the Future of ERP Podcast from SAP, hosted by Richard Howells, vice president for thought leadership at SAP, and Oyku Ilgar, marketing director for SAP Supply Chain. Advances in quantum hardware, middleware, and software will lead to a general-purpose quantum advantage machine being developed by 2030.
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.
Typically new graphics drivers are supposed to make your computer go, in the parlance of the common man, faster. But anyone who's been a PC gamer long enough knows this isn't always so. Take the last Game Ready Driver from Nvidia, which had the unintended effect of spiking some users' CPUs even after they'd closed their games. According to Nvidia representatives, the issue has been patched out in the latest version of the driver, which you can download now. We're already seeing it automatically offered via GeForce Experience.
Earlier this year, an amateur Go player decisively defeated one of the game's top-ranked AI systems. They did so using a strategy developed with the help of a program researchers designed to probe systems like KataGo for weaknesses. It turns out that victory is just one part of a broader Go renaissance that is seeing human players become more creative since AlphaGO's milestone victory in 2016 In a recent study published in the journal PNAS, researchers from the City University of Hong Kong and Yale found that human Go players have become less predictable in recent years. As the New Scientist explains, the researchers came to that conclusion by analyzing a dataset of more than 5.8 million Go moves made during professional play between 1950 and 2021. With the help of a "superhuman" Go AI, a program that can play the game and grade the quality of any single move, they created a statistic called a "decision quality index," or DQI for short.
A strong amateur Go player has beat a highly-ranked AI system after exploiting a weakness discovered by a second computer, The Financial Times has reported. By exploiting the flaw, American player Kellin Pelrine defeated the KataGo system decisively, winning 14 of 15 games without further computer help. It's a rare Go win for humans since AlphaGo's milestone 2016 victory that helped pave the way for the current AI craze. It also shows that even the most advanced AI systems can have glaring blind spots. Pelrine's victory was made possible by a research firm called FAR AI, which developed a program to probe KataGo for weaknesses.
A human player has comprehensively defeated a top-ranked AI system at the board game Go, in a surprise reversal of the 2016 computer victory that was seen as a milestone in the rise of artificial intelligence. Kellin Pelrine, an American player who is one level below the top amateur ranking, beat the machine by taking advantage of a previously unknown flaw that had been identified by another computer. But the head-to-head confrontation in which he won 14 of 15 games was undertaken without direct computer support. The triumph, which has not previously been reported, highlighted a weakness in the best Go computer programs that is shared by most of today's widely used AI systems, including the ChatGPT chatbot created by San Francisco-based OpenAI. The tactics that put a human back on top on the Go board were suggested by a computer program that had probed the AI systems looking for weaknesses.
In March 2016, to much fanfare, AlphaGo beat Go world champion Lee Sedol, convincingly, in a 5 game match, 4 to 1. Computers have only gotten faster since then; one might have thought that the matter was settled. And of course computers have only gotten faster ever since. And the human that won wasn't even a professional Go player, let along a World Champion, just a strong amateur named Kellin Pelrine. And the match wasn't even close; Pelrine beat a top AI system 14 games to 1, in a 15 match series. Almost seven years to the day after AlphaGo beat Sedol.
There has been legal documentation through technological systems, but it goes beyond exploring technology for transmitting and storing legal proceedings to creating a robot lawyer. Pondering what a robot lawyer does? A robot lawyer can advocate like a trained, and experienced lawyer would do. A robot lawyer can "fight corporations, beat bureaucracy, and sue anyone at the press of a button", says Joshua Browder, DoNotPay founder. DoNotPay is a manufacturing company aimed at making legal information and self-help services available to everyone through the use of technology.
Can a robot bring you peace of mind? Find all the exciting innovation from CES 2023 in this ZDNET special feature. This has, for some time, been a conundrum that's wafted around my inner workings. If robots are so clever -- and some surely are -- they can protect us from all sorts of nefarious threats and intrusions. So when I first heard that a company called Knightscope had created security robots that patrolled buildings, I was unnaturally moved.