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.
Give a man a fish, the old saying goes, and you feed him for a day--teach a man to fish, and you feed him for a lifetime. Same goes for robots, with the exception that robots feed exclusively on electricity. The problem is figuring out the best way to teach them. Typically, robots get fairly detailed coded instructions on how to manipulate a particular object. But give it a different kind of object and you'll blow its mind, because the machines aren't great yet at learning and applying their skills to things they've never seen before.
As the number of robots cohabiting the world with us increases, so too does our need to examine our relationship with them. Should we feel bad for robots? Do we need to treat them with respect? It turns out flipping the off switch on Johnny Five and his electronic siblings isn't so easy when robots can beg for their own lives. We talk all about it on this week's episode of the podcast.
DeepMind has shaken the world of Reinforcement Learning and Go with its creation AlphaGo, and later AlphaGo Zero. It is the first computer program to beat a human professional Go player without handicap on a 19 x 19 board. It has also beaten the world champion Lee Sedol 4 games to 1, Ke Jie (number one world ranked player at the time) and many other top ranked players with the Zero version. The game of Go is a difficult environment because of its very large branching factor at every move which makes classical techniques such as alpha-beta pruning and heuristic search unrealistic. I will present my work on reproducing the paper as closely as I could.
Next week, scientists working on artificial intelligence (AI) and games will be watching the latest human-machine matchup. But instead of a single pensive player squaring off against a computer, a team of five top video game players will be furiously casting magic spells and lobbing (virtual) fireballs at a team of five AIs called OpenAI Five. They'll be playing the real-time strategy game Dota 2 at The International in Vancouver, Canada, an annual e-sports tournament that draws professional gamers who compete for millions of dollars. In 1997, IBM's Deep Blue AI bested chess champion Garry Kasparov. In 2016, DeepMind's AlphaGo AI beat Lee Sedol, a world master, at the traditional Chinese board game Go.
DeepMind's AlphaGo Zero algorithm beat the best Go player in the world by training entirely by self-play. It played against itself repeatedly, getting better over time with no human gameplay input. AlphaGo Zero was a remarkable moment in AI history, a moment that will always be remembered. Move 37 in particular is worthy of many philosophical debates. You'll see what I mean and get a technical overview of its neural components (code animations) in this video.
AlphaGo, Google's AI becomes the best Go player in the world by winning three games against world number one, Ke jie. AlphaGo once battled other champions like Fan Hui and Lee Sedol, which allowed him to improve, in addition to millions of games played against himself. Twenty years ago, Deep Blue, IBM's supercomputer, defeated world champion Garry Kasparov with his algorithms and great computing power, sweeping all the gameplay on many shots. But faced with the game of Go, which has an immense number of possible combinations, the computing power is not enough, it is necessary to improve the algorithms. Two methods are used in AlphaGo: the Monte Carlo method and Deep Learning.
BEIRUT: The rise of Artificial Intelligence has grown and developed globally, owing to various advances in many fields. Although many people are still finding it hard to understand the virtual intelligence, others are taking in the challenge and bringing together ways to connect computers and humans, producing new AI material that somehow benefits many. With the inspiration of corralling several key ideas, Nicolas Zaatar and Charlie El Khoury have developed their own startup NAR – Next Automated Robot – whose mission was to transform drones from flying cameras to flying computers. "We thought, what if we use the drone as an inspector gadget?" The ideas they have combined are information, data, algorithm, uncertainty, computing, and finally, optimizing.
If you're totally stumped on a page of Where's Waldo and ready to file a missing persons report, you're in luck. Now there's a robot called There's Waldo that'll find him for you, complete with a silicone hand that points him out. Built by creative agency Redpepper, There's Waldo zeroes in and finds Waldo with a sniper-like accuracy. The metal robotic arm is a Raspberry Pi-controlled uArm Swift Pro which is equipped with a Vision Camera Kit that allows for facial recognition. The camera takes a photo of the page, which then uses OpenCV to find the possible Waldo faces in the photo.
Japan has an impending millennium bug problem. In the lead up to the turn of the millennium, few computers were able to properly represent the year 2000, leaving people worried about what would happen when midnight on New Year's eve struck. Now the same issues could arise in Japan when the current emperor steps down in April next year. The Japanese calendar is based on era names that coincide with the rule of its emperors.