The latest in 3D Printing and Bionics....
Robots Will Transform Fast Food
That might not be a bad thing.
Visitors to henn-na, a restaurant outside Nagasaki, Japan, are greeted by a peculiar sight: their food being prepared by a row of humanoid robots that bear a passing resemblance to the Terminator. The “head chef,” incongruously named Andrew, specializes in okonomiyaki, a Japanese pancake. Using his two long arms, he stirs batter in a metal bowl, then pours it onto a hot grill. While he waits for the batter to cook, he talks cheerily in Japanese about how much he enjoys his job. His robot colleagues, meanwhile, fry donuts, layer soft-serve ice cream into cones, and mix drinks. One made me a gin and tonic.
H.I.S., the company that runs the restaurant, as well as a nearby hotel where robots check guests into their rooms and help with their luggage, turned to automation partly out of necessity. Japan’s population is shrinking, and its economy is booming; the unemployment rate is currently an unprecedented 2.8 percent. “Using robots makes a lot of sense in a country like Japan, where it’s hard to find employees,” CEO Hideo Sawada told me.
Sawada speculates that 70 percent of the jobs at Japan’s hotels will be automated in the next five years. “It takes about a year to two years to get your money back,” he said. “But since you can work them 24 hours a day, and they don’t need vacation, eventually it’s more cost-efficient to use the robot.”
This may seem like a vision of the future best suited—perhaps only suited—to Japan. But according to Michael Chui, a partner at the McKinsey Global Institute, many tasks in the food-service and accommodation industry are exactly the kind that are easily automated. Chui’s latest research estimates that 54 percent of the tasks workers perform in American restaurants and hotels could be automated using currently available technologies—making it the fourth-most-automatable sector in the U.S.
The robots, in fact, are already here. Chowbotics, a company in Redwood City, California, manufactures Sally, a boxy robot that prepares salads ordered on a touch screen. At a Palo Alto café, I watched as she deposited lettuce, corn, barley, and a few inadvertently crushed cherry tomatoes into a bowl. Botlr, a robot butler, now brings guests extra towels and toiletries in dozens of hotels around the country. I saw one at the Aloft Cupertino......
Have now thanks.....Has anyone checked out the AlphaZero vs Stockfish chess match? Pretty astonishing tstl.
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
The game of chess is the most widely-studied domain in the history of artificial intel- ligence. The strongest programs are based on a combination of sophisticated search tech- niques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. In contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go, by tabula rasa reinforce- ment learning from games of self-play. In this paper, we generalise this approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains. Starting from random play, and given no domain knowledge except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a world-champion program in each case.
.......We also analysed the relative performance of AlphaZero’s MCTS search compared to the state-of-the-art alpha-beta search engines used by Stockfish and Elmo. AlphaZero searches just 80 thousand positions per second in chess and 40 thousand in shogi, compared to 70 million for Stockfish and 35 million for Elmo. AlphaZero compensates for the lower number of evalu- ations by using its deep neural network to focus much more selectively on the most promising variations – arguably a more “human-like” approach to search, as originally proposed by Shan- non (27). Figure 2 shows the scalability of each player with respect to thinking time, measured on an Elo scale, relative to Stockfish or Elmo with 40ms thinking time. AlphaZero’s MCTS scaled more effectively with thinking time than either Stockfish or Elmo, calling into question the widely held belief (4, 11) that alpha-beta search is inherently superior in these domains.3
Finally, we analysed the chess knowledge discovered by AlphaZero. Table 2 analyses the most common human openings (those played more than 100,000 times in an online database of human chess games (1)). Each of these openings is independently discovered and played frequently by AlphaZero during self-play training. When starting from each human opening, AlphaZero convincingly defeated Stockfish, suggesting that it has indeed mastered a wide spec- trum of chess play.
Thanks for that. It's probably a lot more complicated than I thought initially and while it might not be SkyNet, still a very nifty piece of software.https://www.digit.in/machine-learni...an-make-phone-calls-on-your-behalf-40951.html
Found this article interesting, explaining the basics of the underlying technology... even if it did end on a sad attempt at a doomsayer note!