The Google DeepMind system significantly improved the power efficiency of the Google datacenter, via tweaks to how servers were run and the operation of power and cooling equipment. While the traditional approach to minimizing power consumption was to run as few cooling systems as possible, the AI instead recommended running all the systems at lower power levels. The difference in datacenter power usage when Google turned the machine learning recommendations on and off. To streamline that training process Google built it own specialized chips, known as Tensor Processing Units (TPUs), which accelerate the rate at which useful machine-learning models can be built using Google's TensorFlow software library.
Rotten Tomatoes vice president Jeff Voris, middle, with senior editor Grae Drake, right, are filmed by creative director Jimmy Johenning at the Beverly Hills offices of the review aggregation website. Rotten Tomatoes vice president Jeff Voris, middle, with senior editor Grae Drake, right, are filmed by creative director Jimmy Johenning at the Beverly Hills offices of the review aggregation website. How Rotten Tomatoes became Hollywood's most influential -- and feared -- website On a recent Wednesday morning, the staff of Rotten Tomatoes gathers in a Beverly Hills office, laptops open -- steeling themselves for the next onslaught of reviews for Hollywood's biggest upcoming movies. That means the vast majority of critics liked the new 20th Century Fox movie -- and the $150-million "Apes" sequel gets the official "certified fresh" label on the movie-rating web site.
Consider the recent comment by Eric Schmidt, executive chairman of Google parent company Alphabet: "The largest taxi company has no taxis, that's Uber. The largest movie theatre has no movie theatres, that's Netflix." The previous miniseries examined who would be most impacted by job destruction in the AI revolution. While many fear that the magnitude of AI's job destruction will be great, augmentation actually provides us with reason for optimism on this front.
Partly about putting the record straight, partly about the workings of Deep Blue and partly musings on the nexus of man and machine, Kasparov's book is readable and worth reading. In a period when machine learning has become all the rage, it is also interesting to be reminded of the utility of something as basic (these days) as tree search, especially as enhanced and nuanced as it was in Deep Blue.
AI trained to win at poker games learned to bluff, handling missing and potentially fake, misleading information. Machine learning (ML), a subset of AI, make machines learn from experience, from examples of the real world: the more the data, the more it learns. Each method might make different errors, so averaging their results can win, at times, over single methods. So it should be the "smaller" AI to claim that the human brain as not real intelligence, but only brute force computation.
Data collected, such as players' vital stats and movements in training and in play on game day are being analyzed to enhance player performance and match strategy. And by studying patterns of play and player movements, coaches can reconfigure play strategy to make use of each player's strengths and offset their weaknesses to improve overall team performance. Another application is the WASP (Winning and Scoring Prediction), which has used machine learning techniques that predict the final score in the first innings and estimates the chasing team's probability of winning in the second innings. The second innings model estimates the probability of winning as a function of balls and wickets remaining, runs scored to date, and the target score.
Last week, investment bank Jefferies released a report warning shareholders not to expect IBM's investments in AI to repay themselves; Watson, it said, risks being eclipsed by competing AI platforms from Google, Amazon, and Microsoft. In fact, like all the AI systems in use today, Watson needs to be carefully trained with example data to take on a new kind of problem. Microsoft CEO Satya Nadella has made so-called "cognitive services" a central part of his effort to build up Microsoft's cloud business. The Mountain View juggernaut has even set up a unit of engineers that work with cloud customers to build up machine learning and AI projects, a model with echoes of IBM's own services business.
Today's smart computers can beat board game champions, master video games, and learn to recognize cats. No wonder artificial intelligence has captured the imaginations of business and IT leaders. And indeed, AI is starting to transform processes in established industries, from retail to financial services to manufacturing. But an organization's success in this area depends on its ability to capture, prepare, and analyze data strategically and effectively.
Seeing this move of the "very secretive" company, we can say that Apple has now started opening up with the world. This new move shows that the company is working on bridging the gap that it has created in these years. And, this not only helps researchers and engineers working in this domain but it will help the company as well because now, with direct interaction with scholars, they will get honest feedback and thousands of different kinds of queries, which eventually will need more brainstorming and lead towards a better outcome.
"A lot of companies working on AI use games to build intelligent algorithms because there's a lot of human-like intelligence capabilities that you need to beat the games," Maluuba program manager Rahul Mehrotra explains in the story, noting that the variety of situations you can encounter while playing the games makes them a good testing ground. That divide between the top agent's egalitarian programming and each individual agent's individual desire to achieve its specific result or collect its specific pellet regardless of the obstacles or ghosts in the way, proved to be the algorithm's secret sauce. "There's this nice interplay between how they have to, on the one hand, cooperate based on the preferences of all the agents, but at the same time each agent cares only about one particular problem," Maluuba research manager Harm Van Seijen says in the story. "It really enables us to make further progress in solving these really complex problems," research manager Van Seijen says.