There is universal proverb that roughly translates into "Empty vessels make the most noise." Some ascribe it to Plato, but having come from Punjab (India), I can safely confirm that the Punjabi translation means exactly that, and Punjab has as much to do with Plato as chicken tikka masala has to do with French cuisine. One of my favourite version of this proverb comes from Polish, which translated into English means, "The cow which moos a lot gives little milk." This pretty much reflects a certain philosopher from Oxford who without having any foundation in principles of engineering, let alone (proper) machine learning, seems to think himself as the leading authority to warn the world against the perils of machine learning. And for the last few years has busied himself making outrageous, pseudoscientific and shamanic claims to sell his book.

Predicting the future is an imprecise art. But after years of crying wolf, one thing is becoming clear: automation is set to change the world of work forever. "It's not easy to measure automation precisely, but anecdotal and economic evidence indicate we have every reason to believe it will move faster and faster," says Martin Ford, global thought leader on automation at work and author of Rise of the Robots: Technology and the threat of a jobless future. Ford, who will be speaking at the upcoming Creative Innovation Conference, first became interested in an automated future while running a small software company in the 1990s. Back then, software still came on CD ROMs that were shipped to customers – a job that, while tech based, still required a human touch.

Japanese Prime Minister Shinzo Abe, dressed as Super Mario, holds a red ball during the closing ceremony of the Rio 2016 Olympic Games in Rio de Janeiro. Japanese PM Shinzo Abe's show-stopping appearance at today's closing ceremony in Rio, dressed as iconic game character Super Mario, already sets the tone for what lies in store. Japan is known internationally for its technological innovations, so Tokyo 2020 organizers are aiming to launch ambitious tech projects that will boost the economy and wow crowds. Tourists staying next to the Olympic Village in Tokyo's Odaiba neighborhood can choose, for example, to hang out with robot helpers of all sizes and sorts that offer up tips on the best transport, food and entertainment options in Tokyo. And that won't be the only place they'll encounter their robotic counterparts.

SINCE nobody really knows how brains work, those researching them must often resort to analogies. A common one is that a brain is a sort of squishy, imprecise, biological version of a digital computer. But analogies work both ways, and computer scientists have a long history of trying to improve their creations by taking ideas from biology. The trendy and rapidly developing branch of artificial intelligence known as "deep learning", for instance, takes much of its inspiration from the way biological brains are put together. The general idea of building computers to resemble brains is called neuromorphic computing, a term coined by Carver Mead, a pioneering computer scientist, in the late 1980s.

Yoshinori Nomura felt like weeping. It was the morning of June 24, Brexit day, and markets were moving against him. It was the hedge fund manager's self-learning computer program that had placed the bet, selling Japanese stock-index futures before a sizable market advance. Nomura had anticipated a rally, but decided not to interfere, and his fund was paying the price. Then, in an instant, everything changed.

In 2004, Charles Lickel was eating in a dinner with some colleagues when he noticed that all of the patrons were rushing to the bar. Curious, he followed them to see what all the commotion was about. As it turned out, they were going to see Ken Jennings' historic six-month run on the game show, Jeopardy! Paul Horn, then director of IBM Research, had been bugging Lickel to come up with an idea for the company's next "grand challenge," Big Blue's tradition of tackling incredibly tough problems just to see if they can be solved. The last one drew wide attention when the firm's Deep Blue computer beat Garry Kasparov at chess in 1996.

Every year and in every sport, digital technology's role in officiating live gameplay expands. Innovations like MLB's Statcast and the NBA's SportVU systems have leveraged data to collect a wealth of information about all aspects of games, offering detailed feedback to help coaches and players improve performance and grant fans greater access. But applying the same technology to help referees make better calls has been much more controversial, in part because the stakes are so high. The decision on a tough call can be the difference between a win or a loss. Officiating is, of course, subjective, and therefore susceptible to human error.

The result of standardization (or Z-score normalization) is that the features will be rescaled so that they'll have the properties of a standard normal distribution with Standardizing the features so that they are centered around 0 with a standard deviation of 1 is not only important if we are comparing measurements that have different units, but it is also a general requirement for many machine learning algorithms. Intuitively, we can think of gradient descent as a prominent example (an optimization algorithm often used in logistic regression, SVMs, perceptrons, neural networks etc.); with features being on different scales, certain weights may update faster than others since the feature values play a role in the weight updates Other intuitive examples include K-Nearest Neighbor algorithms and clustering algorithms that use, for example, Euclidean distance measures – in fact, tree-based classifier are probably the only classifiers where feature scaling doesn't make a difference. In fact, the only family of algorithms that I could think of being scale-invariant are tree-based methods. Let's take the general CART decision tree algorithm. Without going into much depth regarding information gain and impurity measures, we can think of the decision as "is feature x_i some_val?"

If these tasks were part of a bigger project, this gets checked off as well, and I get to see a motivational quote as a reward. Since I keep all of that in Dropbox, it is available across all my computers, and I don't have to worry about platform-specific workarounds. I know, this sounds all weird, but if there really is a person who is interested in this, I can elaborate more and upload an example to GitHub in no time. This article certainly became longer than I intended it to be. You probably didn't read all of it, but I hope that you at least skipped forward to this last section!

IP Cores Designed for HSA Historically GPT has developed IP specifically for the China market. The company recently announced a range of new IP licensing offerings along with an enhanced geographical licensing program. With the company-wide adoption of HSA standards, GPT now licenses IP worldwide. All GPT processors include HSA support and the company is now offering world-class HSA-enabled processors to its customers. The HSA enabled IP core which is sampling now in silicon is a first implementation of GPT's 3-in-1 Unity architecture designed for multidimensional signal processing including image and video processing.