By bringing artificial intelligence to its wearable tech, PIQ is looking to improve on its design. Until now, sports wearables have largely boiled down to high-tech sensors recording basic data. With the addition of GAIA Intelligence, the company will be able to make the PIQ Robot that much better at improving your performance. Both the PIQ Robot and GAIA Intelligence give coaches and athletes the ability to analyze every movement during a game or match. This data can then be compared with any previous performances as well as with a community's performance overall.

When an app claims to be powered by "artificial intelligence" it feels like you're in the future. What does that really mean, though? We're taking a look at what buzzwords like AI, machine learning, and neural networks really mean and whether they actually help improve your apps. Just recently, Google and Microsoft both added neural network learning to their translation apps. Google said it's using machine learning to suggest playlists. Todoist says it's using AI to suggest when you should finish a task.

Tens of thousands of people are calling on Parliament to recall the "most extreme spying powers ever seen". The Investigatory Powers Bill was just passed through the House of Lords and so is now just weeks away from becoming law. But signatories to a new petition hope that process can be stopped, forcing lawmakers to keep the new powers from being published. The new law forces internet companies to keep a full browsing history of all of their users and give it up to a huge range of government agencies if they are asked. It also gives spies unprecedented powers to read people's messages, as well as forcing technology companies like Apple to hack into their own phones if they are asked.

Japan plans to build a super-efficient computer that could vault it to the top of the world's supercomputer rankings by the end of next year. With a processing capacity of 130 petaflops, the planned computer would outperform the current world leader, China's Sunway TaihuLight, which delivers 93 petaflops. One petaflop is one million billion floating-point operations per second. Japan's National Institute of Advanced Industrial Science and Technology (AIST) isn't just aiming to build the world's fastest supercomputers, it also wants to make one of the most efficient. It is aiming for a power consumption of under 3 megawatts -- a staggering figure, given that Japan's current highest entry in the Top500 supercomputer list, Oakforest-PACS, delivers one-tenth the performance (13.6 petaflops) for the same power.

As the interest in data science, predictive analytics and machine learning has grown in direct correlation to the amount of data that is now being captured by everyone from start ups to enterprise organisations, endjin are spending increasing amounts of time working with businesses who are looking for deeper and more valuable insights into their data. As such, we've evolved a pragmatic approach to the machine learning process, based on a series of iterative experiments and relying on evidence-based decision making to answer the most important business questions. In this series of posts, we're going to look at what machine learning really is (and isn't), the endjin process and some examples of how and where we've put it to use. So what do machine learning and data science actually mean? My previous post argued that there's no mad science or dark art at play, just a pragmatic process based around trial and error with statistics.

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With the advent of photography, a tiny fraction of 19th-century scientists believed they could develop methods of accurately identifying criminals by their facial features. While their hypotheses were eventually discredited, new artificial intelligence technology suggests their claims might've been valid after all. Xiaolin Wu and Xi Zhang from Shanghai Jiao Tong University in China have resurrected this facial recognition tradition and built a neural network that can supposedly pick out criminals by simply looking at their faces. To accomplish this, the researchers used an array of machine-vision algorithms to examine a series of facial juxtapositions between photos of criminals and non-criminals with the goal of finding out whether a neural network can reliably tell them apart. In the process, the scientists fed the neural network a total of 1856 ID photos of men with no facial hair between the ages of 18 and 56, only half of whom had a criminal past.

TOKYO (Reuters) - Japan plans to build the world's fastest-known supercomputer in a bid to arm the country's manufacturers with a platform for research that could help them develop and improve driverless cars, robotics and medical diagnostics. The Ministry of Economy, Trade and Industry will spend 19.5 billion yen ($173 million) on the previously unreported project, a budget breakdown shows, as part of a government policy to get back Japan's mojo in the world of technology. The country has lost its edge in many electronic fields amid intensifying competition from South Korea and China, home to the world's current best-performing machine. In a move that is expected to vault Japan to the top of the supercomputing heap, its engineers will be tasked with building a machine that can make 130 quadrillion calculations per second - or 130 petaflops in scientific parlance - as early as next year, sources involved in the project told Reuters. At that speed, Japan's computer would be ahead of China's Sunway Taihulight that is capable of 93 petaflops.

Machine Learning systems are complex. At their core, they ingest data in a certain format, to build models that are able to predict the future. A famous example in the industry is identifying fragile customers, who may stop being customers within a certain number of days (the "churn" problem). These predictions only become valuable when they are used to inform or to automate decisions (e.g. which promotional offers to give to which customers, to make them stay). In many organizations, there is often a disconnect between the people who are able to build accurate predictive models, and those who know how to best serve the organization's objectives.

Your smartphone may soon be able to give you an honest answer, thanks to a new machine learning algorithm designed by engineering researchers at the University of Toronto. The researchers trained their algorithm to identify people's hair in photographs--a much more challenging task for computers than it is for humans. The team designed an algorithm that learns directly from human instructions, rather than an existing set of examples, and outperformed conventional methods of training neural networks by 160 percent. More surprisingly, their algorithm also outperformed its own training by nine percent--it learned to recognize hair in pictures with greater reliability than that enabled by the training, marking a significant leap forward for artificial intelligence. "Our algorithm learned to correctly classify difficult, borderline cases--distinguishing the texture of hair versus the texture of the background," said researcher Parham Aarabi.