PARIS--(BUSINESS WIRE)--PIQ, a leading French start-up in sports wearables today unveiled a breakthrough innovation with the introduction of a genuine Artificial Intelligence interface dedicated to sports activities. After 2 years of R&D and €13m investments, PIQ's 50 engineers developed a revolutionary technology, protected by 10 international patents allowing to identify athletes' Winning Factors, highlighting the key strength they should leverage on to succeed. From a world where connected sports were limited to the capture of basic data, PIQ's two cutting edge innovations are opening new horizons to the Sport Wearables industry: The combination of GAIA and PIQ ROBOTTM enables athletes to identify their Winning Factors, highlighting the key strength they should leverage on to succeed. GAIA – GAIA is the first Artificial Intelligence system that autonomously understands sports movement. GAIA is capable of breaking down and analyze sports movements via specific motion-capture algorithms.

Spotify is looking to make people better, as well as more entertained. The company has launched a new partnership with Headspace, an app meant to help people live "healthier and happier lives". In the new deal, people will be able to suscribe to both of the services for a reduced price. Together, the two apps will sell for £14.99 -- or roughly the same price in other currencies -- rather than the £10 or so that each of them costs. A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

It's the holiday season, which means it's time for retailers to bombard you with sales, special offers, and other attempts to sell you things. For many people, the Black Friday-to-Christmas shopping rush will lead to Amazon. It makes sense: The e-commerce giant sells a whole lot of products and makes it very easy to buy them. Many times, those products are less expensive than they are elsewhere. Though most people seem to be satisfied with the Amazon shopping experience, certain aspects of it can still be misleading.

Would you trust an artificial intelligence to fly an armed combat jet? Software called ALPHA is being used to fly uncrewed jets in simulations and could one day help pilots in real-world missions. ALPHA's developers claim, that unlike many AI systems, its behaviour can be verified at each step, meaning it won't act unpredictably. ALPHA was developed by Psibernetix in Ohio as a training aid for the US air force. It was originally designed to fly aircraft in a virtual air combat simulator, but has now been turned into a friendly co-pilot system that can help human pilots using the simulator.

Deep belief nets are one of the most exciting recent developments in artificial intelligence. The structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a'thought process' that is capable of learning abstract concepts built from simpler primitives. A typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting. This book presents the essential building blocks of the most common forms of deep belief nets. At each step the text provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards.

To stay competitive in a digital economy, businesses increasingly need to move beyond simple reporting and descriptive analytics to a more predictive approach that puts artificial intelligence (AI) strategies to work to engage with customers in new ways. So how can you find a practical way to start applying AI in your business? One path forward follows three steps: leverage predictive models to improve how you engage with customers, put machine learning to work to improve those models, and then validate your models. In this post, I will focus on the validation of predictive models First let me provide a quick overview of predictive analytics and machine learning, and explain why validation is important when you apply these approaches. Predictive analytics is about using algorithms to predict the result of a measurement that you can't make, based on measurements that you can make.

With automation and IT working together to take over routine tasks, IT workers can devote more time to innovation. Don't forget, though, that adding automation to the mix doesn't mean that IT and automation work in silos. As KPMG told Network World, the co-existence between human employees and cognitive systems is creating a new class of digital labor that can enhance human skills and expertise, allowing employees to innovate constantly. Before diving into what this means to IT, let's examine the underlying concepts. Machine learning is a branch of artificial intelligence (AI) that uses data, algorithms, and known outcomes to build systems that learn and adapt without human input.

Google is on a spree to strengthen its cloud offerings after it combined its cloud and corporate software divisions under a single entity. This time around, Google has acquired yet another firm called Qwiklabs that deals in end-to-end cloud platform services and training on how to use them. The acquisition will provide a lab learning on Google's Cloud Platform, G Suite and other products for the developers. The acquisition deal amount has not been disclosed. Separately, it invested in AI research at the Montreal Institute for Learning Algorithms, and is also opening an AI research group at its Montreal office.

It's become clear that the algorithms Facebook and Google designed to deliver news to their users have failed. But while fake news is a headache for those tech giants right now, the underlying research question--whether and how machines tell truth from lies on the internet--is one that will persist as long as the world wide web stays an open forum. Facebook and Google's sizable machine learning divisions have created algorithms that effectively surface information that users want to see. But they've been unable to actually understand or vet that info--and in fact, experts across the tech industry say it's unrealistic to expect any AI or machine learning algorithm to do this task well. All our best efforts so far are built on research in natural language processing, which teaches AI to read a piece of text, understand the concepts within, and provide insight about its meaning. "Modern machine learning for natural language processing is able to do things like translate from one language to another, because everything it needs to know is in the sentence its processing," says Ian Goodfellow, a researcher at OpenAI.

Amazon, Baidu, Facebook, Google and Microsoft, among other technology companies, have been investing heavily in artificial intelligence and related disciplines like machine learning because they see the technology enabling services that become a source of revenue. Consultancy Accenture earlier this week quantified this enthusiasm, predicting that AI "could double annual economic growth rates by 2035 by changing the nature of work and spawning a new relationship between man and machine" and by boosting labor productivity by 40 per cent. Certainly things could work out well for Accenture, which a day later announced a partnership with Google to help companies deploy Google technology like machine learning. It's as if the global services firm has a stake in the future it foresees. But the machine learning algorithms underpinning this harmonious union of people and circuits aren't secure. In a paper [PDF] presented in August at the 25th Annual Usenix Security Symposium, researchers at École Polytechnique Fédérale de Lausanne, Cornell University, and The University of North Carolina at Chapel Hill showed that machine learning models can be stolen and that basic security measures don't really mitigate attacks.