When it comes to the world of "big data", whoever has the most data has an advantage. And whoever has the most and best data scientists with the best tools to crunch that data usefully, build on that advantage. That's a key reason CIOs now look at all the data their companies collect and enlist data scientists and new tools to try to wring competitive advantage from what they learn. Entire economic blocs and countries are also upping the investment ante as they seek to slake their thirst for data and the riches machine learning and other AI technologies can bring. For example, the European Union's Digital Market Commission in April said it would increase its artificial intelligence R&D spending to $1.8 billion.

Aaeon has launched an "UP AI Edge" family of products that builds on a new Apollo Lake based "UP Core Plus" SBC with stacking AI companion boards based on the Movidius Myriad 2 or Intel Cyclone 10GX plus add-ons including a quad-GbE board and a camera. Aaeon Europe quickly met its modest $11K Kickstarter goal for the new UP AI Edge ecosystem, which builds on its UP board products and community. The centerpiece is a new UP Core Plus SBC, although the official, Ubuntu-equipped UP AI Edge development package uses the larger, more feature-rich UP Squared SBC. The Ubuntu stack also includes Intel's OpenVINO computer vision toolkit, which is optimized for the Myriad 2. Also available is the Arduino Create development environent, an Open CL/ Movidius Driver, Intel System Studio, and cloud connectors for Microsoft Azure, Amazon AWS, Google Cloud, and IBM Bluemix. You can also use the Neural Compute Stick SDK available for the Myriad 2 equipped Intel Movidius Neural Compute Stick for "rapid prototyping, validation and deployment of Deep Neural Network (DNN) inference applications at the edge," says Aaeon.

It was a week of near misses and closer hits than the tech industry probably would have wanted. Amazon's Alexa "accidentally" recorded more than a few customers' private conversations, Apple's iPhones turned out to be bendier than anticipated, and that PUBG chicken dinner of yours wound up being harder fought than anybody had previously thought. Nobody: The number of people who saw that coming. Kidding, of course, even the greenest of horned labor attorney could tell you that Tesla CEO Elon Musk was simultaneously talking out of his tailpipe and putting his entire automotive empire in legal jeopardy when he started running his mouth on Twitter the other night. This is why you don't let the guy who owns the business do the ads for said business.

LONDON, UK – Businesses and developers must begin to take their ethical responsibilities seriously as AI impacts more and more walks of life, Microsoft CEO Satya Nadella argued at the firm's'Leading Transformation with AI' event this week in Millbank Tower. The event, which brought together Microsoft executives, their enterprise customers, press, and AI experts, focused on providing a comprehensive overview of Microsoft's AI roadmap for the coming years. Speaking on the back of a visit to the Cambridge research lab, where many of the company's AI efforts are focused, Nadella outlined how and why AI will form the backbone for Microsoft's products and services roadmap in the years to come. "What is perhaps at the core of the experiences [and applications] we create, as well as our platform itself, is AI," Nadella told the audience. "AI is the runtime that is going to shape all of what we do going forward in terms of the applications as well as the platform advances."

Artificial Intelligence, or AI, is empowering people with physical disabilities, allowing them to take charge of their own lives but it's also having a surprising impact on people with neuro-diverse conditions like autism. It's easy to generalise about people on the autism spectrum; they like consistency, take things literally and like routine. They are built to provide consistency. They don't (yet) understand sarcasm and they like logic, a lot. But it's important to remember that although people on the autism spectrum will share certain difficulties, everyone's experience of the condition will be very different.

When it comes to artificial intelligence, NASA and other space agencies are nowhere near building a "Terminator" in space. So, you can rest easy -- Arnold Schwarzenegger isn't about to hunt you down because you're leading a rebellion against the machines. Artificial intelligence is in its infancy, but scientists have used it to find alien planets, classify galaxies and create spacecraft capable of dodging debris. But some critics, like SpaceX founder Elon Musk and the renowned physicist Stephen Hawking (recently deceased), have warned that artificial intelligence could be dangerous if left unchecked. While AI is a popular theme in space exploration, its use (and misuse) has been discussed by several people in other applications as well.

Doppler spectroscopy (also known as the radial-velocity method, or colloquially, the wobble method) is an indirect method for finding extrasolar planets and brown dwarfs from radial-velocity measurements via observation of Doppler shifts in the spectrum of the planet's parent star. Otto Struve proposed in 1952 the use of powerful spectrographs to detect distant planets. He described how a very large planet, as large as Jupiter, for example, would cause its parent star to wobble slightly as the two objects orbit around their center of mass.[2] He predicted that the small Doppler shifts to the light emitted by the star, caused by its continuously varying radial velocity, would be detectable by the most sensitive spectrographs as tiny red shifts and blue shifts in the star's emission. However, the technology of the time produced radial-velocity measurements with errors of 1,000 m/s or more, making them useless for the detection of orbiting planets.[3]

Roundup Here's a quick roundup to keep you updated on what's been happening in AI, beyond what we've already covered, for your long weekend. It includes news of Samsung and Qualcomm setting up new AI research teams, why human radiologists are still better than machines and support for Amazon's Keras-MXNet backend. Hold your horses AI radiologists People are quick to believe that machines will soon replace radiologists because they think computers are much better at spotting abnormalities like tumors or clots in medical scans. But results reported by Stanford University shows that radiologists still trump AI. A group of researchers built a large convolutional neural network (CNN) with 169 layers to predict the probability of an abnormality appearing in a particular scan from the MURA (musculoskeletal radiographs) dataset. It collects 40,561 scans of the elbow, finger, forearm, hand, humerus, shoulder, and wrist of 12,173 patients.

It was barely three decades ago when the whole world was living in the fear of a full-blown nuclear war that could destroy anything and everything. The worst was just waiting to happen as the tensions between the superpowers - Soviet Union and the United States - had reached its peak during what we call the Cold War. One of the key reasons that the Cold War passed without the world descending into the havoc of a nuclear-powered third world war because of nuclear deterrence in the backdrop of a situation termed as MAD or'mutually assured destruction'. In any scenario of a potential first strike, either from the Soviets or the Americans, either side was aware of the fact that the other could and would retaliate, thus preventing an attack in the first place. However, things will not be the same if the military decides to incorporate artificial intelligence (AI) into their strategic decision making.

The hysteria about the future of artificial intelligence (AI) is everywhere. There seems to be no shortage of sensationalist news about how AI could cure diseases, accelerate human innovation and improve human creativity. Just looking at the media headlines, you might think that we are already living in a future where AI has infiltrated every aspect of society. While it is undeniable that AI has opened up a wealth of promising opportunities, it has also led to the emergence of a mindset that can be best described as "AI solutionism". This is the philosophy that, given enough data, machine learning algorithms can solve all of humanity's problems.