Advanced analytics engines will likely require significant computing power. These systems and their CPUs will differ from traditional servers. An Intel x86 or IBM architecture may be supplemented by parallel graphics processors from Nvidia or AMD, or programmable logic chips from Altera or Xilinx within these engines. Universities and cloud vendors such as Google are the primary users. Big data, security, image identification, natural language processing and virtual reality may be among the target applications.

Instagram is completely down, leaving people unable to use either its app or website. Visitors to the website see a message reading "5xx server error" and nothing else. App users will see a message saying that the app "couldn't refresh feed" – though the problem looks temporary, it isn't actually possible to get new pictures at all. It isn't clear what the source of the problem is or if it will be fixed any time soon. Connected company president Shigeki Tomoyama addresses a press briefing as he elaborates on Toyota's "connected strategy" in Tokyo.

Roboticists are putting a tremendous amount of time and effort into finding the right combination of sensors and algorithms that will keep their drones from smashing into things. It's a very difficult problem: With a few exceptions, you've got small platforms that move fast and don't have the payload capability for the kind of sensors or computers that you really need to do real-time avoidance of things like trees or powerlines. And without obstacle avoidance, how will we ever have drones that can deliver new athletic socks to our doorstep in 30 minutes or less? At the University of Pennsylvania's GRASP Lab, where they've been working very very hard at getting quadrotors to fly through windows without running into them, Yash Mulgaonkar, Luis Guerrero-Bonilla, Anurag Makineni, and Professor Vijay Kumar have come up with what seems to be a much simpler solution for navigation and obstacle avoidance with swarms of small aerial robots: Give them a roll cage, and just let them run into whatever is in their way. This kind of "it'll be fine" philosophy is what you find in most small flying insects, like bees: They don't worry all that much about bumbling into stuff, or each other, they just kind of shrug it off and keep on going.

Entry Point: Data - Using Python's sci-packages to prepare data for Machine Learning tasks and other data analyses [IPython nb]

The IoT is a $410 billion opportunity just waiting for the healthcare sector and other industries to leverage their big data. It is likely that the Internet of Things, along with blockchain technology and artificial intelligence, will start to come into their own as the decade draws to a close, opening up the potential for drastic improvements in care quality, the patient experience, and providers' ability to make informed, actionable decisions.

The growing number of services that run machine learning algrithms under the hood will now be able to tap the popular open-source database Redis' capabilities much more easily than before. Typically used to cache applications' most frequently accessed information, Redis has received a new extension today that brings integration with Spark ML. Organizations that employ that framework to power their machine learning projects can now easily load models into Redis to improve their performance. Initial benchmark tests indicate that the system provides five to 10 times lower latency than a standard Spark implementation when performing certain tasks such as categorizing records. The assessment was carried out by Redis Labs Inc., the startup behind the new module and the database's main commercializer.

Does Machine Intelligence Reflect an Engineer's Internal Bias? Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

Imagine being paralysed and having an implanted microchip that could action a message from your brain to move your prosthetic arm. Or a diagnostic system that could pick up Alzheimer's a decade before you develop any symptoms. Or a 3D printing machine that could print a pill with a combination of drugs tailored just for you. The faculty chair of exponential medicine at the Silicon Valley-based Singularity University, no one could be more serious – or ambitious – about the revolutionary impact that digital technologies will have on the future of healthcare. The internet of things, constant connectivity, ever cheaper hardware, big data, machine learning: Kraft's list of converging "meta-trends" goes on.

How Can Lean Six Sigma Help Machine Learning? Does Machine Intelligence Reflect an Engineer's Internal Bias? Can Natural Language Processing Boost Clinical Documentation? Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term.

How Can Lean Six Sigma Help Machine Learning? Does Machine Intelligence Reflect an Engineer's Internal Bias? Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.