Flying can be exhausting when you're a tiny, bee-sized robot, but researchers from Harvard have created a new way to let little winged bots take a break. Using static electricity, robots no bigger than a quarter can latch onto the underside of any flat surfaces, a process that uses between 500 and 1,000 times less power than flying. In a study published in this week's issue of Science, researchers say this new perching ability could be key to creating insect-sized aerial robots that can help with a long-term observational tasks -- traffic control, to search-and-rescue. The mechanism was developed by researchers from Harvard for the RoboBee: a tiny flying robot first unveiled by a team from the university in 2013. The RoboBee weighs just 0.08 grams (that's 31 times lighter than a penny), and has a pair of tiny wings that can beat up to 120 times per second.

It's not easy to put the intelligence in artificial intelligence. Current machine learning techniques generally rely on huge amounts of training data, vast computational resources, and a time-consuming trial and error methodology. Even then, the process typically results in learned concepts that aren't easily generalized to solve related problems or that can't be leveraged to learn more complex concepts. The process of advancing machine learning could no doubt go more efficiently--but how much so? To date, very little is known about the limits of what could be achieved for a given learning problem or even how such limits might be determined.

When AlphaGo, Google's artificial intelligence program, defeated champion Go player Lee Sedol earlier this year, everyone praised its advanced software brain. But the program, developed by Google's DeepMind research team, also had some serious hardware brawn standing behind it. The program was running on custom accelerators that Google's hardware engineers had spent years building in secret, the company said. With the new accelerators plugged into AlphaGo's servers, the program could recognize patterns in its vast library of game data faster than it could with standard processors. The increased speed helped AlphaGo make the kind of quick, intuitive judgments that have escaped other computers trying to conquer the game.

Much of the technology that underpins these systems is shared among the industry. A handful of companies like Bosch, Delphi, and Mobileye provide sensors, control units, and even algorithms to car makers, who then integrate and refine those systems. Depending on the make of car, these advanced driver assistant systems--ADAS in industry speak--might be called Traffic-Aware Cruise Control and Autosteer (Tesla), IntelliSafe Assist and Pilot Assist (Volvo), Distronic Plus with Steering Assist (Mercedes-Benz), Adaptive Cruise Control with Lane Assist and Traffic Jam Assist (Audi), and so on. But all of them work on the same basic principles. A fusion of sensors identify the lane markings on the road, the cars around you, and now even road signs like speed limits or school zones, and use this information to maintain your speed and a safe distance to those other cars.

On May 10, the world-class admin behind John Chambers's success was honored with one of the top awards in her field: Debbie Gross received the Colleen Barrett Award for Administrative Excellence. Clearly, Debbie is a force. This CNBC story gives us a peek into her life keeping John at the top of his game. People are rightly saying she's a role model for next-generation administrators. But what people aren't saying is that some next-gen admins are made not of flesh and blood like Debbie but of compute cycles.

In the'Infrastructure as a Service' game, Amazon is currently dominant. They have a huge number of loyal customers of various sizes and business types who have been using their framework, Amazon Web Services, for years. Their most recent threat, however, may be the forthcoming artificial intelligence revolution. Specifically, they may see some customers who want to use A.I. applications jump ship to other providers who are more A.I. friendly. Google, for example, recently equipped their public cloud servers with special in-house processors called Tensor Processing Units.

Google has developed some awesome mobile applications that realize visual and audio recognition. Google just recently announced an open source Natural Language Understanding (NLU) system called SyntaxNet. This NLU system is built upon Google's TensorFlow, an open source neural network framework. Google has been able to achieve an overall 90 percent accuracy rate with their system. This is quite an accomplishment from just ten years ago, where part of speech tagging consisted of simply identifying entity extraction (verbs, nouns, etc.).

Spot-checking is a way of discovering which algorithms perform well on your machine learning problem. You cannot know which algorithms are best suited to your problem before hand. You must trial a number of methods and focus attention on those that prove themselves the most promising. In this post you will discover 6 machine learning algorithms that you can use when spot checking your classification problem in Python with scikit-learn. You cannot know which algorithm will work best on your dataset before hand.

So now that we've got all of this basic terminology out of the way and you can see what machine learning and intelligence are really all about, we can start thinking of the possibilities from a practical perspective. It should come as no surprise that ML is already in widespread use. One popular use case is fraud detection in financial transactions, and the industry is only getting started with the possibilities. Crooks can get quite creative when it comes to gaming the system, and this why we need intelligent systems that continually monitor people's buying behavior. The easy detections are the ones where there is an obvious outlier in the data.

Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. Because of new computing technologies, machine learning today is not like machine learning of the past.