Editor's note: In this guest post, craft ai describe the thinking and implementation behind their winning entry in the VIVA Tech hackathon hosted by Samsung and Legrand. On July 2, the craft ai team woke up way too early for a Saturday to join fellow developers at the Samsung/Legrand booth at VIVA Tech and hack for a day. Our objective: Show that smart homes can offer a better user experience thanks to artificial intelligence--beyond smartphone remotes, complicated dashboards and manual scenarios! This is the tale of how we used craft ai in conjunction with Samsung ARTIK to make a few Legrand devices learn usage patterns and automate themselves. Managing the light in a house is one of our pet use cases at craft ai.

My cellphone died the other day and I was on the move and had no way to charge it. If at that moment someone had offered me one of those battery packs, I would have happily paid a premium. What if you could accurately predict when demand in your products was going to rise or fall, and change your pricing accordingly? What if you could predict future demand with incredible accuracy? And beyond that--what if that decision-making didn't require human intervention but was automated and could happen instantaneously?

New research, led by the University of Southampton, has demonstrated that a nanoscale device, called a memristor, could be used to power artificial systems that can mimic the human brain. Artificial neural networks (ANNs) exhibit learning abilities and can perform tasks which are difficult for conventional computing systems, such as pattern recognition, on-line learning and classification. Practical ANN implementations are currently hampered by the lack of efficient hardware synapses; a key component that every ANN requires in large numbers. In the study, published in Nature Communications, the Southampton research team experimentally demonstrated an ANN that used memristor synapses supporting sophisticated learning rules in order to carry out reversible learning of noisy input data. Memristors are electrical components that limit or regulate the flow of electrical current in a circuit and can remember the amount of charge that was flowing through it and retain the data, even when the power is turned off.

For example, Acme Motorcycles (a hypothetical electric motorcycle manufacturer) discovered via AI that if it could get a prospective customer into a showroom and get them on a test drive then there was strong chance they would purchase the bike. Velocity data sent from the motorcycle test-drive experience in real time to the dealer and then pushed into the CEP engine indicated that if a customer drove the bike at over 50 MPH then the likelihood of purchase increased 10%. When the customer returns from the test drive, if the dealer's analysis dashboard shows that the customer is in "neutral" state, then the dealer would be provided with a recommendation to advise the customer to go back on the road and try the bike on the highway.

Technology is advancing at record speed as innovations that were a foggy prediction came to life one after the other. This passing month, I decided to explore "future studies" and browsed the web for the latest advancements in the tech world, and especially AI, IT, and SaaS. An artificial intelligence agent developed by two Carnegie Mellon University computer science students has proven to be the game's ultimate survivor -- outplaying both the game's built-in AI agents and human players. The students, Devendra Chaplot and Guillaume Lample, used deep-learning techniques to train the AI agent to negotiate the game's 3-D environment, still challenging after more than two decades because players must act based only on the portion of the game visible on the screen. People have started noticing self-driving Uber cars in downtown San Francisco, fueling speculation the ridesharing company could soon be deploying autonomous vehicles for commercial use right where it all started, in the Bay Area.

A new age is dawning. Whether it is a wonderful one or a terrible one remains to be seen. Look around and the signs of dizzying technological progress are difficult to miss. Driverless cars and drones, not long ago the stuff of science fiction, are now oddities that can occasionally be spotted in the wild and which will soon be a commonplace in cities around the world. With a few flicks of a finger, we can use our phones to order up a meal, or a car, or a translation for a waiter's query in a foreign country. Gadgets such as the Amazon Echo are finding their way into living rooms, where they sit listening, ready to comply with a voice command. Just a few years ago, one could dismiss the digital age as consisting of little more than social networks and cat videos; no longer.

This topic contains 0 replies, has 1 voice, and was last updated by Michael DeVries 11 minutes ago. The Future of Surgery Is Robotic, Data-Driven, and Artificially Intelligent http://bit.ly/2dO7uDT How do you predict that robotics artificial intelligence (/AI) and Big Data will transform surgery and medicine in the future?

When I was young and foolish, I often wondered about why someone would chose to spend all that effort in driving a motorcycle, when they also had the option to chose a car. To me, it seemed as if the comfort and the safety offered by the latter was the only way to travel. After growing up and having experienced the thrill offered by a bike, the adrenaline rush when you are speeding at 80 miles on a level stretch of road, well, I feel even more thankful for the safety features integrated in cars, than ever. However, BMW is working on concept bike that may just change the way people like me, look at motorcycles. Cars are probably going to be way cooler in around a decade or so, thanks to the huge interest in autonomous and other technologies.

This course is about the fundamental concepts of machine learning, focusing on neural networks, SVM and decision trees. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example or we may construct algorithms that can have a very very good guess about stock prices movement in the market. In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together. The first chapter is about regression: very easy yet very powerful and widely used machine learning technique.

H2O Sparkling Water supports a wide array of algorithms, and as illustrated above it's easy to chain these functions together with dplyr pipelines. To learn more see the H2O Sparkling Water section.