If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Instead of relying on heat, this innovative device employs the Coanda jet-flow effect (which helps keep planes airborne) to wrap and style hair. It requires relearning how to coax your locks, but you can say goodbye to heat damage and singed foreheads. The first of Sonos's popular multiroom speakers to come loaded with Amazon's voice assistant, Alexa. "The best all-round music-focused smart speaker available in the UK," says the Guardian. This limited-edition version comes in five colours selected by Danish furniture designer Hay.
This week, the WIRED Transportation team highlighted (as we often do) a few exciting developments in self-driving cars. The Senate is finally considering self-driving car legislation, and might finalize it before the end of the year. An autonomous vehicle shuttle company bagged some new government contracts, and will open its six-seaters to members of the general public this month. Waymo, the putative leader in the space, finally launched its self-driving car service in metro Phoenix, Arizona. And then there were some asterisks.
Fifty years ago today, Doug Engelbart showed 2,000 people a preview of the future. Engelbart gave a demonstration of the "oN-Line System" at the Fall Joint Computer Conference in San Francisco on Dec. 9, 1968. The oN-Line System was the first hypertext system, preceding the web by more than 20 years. But it was so much more than that. When Engelbart typed a word, it appeared simultaneously on his screen in San Francisco and on a terminal screen at the Stanford Research Institute in Menlo Park.
A problem with training neural networks is in the choice of the number of training epochs to use. Too many epochs can lead to overfitting of the training dataset, whereas too few may result in an underfit model. Early stopping is a method that allows you to specify an arbitrary large number of training epochs and stop training once the model performance stops improving on a hold out validation dataset. In this tutorial, you will discover the Keras API for adding early stopping to overfit deep learning neural network models. How to Stop Training Deep Neural Networks At the Right Time With Using Early Stopping Photo by Ian D. Keating, some rights reserved. Callbacks provide a way to execute code and interact with the training model process automatically. Callbacks can be provided to the fit() function via the "callbacks" argument.
Bats – long vilified in fairytales or onscreen for sucking people's blood or being carriers of a bunch of scary diseases may help engineers take UAV technology to the next level. According to Science Nordic, researchers from the University of Lund in Sweden are examining slow-motion footage of bats flying to learn more about exactly how they move through the air. Bats fly in such a unique manner, it difficult to observe with the naked eye. According to the research team, not much is yet known about bats method of flight as they navigate the open skies. In order to learn more about how bats fly, the researchers trained bats to fly in wind tunnels (yes – really).
Using maps plotted by human employees, the AI-powered cleaners will placidly traverse the aisles, sweeping and buffing as they go--just as blue-aproned human employees used to do (and still will, in Walmart stores without an Auto-C, as the robots are called). Perhaps the most striking thing about these robot workers is how not-striking they are. Sci-fi movies suggest a future full of humanoid robots who unnerve us with their "uncanny valley" qualities. Now the future is coming into view, and it looks like a giant Roomba. It's easy to imagine walking absentmindedly past an Auto-C on a shopping trip without even registering its presence.
Machine learning is one of those hot technology categories that has lots of business and technology executives scrambling to see how their organizations can get in on the action. Done right, machine learning can help you create more effective sales and marketing campaigns, improve financial models, more easily detect fraud, and enhance predictive maintenance of equipment--to name a few. But machine learning can also go terribly wrong, making you regret that enthusiastic rush to adopt. Here are five ways machine learning can go wrong, based on the actual experience of real companies that have adopted it. They've shared their lessons so you can avoid the same failures.
At Amazon Web Services (AWS), we are continually innovating with new services and solutions. That's why we're excited to announce several new offerings from AWS Training and Certification to help AWS Partner Network (APN) Partners build new cloud skills and learn about the latest AWS services. Dive deep into the same ML curriculum we use to train Amazon's developers and data scientists. Choose from four role-based learning paths, with more than 30 digital ML courses and hands-on labs totaling 45 hours of training. Take our new AWS Certified Machine Learning – Specialty beta exam.
On the chilly October day the New York City subway opened in 1904, the marvel of engineering and grit was greeted with horns, steam sirens and stations overrun by thousands of revelers. "Fast Trains in Tubes," blared one headline. On Wednesday, 114 years later in sun-swept Arizona, the launch of the 21st-century equivalent came in a blog post and an email invitation. Google offshoot Waymo announced it is launching the nation's first commercial self-driving taxi service in this and other Phoenix suburbs. The 24/7 service, dubbed Waymo One, will let customers summon self-driving minivans by a smartphone app, a la Uber or Lyft.