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A lot of the excitement in AI has focused on deep learning, a machine learning technique that was popularized by a now famous 2012 Google project that used a giant cluster of computers to learn to identify cats in YouTube videos.
Deep learning is a descendent of neural networks, a technology that dates back to the 1940s. It was brought back to life by a combination of factors, including new algorithms, cheap parallel computation, and the widespread availability of large data sets.
There are a variety of new computing platforms currently in the gestation phase that will soon get much better — and possibly enter the growth phase — as they incorporate recent advances in hardware and software.
Although they are designed and packaged very differently, they share a common theme: they give us new and augmented abilities by embedding a smart virtualization layer on top of the world. Here is a brief overview of some of the new platforms: cars, drones, the internet of things, wearables, VR and AR.
The computing industry progresses in two mostly independent cycles: financial and product cycles.
Financial markets get a lot of attention. They tend to fluctuate unpredictably and sometimes wildly. The product cycle by comparison gets relatively little attention, even though it is what actually drives the computing industry forward.
Putting the phone on charge all night does not damage the battery.
Smartphones of today have lithium-ion batteries, and advanced technology that prevents overheating, overcharging and other issues that plagued earlier phones.
According to a new study, relying on caffeine to help you through your day can only get you so far.
Caffeine may help you stay awake to attend to a task, but it doesn't help to prevent procedural errors that can cause, for example, medical mistakes and car accidents.
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