Columnist Jennifer Jolly tries out Alexa on the Ford, peers at TVs held to walls by magnets, and tests a'smart bike'. There's the equivalent of some 43-football fields worth of space filled with gadgets at CES 2017. With that many tech toys to explore, they all start to blur together pretty quickly. Here's what I've seen so far that's made an impression. Ford is making it all possible with its SYNC 3 AppLink software, so you can use Alexa's voice commands to ask for directions, get a rundown of the top headlines, add milk to your shopping list, or catch the latest New York Times bestseller via audiobook.
The recent win of AlphaGo over Lee Sedol--one of the world's highest ranked Go players--has resurfaced concerns about artificial intelligence. We have heard about A.I. stealing jobs, killer robots, algorithms that help diagnose and cure cancer, competent self-driving cars, perfect poker players, and more. It seems that for every mention of A.I. as humanity's top existential risk, there is a mention of its power to solve humanity's biggest challenges. Demis Hassabis--founder of Google DeepMind, the company behind AlphaGo--views A.I. as "potentially a meta-solution to any problem," and Eric Horvitz--director of research at Microsoft's Redmond, Washington, lab--claims that "A.I. will be incredibly empowering to humanity." By contrast, Bill Gates has called A.I. "a huge challenge" and something to "worry about," and Stephen Hawking has warned about A.I. ending humanity.
Machine learning and deep learning, in particular, are developing at amazing speeds. Today, machine learning can be used to solve ever more complex tasks that have been considered impractical just a few years ago. Examples include autonomous cars, AlphaGo's win against the world's Go champion, the photo-realistic transformation of pictures, and neural machine translation systems. In this blog post, we describe a simple system to recognize monetary amounts on Swiss payment slips. The user interface is implemented using Eclipse Scout and we build, train, and run the deep neural net using Deeplearning4j.
Until recently, artificial intelligence (AI) was primarily limited to computer chess players and jeopardy. In the last few years, however, the pace of innovation in AI has skyrocketed, driven by tipping points in algorithms, processing (GPUs), and increasing volumes of data. While there is an infinite set of use cases for AI, the Internet of Things is a particularly interesting breeding ground for new AI-driven solutions and experiences, from self-driving cars to intelligent homes to mHealth. In this talk at Bosch ConnectedWorld Chicago, MongoDB's Dev Ittycheria discusses how the massive increase in data driven by sensors will drive the next wave of innovation in AI.
When it comes to the future of artificial intelligence, the ultimate battle between man and machine may come to mind -- but that's really the stuff of science fiction. AI actually has a presence in our daily lives on a much more useful and less apocalyptic level. Think personal assistant devices and apps like Alexa, Cortana and Siri, web search predictions, movie suggestions on Netflix and self-driving cars. The term "artificial intelligence" was coined back in 1956. It describes a machine's ability to perform intelligent behavior such as decision-making or speech recognition.