The use of artificial intelligence has been of enormous economic benefit for dairy farmers in many countries through the improvement of their stock. Affordable tools with the ability to continuously monitor the growth rate of livestock animals are highly sought after by the livestock industries. This demand is driven by the potential for these tools to assist in improving animal welfare and production efficiency. In a rapidly growing population, the demand for meat is escalating, especially in Asia, where the middle class is currently expanding. Meanwhile, in the western world there is growing consumer concern surrounding animal husbandry, with certain organisations labelling some of the current husbandry practices cruel or sub-standard.

A metal typewriter sits on a pedestal churning out the next sentence of a collaborative story. For every line I typed onto paper, the AI computer controlling the hunk of metal replies with what it thinks would be a good follow up. The goal of the experiment is to see what happens when you feed an artificial intelligence only fairy tales. It's the kind of seemingly idiosyncratic thing you'd expect from IBM, Google or Apple. But I actually found it inside Mercedes Benz's R&D North American headquarters in Silicon Valley.

New York City could operate with no more than a few thousand taxis or rides if we took carpooling more seriously, researchers have suggested. When commuters share their rides through carpooling, the cost of individual transport lowers and there are potentially fewer cars on the road which can cause increased congestion and pollution. Modern ride-hailing services including Uber and Lyft, in which passengers can hail a ride through mobile devices and also share their vehicle with others, have caused controversy and are often less-than-popular with traditional taxi firms, but researchers say these services could have a real impact on how many vehicles are needed on our city roads. Currently, New York City has issued almost 14,000 official taxi medallion licenses, but according to researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), the city could make do with no more than a quarter of rides. The team has developed a new algorithm which crunched the numbers and, assuming carpooling becomes more popular, MIT has come to the conclusion that NYC only needs 3,000 four-person cars to serve 98 percent of transport demand in New York City.

Most of the attention around automation focuses on how factory robots and self-driving cars may fundamentally change our workforce, potentially eliminating millions of jobs. But AI that can handle knowledge-based, white-collar work are also becoming increasingly competent. One Japanese insurance company, Fukoku Mutual Life Insurance, is reportedly replacing 34 human insurance claim workers with "IBM Watson Explorer," starting by January 2017. The AI will scan hospital records and other documents to determine insurance payouts, according to a company press release, factoring injuries, patient medical histories, and procedures administered. Automation of these research and data gathering tasks will help the remaining human workers process the final payout faster, the release says.

Here's a no-brainer: 2017 will be a big year for the cloud. Cloud computing is an innovation rivaling the advent of client-server, the PC or the internet, and it's going to enjoy continued vigorous growth in the new year. Though the essential balance of power within the public-cloud world won't change much, competition may favor companies that best serve the organizations straddling private data centers and the public cloud -- which is to say, most of them. Here are some of the key cloud trends to watch this year. Revenue will rise sharply for the big public-cloud providers.

Although it's a digital technology, voicemail requires people to stop what they are doing and pay complete attention to a message that may or may not be important. Consumers, buyers, and decision makers are becoming increasingly more comfortable using self-service, digital channels to not only conduct their research, but make purchase decisions. And, with the introduction of bots, natural language processing technology, and robotic process automation (RPA) to name a few, people may not even realize they are talking to a robot!

IoT is one of the transformational trends that will shape the future of businesses in 2017 and beyond. Many firms see big opportunity in IoT uses and enterprises start to believe that IoT holds the promise to enhance customer relationships and drive business growth by improving quality, productivity, and reliability on one side, and on the other side reducing costs, risk, and theft. By having the right IoT model companies will be rewarded with new customers, better insights, and improved customer satisfaction to mention few benefits. With all this in mind, let's explore some of the trends of IoT impacting business and technology in 2017: Blockchain is more than a concept now and has applications in many verticals besides FinTech including IoT. Blockchain technology is considered by many experts as the missing link to settle scalability, privacy, and reliability concerns in the Internet of Things.

I'm going to start this post by saying that it makes no sense for anyone to pay management fees to get a return stream that… The Ideal ETF Most investors choose an ETF to act on a market view -- gold will rise, U.S. equities will fall, and so on.

Researchers from Google's DeepMind division say they have shown how their differentiable neural computer is able to process information using so-called'slow' thinking thought processes. Researchers are successfully teaching machines to process information in ways that emulate the subtleties and complexities of human thought processes.

This is a hands-on guide to machine learning for programmers with no background in AI. Using a neural network doesn't require a PhD, and you don't need to be the person who makes the next breakthrough in AI in order to use what exists today. What we have now is already breathtaking, and highly usable. I believe that more of us need to play with this stuff like we would any other open source technology, instead of treating it like a research topic. In this guide our goal will be to write a program that uses machine learning to predict, with a high degree of certainty, whether the images in data/untrained-samples are of dolphins or seahorses using only the images themselves, and without having seen them before. Here are two example images we'll use: To do that we're going to train and use a Convolutional Neural Network (CNN). We're going to approach this from the point of view of a practitioner vs. from first principles. There is so much excitement about AI right now, but much of what's being written feels like being taught to do tricks on your bike by a physics professor at a chalkboard instead of your friends in the park.