Volvo is set to run self-driving versions of its family 4x4s on roads around London next year as the motor industry's trial of autonomous vehicles accelerates. While self-driving pods and shuttles were already due to operate on pavements in Greenwich and Milton Keynes this summer, the Swedish carmaker is planning to test autonomous vehicles on public roads in the capital from 2017. Volvo's UK test, called Drive Me London, will go a step further than other programmes by using real families driving autonomous cars on public roads. The manufacturer has conducted tests with the same vehicles in Gothenburg since 2014, and plans a parallel public trial in the Swedish city next year. The cars will record data from everyday users to help develop driverless cars for real-world conditions.

Such is not the case with IoT. The post How Machine Learning Can Bring Security to IoT Devices appeared first on DATAVERSITY.

With Facebook Bots and everyone's ongoing addiction to Slack, Chatbots are all the hype in the world of AI these days. This article goes against that current and questions whether they really are all that. "Personified user interfaces, like chat bots or agents, are the new thing once again. But despite advances in artificial intelligence, they still have many issues and drawbacks compared to direct-manipulation interfaces. There was a debate around these interfaces in the 1990s, and it seems to be bound to repeat itself. In the last few weeks, Facebook unveiled a new push to use chat bots on Messenger, and Microsoft has a new platform for building bots. These bots are supposed to be the new way of doing everything, from delivering news (like Quartz' recent iPhone app) to letting you order pizza, flowers, and everything else. These bots act like people, in that they talk to you via text chat and try to understand free-form text. They don't attempt to pass theTuring Test, but they promise to be smarter (and more attentive) than an overworked call center worker tending to a dozen people at the same time."

Compare this to the conventional self-driving car approach which breaks the task into different components - lane finding, other car detection, guard rail location and so on. In most cases a detailed and accurate map of the road is also required and the program is essentially a set of if..then.. rules that use the input features to deterministically generate the outputs. This approach to self-driving cars comes with a measure of verifiability that gives confidence that the design works and why it doesn't. For example, the recent Google self driving car crash with a bus resulted in a bus detector being added to the system so it wouldn't happen again. The problem with this "engineering" approach is that the system can't cope with anything that it encounters that is new.

In a recent survey in the UK, one in four young people agreed that they would date a robot. Try looking at the idealized view in the first video from the 2015 movie Ex Machina, but then check out the more realistic actual robot in the second (linked).

That's the reason why I was shocked by a piece of news that came out of London on January 27 this year. AlphaGo, a program created by Google subsidiary DeepMind, defeated the European Go champion, five games to nothing. Maybe you think that's no big deal. After all, it's almost 20 years since IBM's Deep Blue beat Kasparov at chess in 1997. Chess is about logic; Go involves imagination and intuition.

Virtual reality and artificial intelligence (AI) are becoming powerful tools needed to revolutionize education. Experts suggest the use of technology, with the integration of the ever-evolving cyber tools, will unify education and research environment and network. Despite the threat of artificial intelligence to rise up against humans and destroy humanity within decades, AI continues to prove its usefulness to mankind. Recently, artificial intelligence makes headlines for having a potential to provide solutions to various global issues such as poaching, illegal logging, cyber-\attacks, in aiding cancer diagnosis and in education. The rise of technology has changed the way students communicate and entertain themselves.

More than six billion dollars: That's how much health care providers and consumers will be spending every year on artificial intelligence tools by 2021--a tenfold increase from today--according to a new report from research firm Frost & Sullivan. AI will be everywhere--from diagnosing cancer to providing weight-loss coaching, says Venkat Rajan, who has the great title of global director for the company's Visionary Healthcare Program. "Prior to 2015, most of what was happening was sort of academic: pilot programs, exploratory, proof of concept-type stuff," he says. AI's ability to sort through scads of information, and remember everything it has ever seen, could enable a digital (and congenial) version of Dr. House, the brilliant diagnostician from the eponymous TV show, says Rajan. "At first, it's a complete mystery, it could be one of ten different things," he says, about the process in the show, and real life, called differential diagnosis. "And then he's able to sort through various issues, you know, illuminate certain factors on why it's not one of these other conditions, and he's able to pull something from memory that figures out ultimately what it is, and they can provide the appropriate treatment." Robots won't steal doctors' jobs, says Rajan, but they will spare overworked docs some of the dangerous fatigue that can lead to mistakes.

Use this easy-to-understand, downloadable infographic overview of machine learning basics to identify the popular algorithms used to answer common machine learning questions. Algorithm examples help the machine learning beginner understand which algorithms to use and what they are used for. Azure Machine Learning Studio comes with a large number of machine learning algorithms that you can use to solve predictive analytics problems. The downloadable infographic below demonstrates how the four types of machine learning algorithms - regression, anomaly detection, clustering, and classification - can be used to answer your machine learning questions. Get the most out of the infographic by downloading it - the PDF has links to examples of each algorithm.

Specifically, Aarki has promoted Dr. Yumio Saneyoshi to senior vice president of product, and Mark Kalygulov to vice president of engineering. The company's proprietary mobile advertising platform - Aarki Encore - is widely recognized as the leading technology in the industry. About Mark Kalygulov Mark Kalygulov is the head of Aarki's diversely skilled engineering team. As vice president of engineering, Kalygulov will continue to grow Aarki's global infrastructure, expand the scale of existing projects and drive new technological initiatives.