Why it's so hard to create unbiased artificial intelligence When the Singularity Comes, Will A.I. Fear Death? Google's DeepMind to start playing StarCraft II as part of machine learning research Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

Over the last few years, there has been an ongoing conversation about Artificial Intelligence and how it is going to change our lives and how we do business. So, if you've been keeping up with the latest technology trends, then you know that artificial intelligence has the potential to be the most disruptive technology ever. Today, we can ask Siri or Google or Cortana to help us with simple questions or tasks, but much of their actual potential is still untapped. The reason why involves language. This is where natural language processing (NLP) comes into play in artificial intelligence applications.

Facebook has been forced to end a hugely controversial data sharing agreement with WhatsApp. The decision would have seen WhatsApp hand out information on all of its users to Facebook, letting the latter use data about people's chats to inform its advertising. It would also have gone the other way – allowing companies to send WhatsApp's to people based on things they've bought on Facebook, for instance. But now the UK's Information Commissioner's Office has told the company that it needs to bring that arrangement to an end because it does not have "valid consent" from its users. Facebook had looked to gain permission from its users to have their data used as part of the deal.

Is that you, HAL? AI can now see secrets through lipreading – kinda Tend.ai raises $2M for robot arms that operate multiple 3D printers and workshop machines Why it's so hard to create unbiased artificial intelligence Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

Lip reading is a tricky business. Test results vary, but on average, most people recognize just one in 10 words when watching someone's lips, and the accuracy of self-proclaimed experts tends to vary -- there are certainly no lip-reading savants. Now, though, some researchers claim that AI techniques like deep learning could help solve this problem. After all, AI methods that focus on crunching large amounts of data to find common patterns have helped improve audio speech recognition to near-human levels of accuracy, so why can't the same be done for lip reading? The researchers from the University of Oxford's AI lab have made a promising -- if crucially limited -- contribution to the field, creating a new lip-reading program using deep learning.

Splunk announced new versions of Splunk Enterprise, Splunk IT Service Intelligence (ITSI), Splunk Enterprise Security (ES) and Splunk User Behavior Analytics (UBA). These products leverage machine learning to speed-up and facilitate extracting insights from machine-generated data. Splunk was founded in 2003 and brought "big data" to Wall Street's attention with its 2012 IPO. It has always focused on machine-generated data and its platform captures, indexes and analyzes real-time data in a searchable repository, on premises or in the cloud. Machine learning extends the Splunk platform by adding outlier and anomaly detection, adaptive thresholding and predictive analytics capabilities, applying over 25 commonly-used machine learning algorithms or custom algorithms to build data models that forecast future events.

Agilience mission is to find people and stories that matter to you, to your community, to your business. Their ambition is to nurture communities by pointing out key authorities who inspire and educate, and they do with Agilience Authority Index. Here we review their list of top 10 social media authorities in Artificial Intelligence and Machine Learning. The most trusted #MachineLearning brand in the world. Driven by the crazy idea that #DataScience must be simple & profitable.

The ability for machines to "visually understand the world around them" is the final piece of the puzzle needed for true artificial intelligence, according a researcher at one of the Australian Research Council's centres of excellence. "For an artificially intelligent agent or a robot to be able to operate in its environment, it needs to be able to do three things," said Stephen Gould, chief investigator at ARC's Centre of Excellence in Robotic Vision. "The first thing is to be able to see, the second thing is to be able to think, and the third thing is to be able to act." Gould said that we've long possessed the algorithms required for AI to think and reason, and make decisions. "And the machines that they're reasoning on are getting faster thanks to Moore's Law, and we now have distributed computing and specialised architecture that can run these algorithms," he added.

Artificial intelligence is a $15 billion dollar industry and growing. With more than 2,600 companies developing intelligent technology, the value of AI is expected to rise to more than $70 billion by 2020. And it's not just attracting the tech giants: USAA is using AI to protect its users from identity theft, and Under Armour has connected its health app, MyFitnessPal, to IBM Watson so users can get a more thorough read of their health. For designers, that represents a major business opportunity. But AI is also a challenge requiring every strength and skill they've learned and many they haven't.

Over the next decade, mobile, the Internet of Things, machine learning, robotics, and blockchain technologies will change a great deal about how the oil and gas industry works. Five technologies will change the oil and gas industry: mobile will speed oilfield transactions, increase efficiency, and improve safety by removing people from harm's way; the Internet of Things (IoT) will reduce the cost of repairs; machine learning will provide ever more optimal solutions to field challenges; robotics will upend the question of who does the work, and blockchain will make contracting faster and smoother than ever before. Adopting these technologies will be a challenge for many in our industry, requiring a change in mind-set. Engineers tend to focus less on investing for the future than on fixing what's broken now, as do companies trying to maximize their return on investment. But investments in these transformative technologies now will mean less to fix in the future, and more time to innovate, operate, and develop resources as fully as possible--which is what we're all trying to do, correct?