Microsoft is clearly in the midst of a renaissance, and nothing more clearly indicates this than its aggressive use of open source. Initially a "cancer," and then an afterthought, open source has become Microsoft's single greatest weapon in recovering its industry stature. For evidence, look no further than its opening up of CNTK, Microsoft's artificial intelligence framework that powers everything from its digital assistant Cortana to the Skype Translator. Microsoft has always been a technology pioneer. The problem is that the company too often bottled up such innovations, as Bloomberg captures.

In the run up to next week's Hadoop confab in Silicon Valley, vendors are releasing a flock of automation and other tools aimed at beefing up the mainstream data processing framework. Among them is an attempt to incorporate data science with a leading Hadoop distribution via a machine-learning approach. Boston-based data science automation specialist DataRobot said this week its machine-learning platform designed to fill the data science skills gap has been certified on Cloudera Enterprise 5. The Cloudera platform is used for data management and analytics. DataRobot said the certification means Cloudera users could incorporate machine learning into their analytics operations without additional interfaces or protocols.

Cloud Machine Learning relies on the open source TensorFlow machine learning library, according to Google. The tool will take machine learning mainstream, offering data scientists and developers a way to build a new class of intelligent applications, the company said. It provides access to the same technologies that power Google Now, Google Photos and voice recognition in Google Search as application program interfaces. In a blog post about Cloud Machine Learning, Google said the tool makes it easy for developers to build large-scale machine learning models in less time. Cloud Machine Learning is portable, fully managed and scalable, and works with data in several formats.

New lip-reading technology could help solve crimes by deciphering what people caught on CCTV are saying, researchers have claimed. The visual speech recognition technology developed by the University of East Anglia in Norwich can be used to determine what people are saying in situations where audio is not good enough to hear - such as on security camera footage. Helen Bear, from the university's school of computing science, said the technology could be applied to a wide range of situations from criminal investigations to entertainment. She added: "Lip-reading has been used to pinpoint words footballers have shouted in heated moments on the pitch, but is likely to be of most practical use in situations where there are high levels of noise, such as in cars or aircraft cockpits. "Crucially, whilst there are still improvements to be made, such a system could be adapted for use for a range of purposes - for example, for people with hearing or speech impairments."

If you have ever tried your hand at lip-reading in a noisy environment, you'll know it isn't easy. Now, researchers have invented a machine that can tell the difference between sounds that look the same on the lips to give anyone the ability to decipher what's being said. It is hoped the new technology could help people with hearing and speech impairments communicate more easily and even help solve crimes. Researchers have invented a machine that can tell the difference between sounds that look the same on the lips to give anyone the ability to decipher what's being said. The visual speech recognition technology, can be applied'any place where the audio isn't good enough to determine what people are saying,' according to Helen Bear, who created the machine alongside Richard Harvey at the University of East Anglia (UEA).

UPDATE: March 24, 2016, 3:32 p.m. EDT: Microsoft has "taken Tay offline" and is "making adjustments" to her. The company gave TechCrunch the following statement on Tay's status: "The AI chatbot Tay is a machine learning project, designed for human engagement. It is as much a social and cultural experiment, as it is technical. Unfortunately, within the first 24 hours of coming online, we became aware of a coordinated effort by some users to abuse Tay's commenting skills to have Tay respond in inappropriate ways. As a result, we have taken Tay offline and are making adjustments."

To paraphrase William Gibson, "The future is already here and, thanks to intelligent assistants (IA), it is more evenly distributed than ever before." Based on our spoken input, mobile personal assistants (like Siri and Google Now) answer questions, navigate routes, and organize meetings. Close cousin Alexa, running on the Echo, understands our utterances when we order pizza, hail an Uber, or complete an order from Amazon. Meanwhile, a large set of distant cousins – call them text'bots – understand "plain English" requests and can recommend restaurants, movies, or even bridal fashions. We humans are learning to take over our digital lives using our own words and our choice of digital device.

Earlier this month, Google's DeepMind team made history when its AlphaGo software managed to defeat professional Go player Lee Sedol in a five-game match. The contest was billed as a battle between man and machine -- and it saw the human player largely outclassed by his AI opponent. Artificial intelligence is only going to grow more sophisticated in coming years, becoming more of a factor in everyday life as the development of this technology continues to progress. With artificial minds growing ever more powerful, humans may have to change the game to maintain superiority. To get a true impression of how much progress has been made in the field of artificial intelligence in recent years, it's useful to compare the AlphaGo AI facing Lee with IBM's Deep Blue computer, which faced Chess grandmaster Garry Kasparov in the 1990s.

Let me put it plainly. Despite what you may hear, Microsoft's racist, Hitler-loving A.I. is not how the robot uprising begins. You might have seen some reports by now about Tay, a bot designed to sound like a teenager on the Internet and to learn from her interactions with other people. She knew how to use slang, deploy emoji and crack jokes. The goal was for Tay to become smarter, more conversant and a better interlocutor over time.

For interaction designers, it's becoming common to encounter privacy concerns as part of the design process. Rich online experiences often require the personalization of services, involving the use of people's information. Because gathering information to personalize a customer experience can interfere with the overall experience -- with negative consequences for the business -- how do we navigate this increasingly difficult territory? What are the guidelines to follow when using data to personalize digital experiences, and how can organizations help people feel comfortable with personalization services that research clearly shows people want? Exchanging personal information for things we want, whether physical or virtual, is nothing new. Long before the Internet, we freely published our home phone numbers and addresses in public directories.