Nick Triantos spent the past year trying to commercialize the technology being developed at SRI International, the Stanford research offshoot that helped invent the Internet and Siri, among other things. Now he has joined Ignition Partners at its new office in Los Altos, where he says he will be able to help create companies from a broader range of innovation. Triantos said his focus will be on business-focused startups, particularly ones working in artificial intelligence, cybersecurity and augmented and virtual reality. But he doesn't expect that will include startups in the currently hot space of chatbots, despite his background with voice recognition and machine learning at SRI. The following Q&A about these and other topics has been edited for length and clarity. What was your role at SRI and why are you leaving there? Unfortunately, not many people know about SRI.

Correction: Because of incorrect data initially provided in the Narrative Science report, a previous version of this article reported that 56% of survey respondents were planning to deploy AI technologies within the next two years. The study also found that a lack of data science talent is currently the biggest barrier to adopting AI, with 59% of respondents naming it as the primary obstacle to getting value out of the Big Data available to them. A recent study from Square Root showed that despite spending up to 20 hours per week collecting, analyzing and reporting on data, nearly one in three companies fail to act on their collected data. The Narrative Science study also found that 61% of the respondents who have an innovation strategy are using AI to identify opportunities in data that would be otherwise missed. "One of the major compelling differences in this year's survey compared to last year is the changing perception of AI technologies," said Stuart Frankel, CEO of Narrative Science in an an announcement.

Medical data is an essential part of any healthcare system. This valuable information is the result of hundreds of years work by various people in the medical profession and without it, new ways to diagnose patients and new cures would never be a possibility. However, this amount of data does not come without its downfalls. Having this much data is a lot to handle and is currently an area the healthcare industry is struggling to get to grips with. Most counties welcome the idea of having a global healthcare data center that will allow those in the medical profession to have instant access to previous research, diagnosis', as well as new, revolutionary breakthrough techniques from all around the globe.

Machine learning and artificial intelligence startup Turi has been acquired by Apple in a deal characterized as a blockbuster exit for the Seattle-based company, formerly known as Dato and GraphLab, GeekWire has learned. The acquisition reflects a larger push by Apple into artificial intelligence and machine learning. It also promises to further increase the Cupertino, Calif.-based company's presence in the Seattle region, where Apple has been building an engineering outpost for the past two years. "Apple buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans," said Apple in a statement when contacted by GeekWire about the deal, its standard comment after making such acquisitions. Multiple sources with knowledge of the deal confirmed that Turi has been acquired.

We don't understand AI because we don't understand intelligence

Martin Legowiecki is the creative technology director at Deutsch. Interaction with the world around us should be as easy as walking into your favorite bar and getting your favorite drink in hand before your butt hits the bar stool. The bartender knows you, knows exactly what drink you like and knows you just walked through the door. Advances in AI help make new human-to-machine and machine-to-human interaction possible. Traditional interfaces get simplified, abstracted, hidden -- they become ambient, part of everything.

It could be a couple breaking up on the sidewalk outside your apartment window, or on a bench in the park. "This mission is too important for me to allow you to jeopardize it," the man explains, coldly. It feels like something you shouldn't be listening to, a private conversation between two individuals at their most raw and exposed. "Are these feelings even real?" the woman asks. "Or are they just programming?" It's that uncertainty around the realness of feelings that drives this mashup by Tillmann Ohm, who pulled original lines delivered by Samantha and HAL, the body-less, voice-based learning machines from Spike Jonze's Her (2013) and Stanley Kubrick's 2001: A Space Odyssey (1968), respectively.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

This story was delivered to BI Intelligence Apps and Platforms Briefing subscribers. To learn more and subscribe, please click here. Bot creation platform API.AI announced last week its launch of one-click integrations, which enable developers to more easily convert bots for use on chat platforms like Facebook Messenger, Slack, and Kik. With this capability, bots on different platforms can "share knowledge" with each other via machine learning, thereby cutting down on developer costs and time spent on building and maintaining these bots. For context, platforms such as Messenger and Slack have different natural language processing (NLP) platforms.

Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chat bots, or search engines. Given high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google's Tensorflow). Today, machine learning has come to play an integral role in many phases of the financial ecosystem, from approving loans, to managing assets, to assessing risks. Yet, few technically-savvy professionals have an accurate view of just how many ways machine learning finds it's way into their daily financial lives.