In this video H2O.ai Interaction Designer Tony Chu talks about creating interfaces and experiences for AI which bring interpretability and context to data products.

Every Monday morning, 75 million Spotify users logon to their devices and play a "Discover Weekly" playlist that's been curated to their musical preferences. Unbeknownst to many, the custom playlist they're enjoying was curated by a machine learning algorithm that has learned their unique musical preferences based on previous interactions with songs, musicians, and playlists. Machine learning, and a more advanced technology called deep learning, are types of artificial intelligence that allow a computer to learn information based on the data it is given. To borrow the words of Drew Breunig, "In a nutshell, deep learning is human recognition at computer scale." Essentially, the more information the computer is given, the better it can learn -- and in the case of platforms like Spotify or Netflix, the more interaction you have with the program, the better it can recommend music, movies, or TV shows that you'll like.

Stewart Butterfield (left), CEO of Slack, talks about the future of work with Box CEO Aaron Levie. "We need self-driving Ubers, like now," said Levie, 30, referencing the ride-sharing company's Thursday announcement that it would soon begin testing autonomous Volvos in Pittsburgh. Apparently, Levie's Uber driver got lost en route to this lunchtime session Friday on the future of work, a discussion organized for a few journalists featuring Levie and Stewart Butterfield, 43, CEO of messaging platform Slack. Over the next hour, the two startup entrepreneurs discussed how workplace expectations have been upended by increasingly easy access to information; how work-life balance rules may need to be rewritten; and how artificial intelligence will be able to bail us out of information overload. "I'd say right now (workplace tools) are a net cultural positive, but we're also going to have to learn how to handle it," said Butterfield, who added that his company instituted a Do Not Disturb feature to help create a work-life divide.

Can FinTech and AI solve the ESG data puzzle?

Stewart Butterfield (left), CEO of Slack, talks about the future of work with Box CEO Aaron Levie. "We need self-driving Ubers, like now," said Levie, 30, referencing the ride-sharing company's Thursday announcement that it would soon begin testing autonomous Volvos in Pittsburgh. Apparently, Levie's Uber driver got lost en route to this lunchtime session Friday on the future of work, a discussion organized for a few journalists featuring Levie and Stewart Butterfield, 43, CEO of messaging platform Slack. Over the next hour, the two startup entrepreneurs discussed how workplace expectations have been upended by increasingly easy access to information; how work-life balance rules may need to be rewritten; and how artificial intelligence will be able to bail us out of information overload. "I'd say right now (workplace tools) are a net cultural positive, but we're also going to have to learn how to handle it," said Butterfield, who added that his company instituted a Do Not Disturb feature not to allow workers to shirk responsibility but rather to create a work-life divide.

In this special guest feature, Rob Farber from TechEnablement writes that the Intel Scalable Systems Framework is pushing the boundaries of Machine Learning performance. The challenge of training a machine learning algorithm to accurately solve complex problems requires large amounts of data that greatly increase a system's computational, memory, and network requirements. Meeting this challenge with the right technology mix amplifies the ability of a system to train machine and deep learning neural networks to solve complex pattern recognition tasks. To help customers create systems run deep learning--as well as other HPC, Big Data, and visualization workloads--Intel introduced Intel Scalable System Framework (Intel SSF). It provides a common framework that can support workloads running on everything from small workgroup clusters to the world's largest supercomputers and on-demand cloud computing.

It's not surprising that Apple is warming up to the idea of machine intelligence and AI agents with its 200 million purchase of Turi. The company needs to do that to remain competitive with Google and Microsoft. But, over and above that, the beneficial side effects will have even deeper implications for Apple as a company and its future. Back when Apple was selling iPods by the tens of millions, university and national laboratory computer scientists were building supercomputers to solve some very special problems. Back when Apple launched the iPhone, a whole other world of computer scientist were were working on artificial intelligence. Yet Apple's success in the consumer market was unparalleled, and we accepted that as Apple's forte.

When it comes to enterprise-level customer care, machine learning enables Virtual Assistant solutions to automate tasks that used to require a live agent: password resets; address and complex information collection; even sales support. Integrating machine learning into customer care opens doors to more flexible automated solutions. It also frees up live agents to focus on handling complex or revenue-generating tasks. With the growing challenges and volume of customer interactions that most companies' must handle, that flexibility, efficiency, and accuracy is exactly what's needed. With a little help and vetting from the IT department, the contact center can take customer care to the next level.

Intel is committed to producing CPUs to target machine learning systems, setting up an intriguing rivalry with graphical processing unit (GPU) vendor Nvidia. At the Intel Developer Forum yesterday, the company even brought out an executive from Chinese cloud giant Baidu to talk about the Xeon Phi, Intel's machine learning chip. The choice was interesting considering Baidu has been a vocal Nvidia customer. The potential ace up Intel's sleeve is the pending acquisition of Nervana, a deep learning startup reportedly working on a chip of its own. Intel executive vice president Diane Bryant mentioned Nervana during yesterday's keynote, but with the deal still not closed, it's understandable that she didn't articulate Intel's plans for the startup. The more immediate news for Intel was the announcement of its latest processor for machine learning.

Could a computer be better at identifying depression than a primary care physician? That's the suggestion of a new study that focused on using machine learning to analyze Instagram photos. The study, conducted by a researcher from the department of psychology at Harvard University and another from the University of Vermont, analyzed nearly 44,000 photographs posted to Instagram, exploring factors like what filter was used and how makes "likes" a photo received. The study included photographs from 166 people, some of whom were depressed, and some of whom were not. Instagram offers a variety of filters to change how a photo appears, and the researchers discovered that healthy participants were more likely to use a filter than depressed people.