Jean Brillat-Savarin was a 19th-century French lawyer famed for his writings on gastronomy. In his most famous work, he said: "Dis-moi ce que tu manges, je te dirai ce que tu es." Or "Tell me what you eat and I will tell you what you are." This idea--that you are what you eat--has become increasingly popular. Since Brillat-Savarin's time it has been used as the title of various cookbooks and health guides; for some it is a way of life.

As healthcare systems strive to realize IOM's vision for continuous improvement in care delivery, many are recognizing that they have outgrown their data management and reporting capacity. Those that have turned to new machine-learning approaches have found they can expand capacity and capabilities while reducing administrative burden on clinicians. Here's an example of how one health system used machine-learning tools to improve care delivery for intestinal surgery: Until recently, the health system's surgical services team used traditional methods of hospital data analysis to inform their creation of order sets, protocols, and provider and patient education materials spanning the pre-op, intraoperative and post-op phases of care. Then they applied a "machine intelligence" platform that pairs machine learning algorithms with topological data analysis (TDA)--a mathematical process that uses shape as an organizing principal for understanding complex data. By giving visible form to their data, the health system was able to replicate and validate years of analytical insights in a matter of days.

When people look back at the current decade, what will they single out as the most significant technological breakthrough? In the future, there will be billions of artificial minds, intelligently organizing business processes. The cost of intelligence can eventually fall to zero. We're a little way off that scenario today, but we're in the midst of a second big wave of automation that all businesses can benefit from. Many tasks that we do today in business are time-consuming and expensive.

In celebration of our 5th anniversary, this month we're publishing a series of interviews with innovative leaders about what the next five years hold. To read more about this series, read our editor Nilay Patel's introduction here. Few subsidiaries at Alphabet Inc. inspire as much curiosity as Google X, now called simply "X." X is the company's innovation lab, where ambitious but far-fetched tech ideas are pitched, tested, and either come to life or are ultimately killed. It's where Google's self-driving car concept was developed, where giant internet access balloons were conceived, where glucose-monitoring contact lenses were first experimented with, and where burrito-delivering drones are part of a beta test for bigger things. And while more than 250 employees are behind these far-fetched projects, for the past five years the face of X has been Astro Teller, the so-called "Captain of Moonshots."

The new platform is part of a larger AI effort that includes the machine-vision Lumos app used to suss out images that violate its community standards. It has also open-sourced similar tech on Github to non-Facebook developers. Google released its Tensorflow framework to the open source community and Microsoft recently made its Cognitive Toolkit available to developers. Facebook first flaunted Caffe2Go last month, then brought some of the effects to a new camera in a limited European release. Much like the Prisma app, it transfers styles from Van Gogh or Monet onto any still or moving image.

The use of Apache Spark is dramatically increasing as new workloads create more use cases. The open source cluster computing framework Apache Spark is now being actively used by 54% of people and the majority of them (64%) are finding that it's proving invaluable. That's according to a Cloudera study, conducted by Taneja Group on 7,000 people from technical and managerial roles that are directly involved in big data. According to the study the technology is being used for the most important use cases by 57% of people, when that technology is provided by Cloudera. Those use cases aren't always for the likes of data processing, engineering and ETL workloads that are said to make up 55% of current Spark use.

"Machine learning and AI is something we've just started to track as it will have an impact at some point on the operation of the railway, but it's early days from a railways systems perspective and there is always a bit of reticence around bleeding-edge technology where safety is critical." "We've deployed some AI/ML capability within our sentiment dashboard application which uses machine learning services in the cloud combined with in-house data to build a picture for the licensee. "I've seen some early prototypes in our North American labs of virtual agents – be that chat bots, be that the recently announced integration into Amazon's Alexa product and I think we'll see a lot more of virtual agents in the financial services industry and other industries; I think it's a good example of helping customers interact with financial services companies with a lot less friction." "Things like using Tensor Flow (Google's open source AI framework), AI is really starting to get interesting and seeing how we can use that to help UK Households to save more money will be fun!" Tim Jones, Moneysupermarket.com "AI, analytics using a wide range of techniques (including NoSQL), IoT, automation and wearables are all things we are either doing or continue to explore today.

Facebook has built a simple-looking video tool to show off a sophisticated use of artificial intelligence on cell phones. During an event at its office fb in Menlo Park, Calif., last Friday afternoon, Facebook CTO Mike Schroepfer showed off software that takes a live Facebook video feed from a cell phone and converts the image in real time into a selection of artistic styles, such as that of Van Gogh. It might sound like a simple filter, but usually an algorithm of this nature would need to send that type of information back to a server in a data center to process the pixels on more powerful machines. The Facebook crew crafted a less power-hungry and computing-intensive deep learning system they call "Caffe2Go," that uses the computing power in a cell phone. Facebook's Schroepfer showed the algorithm and other applications of artificial intelligence at the Web Summit conference in Lisbon, Portugal on Tuesday.

Chatbots are getting chattier and may say hello if you click on a News Feed ad. Facebook is letting brands try to get chummier with you by expanding the capabilities of chatbots in Messenger. David Marcus, Facebook's vice president of messaging products, said Tuesday the initial success that companies have seen in reaching customers via Messenger spurred the expansion. He spoke at Web Summit 2016 in Dublin. As early as this week, you may start to see ads in your News Feed that enable you to click straight into a Messenger conversation with a company.

They say what happens in Vegas stays in Vegas, but I keep telling everyone I know about the remarkable innovations I saw at IBM World of Watson 2016 conference, held from Oct. 24 to 27 at beautiful Mandalay Bay, where I was among the 17,000 attendees. As I was "welcomed to the World of Watson," I learned that Watson (yes, the computer that was on Jeopardy) is IBM's researchers' vision "to design an intelligent system that brings man and machine together to create a better world." If that sounds like a utopian fantasy, prepare to be amazed at how real that vision has become: Watson is changing how doctors cure disease, how companies analyze their social media footprints, and how financial services firms adapt to ever-changing regulations. I could write an entire book on all that Watson has to offer, but my focus here is on Watson's ability to help financial services firms meet compliance demands more efficiently and with less cost – a much needed innovation as firms spend $99 billion on addressing compliance, thus limiting their ability to invest in growth, according to Marc Andrews, VP of Industry Analytics Solutions for IBM. If you're scratching your head at why regulatory compliance costs are so high, picture this: Linda, a trader at a high-profile brokerage firm, receives a bad performance review from her supervisor.