Bottom line: Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises' data, leading to diverse company-wide strategies succeeding faster and more profitably than before. The good news for businesses is that all the data they have been saving for years can now be turned into a competitive advantage and lead to strategic goals being accomplished. Revenue teams are using machine learning to optimize promotions, compensation and rebates drive the desired behavior across selling channels. Predicting propensity to buy across all channels, making personalized recommendations to customers, forecasting long-term customer loyalty and anticipating potential credit risks of suppliers and buyers are Figure 1 provides an overview of machine learning applications by industry. Unlike advanced analytics techniques that seek out causality first, machine learning techniques are designed to seek out opportunities to optimize decisions based on the predictive value of large-scale data sets.

Back in December, when Slack integrations launched, I wrote Clippy's Revenge about the potential of "smart messaging" to become a new platform. Since then, big players have done much to nurture that possibility -- as if on some secret, jointly agreed-upon master schedule. This looks like the battleground of the next tech war, and all eyes are on Apple this week. But how much of the bot craze is hype, and what's worth paying attention to? The frenetic energy around this emerging ecosystem is well-placed, but often confusing. After meeting with more than 50 founders in this space, I'd like to offer a structured explanation of the emerging Conversational Economy, and propose some opportunities (for both big companies and startups).

We know of humans that we consider super-intelligent, but we don't yet know how to engineer that intelligence - or exceed it - in people. But some researchers think that advances in genomic science and machine learning are going to open up new avenues of possibility in that realm, potentially leading to individuals whose cognitive abilities leave the greatest minds of history in the dust. That future of superintelligent humans may be upon us sooner than we think. Consider individuals that we consider the smartest of all time, those like Carl Friedrich Gauss or John von Neumann, says Stephen Hsu, a physicist who is the vice president for research and graduate studies at Michigan State University and an advisor to the genomics researchers at BGI. Hsu is a member of BGI's Cognitive Genomics Lab, a research group that's trying to unlock the genetic codes that account for complex traits like height, susceptibility to conditions like obesity, and - perhaps most controversially - intelligence. Most researchers believe that intelligence is influenced by genes and environment, and when it comes to genes, we think a large number of genetic variants all make very small contributions.

It's a strong contender for the geekiest video ever made: a close-up of a smartphone with line upon line of numbers and symbols scrolling down the screen. But when visitors stop by Nicola Marzari's office, which overlooks Lake Geneva, he can hardly wait to show it off. "It's from 2010," he says, "and this is my cellphone calculating the electronic structure of silicon in real time!" Even back then, explains Marzari, a physicist at the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, his now-ancient handset took just 40 seconds to carry out quantum-mechanical calculations that once took many hours on a supercomputer -- a feat that not only shows how far such computational methods have come in the past decade or so, but also demonstrates their potential for transforming the way materials science is done in the future. Instead of continuing to develop new materials the old-fashioned way -- stumbling across them by luck, then painstakingly measuring their properties in the laboratory -- Marzari and like-minded researchers are using computer modelling and machine-learning techniques to generate libraries of candidate materials by the tens of thousands.

Google today hinted at a near future where its artificial intelligence capabilities underpin its collection of cloud-based applications aimed at the workplace, Google Apps. At an event in Tokyo, the Internet giant announced a new application and a revamp of another, with some AI smarts injected into both. The new product is called Springboard, and it's intended to let Google Apps customers search through the content of the documents they store in their Google Drive, along with their contacts and calendar entries, all in one place. It will also use AI, Google says, to proactively find information that may be relevant to what you're working on. Google also says it has rebuilt Google Sites, its lightweight tool for creating websites and intranet sites for use by work teams.

When IBM rolled out its artificial intelligence system, Watson, it also welcomed entrepreneurs to help it monetize the revolutionary technology. A key com ponent to Watson is its cognitive capability -- to learn as it goes along. Able to grind through millions of websites, documents, articles, and images in seconds, Watson can remember the research it gathers, as well as your response to it. So Watson becomes progressively more accurate -- intelligent, if you will -- as people use it. It can be everything from an über-helpful call-center rep to an oncology adviser, recommending cancer treatments on the basis of a patient's genetic profile and thousands of clinical trials and medical journal articles it has analyzed.

In a talk in Pittsburgh in 1997, the late evolutionary biologist Stephen J. Gould allegedly characterized humans as "the primates who tell stories." Psychologist Robyn Dawes went much further, suggesting humans are "the primates whose cognitive capacity shuts down in the absence of a story." To be sure, we love a good story. Research suggests that anecdotes can be as persuasive as hard data, and that jurors are influenced by the quality of the prosecution's and defense's "stories" when deciding whether to find a defendant guilty. Even in science, we seek explanations, not mere descriptions; in history, we want a good narrative, not a mere sequence of events. Or do they offer something more?

Apple, like practically every mega-corporation, wants to know as much as possible about its customers. But it's also marketed itself as Silicon Valley's privacy champion, one that--unlike so many of its advertising-driven competitors--wants to know as little as possible about you. So perhaps it's no surprise that the company has now publicly boasted about its work in an obscure branch of mathematics that deals with exactly that paradox. At the keynote address of Apple's Worldwide Developers' Conference in San Francisco on Monday, the company's senior vice president of software engineering Craig Federighi gave his familiar nod to privacy, emphasizing that Apple doesn't assemble user profiles, does end-to-end encrypt iMessage and Facetime and tries to keep as much computation as possible that involves your private information on your personal device rather than on an Apple server. But Federighi also acknowledged the growing reality that collecting user information is crucial to making good software, especially in an age of big data analysis and machine learning.

Apple's annual Worldwide Developers Conference kicks off Monday, June 13, in San Francisco with one of the company's signature product keynotes. Many expect Apple to announce big new features for Siri, its AI-powered assistant. These include new tools that would finally allow other companies to make their apps and information accessible via Siri, according to a May report from Amir Efrati at The Information. Opening up Siri -- if true -- would be a big step for Apple, which has tightly controlled Siri's capabilities since its debut almost five years ago. Select third-party information and features, such as Yelp restaurant reviews and OpenTable bookings, have long been available via specific partnerships.

Everyone makes mistakes, and many people try to cover them up. But if you try to hide an error made behind the wheel of a car made by Tesla Motors, you are liable to be caught out. In fact, trying to hide what really happened in any kind of car accident could soon become just about impossible. That's the lesson of an incident over the weekend in which the owner of a Tesla Model X SUV crashed into a building and claimed it had suddenly accelerated on its own. "Data shows that the vehicle was traveling at 6 mph when the accelerator pedal was abruptly increased to 100 percent … Consistent with the driver's actions, the vehicle applied torque and accelerated as instructed."