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.

In July, we talked about whether the change in law should be characterized as "Disruption, Eruption or Interruption?" This week, we drill down into one likely source of change, IBM's Watson. Lawyers have been thinking for a while about whether artificial intelligence would ever start to displace or complement lawyers. Richard Susskind, the leading legal futurist/technologist, did his work in this area starting in the mid-1980s. In the August issue of the ABA Journal, one of the commenters to an article about LegalZoom feared: "Once we have fully artificial intelligence enhanced programs like LegalZoom, there will be no need for lawyers, aside from the highly specialized and expensive large-law-firm variety."

Bibhrajit Halder left the Midwest and a job developing autonomous trucks for Caterpillar Inc. about a year and a half ago to join Ford Motor Co. in the San Francisco Bay Area, where the auto maker is working on self-driving vehicles. The Dearborn, Mich., auto maker, however, soon lost the software engineer to Faraday Future Inc., an electric-car startup luring auto industry veterans with Silicon Valley-like perks including stock options, free health care, catered lunches and foosball tables. "The work is exciting," Mr. Halder said in an interview about six months after joining Faraday, where he says he has more responsibility than at the blue chip companies he left. "The company is dependent on you to deliver." Ford is at the center of a ferocious hiring battle now pitting traditional car makers against startups out to force a shift to electric and autonomous-driving vehicles.

Coming off its first quarterly revenue decline in 13 years, Apple Inc. kicks off its annual developers' conference Monday in San Francisco facing questions about whether the company's best days are behind it. The focus of the weeklong Worldwide Developers Conference is expected to be on Siri, Apple's digital assistant. When Apple introduced Siri as an iPhone feature in 2011, it heralded a future of people finding information or completing tasks on their devices by speaking rather than typing or tapping. That vision of Siri hasn't fully materialized, leaving the door open for other technology companies to push into Apple's turf. Google parent Alphabet Inc., Amazon.com Inc. and Microsoft Corp. have all introduced voice-activated digital assistants that rely on artificial intelligence?technology that allows computers to understand inferences and context so they can make decisions like a human brain instead of following programmed instructions.

Bibhrajit Halder left the Midwest and a job developing autonomous trucks for Caterpillar Inc. CAT -1.46 % about a year and a half ago to join Ford Motor Co. F -1.21 % in the San Francisco Bay Area, where the auto maker is working on self-driving vehicles. The Dearborn, Mich., auto maker, however, soon lost the software engineer to Faraday Future Inc., an electric-car startup luring auto industry veterans with Silicon Valley-like perks including stock options, free health care, catered lunches and foosball tables. "The work is exciting," Mr. Halder said in an interview about six months after joining Faraday, where he says he has more responsibility than at the blue chip companies he left. "The company is dependent on you to deliver."

There's a short science fiction film that just made its debut online;it's about three people living in a dystopian future on a space station. Thomas Middleditch of Silicon Valley fame stars in it, opposite Elisabeth Gray and Humphrey Ker, who could all possibly be in a love triangle. Stuff Hollywood B-movies are made of. It's called Sunspring--and it was written entirely by artificial intelligence (AI). Having been written by a neural network called long short-term memory dubbed Benjamin, the very fact that it's a film penned by an AI makes it compelling to watch. At the helm of the film is director Oscar Sharp, who collaborated with NYU AI researcher Ross Goodwin.

Light globes that change colour with the tap of an app, coffee machines you can talk to, and ovens that know exactly how long to cook your food: our homes are getting smart. These devices, just a few examples of what is known as "the internet of things" (or IOT), have been called the "next great disruptor" and "the second digital revolution". One of the great hopes of this revolution is that it will help households save energy. Sensors can turn off lights and appliances when not in use, or turn the heating down when people go to bed. Smartphone apps can provide households with more insight into the energy use of their appliances.

The American Diabetes Association and IBM Watson Health will partner on a long-term collaboration to bring together the cognitive computing power of Watson and the ADA's vast clinical and research data. Kevin L. Hagen, ADA CEO, said the ADA and IBM Watson Health will work to build a first-of-its-kind diabetes advisor for patients and caregivers, as well as develop Watson-powered solutions to optimize clinical, research and lifestyle decisions. "The goal of this endeavor is to develop and introduce cognitive technologies that support clinicians, researchers and people living with diabetes so that we can accelerate discoveries, personalize treatment and care, and improve the lives of all people affected by diabetes," Hagen said while announcing the partnership. "We also see potential to address social determinants of health." Unlike traditional computing systems that are programmed, systems like IBM Watson learn at scale, explained IBM General Manager David Kenny.

In this work, we consider compressed sensing reconstruction from $M$ measurements of $K$-sparse structured signals which do not possess a writable correlation model. Assuming that a generative statistical model, such as a Boltzmann machine, can be trained in an unsupervised manner on example signals, we demonstrate how this signal model can be used within a Bayesian framework of signal reconstruction. By deriving a message-passing inference for general distribution restricted Boltzmann machines, we are able to integrate these inferred signal models into approximate message passing for compressed sensing reconstruction. Finally, we show for the MNIST dataset that this approach can be very effective, even for $M < K$.

The estimation of class prevalence, i.e., the fraction of a population that belongs to a certain class, is a very useful tool in data analytics and learning, and finds applications in many domains such as sentiment analysis, epidemiology, etc. For example, in sentiment analysis, the objective is often not to estimate whether a specific text conveys a positive or a negative sentiment, but rather estimate the overall distribution of positive and negative sentiments during an event window. A popular way of performing the above task, often dubbed quantification, is to use supervised learning to train a prevalence estimator from labeled data. Contemporary literature cites several performance measures used to measure the success of such prevalence estimators. In this paper we propose the first online stochastic algorithms for directly optimizing these quantification-specific performance measures. We also provide algorithms that optimize hybrid performance measures that seek to balance quantification and classification performance. Our algorithms present a significant advancement in the theory of multivariate optimization and we show, by a rigorous theoretical analysis, that they exhibit optimal convergence. We also report extensive experiments on benchmark and real data sets which demonstrate that our methods significantly outperform existing optimization techniques used for these performance measures.