I travel a lot for work, and one of the most rewarding parts of travel is meeting with customers and listening to their stories. Very often I'll hear a story that offers particular insight into the state of the industry – and that I want to share with you in my blog. I was in San Francisco recently to have lunch with Joe, the AI leader of a strategic customer. During the meal he told me he had a story about AI and machine learning (ML) that he thought had good lessons for Informatica – and for anyone working now with data and AI. "Building algorithms for our customers is a key part of my job," he started.

CEOs often live by the numbers--profit, earnings before interest and taxes, shareholder returns. These data often serve as hard evidence of CEO success or failure, but they're certainly not the only measures. Among the softer, but equally important, success factors: making sound decisions that not only lead to the creation of value but also "do no harm." While artificial intelligence (AI) is quickly becoming a new tool in the CEO tool belt to drive revenues and profitability, it has also become clear that deploying AI requires careful management to prevent unintentional but significant damage, not only to brand reputation but, more important, to workers, individuals, and society as a whole. Legions of businesses, governments, and nonprofits are starting to cash in on the value AI can deliver.

Fox News Flash top headlines for August 30 are here. Check out what's clicking on Foxnews.com A personal assistant to President Trump who had been with the administration since Trump took office, Madeleine Westerhout, abruptly resigned Thursday, reportedly after it was determined she shared private information about the president with reporters. Westerhout allegedly shared intimate details about the president's family and life during an off-the-record dinner with reporters near Bedminster, N.J., where Trump was on vacation. Her desk sat outside the Oval Office and she was often seen alongside Trump.

The Distributed Information Systems Laboratory (LSIR) headed by Prof. Karl Aberer at EPFL in Lausanne is currently looking for a software engineer with experience in Web development and Data Science. If you are interested in being at the crossroad of Web technologies, Machine learning and Computational Journalism, this job is for you. You will join our research team. Our goal is to develop a web platform to collect news articles from all around the world and to analyze the media landscape in real-time. You will be in charge of the Web platform development and will help us integrating some of the algorithms developed by our researchers.

The Distributed Information Systems Laboratory (LSIR) headed by Prof. Karl Aberer at EPFL in Lausanne is currently looking for a software engineer with experience in Web development and Data Science. If you are interested in being at the crossroad of Web technologies, Machine learning and Computational Journalism, this job is for you. You will join our research team. Our goal is to develop a web platform to collect news articles from all around the world and to analyze the media landscape in real-time. You will be in charge of the Web platform development and will help us integrating some of the algorithms developed by our researchers.

National artificial intelligence (AI) plans, including those drafted by China, should promote international collaboration, not just permit it, according to Tom Mitchell, former dean of Carnegie Mellon University's computer science school. Why it matters: Mitchell, known as the father of machine learning, was speaking at the opening ceremony of the World Artificial Intelligence Conference (WAIC) in Shanghai on Thursday. "What I think these national strategies need is a distinction that says for win-win applications the rational strategy for every country is not just to allow collaboration but actually to promote it. And to find ways to, for example, share medical data internationally, and share algorithms and the hard engineering work." Details: Mitchell said that AI applications in healthcare, education, and smart cities could benefit from researchers in different countries working together.

Humans have an innate ability to recognize and understand the nuance and context of language. Here's a simple example: An AI-powered data extraction solution may recognize a word in a policy document, but if there is no meaning or context to the word, the data extracted from that document is not actionable. For example, a system may recognize that "John Smith" is a name, but is it the name of the broker or the policyholder? Likewise, is the word "orange" in an insurance document a color or a county in California? In order to be effective, AI solutions not only need to read like humans, they need to master the unique lexicon and business logic of insurance at least as well as an insurer's human knowledge workers on their best day.

With more than 16,000 franchised car dealerships in the U.S. alone, many of which are concentrated in the same urban areas, the struggle to win customers is real. And the stakes are only getting higher. To keep up with the competition, every dealership tries to hire great people--an extremely important component to sales success. But not all dealerships have the same tools. And often, the difference between the dealerships that thrive and those that lose out to the competition comes down to technology. In today's competitive auto sales market, many dealerships are using artificial intelligence to analyze large volumes of behavioral customer data.

The rush of intelligent machines and sophisticated analytics into many aspects of work means that trainees are losing opportunities to acquire skills through on-the-job learning (OJL). In medicine, policing, and other fields, people are finding rule-breaking ways to acquire needed expertise out of the limelight. This "shadow learning" is tolerated for the results it produces, but it can exact a personal and an organizational toll. In response, organizations should carefully uncover and study shadow learning; adapt practices that develop organizational, technological, and work designs that enhance OJL; and make intelligent machines part of the solution. It's 6:30 in the morning, and Kristen is wheeling her prostate patient into the OR. Today she's hoping to do some of the procedure's delicate, nerve-sparing dissection herself. The attending physician is by her side, and their four hands are mostly in the patient, with Kristen leading the way under his watchful guidance. The work goes smoothly, the attending backs away, and Kristen closes the patient by 8:15, with a junior resident looking over her shoulder.

In her new book Race After Technology: Abolitionist Tools for the New Jim Code, Ruha Benjamin breaks down the "New Jim Code," technology design that promises a utopian future but serves racial hierarchies and racial bias. When people change how they speak or act in order to conform to dominant norms, we call it "code-switching." And, like other types of codes, the practice of code-switching is power-laden. Justine Cassell, a professor at Carnegie Mellon's Human-Computer Interaction Institute, creates educational programs for children and found that avatars using African American Vernacular English lead Black children "to achieve better results in teaching scientific concepts than when the computer spoke in standard English." But when it came to tutoring the children for class presentations, she explained that, "We wanted it [the avatar] to practice with them in'proper English.' Standard American English is still the code of power, so we needed to develop an agent that would train them in code-switching."