As the hundreds of people who have publicly posted pictures of their debit cards on Twitter can attest, it's often easy to unwittingly expose private information in the age of social media. But what if a friendly automated assistant, similar to Siri or Alexa, warned you before you share sensitive images, potentially mitigating threats like online stalking and identity theft? That's the idea behind a recent study from researchers at the Max Planck Institute for Informatics in Germany, who say they've built an AI-powered privacy watchdog that can learn a person's privacy preferences and caution them whenever private information might be exposed in the pictures they post to social media. "Our model is trained to predict the user specific privacy risk and even outperforms the judgment of the users, who often fail to follow their own privacy preferences," the researchers write in a recent paper, which awaits peer review. "In fact -- as our study shows -- people frequently misjudge the privacy relevant information content in an image -- which leads to failure of enforcing their own privacy preferences."

In his session at @ThingsExpo, Eric Lachapelle, CEO of the Professional Evaluation and Certification Board (PECB), will provide an overview of various initiatives to certifiy the security of connected devices and future trends in ensuring public trust of IoT. Speaker Bio Eric Lachapelle is the Chief Executive Officer of the Professional Evaluation and Certification Board (PECB), an international certification body. His role is to help companies and individuals to achieve professional, accredited and worldwide recognized certification against various international standards. He also has extensive experience as a trainer and an educator in the fields of Information Security, Risk Management and IT. Throughout his career, he has worked in North America, Latin America and Asia with individuals and various companies of all sizes.

Here's a frustrating fact for anyone who has been prescribed medication or therapy for depression: Your doctor doesn't know what treatment will work for you. "It is currently complete primitive guesswork," Leanne Maree Williams, a professor at Stanford University, says. "It's hard to imagine how you can do worse than the current situation, to be honest." Depression means being stuck in a chronic state of sad mood or lack of enjoyment in life, to a degree where it starts to degrade quality of life. The two main treatments are cognitive behavioral therapy (CBT), a talk-centered approach that gets patients to readjust their habits, and antidepressant medications.

Adam Marcus will host a session, Human-assisted AI at B12: 10 lessons in giving humans superpowers, at the O'Reilly Artificial Intelligence Conference, June 26-29, 2017, in New York City. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the O'Reilly Data Show, I spoke with Adam Marcus, co-founder and CTO of B12, a startup focused on building human-in-the-loop intelligent applications. We talked about the open source platform Orchestra,for coordinating human-in-the-loop projects; the current wave of human-assisted AI applications; best practices for reviewing and scoring experts; and flash teams.

The history of healthcare delivery across the globe is more or less the same. While we have seen eradication of some life-threatening diseases, daily medical delivery has seen no significant development, in sync with times. The last transformative change to take place, drastically improving healthcare delivery systems, was with the adoption of computers. Mere incremental changes in a field that needs to be synonymous with transformative ideas, has crippled its pace. At the same time, the world's population is growing at a rapid pace & number of outbreaks and the number of kinds of disease have both increased significantly since 1980, as per a 2014 study published in Journal of Royal Society Interface.

IBM today announced that its scientists have been granted a patent on machine learning models to predict therapeutic indications and side effects from various drug information sources. IBM Research has implemented a cognitive association engine to identify significant linkages between predicted therapeutic indications and side effects, and a visual analytics system to support the interactive exploration of these associations. This approach could help researchers in pharmaceutical companies to generate hypotheses for drug discovery. For instance, strongly correlated disease-side-effect pairs identified by the patented invention could be beneficial for drug discovery in many ways. One could use the side-effect information to repurpose existing treatments (e.g.

In certain parts of the world, cow collisions are the stuff of legend. A car traveling at any significant speed up against a full-grown cow travelling at no speed is most often the end of the car, the cow, and maybe the human(s) inside of the car. That's up to 1,800 pounds of beef standing there with a center of gravity well-optimized to ensure that those pounds wind up where they can be most dangerous. Cows on roads are a big problem in India, where rapid urbanization and industrialization has meant that new roads are increasingly being laid through rangeland. A 2015 study found that some 6 percent of accidents in India can be attributed to animals on the road.

Last week, Treasury Secretary Steven Mnuchin said he wasn't worried at all about advancing artificial intelligence taking over jobs anytime soon. In fact, he said, he wouldn't be worried about it for for another 50 to 100 years. As I wrote recently, many experts would disagree with the notion that displacement--or at the very least, shifts--in the labor market due to automation are that far afield. Recent studies from McKinsey and the economists Carl Benedikt Frey and Michael A. Osborne estimate that around 45 percent of workers currently perform tasks that could be automated in the near future. And the World Bank estimates that around 57 percent of jobs could be automated within the next 20 years.

A new white paper by the Robotics Industries Association (RIA) says that as many as 2 million US manufacturing jobs will go unfilled in the next ten years due to a lack of skilled workers. According to the paper: "80% of manufacturers report a shortage of qualified applicants for skilled production positions, and the shortage could cost US manufacturers 11% of their annual earnings." Sought-after skills include: computer vision, algorithm design, robotics, vision systems, motion control, robot design, safety expertise, application developers, human-robot interface design, PLC controls, mechatronics, networking, and integration. According to Deloitte's 2016 Global Manufacturing Competitiveness Index, manufacturers rank talent as the most critical driver of global manufacturing competitiveness. "The skills gap is the industry's number one concern," said A3 President Jeff Burnstein in an interview, "and it's threatening the US manufacturing industry's ability to compete globally."

With more than 12,500 patents, eight Nobel prizes, and a 140 year history of field-testing crazy ideas, no one should be surprised that AT&T would be an important player in artificial intelligence. "AT&T is a backbone of the internet," explains Nadia Morris, Head of Innovation at the AT&T Connected Health Foundry. The company manages wireless, landline, and even private secure networks to power connectivity for both individuals and corporations. All these networks generate incredible volumes of data ripe for machine analysis. AT&T has built AI and machine learning systems for decades, using algorithms to automate operations such as common call center procedures and the analysis and correction of network outages.