Artificial intelligence is getting personal. Advances in massive data gathering and processing, coupled with wholesale leaps in machine processing power are setting the stage for a world where AI could closely simulate the human brain and become ingrained in many aspects of daily life. How soon that becomes reality could depend a lot on the humans who control it, because the ability to run basic AI initiatives has evolved to a point where generating a machine-learning model is as easy as planting roses in the backyard. "People like Google and Facebook have made it so easy for the average person to actually do an AI project," said Bob Friday (pictured, second from right), vice president and chief technology officer of Mist, a Juniper company. "Anyone in your audience could actually train a machine-learning model over the weekend. You personally could become a data scientist over the weekend."

Tempus, a technology company advancing precision medicine through the practical application of artificial intelligence in healthcare, today announced that Dr. Scott Gottlieb, MD, has joined its Board of Directors. Dr. Gottlieb will serve as an advisor to Tempus and its leadership team, leveraging his vast experience as a medical policy expert and public health advocate to support Tempus as it harnesses the power and promise of big data and artificial intelligence to personalize patient care. Dr. Gottlieb most recently served as the 23rd Commissioner of the Food and Drug Administration (FDA), where he focused on developing innovative approaches to improving medical outcomes, reshaping healthcare delivery, and expanding patient choice and safety. Previously, he also served as the agency's Deputy Commissioner for Medical and Scientific Affairs and as a Senior Advisor to the FDA Commissioner. During his time as a Senior Advisor to the Administrator of the Centers for Medicare and Medicaid Services, Dr. Gottlieb worked on the implementation of the Medicare drug benefit, where he supported policy work on quality improvement and the agency's coverage process, particularly as it related to new medical technologies.

The Ethics Guidelines for Trustworthy Artificial Intelligence (AI) is a document prepared by the High-Level Expert Group on Artificial Intelligence (AI HLEG). This independent expert group was set up by the European Commission in June 2018, as part of the AI strategy announced earlier that year. The AI HLEG presented a first draft of the Guidelines in December 2018. Following further deliberations by the group in light of discussions on the European AI Alliance, a stakeholder consultation and meetings with representatives from Member States, the Guidelines were revised and published in April 2019. In parallel, the AI HLEG also prepared a revised document which elaborates on a definition of Artificial Intelligence used for the purpose of its deliverables.

An amazing opportunity at Regions Bank put a move from Pennsylvania to Alabama on my radar, but one thing stood in the way: my faith. My wife and I are Jewish, and we were concerned we'd lose the religious community we had surrounding us in Pittsburgh. But upon visiting Birmingham, we found exactly what we needed, and more. Living in Alabama has allowed me to build meaningful connections in my work, my faith and in public service. My team and I serve Regions' mission of making life better for our customers, associates and communities by making sure our mathematical models perform well.

Geoscientists invented a machine learning tool to sift through satellite data. Today's Earth scientists are spending less time standing in fields collecting soil samples, and more time behind a computer screen. Most geoscience data is automatically collected by sensors and satellites. The big challenge is making sense of all that data so that scientists can get back to what they do best: Observing the world, asking questions, conducting experiments, and finding evidence. Scientists use large, publicly available datasets from government programs such as NASA, NOAA, and USGS (that's the National Aeronautics and Space Administration, the National Oceanic and Atmospheric Administration, and US Geological Survey, in non-acronym speak). Many Earth scientists also have private sources, and combining these public and private datasets is difficult and time-consuming.

It's a well-established fact that object- and face-detecting algorithms are vulnerable to adversarial attack, as evidenced by a 2014 study conducted by researchers at Google and New York University. That's to say the models can be deceived by specially-crafted patches attached to real-world targets. Most research in adversarial attacks involves rigid objects like glass frames, stop signs, or cardboard. But scientists at Northwestern University and the MIT-IBM Watson AI Lab propose what they call an'adversarial' T-shirt, a t-shirt with a printed adversarial example that evades person detectors even when it's deformed by a wearer's changing pose. In a preprint paper, they claim that it manages to achieve up to 79% and 63% attack success rates in digital and physical worlds, respectively, against the popular YOLOv2 model.

When the price went way up on a key service a small Washington, D.C., firm was using to protect its customers' internet connectivity, the company balked. After not finding a suitable alternative, the company decided to build its own. The result was a whole new business, called DNSFilter, which is casting a wide net around the market to combat phishing and malware. Its innovation: It ditched the crowdsourcing model that has served for more than a decade as the bedrock for identifying whether websites are valid or corrupt. It opted, instead, for GPU-powered AI to make web surfing safer by identifying threats and objectionable content much faster than traditional offerings. "We figured that if we built a whole new DNS from the ground up, built on artificial intelligence and machine learning, we could find threats faster and more effectively," said Rustin Banks, chief revenue officer and one of four principals at DNSFilter.

Close to 10 percent of the U.K.'s workforce -- about 1.5 million workers -- occupy jobs that are at "high risk" of automation. That's according to a study today published by the U.K.'s Office for National Statistics (ONS), which found that of the 19.9 million people the department surveyed in 2017, approximately 7.4 percent could be replaced by autonomous machines in the coming years. ONS forecasts that service workers -- chiefly waiters and waitresses, retail inventory restockers, and entry-level salespeople -- will be disproportionately affected; it reports that 25.3 percent of supermarket checkout jobs disappeared between 2011 and 2017. And it expects agricultural, automotive, and service industries won't fare much better -- they've seen a collective 15 percent decline in the same timeframe. Women, who in 2017 held 70.2

IATA sees RFID (radio frequency identification) as one of the keys to transforming the baggage handling process. SITA worked with IATA back in 2017 on a detailed business case, estimating that RFID could reduce the number of mishandled bags by an extra 25% and could potentially save the air transport industry $3 billion in baggage mishandling costs. Airlines and airports are now proactively working together to boost their baggage handling efforts as part of IATA's Resolution 753, which requires airlines to "maintain an accurate inventory of baggage by monitoring the acquisition and delivery of baggage". RFID tagging is now 99.98% accurate, according to IATA. Within the next four years most baggage systems will be RFID enabled, which is a huge improvement on barcodes alone.

For private companies and government agencies trying to track peoples' movements, technology is making the task increasingly easy. Facial recognition and analysis are becoming increasingly popular surveillance tools -- the technology was rolled out in airports across the world this summer as a tool for verifying flyers' identity, and is widely used by police departments for tracking suspected criminals. Privacy-minded activists and lawmakers are now hitting back at facial recognition. The technology has been banned for law-enforcement purposes across California, and a similar bill is being weighed in Massachusetts. Meanwhile, artists and researchers have begun to develop clothes designed to thwart algorithms that detect human faces. But emerging technology presents alternate means of identifying and tracking humans beyond facial recognition.