WASHINGTON – The United States said new evidence and analysis of weapons debris recovered from an attack on Saudi oil facilities on Sept. 14 indicates the strike likely came from the north, reinforcing its earlier assessment that Iran was behind the offensive. In an interim report of its investigation -- seen by Reuters ahead of a presentation on Thursday to the United Nations Security Council -- Washington assessed that before hitting its targets, one of the drones traversed a location approximately 200 km (124 miles) to the northwest of the attack site. "This, in combination with the assessed 900 kilometer maximum range of the Unmanned Aerial Vehicle (UAV), indicates with high likelihood that the attack originated north of Abqaiq," the interim report said, referring to the location of one of the Saudi oil facilities that were hit. It added the United States had identified several similarities between the drones used in the raid and an Iranian designed and produced unmanned aircraft known as the IRN-05 UAV. However, the report noted that the analysis of the weapons debris did not definitely reveal the origin of the strike that initially knocked out half of Saudi Arabia's oil production.

NASA's Mars 2020 rover has successfully'passed its driving test' in a major mission milestone that saw it move under its own weight ahead of its launch next year. The rover will leave for Mars in July or August 2020 from the Cape Canaveral Air Force Station in Florida and will travel aboard the new Space Launch System rocket. NASA's robotic vehicle had to demonstrate it could move forwards, backwards and pirouette during the more than 10-hour marathon'driving test' on Tuesday. The next time the Mars 2020 rover drives, it will be rolling over Martian soil. The semi-autonomous vehicle will search for signs of ancient microbial life within the Jezero crater, which contains a dried up lake once filled with water.

From 2009 – 2014, I built one of the first commercially viable robo-advisors, the first deep-learning network that could detect market-moving Congressional legislation, and one of the earliest trading algorithms for illiquid markets. I built those products inside some of the largest financial institutions in the world. And every one of those products was shut down by those banks. Everyone who was part of those teams quit or was fired. But none of the innovations died.

Our aim was to evaluate the performance of machine learning (ML), integrating clinical parameters with coronary artery calcium (CAC), and automated epicardial adipose tissue (EAT) quantification, for the prediction of long-term risk of myocardial infarction (MI) and cardiac death in asymptomatic subjects. Our study included 1912 asymptomatic subjects [1117 (58.4%) male, age: 55.8 9.1 years] from the prospective EISNER trial with long-term follow-up after CAC scoring. EAT volume and density were quantified using a fully automated deep learning method. ML extreme gradient boosting was trained using clinical co-variates, plasma lipid panel measurements, risk factors, CAC, aortic calcium, and automated EAT measures, and validated using repeated 10-fold cross validation. During mean follow-up of 14.5 2 years, 76 events of MI and/or cardiac death occurred. ML obtained a significantly higher AUC than atherosclerotic cardiovascular disease (ASCVD) risk and CAC score for predicting events (ML: 0.82; ASCVD: 0.77; CAC: 0.77, P 0.05 for all). Subjects with a higher ML score (by Youden's index) had high hazard of suffering events (HR: 10.38, P 0.001); the relationships persisted in multivariable analysis including ASCVD-risk and CAC measures (HR: 2.94, P 0.005). Age, ASCVD-risk, and CAC were prognostically important for both genders. Systolic blood pressure was more important than cholesterol in women, and the opposite in men.

According to McKinsey Global Institude study on impact of AI and automation, transportation and warehousing are one of the most automatable sectors of economy (3rd place), with 60% potential for automation. Predicting the future demands for production and supplies, improving transportation routines, or automating physical inspection and maintenance are some of vast possibilities to use data science in supply chain management. While self-driving cars seem to be a future of transport, we would like to focus on optimizing "here and now", without changing the market – just with smart, data-driven decisions. We would like to focus on a problem of choosing location of warehouse to minimise cost of both freight and warehouse maintenance. It is a complex Data Science problem, that is composed of various independent components that need to be optimised.

The doomsday headlines about automation and job displacement continue to pile up. Nearly half of all jobs are at risk, one report said. Another found that white-collar jobs once thought safe are in the crosshairs. "I fundamentally don't believe that," Mader said of automation displacing huge swaths of workers during a recent speech at Seattle University's Albers School of Business and Economics. "There is so much unstructured work for which you can't actually program the robot to do that work."

To judge from the hype, artificial intelligence is inches away from ripping through the economy and destroying everyone's jobs--save for the AI scientists who build the technology and the baristas and yoga instructors who minister to them. But one critic of that view comes from within the tent of AI itself: Gary Marcus. From an academic background in psychology and neuroscience--rather than computer science--Mr Marcus has long been an AI gadfly. He relishes poking holes in the popular AI technique of deep-learning because of its inability to perform abstractions even as it does an impressive job at pattern-matching. Yet his unease with the state of the art didn't prevent him from advancing the art with his own AI startup, Geometric Intelligence, which he sold to Uber in 2016.

This is the third Synced year-end compilation of "Artificial Intelligence Failures." Despite AI's rapid growth and remarkable achievements, a review of AI failures remains necessary and meaningful. Our aim is not to downplay or mock research and development results, but rather to take a look at what went wrong with the hope we can do better next time. A leading facial-recognition system identified three-time Super Bowl champion Duron Harmon of the New England Patriots, Boston Bruins forward Brad Marchand, and 25 other New England professional athletes as criminals. Amazon's Rekognition software incorrectly matched the athletes to a database of mugshots in a test organized by the Massachusetts chapter of the American Civil Liberties Union (ACLU).

The year was 2008 and the Interop tech trade show in New York City was crammed full of booths. Sales reps offered trinkets as they hawked their next-gen software and hardware. I wondered through blinking displays and the noise of a thousand buzzwords. The brand name vendors – Microsoft, Intel, Oracle, IBM – had paid big bucks for booths the size of small houses. Staffers gave product lectures backed by full-size video screens. After touring these big outfits, I investigated the smaller booths hosted by mid-sized players. With minimal staff, they worked still harder to lure you to their pitch. In an era before "Booth Babes" were outlawed, some booths included twenty-something women in skimpy sequined outfits, handing out t-shirts or glow-in-the-dark key chains. Tiny booths staffed mostly by bare bones crews. There I saw a modest booth by an outfit called Amazon Web Services. A sole rep manned it, and he wasn't wearing a company shirt.

Is it possible that lakes and forests might have rights before robots? Voters in Toledo have granted "irrevocable rights for the Lake Erie Ecosystem to exist, flourish and naturally evolve" which, according to this story, would give it legal standing to file lawsuits to protect itself from polluters (through the mouthpiece of a human guardian). It's an amazingly bold statement that is rife with thorny questions. Humans have had say over nature ever since Adam and Eve, and most political and cultural uses or abuses have been based on the shifting perspectives of their progeny. Nature is something "out there" that only gains meaning or purpose when defined by us.