Xenofon Koutsoukos V anderbilt University Nashville, TN firstname.lastname@example.org Abstract --Cyber-physical systems (CPS) greatly benefit by using machine learning components that can handle the uncertainty and variability of the real-world. Typical components such as deep neural networks, however, introduce new types of hazards that may impact system safety. The system behavior depends on data that are available only during runtime and may be different than the data used for training. Out-of-distribution data may lead to a large error and compromise safety. The paper considers the problem of efficiently detecting out-of-distribution data in CPS control systems. Detection must be robust and limit the number of false alarms while being computational efficient for real-time monitoring. The proposed approach leverages inductive confor-mal prediction and anomaly detection for developing a method that has a well-calibrated false alarm rate. We use variational autoencoders and deep support vector data description to learn models that can be used efficiently compute the nonconformity of new inputs relative to the training set and enable real-time detection of out-of-distribution high-dimensional inputs. We demonstrate the method using an advanced emergency braking system and a self-driving end-to-end controller implemented in an open source simulator for self-driving cars. The simulation results show very small number of false positives and detection delay while the execution time is comparable to the execution time of the original machine learning components. I NTRODUCTION Learning-enabled components (LECs) such as neural networks are used in many classes of cyber-physical systems (CPS). Semi-autonomous and autonomous vehicles, in particular, are CPS examples where LECs can play a significant role for perception, planning, and control if they are complemented with methods for analyzing and ensuring safety , . However, there are several characteristics of LECs that can complicate safety analysis. LECs encode knowledge in a form that is not transparent.
Google, Facebook and other internet giants would disclose the algorithms they use to return search results under new legislation proposed by US law makers. The bipartisan Filter Bubble Transparency Act also would require the online companies to offer users an unfiltered search option that delivers results without any algorithmic tinkering. Senator John Thune, a Republican from North Dakota, filed the bill on Friday. The legislation was co-sponsored by Republican senators Jerry Moran of Kansas and Marsha blackburn of Tennessee, as well as Democrats Richard Blumenthal of Connecticut and Mark Warner of Virginia. Senator John Thune, a Republican from North Dakota, filed the bipartisan'Filter Bubble Transparency Act,' which would require internet companies to reveal algorithms used to determine online searches The online firm, owned by Alphabet, like other internet companies relies on algorithms - a highly-specific set of instructions to computers - that track users' behavior and location Thune says the legislation is needed because'people are increasingly impatient with the lack of transparency,' on the internet, reports the Wall Street Journal.
NASHVILLE, Tenn.--(BUSINESS WIRE)--Change Healthcare (Nasdaq: CHNG), today announced that its artificial intelligence (AI) technology has been added to the CareSelect Imaging decision support solution. The new AI capabilities will help healthcare providers using leading electronic health record (EHR) systems enhance workflow efficiency, improve patient safety, provide higher-value care, and meet pending regulatory requirements. CareSelect Imaging now uses Change Healthcare AI in EHR workflow to help physicians streamline imaging orders. In addition, it helps providers comply with new Protecting Access to Medicare Act (PAMA) requirements governing advanced imaging ordered under Medicare Part B. "Bringing Change Healthcare AI to CareSelect Imaging helps providers ensure they're delivering the highest quality, most appropriate care, while reducing their administrative and regulatory burdens through advanced automation," said Michael Mardini, CEO of National Decision Support Company, a Change Healthcare Company. "This is a perfect example of how strategic applications of AI will continue to improve healthcare processes and benefit all stakeholders."
In 2012, Geoffrey Hinton's research team used only two NVIDIA GPUs to train AlexNet, the revolutionary network architecture that handily won the ImageNet Large Scale Visual Recognition Challenge. It probably never occurred to these groundbreaking researchers that just seven years later, a new team of researchers would use almost 10,000 times more GPUs to train their AI model. A research team from NVIDIA, Oak Ridge National Laboratory (ORNL), and Uber has introduced new techniques that enabled them to train a fully convolutional neural network on the world's fastest supercomputer, Summit, with up to 27,600 NVIDIA GPUs. They managed to achieve an impressive, near-linear scaling of 0.93 on distributed training and produce a model capable of atomically-accurate reconstruction of materials -- a longstanding scientific problem involving materials imaging. In June 2018 the US Department of Energy's Oak Ridge National Laboratory in Tennessee unveiled the world's fastest supercomputer Summit, whosecomputing power reaches 200 petaflops.
Jurisdictions might be on-the-hook for their self-driving car laws that allow autonomous cars and for which might get into mishaps or crashes. Florida just passed a law that widens the door for self-driving driverless cars to roam their public roadways and do so without any human back-up driver involved. Some see dangers afoot, others see progress and excitement. Ron DeSantis, governor of Florida, declared that by approving the new bill it showed that "Florida officially has an open-door policy to autonomous vehicle companies." There are now 29 states that have various driverless laws on their books, per the National Conference of State Legislatures: Alabama, Arkansas, California, Colorado, Connecticut, Florida, Georgia, Illinois, Indiana, Kentucky, Louisiana, Maine, Michigan, Mississippi, Nebraska, New York, Nevada, North Carolina, North Dakota, Oregon, Pennsylvania, South Carolina, Tennessee, Texas, Utah, Virginia, Vermont, Washington, and Wisconsin, plus Washington, D.C. Here's a question that some politicians and regulators are silently grappling with, albeit some think that they have the unarguably "right" answer and thusly have no need to lose sleep over the matter: Should states, counties, cities and townships be eagerly courting self-driving autonomous cars onto their public roadways, or should those jurisdictions be neutral about inviting them into their locales, or should they be highly questioning and require "proof until proven safe" before letting even one such autonomous car onto their turf?
On an isolated stretch of industrial flatland outside Knoxville, Tenn., a minibus is taking shape in a car factory unlike any other. The space is small, the size of a supermarket, and all but tool-free. Instead, perched in the center is the world's largest 3D printer, a gangly 10-by-40-foot behemoth with a steel-gray exterior, thick columnar footings, and derrick-like roof beams to true its frame. When the print heads are in motion, the equipment emits little more than a whisper, dexterously cutting sharp angles and rounded edges. Programmers on laptops and quality-control experts with tablets mill around, inputting design changes and fine-tuning the minibus's sensor instructions. Beyond the assembly room lies a kind of alchemist's playground, where young staffers with advanced degrees in materials science and mechanical engineering synthesize nanopolymers or test exotic particles for strength or thermal and electrical conductivity.
Along America's west coast, the world's most valuable companies are racing to make artificial intelligence smarter. Google and Facebook have boasted of experiments using billions of photos and thousands of high-powered processors. But late last year, a project in eastern Tennessee quietly exceeded the scale of any corporate AI lab. It was run by the US government. The record-setting project involved the world's most powerful supercomputer, Summit, at Oak Ridge National Lab.
Along America's west coast, the world's most valuable companies are racing to make artificial intelligence smarter. Google and Facebook have boasted of experiments using billions of photos and thousands of high-powered processors. Late last year, a project in eastern Tennessee quietly exceeded the scale of any corporate AI lab. It was run by the US government. The record-setting project involved the world's most powerful supercomputer, Summit, at Oak Ridge National Lab.
To what extent can your doctor's functions be automated -- replaced or enhanced by intelligent machines? How might such automation improve care and reduce costs? These questions are central to understanding Clover Health -- a California-based company providing Medicare Advantage insurance plans in seven states: New Jersey, Pennsylvania, Tennessee, Georgia, Arizona, South Carolina and Texas. A while back, I hosted a dinner in New York for a dozen-plus health care innovators -- entrepreneurs, medical school professors, futurists, etc. Someone in the room asked, "How much of today's physician services can be reduced to algorithms?" An algorithm is a set of instructions (like a computer program) leading to unambiguous results.
Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. On May 9, the Department of Transportation announced the first 10 project sites it chose to participate in its new three-year Drone Integration Pilot Program aimed at expanding the testing of new drone technology in a select number of local, state, and tribal jurisdictions. Selected from 149 lead applicants and over 2,800 private sector "interested parties," they're an eclectic bunch: the Choctaw Nation of Oklahoma; projects in the city of San Diego; the Innovation and Entrepreneurship Investment Authority in Herndon, Virginia; the Lee County Mosquito Control District in Florida; the Memphis–Shelby County Airport Authority in Tennessee; the North Carolina, Kansas, and North Dakota departments of transportation; the city of Reno, Nevada; and the University of Alaska–Fairbanks all saw their specific public-private partnership proposals get the greenlight. The projects include plans to test various kinds of unmanned aircraft systems (UAS for short, as they are formally known), including drone-based mapping, inspections, traffic and weather monitoring, commercial and medical delivery, and law enforcement surveillance systems. Selected applicants will be given special attention from the Federal Aviation Administration.