Urban traffic roughly follows a periodic pattern associated with the typical "9 to 5" work schedule. However, when an accident happens, traffic patterns are disrupted. Designing accurate traffic flow models, for use during accidents, is a major challenge for traffic engineers, who must adapt to unforeseen traffic scenarios in real time. A team of Lawrence Berkeley National Lab computer scientists are working with the California Department of Transportation (Caltrans) to use high performance computing (HPC) and machine learning to help improve Caltrans' real-time decision making when incidents occur. The research was done in conjunction with California Partners for Advanced Transportation Technology (PATH), part of UC Berkeley's Institute for Transportation Studies (ITS), and Connected Corridors, a collaborative program to research, develop, and test an Integrated Corridor Management approach to managing transportation corridors in California.

Arterial streets surrounding the I-210 freeway in southern California, where the first traffic prediction pilot is taking place. Urban traffic roughly follows a periodic pattern associated with the typical "9 to 5" work schedule. However, when an accident happens, traffic patterns are disrupted. Designing accurate traffic flow models, for use during accidents, is a major challenge for traffic engineers, who must adapt to unforeseen traffic scenarios in real time. A team of Lawrence Berkeley National Laboratory (Berkeley Lab) computer scientists is working with the California Department of Transportation (Caltrans) to use high performance computing (HPC) and machine learning to help improve Caltrans' real-time decision making when incidents occur.

A recent video leaked from ABC appears to show anchor Amy Robach admit that her network let outside pressure influence its coverage of the case against infamous pedophile Jeffrey Epstein. Meanwhile, Epstein's rich and powerful alleged co-conspirators have managed to dodge prosecution. Police are subpoenaing an Alexa Echo device as part of a murder investigation regarding a Florida woman whose boyfriend allegedly killed her with a spear in July. A new report by the National Security Commission on Artificial Intelligence warns of the inseparability of AI development from "emerging strategic competition with China." Then former naval intelligence officer John Jordan shares his insights.

Accelerated design, optimization, and tuning of materials via machine learning is receiving increasing interest in science and industry. A major driver of this interest is the potential to reduce the substantial cost and effort involved in manual development, synthesis, and characterization of large numbers of candidate materials. The primary aim is to reduce the number of both failed candidates and development cycles. A data-driven approach to achieve this acceleration is active learning (AL) [23], an iterative procedure in which a machine-learning model suggests candidate materials, a selection of which are synthesized, characterized, and fed back into the model to complete a learning iteration. The objective of this procedure varies; in materials informatics it is often to identify promising material candidates by optimizing properties of interest.

Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded. Over 100 organizations are building ML inference chips, and the systems that incorporate existing models span at least three orders of magnitude in power consumption and four orders of magnitude in performance; they range from embedded devices to data-center solutions. Fueling the hardware are a dozen or more software frameworks and libraries. The myriad combinations of ML hardware and ML software make assessing ML-system performance in an architecture-neutral, representative, and reproducible manner challenging. There is a clear need for industry-wide standard ML benchmarking and evaluation criteria. MLPerf Inference answers that call. Driven by more than 30 organizations as well as more than 200 ML engineers and practitioners, MLPerf implements a set of rules and practices to ensure comparability across systems with wildly differing architectures. In this paper, we present the method and design principles of the initial MLPerf Inference release. The first call for submissions garnered more than 600 inference-performance measurements from 14 organizations, representing over 30 systems that show a range of capabilities.

Convolution Neural Networks (CNN) have performed well in many applications such as object detection, pattern recognition, video surveillance and so on. CNN carryout feature extraction on labelled data to perform classification. Multi-label classification assigns more than one label to a particular data sample in a data set. In multi-label classification, properties of a data point that are considered to be mutually exclusive are classified. However, existing multi-label classification requires some form of data pre-processing that involves image training data cropping or image tiling. The computation and memory requirement of these multi-label CNN models makes their deployment on edge devices challenging. In this paper, we propose a methodology that solves this problem by extending the capability of existing multi-label classification and provide models with lower latency that requires smaller memory size when deployed on edge devices. We make use of a single CNN model designed with multiple loss layers and multiple accuracy layers. This methodology is tested on state-of-the-art deep learning algorithms such as AlexNet, GoogleNet and SqueezeNet using the Stanford Cars Dataset and deployed on Raspberry Pi3. From the results the proposed methodology achieves comparable accuracy with 1.8x less MACC operation, 0.97x reduction in latency and 0.5x, 0.84x and 0.97x reduction in size for the generated AlexNet, GoogleNet and SqueezeNet CNN models respectively when compared to conventional ways of achieving multi-label classification like hard-coding multi-label instances into single labels. The methodology also yields CNN models that achieve 50\% less MACC operations, 50% reduction in latency and size of generated versions of AlexNet, GoogleNet and SqueezeNet respectively when compared to conventional ways using 2 different single-labelled models to achieve multi-label classification.

The package company and its partner, CVS, have completed the first commercial medical drone delivery in the US. Using a M2 drone, the prescriptions were lowered down to two separate destinations via a cable while the unmanned aerial vehicle hovered 20 feet above each home. UPS and CVS, have completed the first commercial medical drone delivery in the US. The milestone is a result of UPS becoming the first drone delivery service to receive full approval from the Federal Aviation Administration. UPS and CVS carried out two flights on Friday, November 1st – both dropped off prescriptions to paying customers in Cary, North Carolina.

I've encountered a lot of artificial intelligences, both the ones I've trained for my blog AI Weirdness, and the ones I've written about for my book on artificial intelligence, You Look Like a Thing and I Love You: How AI Works and Why it's Making the World a Weirder Place. I focus on the machine learning algorithms that exist today, the ones that sort spam, tag photos, and drive cars. We call them AI, but they're as different from the AI of science fiction as a toaster is from a person. In the book, I spend a lot of time explaining why today's AIs, with their tiny worm brains, don't understand their tasks or the human world. They won't be taking over from people, but they also won't be saving us by questioning bad orders.

This article was submitted in response to the call for ideas issued by the co-chairs of the National Security Commission on Artificial Intelligence, Eric Schmidt and Robert Work. It responds to question 3 (parts a. and b.), which asks what types of AI research the national security community should focus on how the government, academia, and the private sector should work together, and what type of infrastructure the United States needs. The U.S. Army tank brigade was once again fighting in the Middle East. Its tanks were recently equipped with a computer vision-based targeting system that employed remotely controlled drones as scouts. Unfortunately, adversary forces deceived the vision system into thinking grenade flashes were actually cannon fire.

Paris, October 02, 2019 – Capgemini has developed an intelligent data platform called Project FARM (Financial and Agricultural Recommendation Models), which is designed to optimize the agricultural value chain and bolster global food supply. The platform uses Artificial Intelligence (AI) to determine farming patterns through big data, generating insights from the data to make recommendations. It uses Machine Learning to make the platform applicable at scale by connecting it with cell phones. This solution has been built in collaboration with Agrics, a social enterprise operating in East Africa, which provides local farmers with agricultural products and services on credit. Global demand for food is anticipated to increase by 60% by 2050[1].