Artificial intelligence has already started to shape our lives in ubiquitous and occasionally invisible ways. In its new documentary, In The Age of AI, FRONTLINE examines the promise and peril this technology. AI systems are being deployed by hiring managers, courts, law enforcement, and hospitals -- sometimes without the knowledge of the people being screened. And while these systems were initially lauded for being more objective than humans, it's fast becoming clear that the algorithms harbor bias, too. It's an issue Joy Buolamwini, a graduate researcher at the Massachusetts Institute of Technology, knows about firsthand. She founded the Algorithmic Justice League to draw attention to the issue, and earlier this year she testified at a congressional hearing on the impact of facial recognition technology on civil rights. "One of the major issues with algorithmic bias is you may not know it's happening," Buolamwini told FRONTLINE.

GRAVE NEW WORLD: The U.S. needs to tackle the challenges of adapting artificial intelligence systems for modern warfare, the same way the "titans of industry" transformed Detroit into an "arsenal of democracy" during World War II, Defense Secretary Mark Esper said at a conference hosted by the National Security Commission on Artificial Intelligence. "Mastering artificial intelligence will require similar vision, ambition and commitment," Esper said. "We need the full force of American intellect and ingenuity working in harmony across the public and private sectors." Artificial Intelligence, sometimes called "machine learning," refers to advanced computer algorithms that can use data to "learn" and therefore make choices without human input. Last week a Pentagon advisory board released proposed guidelines for the ethical deployment of AI-enabled weapons on the battlefield.

Two doctors are using data analysis and predictive algorithms to stretch healthcare resources in South Africa and help millions of people live with HIV. Dr. John Sargent and Dr. Ernest Darkoh co-founded BroadReach in 2003 to make the healthcare system more efficient and treat more patients. In 2010, the two developed Vantage, a data analysis platform and recommendation engine that runs on Microsoft Azure. The initial idea was to use the platform to manage and improve the public-private partnerships that support many healthcare services in Africa. The two realized that the analytic work could also improve access to healthcare in countries where there are many more people than doctors.

Prescient are the entrepreneurs who predicted data would become the new oil, like Ali Ghodsi, Andy Konwinski, Ion Stoica, Matei Zaharia, Patrick Wendell, Reynold Xin, and Scott Shenker. They're the cofounders of Databricks, a San Francisco-based company that provides a suite of enterprise-focused scalable data science and data engineering tools. Since 2013, the year Databricks opened for business, it's had no trouble attracting customers. But this week kicked into high gear the company's uninterrupted march toward market domination. Databricks this morning announced that it's closed a $400 million series F fundraising round led by Andreessen Horowitz with participation from Microsoft, Alkeon Capital Management, BlackRock, Coatue Management, Dragoneer Investment Group, Geodesic, Green Bay Ventures, New Enterprise Associates, T. Rowe Price, and Tiger Global Management.

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.