The United States is blacklisting a group of Chinese tech companies that develop facial recognition and other artificial intelligence technology that the U.S. says is being used to repress China's Muslim minority groups. A move Monday by the U.S. Commerce Department seeks to put the companies on a so-called Entity List for acting contrary to American foreign policy interests. The blacklist effectively bars U.S. firms from selling technology to the Chinese companies without government approval. The blacklisted companies include Hikvision, a global provider of video surveillance technology. Prominent Chinese AI firms such as Sense Time, Megvii and iFlytek are also on the list.

We show how complexity theory can be introduced in machine learning to help bring together apparently disparate areas of current research. We show that this new approach requires less training data and is more generalizable as it shows greater resilience to random attacks. We investigate the shape of the discrete algorithmic space when performing regression or classification using a loss function parametrized by algorithmic complexity, demonstrating that the property of differentiation is not necessary to achieve results similar to those obtained using differentiable programming approaches such as deep learning. In doing so we use examples which enable the two approaches to be compared (small, given the computational power required for estimations of algorithmic complexity). We find and report that (i) machine learning can successfully be performed on a non-smooth surface using algorithmic complexity; (ii) that parameter solutions can be found using an algorithmic-probability classifier, establishing a bridge between a fundamentally discrete theory of computability and a fundamentally continuous mathematical theory of optimization methods; (iii) a formulation of an algorithmically directed search technique in non-smooth manifolds can be defined and conducted; (iv) exploitation techniques and numerical methods for algorithmic search to navigate these discrete non-differentiable spaces can be performed; in application of the (a) identification of generative rules from data observations; (b) solutions to image classification problems more resilient against pixel attacks compared to neural networks; (c) identification of equation parameters from a small data-set in the presence of noise in continuous ODE system problem, (d) classification of Boolean NK networks by (1) network topology, (2) underlying Boolean function, and (3) number of incoming edges.

Artificial intelligence (AI) holds great promise to empower us with knowledge and augment our effectiveness. We can -- and must -- ensure that we keep humans safe and in control, particularly with regard to government and public sector applications that affect broad populations. How can AI development teams harness the power of AI systems and design them to be valuable to humans? Diverse teams are needed to build trustworthy artificial intelligent systems, and those teams need to coalesce around a shared set of ethics. There are many discussions in the AI field about ethics and trust, but there are few frameworks available for people to use as guidance when creating these systems. The Human-Machine Teaming (HMT) Framework for Designing Ethical AI Experiences described in this paper, when used with a set of technical ethics, will guide AI development teams to create AI systems that are accountable, de-risked, respectful, secure, honest, and usable. To support the team's efforts, activities to understand people's needs and concerns will be introduced along with the themes to support the team's efforts. For example, usability testing can help determine if the audience understands how the AI system works and complies with the HMT Framework. The HMT Framework is based on reviews of existing ethical codes and best practices in human-computer interaction and software development. Human-machine teams are strongest when human users can trust AI systems to behave as expected, safely, securely, and understandably. Using the HMT Framework to design trustworthy AI systems will provide support to teams in identifying potential issues ahead of time and making great experiences for humans.

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.

Sepsis is a major medical issue. In the next week, an estimated 5,000 people will die from sepsis in the U.S. alone, and one third of all hospital deaths are related to sepsis (according to U.S. Centers for Disease Control and Prevention figures). These deaths are preventable, but by the time sepsis is detected, it's often already too late. One way to reduce incidences of sepsis is with the application of artificial intelligence. The staff at Sentara Healthcare are using an AI-enabled prescriptive analytic tool developed by Jvion, which identifies who is at risk of sepsis, alerts clinicians and suggests interventions tailored to each patient's needs.

His incentive at the time, he wrote in a reminiscence, was to assist the National Security Agency, which employed "vast numbers of transcribers and translators to make sense of a multitude of communication channels they monitored." In one instance he had ARPA researchers work on using artificial intelligence to transcribe manual Morse code. "In my view, he was one of the few people who really thought about how science and technology serve national security," said Sharon Weinberger, author of "The Imagineers of War: The Untold Story of DARPA, the Pentagon Agency That Changed the World" (2017). "He saw the role of strategy, not just widgets or weapons to serve the Pentagon, but the bigger picture around it." Dr. Lukasik was an early champion of the Arpanet, which began as an experiment in computer networking.

Deepfake videos have the potential to do unprecedented amounts of harm so California has introduced two bills designed to limit them. For those unaware, deepfakes use machine learning technology in order to make a person appear like they're convincingly doing or saying things which they're not. Many celebrities have become victims of deepfake porn. One of the bills signed into law by the state of California last week allows victims to sue anyone who puts their image into a pornographic video without consent. Earlier this year, Facebook CEO Mark Zuckerberg became the victim of a deepfake.

It's karaoke-rehearsal time at Knollwood Military Retirement Community, a 300-bed facility tucked away in a leafy corner of northwest Washington, D.C. Knollwood resident and retired U.S. Army Colonel Phil Soriano, 86, has hosted the facility's semi-monthly singalongs since their debut during a boozy snowstorm happy hour in 2016. For the late August 2019 show, he'll share emcee duties with a special guest: Stevie, a petite and personable figure who's been living at Knollwood for the last six weeks. Soriano wants to sing the crowd-pleasing hit "YMCA" while Stevie leads the crowd through the song's signature dance moves. But Stevie is a robot, and this is harder than it sounds. "We could try to make him dance," says Niamh Donnelly, the robot's lead AI engineer, though she sounds dubious. She enters commands on a laptop.

By the time today's youth retire, or perhaps sooner, they might see artificial intelligence and machine learning change just about everything in the fab shop. There's an old saying in manufacturing: Automation is only as good as what you tell it to do. Richard Boyd has spent a career proving this statement wrong. Boyd has worked with Hollywood studios and computer gaming companies; launched Virtual World Labs that concentrated on virtual reality, augmented reality, and artificial intelligence; and then sold that company to Lockheed-Martin, where he worked for a time before striking out on his own again. A speaker at this year's FABTECH show in Chicago, Boyd is founder and CEO of Tanjo (rhymes with "bongo"), a Carrboro, N.C., company specializing in AI and machine learning.

A new California law will ban the creation and distribution of deepfake pornography produced without the consent of the person it depicts. Statutory damages range between $1,500 and $30,000, while cases in which malice can be demonstrated, damages rise to $150,000. The bill is part of a larger deepfake package that will also make it illegal to create and distribute deepfake videos of political figures within 60 days of an election. Katy Perry's face (pictured above) was swapped onto an adult film actress' body for a short video, something that the new California law will make illegal. Almost all deepfake videos are circulated online are pornographic, with one study suggesting the figure is 96 percent.