In this special guest feature, Gary M. Shiffman, PhD, Co-founder and CEO, Consilient, takes a look at Federated Machine Learning, the branch of machine learning that's sure to be a revolution for FCC professionals by enabling collaboration while preserving privacy. Gary is an applied micro-economist and business executive working to combat organized violence, corruption, and coercion. Past experiences include senior positions at the Pentagon, U.S. Senate, and the Department of Homeland Security. He is the Founder and CEO of Giant Oak, Inc. and the Co-Founder and CEO of Consilient, Inc., machine learning and artificial intelligence companies building solutions to support professionals promoting national security and combating financial crime. Dr. Shiffman is the author of The Economics of Violence: How Behavioral Science Can Transform Our View of Crime, Insurgency, and Terrorism with Cambridge University Press in 2020.

Bishop Garrison is vice president of government affairs and public policy at Paravision. It is an iron law of progress that any innovation that benefits society also has the potential for harm. We saw it with the train and the automobile. We can already see it with genetic engineering. And now we are seeing it with artificial intelligence.

Nvidia Corp, a chip designer, said on Wednesday that U.S. officials had told it to stop exporting two top AI chips for artificial intelligence work to China, a move that could cripple Chinese firms' ability to perform advanced work such as image recognition and harm Nvidia's business in the country, Reuters reports. The statement marks a significant increase in the United States' crackdown on China's technical capabilities as tensions rise over the destiny of Taiwan, where AI chips for Nvidia and nearly every other major semiconductor business are built. The embargo, which affects its A100 and H100 chips meant to accelerate machine learning operations, might impede the completion of the H100, the business's flagship AI chip launched this year, according to the company. Advanced Micro Devices, Inc. (AMD) shares declined 3.7% after hours. According to an AMD spokeswoman, the company has received new licensing requirements to prevent its MI250 artificial intelligence processors from being transported to China.

The US Air Force's AFLCMC Armament Directorate has awarded a one-year contract to Liteye Systems and Unmanned Experts to build Web Weasels (WW) autonomous swarming artificially intelligent munitions. WW is part of Unmanned Experts' parent program Air Commons – Swarm which allows commanders to plan, task, and manage multiple swarming assets through a Swarm ATO and Swarm Engine. According to Liteye, squadrons of autonomous collaborative munitions operating at range, and at risk, need the training, Tactics, Techniques and Procedures (TTPs) to handle the speed-of-datalink environment that occurs in modern combat. Teamwork, communication, shared mental models, and a robust set of tried and tested strategies are needed to survive and dominate. WW aims to overlay Artificial Intelligence and Machine Learning (AI/ML)-trained algorithms onto Air Commons – Swarm's capabilities to provide pre-launch munitions with a series of TTPs in a'Playbook' for a given mission set (i.e., SEAD).

The development of China's artificial intelligence sector is expected to be slowed in the coming few years by the United States' new ban on exports of several high-end chips made by Nvidia and AMD, say Chinese IT experts. Nvidia said last Friday it had been informed by the US government that it must stop exporting its graphics processing unit (GPU) chips, namely A100 and H100, to China and Russia. It said its DGX, an AI server, was also barred from being shipped to China if a unit contained the two chips. At the same time, media reports said the US had also restricted sales of AMD's MI250 Accelerator AI chip to China. China's Foreign Ministry said the US had typically exerted its "sci-tech hegemony" and violated the rules of the market economy with its latest chip-export restrictions.

Talk is cheap regarding companies talking up their credentials in Environment Social and Governance (ESG). But artificial intelligence and natural language processing can help identify those who are more serious about it than others. At U.S.-based fund manager Acadian Asset Management, the firm has developed a tool that uses artificial intelligence and machine learning to rank corporates by the seriousness of their intent. In the investment world, many fund managers base their decisions on which companies to invest in on how much they disclose about their activities. But this is only part of the story, says Acadian's director of responsible investing, Andy Moniz.

Matthew is Senior Editor at Emerj, focused on enterprise AI use-cases and trends. He previously served as podcast producer with CrossBorder Solutions, a venture-back AI-enabled tax solutions firm. Prior, Matthew served three years at the World Policy Institute as a news editor and podcast producer. Healthcare is an increasingly complex sector of the global economy, and AI is playing an active role in the worldwide evolution of the industry throughout its many disciplines. In a Deloitte study released earlier this year, 85% of respondents among healthcare business leaders said their enterprise was increasing AI spend before 2023.

Physics-constrained machine learning is emerging as an important topic in the field of machine learning for physics. One of the most significant advantages of incorporating physics constraints into machine learning methods is that the resulting model requires significantly less data to train. By incorporating physical rules into the machine learning formulation itself, the predictions are expected to be physically plausible. Gaussian process (GP) is perhaps one of the most common methods in machine learning for small datasets. In this paper, we investigate the possibility of constraining a GP formulation with monotonicity on three different material datasets, where one experimental and two computational datasets are used. The monotonic GP is compared against the regular GP, where a significant reduction in the posterior variance is observed. The monotonic GP is strictly monotonic in the interpolation regime, but in the extrapolation regime, the monotonic effect starts fading away as one goes beyond the training dataset. Imposing monotonicity on the GP comes at a small accuracy cost, compared to the regular GP. The monotonic GP is perhaps most useful in applications where data is scarce and noisy, and monotonicity is supported by strong physical evidence.

We present DAVE Aquatic Virtual Environment (DAVE), an open source simulation stack for underwater robots, sensors, and environments. Conventional robotics simulators are not designed to address unique challenges that come with the marine environment, including but not limited to environment conditions that vary spatially and temporally, impaired or challenging perception, and the unavailability of data in a generally unexplored environment. Given the variety of sensors and platforms, wheels are often reinvented for specific use cases that inevitably resist wider adoption. Building on existing simulators, we provide a framework to help speed up the development and evaluation of algorithms that would otherwise require expensive and time-consuming operations at sea. The framework includes basic building blocks (e.g., new vehicles, water-tracking Doppler Velocity Logger, physics-based multibeam sonar) as well as development tools (e.g., dynamic bathymetry spawning, ocean currents), which allows the user to focus on methodology rather than software infrastructure. We demonstrate usage through example scenarios, bathymetric data import, user interfaces for data inspection and motion planning for manipulation, and visualizations.

We are facing a global healthcare crisis today as the healthcare cost is ever climbing, but with the aging population, government fiscal revenue is ever dropping. To create a more efficient and effective healthcare system, three technical challenges immediately present themselves: healthcare access, healthcare equity, and healthcare efficiency. An autonomous mobile clinic solves the healthcare access problem by bringing healthcare services to the patient by the order of the patient's fingertips. Nevertheless, to enable a universal autonomous mobile clinic network, a three-stage technical roadmap needs to be achieved: In stage one, we focus on solving the inequity challenge in the existing healthcare system by combining autonomous mobility and telemedicine. In stage two, we develop an AI doctor for primary care, which we foster from infancy to adulthood with clean healthcare data. With the AI doctor, we can solve the inefficiency problem. In stage three, after we have proven that the autonomous mobile clinic network can truly solve the target clinical use cases, we shall open up the platform for all medical verticals, thus enabling universal healthcare through this whole new system.