There is a growing need for fast and accurate methods for testing developmental neurotoxicity across several chemical exposure sources. Current approaches, such as in vivo animal studies, and assays of animal and human primary cell cultures, suffer from challenges related to time, cost, and applicability to human physiology. We previously demonstrated success employing machine learning to predict developmental neurotoxicity using gene expression data collected from human 3D tissue models exposed to various compounds. The 3D model is biologically similar to developing neural structures, but its complexity necessitates extensive expertise and effort to employ. By instead focusing solely on constructing an assay of developmental neurotoxicity, we propose that a simpler 2D tissue model may prove sufficient. We thus compare the accuracy of predictive models trained on data from a 2D tissue model with those trained on data from a 3D tissue model, and find the 2D model to be substantially more accurate. Furthermore, we find the 2D model to be more robust under stringent gene set selection, whereas the 3D model suffers substantial accuracy degradation. While both approaches have advantages and disadvantages, we propose that our described 2D approach could be a valuable tool for decision makers when prioritizing neurotoxicity screening.

Tactical decision making for autonomous driving is challenging due to the diversity of environments, the uncertainty in the sensor information, and the complex interaction with other road users. This paper introduces a general framework for tactical decision making, which combines the concepts of planning and learning, in the form of Monte Carlo tree search and deep reinforcement learning. The method is based on the AlphaGo Zero algorithm, which is extended to a domain with a continuous state space where self-play cannot be used. The framework is applied to two different highway driving cases in a simulated environment and it is shown to perform better than a commonly used baseline method. The strength of combining planning and learning is also illustrated by a comparison to using the Monte Carlo tree search or the neural network policy separately.

The National Institute of Standards and Technology is seeking industry input on ways to develop standards for artificial intelligence. Under a federal executive order issued on February 11, NIST is to develop the plan within 180 days. "Timely and fit-for-purpose AI technical standards, whether developed by national or international organizations, will play a crucial role in the development and deployment of AI technologies, and will be essential to building trust and confidence about AI technologies and for achieving economies of scale," according to the agency. Consequently, NIST will host a workshop on May 30 in Gaithersburg, Md., as well as a webcast. Issues that NIST seeks to better understand include current status and plans regarding the availability, use and development of AI technical standards and tools in support of reliable, robust and trustworthy systems that use AI; needs and challenges regarding the existence, availability, use and development of AI standards and tools; and the role of federal agencies in finding tools to meet the nation's needs.

AI can now write realistic-sounding text, give debating champs a run for their money, diagnose illnesses, and generate fake human faces--among much more. After training these systems on massive datasets, their creators essentially just let them do their thing to arrive at certain conclusions or outcomes. The problem is that more often than not, even the creators don't know exactly why they've arrived at those conclusions or outcomes. There's no easy way to trace a machine learning system's rationale, so to speak. The further we let AI go down this opaque path, the more likely we are to end up somewhere we don't want to be--and may not be able to come back from.

Artificial intelligence–connected sensors, tracking wristbands, and data analytics: We've seen this type of tech pop up in smart homes, cars, classrooms, and workplaces. And now, we're seeing these types of networked systems show up in a new frontier--prisons. Specifically, China and Hong Kong have recently announced that their governments are rolling out new artificial intelligence (AI) technology aimed at monitoring inmates in some prisons every minute of every day. In Hong Kong, the government is testing Fitbit-like devices to monitor individuals' locations and activities, including their heart rates, at all times. Some prisons will also start using networked video surveillance systems programmed to identify abnormal behavior, such as self-harm or violence against others.

The Receiver Operating Characteristic (ROC) curve is a representation of the statistical information discovered in binary classification problems and is a key concept in machine learning and data science. This paper studies the statistical properties of ROC curves and its implication on model selection. We analyze the implications of different models of incentive heterogeneity and information asymmetry on the relation between human decisions and the ROC curves. Our theoretical discussion is illustrated in the context of a large data set of pregnancy outcomes and doctor diagnosis from the Pre-Pregnancy Checkups of reproductive age couples in Henan Province provided by the Chinese Ministry of Health.

A sister company of Google, Alphabet's Wing Aviation, just got federal approval to start using drones for commercial delivery. Amazon's own drone-delivery program is ready to launch as well. As drones take flight, the world is about to get a lot louder – as if neighborhoods were filled with leaf blowers, lawn mowers and chainsaws. Small recreational drones are fairly loud. Serious commercial drones are much louder.

The rush by China to incorporate artificial intelligence into its military could trigger a new arms race in the region, analysts have warned. China is lagging far behind the United States in developing its AI capability but the gap is likely to narrow as Beijing pursues its plan to speed up development of an "intelligent military", first outlined by President Xi Jinping in 2017 at the 19th National Party Congress, according to a report by the Centre for a New American Security. The country has set up two major research organisations focused on AI and unmanned systems and present trends suggest that the gap with the US will narrow as China is moving quickly to develop its military AI capability, the report said. China's pursuit of artificial intelligence for its People's Liberation Army forces is aimed at leveraging the emerging technology to enhance national power, according to Adam Ni, China researcher from Macquarie University in Sydney. "Simulations and other exercises aided by AI are important to improve the PLA's training and combat readiness, and form a defensive network that makes it risky for adversaries to undertake military operations," Ni said.

The fast pace of artificial intelligence (AI) and automation is propelling strategists to reshape their business models. This is fostering the integration of AI in the business processes but the consequences of this adoption are underexplored and need attention. This paper focuses on the overall impact of AI on businesses - from research, innovation, market deployment to future shifts in business models. To access this overall impact, we design a three-dimensional research model, based upon the Neo-Schumpeterian economics and its three forces viz. innovation, knowledge, and entrepreneurship. The first dimension deals with research and innovation in AI. In the second dimension, we explore the influence of AI on the global market and the strategic objectives of the businesses and finally, the third dimension examines how AI is shaping business contexts. Additionally, the paper explores AI implications on actors and its dark sides.

This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers.