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NSF has long supported transformative research in artificial intelligence (AI) and machine learning (ML). The resulting innovations offer new levels of economic opportunity and growth, safety and security, and health and wellness. At the same time, broad acceptance of large-scale deployments of AI systems relies critically on their trustworthiness which, in turn, depends upon the collective ability to ensure, assess, and ultimately demonstrate the fairness, transparency, explainability, and accountability of such systems. Importantly, the beneficial effects of AI systems should be broadly available across all segments of society. NSF and Amazon are partnering to jointly support computational research focused on fairness in AI, with the goal of contributing to trustworthy AI systems that are readily accepted and deployed to tackle grand challenges facing society. Specific topics of interest include, but are not limited to transparency, explainability, accountability, potential adverse biases and effects, mitigation strategies, validation of fairness, and considerations of inclusivity. Funded projects will enable broadened acceptance of AI systems, helping the U.S. further capitalize on the potential of AI technologies. Although Amazon provides partial funding for this program, it will not play a role in the selection of proposals for award.

The DOE SC program in Advanced Scientific Computing Research (ASCR) hereby announces its interest in making research data and artificial intelligence (AI) models findable, accessible, interoperable, and reusable (FAIR1) to facilitate the development of new AI applications in SC's congressionally authorized mission space, which includes the advancement of AI research and development. In particular, ASCR is interested in supporting FAIR benchmark data for AI; and FAIR frameworks for relating data and AI models. For this FOA, AI is inclusive of, for example, machine learning (ML), deep learning (DL), neural networks (NN), computer vision, and natural language processing (NLP). Data, in this context, are the digital artifacts used to generate AI models and/or employed in combination with AI models during inference. An AI model is an inference method that can be used to perform a "task," such as prediction, diagnosis, or classification. The model is developed using training data or other knowledge. An AI task is the inference activity performed by an artificially intelligent system.

NSF has long supported transformative research in artificial intelligence (AI) and machine learning (ML). The resulting innovations offer new levels of economic opportunity and growth, safety and security, and health and wellness, intended to be shared across all segments of society. Broad acceptance and adoption of large-scale deployments of AI systems rely critically on their trustworthiness which, in turn, depends on the ability to assess and demonstrate the fairness (including broad accessibility and utility), transparency, explainability, and accountability of such systems. For example, the behavior of algorithms for face recognition, speech, and language, especially when integrated into decision support systems applied across different segments of society, would benefit from new foundational research in fairness of AI systems. NSF and Amazon are partnering to jointly support computational research focused on fairness in AI, with the goal of contributing to trustworthy AI systems that are readily accepted and deployed to tackle grand challenges facing society. Specific topics of interest include, but are not limited to transparency, explainability, accountability, potential adverse biases and effects, mitigation strategies, algorithmic advances, fairness objectives, validation of fairness, and advances in broad accessibility and utility. Funded projects will enable broadened acceptance of AI systems, helping the U.S. further capitalize on the potential of AI technologies. Although Amazon provides partial funding for this program, it will not play a role in the selection of proposals for award.

NSF has long supported transformative research in artificial intelligence (AI) and machine learning (ML). The resulting innovations offer new levels of economic opportunity and growth, safety and security, and health and wellness. At the same time, broad acceptance of large-scale deployments of AI systems relies critically on their trustworthiness which, in turn, depends upon the collective ability to ensure, assess, and ultimately demonstrate the fairness, transparency, explainability, and accountability of such systems. Importantly, the beneficial effects of AI systems should be broadly available across all segments of society. NSF and Amazon are partnering to jointly support computational research focused on fairness in AI, with the goal of contributing to trustworthy AI systems that are readily accepted and deployed to tackle grand challenges facing society. Specific topics of interest include, but are not limited to transparency, explainability, accountability, potential adverse biases and effects, mitigation strategies, validation of fairness, and considerations of inclusivity. Funded projects will enable broadened acceptance of AI systems, helping the U.S. further capitalize on the potential of AI technologies. Although Amazon provides partial funding for this program, it will not play a role in the selection of proposals for award.

Scientific machine learning is a core component of artificial intelligence and a computational technology that can be trained, with scientific data, to augment or automate human skills. Major research advances will be enabled by harnessing DOE investments in massive amounts of scientific data, software for predictive models and algorithms, high-performance computing (HPC) and networking platforms, and the national workforce. The crosscutting nature of machine learning and AI provides strong motivation for formulating a prioritized research agenda. Scientific Machine Learning and Artificial Intelligence (Scientific AI/ML) will have broad use and transformative effects across the research communities supported by DOE. Accordingly, a 2019 Basic Research Needs workshop report identified six Priority Research Directions. The first three PRDs describe foundational research themes that are common to the development of Scientific AI/ML methods and correspond to the need for domain-awareness, interpretability, and robustness. The other three PRDs describe capability research themes and correspond to the three major use cases of massive scientific data analysis (PRD #4), machine learning-enhanced

Artificial Intelligence (AI) has advanced tremendously and today promises personalized healthcare; enhanced national security; improved transportation; and more effective education, to name just a few benefits. Increased computing power, the availability of large datasets and streaming data, and algorithmic advances in machine learning (ML) have made it possible for AI development to create new sectors of the economy and revitalize industries. Continued advancement, enabled by sustained federal investment and channeled toward issues of national importance, holds the potential for further economic impact and quality-of-life improvements.

For FY2021, Artificial Intelligence (AI) and Quantum Information Science and Engineering (QISE) have been added to the national priority areas in which the NRT Program encourages proposals. We seek proposals on any interdisciplinary research theme of national priority, with special emphasis on AI and QISE and the six research areas within NSF's 10 Big Ideas. The NSF research Big Ideas are Harnessing the Data Revolution (HDR), The Future of Work at the Human-Technology Frontier (FW-HTF), Navigating the New Arctic (NNA), Windows on the Universe: The Era of Multi-Messenger Astrophysics (WoU), The Quantum Leap: Leading the Next Quantum Revolution (QL), and Understanding the Rules of Life: Predicting Phenotype (URoL). Proposals that align with one of these designated priority areas should contain a title to reflect that alignment, as described in the program solicitation (e.g., NRT-AI: title, NRT-HDR: title, NRT-QL: title). Proposals may be submitted under two tracks (i.e., Track 1 and Track 2). Track 1 proposals may request a total budget (up to five years in duration) up to $3 million for projects with a focus on STEM graduate students in research-based PhD and/or master's degree programs. Track 2 proposals may request a total budget (up to five years in duration) up to $2 million; NSF requires that Track 2 proposals focus on programs from institutions not classified as Doctoral Universities: Very High Research Activity (R1). Requirements for Track 1 and Track 2 are identical.

Artificial Intelligence (AI) has advanced tremendously and today promises personalized healthcare; enhanced national security; improved transportation; and more effective education, to name just a few benefits. Increased computing power, the availability of large datasets and streaming data, and algorithmic advances in machine learning (ML) have made it possible for AI development to create new sectors of the economy and revitalize industries. Continued advancement, enabled by sustained federal investment and channeled toward issues of national importance, holds the potential for further economic impact and quality-of-life improvements.

The National Science Foundation (NSF) through its Divisions of Electrical, Communications and Cyber Systems (ECCS); Chemical, Bioengineering, Environmental and Transport Systems (CBET); Civil, Mechanical and Manufacturing Innovation (CMMI); Information and Intelligent Systems (IIS); and Mathematical Sciences (DMS) announces a solicitation on Multimodal Sensor Systems for Precision Health enabled by Data Harnessing, Artificial Intelligence (AI), and Learning. Next-generation multimodal sensor systems for precision health integrated with AI, machine learning (ML), and mathematical and statistical (MS) methods for learning can be envisioned for harnessing a large volume of diverse data in real time with high accuracy, sensitivity and selectivity, and for building predictive models to enable more precise diagnosis and individualized treatments. It is expected that these multimodal sensor systems will have the potential to identify with high confidence combinations of biomarkers, including kinematic and kinetic indicators associated with specific disease and disability. This focused solicitation seeks high-risk/high-return interdisciplinary research on novel concepts, innovative methodologies, theory, algorithms, and enabling technologies that will address the fundamental scientific issues and technological challenges associated with precision health.

Solid research is the underlying driving force to crystallize fearless creativity and innovation. While we are committed to run in-house research and engineering, we are also excited to collaborate with academic partners to facilitate exploration of new and promising research. The Sony Focused Research Award provides an opportunity for university faculty and Sony to conduct this type of collaborative, focused research. The award provides up to $150K USD* in funds, and may be renewed for subsequent year(s). A list of candidate research topics appears below: - Manipulation Secure Image Sensing - Self-Supervised Learning for Spiking Neural Networks with Event-Based Vision Sensor - Deep Learning & Deep Fusion Towards Automotive Scene Perception - Designing and Implementing Camera ISP Algorithms Using Deep Learning and Computer Vision - Robust Mesh Tracking for Volumetric Capture - Advanced Image Processing Enabled by AI - Novel Actuator - Machine Learning/AI for Wireless Communications - Reconfigurable Reflector Type Materials - Individual Treatment Effect Estimation - Acoustic Metamaterials - Novel Technologies for GaN-based VSCELs - Intelligent Sensing of Patient-Reported Outcomes

The purpose of this Funding Opportunity Announcement (FOA) is to solicit applications for early-stage translational research projects focused on the development of innovative, unbiased next generation, differential diagnostic technologies for acute febrile illnesses caused by infectious pathogens, excluding HIV.

The Defense Advanced Research Projects Agency (DARPA) Defense Sciences Office (DSO) is issuing a Disruption Opportunity (DO) Special Notice (SN) inviting submissions of innovative basic research concepts exploring radically new architectures and approaches in Artificial Intelligence (AI) that incorporate prior knowledge, such as known physical laws, to augment sparse data and to ensure robust operation.

The objective of this Funding Opportunity Announcement (FOA) is to support the development of new and innovative multipurpose prevention technologies (MPT) with rheological/biophysical properties and product user perceptions compatible with current long-acting reversible contraceptive (LARC) strategies (look, feel, effectiveness, safety and duration of action) for the dual purpose of preventing pregnancy and HIV infection in women. MPTs proposed for development must be dual indication and prevent pregnancy and HIV infection and have drug delivery systems (DDS) capable with sustained/extended release of both drugs. MPTs proposed for development must use a licensed contraceptive. This FOA requires an industry partner, milestones linked to Go/No Go decisions and year 5 funding requires submission of a pre-IND application to the FDA.

Devices to maintain beauty are already available on the market. However, care provided by these devices is still limited to one based on the age or skin characteristics measured at special facilities. Devices for personalized treatment suitable for every individual condition and every environment is not yet provided. A personalized beauty care that incorporates innovative technologies is therefore supposed to possibly offer new beauty care experiences. The Client aims at building a device or service to meet any one of the following needs through cooperation with a partner: Allow individuals to grasp their beauty conditions and environment by using a device or service in an easy, speedy, and accurate manner at home. The beauty conditions include: skin conditions (e.g., wrinkles, blemishes, pores, texture, moisture content, sebum quantity, blood flow volume, color, elasticity, inflammation, pimples, porphyrin, desquamation, lymph, hidden blemishes, infiltration of cosmetics),  hair characteristics (e.g., temperature, moisture content, color, glow, electrostatic charge amount, pH, damages [e.g., cuticle peel-off, porosity, component balance, breakage, split hair], waviness, length, amount), and  beard characteristics (e.g., length, thickness, color, hair-raising angle, direction, ingrown hair, hairline).  The environment includes temperature, humidity, and ultraviolet quantity.

The need to process large data sets arising in many practical problems require real-time learning from data streams makes high-performance hardware necessary, and yet the very nature of these problems, along with currently known algorithms for addressing them, imposes significant hardware challenges. Current versions of deep-learning algorithms operate by using millions of parameters whose optimal values need to be determined for good performance in real time on high-performance hardware. Conversely, the availability of fast hardware implementations can enable fuller use of Bayesian techniques, attractive for their ability to quantify prediction uncertainty and thus give estimates of reliability and prediction breakdown. The abilities of ML systems to self-assess for reliability and predict their own breakdowns (and also recover without significant ill effects) constitute critical areas for algorithm development as autonomous systems become widely deployed in both decision support and embodied AI agents. Only with attention to these challenges can we construct systems that are robust when they encounter novel situations or degradation and failure of sensors.

The research opportunity described in this announcement falls under the following sections of the BAA: Appendix 1 "Program Description," * Section I entitled "Expeditionary Maneuver Warfare & Combating Terrorism (Code 30); specific thrusts and focused research area: * Paragraph E. "ONR 30 Decision Support, AI, Machine Learning and Graph Analysis Program," Technology Investment area 3, entitled "Modeling/ Machine Learning/ /Artificial Intelligence" *

The objectives of the DoD STTR Program include stimulating technological innovation, strengthening the role of small business in meeting DoD research and development needs, fostering and encouraging participation by minority and disadvantaged persons in technological innovation, and increasing the commercial application of DoD-supported research or research and development results. Focus areas: - 5G - AI and ML - Autonomy - Biotechnology - Cybersecurity - Direct Energy - Hypersonics - Microelectronics - Networked Command, Control & Communications - Nuclear - Quantum Science - Space - General Warfighting Requirements

The Pandemic Response Challenge is a $500K, four-month challenge that focuses on the development of data-driven AI systems to predict COVID-19 infection rates and prescribe Intervention Plans (IPs) that regional governments, communities, and organizations can implement to minimize harm when reopening their economies.

The NIAID Radiation/Nuclear Countermeasures Development Program supports extramural research to develop safe and effective radiological/nuclear medical countermeasures for clinical use under emergency situations. This program spans basic through applied research. The role of the endovascular network in radiation injury pathogenesis is not well understood; however, the importance of this biological system in the observed multi-organ dysfunction and failure that occurs following radiation exposure has recently been established. The purpose of the Funding Opportunity Announcement (FOA) is to provide an opportunity for academic, industry and government laboratory researchers to address gaps in the understanding of the pathophysiology of radiation injury in the vasculature, and how this damage contributes to overall mortality following radiation exposure. This funding will also advance the development of post-exposure treatment approaches targeting the vascular endothelium, with the ultimate goal of licensure of candidate medical countermeasures by the Food and Drug Administration (FDA) for the radiation/nuclear public health emergency indication.

The NIAID Radiation/Nuclear Countermeasures Development Program supports extramural research to develop safe and effective radiological/nuclear medical countermeasures for clinical use under emergency situations. This program spans basic through applied research. The role of the endovascular network in radiation injury pathogenesis is not well understood; however, the importance of this biological system in the observed multi-organ dysfunction and failure that occurs following radiation exposure has recently been established. The purpose of the Funding Opportunity Announcement (FOA) is to provide an opportunity for academic, industry and government laboratory researchers to address gaps in the understanding of the pathophysiology of radiation injury in the vasculature, and how this damage contributes to overall mortality following radiation exposure. This funding will also advance the development of post-exposure treatment approaches targeting the vascular endothelium, with the ultimate goal of licensure of candidate medical countermeasures by the Food and Drug Administration (FDA) for the radiation/nuclear public health emergency indication.

The purpose of this Funding Opportunity Announcement (FOA) is to solicit applications to support screening of target-based pharmaceutical libraries to identify candidate therapeutics against select eukaryotic pathogens and subsequent preclinical development activities.