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National Artificial Intelligence (AI) Research Institutes: Accelerating Research, Transforming Society, and Growing the American Workforce

Full Proposal Deadline(s) (due by 5 p.m. submitter's local time): January 28, 2020 for Institute proposals in one of the six specified themes January 30, 2020 for Planning proposals Program Title: Synopsis of Program: Cognizant Program Officer(s): Please note that the following information is current at the time of publishing.

In the event that an individual exceeds these limits, proposals will be accepted based on earliest date and time of proposal submission, i.e., the first proposal will be accepted, and the remainder will be returned without review.

The full AI endeavor is inherently multidisciplinary, encompassing the research necessary to understand and develop systems that can perceive, learn, reason, communicate, and act in the world;

It includes research in all matters of learning, abstraction, and inference required for intelligent behavior as well as general architectures for intelligence, integrated intelligent agents, and multiagent systems.

Machine learning, that is, methods for solving tasks by generalizing from data, has made great advances in recent years through the combination of new algorithms, increases in computing power, and the growing availability of data.

Behavioral and cognitive science informs much of the motivation and design of systems seeking to implement behavior typical of human perceptual, motor, and cognitive processes and their interactions.

Human language technologies (also known as 'natural language processing' and 'natural language understanding') research enables intelligent systems to analyze, produce, translate, and respond to human text and speech.

While an embodied AI may be a robot, this solicitation does not include in its scope work on teleoperated robots or industrial robots that simply repeat programmed patterns of motion.

As intelligent systems amplify humans' capabilities to accomplish individual and collective goals, research is needed to assess the benefits, effects, and risks of AI-enabled computing systems;

Relevant research areas therefore include consideration of explainable and trustworthy AI, validation of AI-enabled systems, AI safety, security, and privacy (including, for example, AI for security and security for AI), and the role of emotion and affect in the design and perception of increasingly-sophisticated machine intelligence.

Importantly, work in AI encompasses novel software and hardware architectures, as well as methods for carrying out AI algorithms on a variety of computing systems and platforms, including those that operate under additional constraints such as time (e.g., real-time) or energy, or those targeting specific application classes or use cases.

For example, this solicitation supports advances in the theoretical explanations for the performance of and justification for use of AI and ML algorithms, including improved algorithms and analysis leading to greater accuracy and resource usage;

information theoretic foundations of AI that seek to remove the guesswork in designing ML algorithms, transitioning from 'black box' to 'grey/white box' models offering interpretability and affording targeted information extraction;

and information authenticity in an era of 'deep fakes,' tackling verifying information provenance through forensics, authentication, consistency checks, and natural or engineered watermarks.

the current renaissance in ML is directly tied to progress in hardware technology including improved memory, input/output, clock speeds, parallelism, and energy efficiency.

More advanced ML-based feedback methods can allow AI systems to intelligently sample or prioritize data from large-scale simulations, experimental instruments, and distributed sensor systems.

As an example, foundational research in machine learning gave rise to breakthroughs in deep neural networks motivated by performance in controlled contexts like character recognition.

Later, use-inspired research in the intersection of machine learning and linguistics led to the development of recurrent neural networks in AI while also revolutionizing language modeling for speech and text processing.

We use the phrase 'use-inspired' rather than 'applied' to emphasize that this solicitation seeks to support work that goes beyond merely applying known techniques, and adds new knowledge and understanding in both foundational AI and use-inspired domains.

Ideally there is a virtuous cycle between foundational and use-inspired research, where foundational results provide a starting point for use-inspired research, and the results from use-inspired research are generalized and made foundational.

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 2019 update to the National Artificial Intelligence Research and Development Strategic Plan, informed by visioning activities in the scientific community as well as interaction with the public, identifies as its first strategic objective the need to make long-term investments in AI research in areas with the potential for long-term payoffs in AI.

They will accelerate the development of transformational technologies by grounding that research in critical application sectors that can serve as motivation for foundational research advances and provide opportunities for the effective fielding of AI-powered innovation.

Funding Tracks Planning Track: This track will support planning grants for durations of up to two years, and for up to a total of $500,000 to enable teams to develop communities and capacity for full Institute operations through diverse and sustained activities.

While novel approaches are encouraged, it is anticipated that such planning activities might include workshops, development of partnerships, preliminary research and analysis, and engagement of stakeholders most appropriate for the Institute vision.

Institute proposals must convey clear and concrete plans for foundational AI research, use-inspired motivation and technology transition opportunities, the education and workforce development activities to be undertaken, and plans for multidisciplinary research community building appropriate to the proposed Institute's vision and mission.

These themes represent a subset of research areas that NSF supports, and future solicitations may target additional themes or even invite Institute proposals in areas not specifically called out, or offer open tracks.

There are numerous opportunities to apply transformative, data-driven research methods and algorithm development to the food and agricultural sector to yield meaningful insights and possibilities for producers, labor, food handling and processes, transportation and storage, wholesale and retail marketing, and high-quality products and information for consumers.

The Green Revolution of the 1960s greatly enhanced food production and resulted in positive impacts on food security, human health, employment, and overall quality of life for many.

An AI-based approach to agriculture can go much further by addressing whole food systems, inputs and outputs, internal and external consequences, and issues and challenges at micro, meso, and macro scales.

While challenges in methods, data, privacy, and fairness are universal to the broader AI endeavor, these considerations take on particular urgency when associated with a need as fundamental as the food supply.

AI Research Institutes that simultaneously advance foundational AI research and agriculture and food systems might address a wide range of research foci, build new multidisciplinary communities, and create the workforce needed for an AI-powered revolution in agriculture.

Efforts resulting from the theme will ideally support the research, education, extension, and economics endeavors designed to advance public knowledge and responsible commercial interests.

Theme 4: AI-Augmented Learning The primary focus of an institute in the theme of AI-Augmented Learning includes research and development of AI-driven innovations to radically improve human learning and education writ large –

in formal settings (e.g., preK-12, undergraduate, graduate, vocational education), training, on-the-job, and across the lifespan as well as informal settings (e.g., museums, nature centers, libraries;

This could be in support of cognitive, neural, perceptive and affective processes as well as well-defined learning outcomes in STEM fields, and STEM-enabling content such as literacy, self-regulation, creativity, curiosity, communication, collaboration and social skills.

Such collaborative intelligent learning systems could include, for example, research on the design of conversational agents, intelligent cognitive assistants, supportive multimodal dashboards, or social robots.

An important purpose of this Institute is also to work toward a grand challenge of 'Education for All' through research of AI-supported learning systems to radically expand access of learning to all Americans and in response to the rapidly changing landscape of jobs and work.

Here, research could include the design and implementation of AI technologies through highly adaptable and distributed systems to expand access, equity, and depth of learning across diverse people, institutions, and settings.

Advances in data science could provide diagnostic information to support formative, continuous, and summative assessments, drawing upon multimodal and smart and connected data such as from sensors and other cyber-physical systems.

Theme 5: AI for Accelerating Molecular Synthesis and Manufacturing The primary focus of this theme is the development of AI advances and AI-based tools to drive molecular discovery and identify chemical transformation pathways that support energy-efficient, sustainable chemical manufacturing.

This Institute track seeks to develop AI tools and approaches that increase the pace of discovery of new molecules and promote alternative, energy-sustainable processes for the production of chemicals.

These goals will be achieved through the development of closed-loop systems that integrate tools for extracting knowledge from existing databases and text, executing autonomous experimental measurements and optimization, and incorporating computational and machine-learning approaches to develop physics- and/or descriptor-based predictive tools.

The extraction of information from the chemical literature requires mining and use of sparse and noisy data from various sources (figures, spectra, tables, and text), relationship extraction between text and images, overcoming the lack of negative data, and the use of non-standard terminologies.

Beyond the chemical literature, both classical and ab initio computational methods, in combination with machine learning, provide opportunities to screen vast arrays of molecular structures and to develop phenomenological insight from complex datasets.

A successful AI Research Institute in the theme of Accelerating Molecular Synthesis and Manufacturing will develop methods to extract useful information from many sources to provide a knowledge-based user database, available in a machine-readable format, of predictable reactivity patterns informed by reaction rules, kinetic and selectivity data, thermodynamics, and materials properties.

However, research in information technology that applies engineering or computer science principles to problems in biology and medicine while advancing engineering or computer science knowledge within the scope of this program is eligible for support.

Realizing the full potential of AI for Discovery in Physics will improve the operations and exploitation of Division of Physics facilities, promote the integration and interpretation of heterogenous datasets, accelerate model-building and quantification of uncertainties, and enable novel ways to interrogate high-dimensional features of complex data sets.

Support for each year of the cooperative agreement of a funded AI Research Institute will be contingent upon a satisfactory annual review (possibly including a site visit or reverse site visit) by NSF of the Institute's progress and future plans, with an emphasis on the quality of the research, education, broadening participation, and knowledge transfer activities.

In the event that an individual exceeds these limits, proposals will be accepted based on earliest date and time of proposal submission, i.e., the first proposal will be accepted, and the remainder will be returned without review.

Education and Workforce Development: A description of the planned new and innovative approaches for education and workforce development of the Nation's undergraduate and graduate students, post-doctoral researchers, community colleges and skilled technical workforce training, as well as other opportunities to advance knowledge and education of AI.

Management and Integration Plan: Plans to develop all aspects of an AI Research Institute, including initiation of multidisciplinary research, planning for workforce development, identification and refinement of infrastructure needs, and exploration of strategic and synergistic partnerships with industry, nonprofits/foundations, other federal agencies, national labs and any international partners.

Describe the relevant experience and qualifications of the lead PI and other key personnel of the management team to build and manage a complex, multi-faceted, and innovative enterprise that integrates research, education, broadening participation, and knowledge transfer.

Explain how the proposed research relates to other state and national research capabilities (including related centers, institutes, facilities and national laboratories) as well as international programs in the proposed fields of research.

Education and Workforce Development: With the goal of advancing AI knowledge and education, present plans to actively build the next generation of talent for a diverse well-trained workforce through new and innovative approaches to education and workforce development.

Participants may include undergraduate and graduate students, community colleges and post-doctoral researchers, skilled technical workforce, K12 students as well as professionals looking to shift career focus.

Describe all proposed activities in sufficient detail to allow assessment of their intrinsic merit, potential effectiveness, and their anticipated contribution toward a highly competent new generation of AI workforce.

Describe plans for increasing diversity through the participation of underrepresented groups, including women, minorities, and persons with disabilities, in all organizational levels of institute activities, and cite the relevant literature on effective practices.

This could, for example, intentionally target specific combinations of groups (e.g., by race/ethnicity, gender and/or disability) with an analysis of how institute activities impact their participation in the AI workforce.

Key Personnel, Management and Integration Plan: Describe the multidisciplinary group of scientists, engineers and educators comprising the Institute and their suitability to conduct large-scale, long-term research agenda for the advancement of AI and the fielding of AI-powered innovation in application sectors of national importance.

Describe the relevant experience and qualifications of the lead PI, Managing Director/Project Manager (if different), and other key members of the management team to lead and manage a complex, multi-faceted, and innovative enterprise that integrates research, education, broadening participation, and knowledge transfer.

Facilities, Equipment and Other Resources: Provide a synopsis of organizational resources that will be available to the Institute (dedicated space, access to facilities and instrumentation, faculty and staff positions, access to programs that assist with curriculum development or broadening participation, or other organizational programs that could provide support to the Institute).

Indirect Cost (F&A) Limitations: The following instructions apply to awards made by USDA-NIFA: For awards made by USDA-NIFA, Section 1462(a) and (c) of the National Agricultural Research, Extension, and Teaching Policy Act of 1977 (NARETPA) limits indirect costs for the overall award to 30 percent of Total Federal Funds Awarded (TFFA) under a research, education, or extension grant.

The maximum indirect cost rate allowed under the award is determined by calculating the amount of indirect costs using: 1) the sum of an institution's negotiated indirect cost rate and the indirect cost rate charged by subawardees, if any;

The subawardee may charge its negotiated indirect cost rate on its portion of the award, provided the sum of the indirect cost rate charged under the award by the prime awardee and the subawardee(s) does not exceed 30 percent of the TFFA.

In the event of an award, the prime awardee is responsible for ensuring the maximum indirect cost allowed for the award is not exceeded when combining indirect costs for the Federal portion (i.e., prime and subawardee(s)) and any applicable cost-sharing (see 7 CFR 3430.52(b)).

Other Budgetary Limitations: Cost Sharing Requirements for awards made by USDA-NIFA: In accordance with 7 USC 450i(b)(9), if a funded applied Research or Integrated Project with an applied research component, is commodity-specific and not of national scope, the grant recipient is required to match the USDA funds awarded on a dollar-for-dollar basis from non-Federal sources with cash and/or in-kind contributions.

The Secretary of Agriculture may waive all or part of the matching requirement if all three of the following criteria are met: (1) applicants must be a college, university, or research foundation maintained by a college or university that ranks in the lowest one third of such colleges, universities, and research foundations on the basis of Federal research funds received (see Additional Eligibility Information for proposals that designate USDA-NIFA as the requested funding agency);

All proposals are carefully reviewed by a scientist, engineer, or educator serving as an NSF Program Officer, and usually by three to ten other persons outside NSF either as ad hoc reviewers, panelists, or both, who are experts in the particular fields represented by the proposal.

NSF's mission calls for the broadening of opportunities and expanding participation of groups, institutions, and geographic regions that are underrepresented in STEM disciplines, which is essential to the health and vitality of science and engineering.

The National Science Foundation strives to invest in a robust and diverse portfolio of projects that creates new knowledge and enables breakthroughs in understanding across all areas of science and engineering research and education.

To identify which projects to support, NSF relies on a merit review process that incorporates consideration of both the technical aspects of a proposed project and its potential to contribute more broadly to advancing NSF's mission 'to promote the progress of science;

Merit Review Principles These principles are to be given due diligence by PIs and organizations when preparing proposals and managing projects, by reviewers when reading and evaluating proposals, and by NSF program staff when determining whether or not to recommend proposals for funding and while overseeing awards.

Given that NSF is the primary federal agency charged with nurturing and supporting excellence in basic research and education, the following three principles apply: With respect to the third principle, even if assessment of Broader Impacts outcomes for particular projects is done at an aggregated level, PIs are expected to be accountable for carrying out the activities described in the funded project.

To that end, reviewers will be asked to evaluate all proposals against two criteria: The following elements should be considered in the review for both criteria: Broader impacts may be accomplished through the research itself, through the activities that are directly related to specific research projects, or through activities that are supported by, but are complementary to, the project.

Additional Solicitation Specific Review Criteria In addition to the National Science Board merit review criteria, reviewers will be asked to apply the following criteria when reviewing proposals submitted to the Planning track: In addition to the National Science Board merit review criteria, reviewers will be asked to apply the following criteria when reviewing proposals submitted to the Institute track: Proposals submitted in response to this program solicitation will be reviewed by Ad hoc Review and/or Panel Review, or Reverse Site Review.

for additional information on the review process.) An NSF award consists of: (1) the award notice, which includes any special provisions applicable to the award and any numbered amendments thereto;

The cooperative agreements will have an extensive section of Special Conditions relating to the period of performance, statement of work, awardee responsibilities, NSF responsibilities, joint NSF-awardee responsibilities, funding and funding schedule, reporting requirements, key personnel, and other conditions.

Once awarded, at the request of an awardee, or of the funding agency with the awardee's consent, agencies may separately fund their own personnel to participate in research, part-time or full-time, with organizations awarded under the AI Research Institutes program.

USDA-NIFA Award Administration and Conditions Within the limit of funds available for such purpose, the USDA-NIFA awarding official shall make grants to those responsible, eligible applicants whose applications are judged most meritorious under the procedures set forth in this solicitation.

The date specified by the USDA-NIFA awarding official as the effective date of the grant shall be no later than September 30 of the federal fiscal year in which the project is approved for support and funds are appropriated for such purpose, unless otherwise permitted by law.

The project need not be initiated on the grant effective date, but as soon thereafter as practical so that project goals may be attained within the funded project period.

All funds granted by USDA-NIFA under this solicitation may be used only for the purpose for which they are granted in accordance with the approved application and budget, regulations, terms and conditions of the award, applicable federal cost principles, USDA assistance regulations, and USDA-NIFA General Awards Administration Provisions at 7 CFR part 3430, subparts A through E.

Responsible and Ethical Conduct of Research In accordance with sections 2, 3, and 8 of 2 CFR Part 422, institutions that conduct USDA-funded extramural research must foster an atmosphere conducive to research integrity, bear primary responsibility for prevention and detection of research misconduct, and maintain and effectively communicate and train their staff regarding policies and procedures.

For all multi-year grants (including both standard and continuing grants), the Principal Investigator must submit an annual project report to the cognizant Program Officer no later than 90 days prior to the end of the current budget period.

Failure to provide the required annual or final project reports, or the project outcomes report, will delay NSF review and processing of any future funding increments as well as any pending proposals for all identified PIs and co-PIs on a given award.

General inquiries regarding this program should be made to: For questions related to the use of FastLane or, contact: For questions relating to contact: For the Institute track: Theme 1: Trustworthy AI Theme 2: Foundations of Machine Learning Theme 3: AI-Driven Innovation in Agriculture and the Food System Theme 4: AI-Augmented Learning Theme 5: AI for Accelerating Molecular Synthesis and Manufacturing Theme 6: AI for Discovery in Physics For the Planning track: Disciplines not named below, please use (general email) Biological Sciences Computer and Information Science and Engineering Computing and Communication Foundations Computer and Network Systems Information and Intelligent Systems Advanced Cyberinfrastructure Education and Human Resources Research on Learning in Formal and Informal Settings Division of Undergraduate Education Division of Graduate Education Engineering Chemical, Bioengineering, Environmental and Transport Systems Civil, Mechanical and Manufacturing Innovation Electrical, Communications and Cyber Systems Engineering Education and Centers Geosciences Mathematical and Physical Sciences Astronomy Chemistry Materials Research Mathematical Sciences Physics Social and Behavioral Sciences Behavioral and Cognitive Sciences Integrative Activities EPSCoR Other Agency Contacts The NSF website provides the most comprehensive source of information on NSF Directorates (including contact information), programs and funding opportunities.

In addition, 'NSF Update' is an information-delivery system designed to keep potential proposers and other interested parties apprised of new NSF funding opportunities and publications, important changes in proposal and award policies and procedures, and upcoming NSF Grants Conferences.

to other government agencies or other entities needing information regarding applicants or nominees as part of a joint application review process, or in order to coordinate programs or policy;

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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.

Commerce Secretary Wilbur Ross said in a written statement Monday that the U.S. government “will not tolerate the brutal suppression of ethnic minorities within China.” The blacklisted companies include Hikvision and Dahua, both of which are global providers of video surveillance technology.

The filing said the listed groups have been implicated in “China’s campaign of repression, mass arbitrary detention, and high-technology surveillance” against Uighurs, Kazakhs and other predominantly Muslim minority groups.

Ross said Monday’s action will ensure U.S. technologies “are not used to repress defenseless minority populations.” China is estimated to have detained up to 1 million Muslims in prison-like detention centers in the region.

CBSE Releases Teacher's Handbook For Artificial Intelligence (AI) Subject

Central Board of Secondary Education, following up on its plan to introduce Artificial Intelligence (AI) as a skill subject in classes 8 and 9, has released Facilitators Handbooks on Artificial Intelligence.

The handbook comes with a booklet which details 15 student projects from schools which were put on a fast track mode by the board to study the impact of the curriculum.

CBSE, early this year, had announced that AI would be introduced as a skill subject for students of classes 8 and 9 and had also released the curriculum with learning outcomefor the subject.

An AI Pioneer Wants His Algorithms to Understand the 'Why'

In March, Yoshua Bengio received a share of the Turing Award, the highest accolade in computer science, for contributions to the development of deep learning—the technique that triggered a renaissance in artificial intelligence, leading to advances in self-driving cars, real-time speech translation, and facial recognition.

He believes it won’t realize its full potential, and won’t deliver a true AI revolution, until it can go beyond pattern recognition and learn more about cause and effect.

The 55-year-old professor at the University of Montreal, who sports bushy gray hair and eyebrows, says deep learning works well in idealized situations but won’t come close to replicating human intelligence without being able to reason about causal relationships.

In real life it is often not the case.” Machine learning systems including deep learning are highly specific, trained for a particular task, like recognizing cats in images, or spoken commands in audio.

Over the past several decades, he helped develop the ideas and engineering techniques that unleashed the potential of deep learning, together with this year’s other Turing Award recipients: Geoffrey Hinton, of the University of Toronto and Google, and Yann LeCun, who works at NYU and Facebook.

A deep learning program can be trained to recognize objects in photographs with high accuracy, providing it sees lots of training images and is given plenty of computing power.

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