AI News, Department of Energy Advances Artificial Intelligence

Applications of artificial intelligence

Artificial intelligence, defined as intelligence exhibited by machines, has many applications in today's society.

More specifically, it is Weak AI, the form of AI where programs are developed to perform specific tasks, that is being utilized for a wide range of activities including medical diagnosis, electronic trading platforms, robot control, and remote sensing.

Crop and soil monitoring uses new algorithms and data collected on the field to manage and track the health of crops making it easier and more sustainable for the farmers.[3]

The AOD has use for artificial intelligence for surrogate operators for combat and training simulators, mission management aids, support systems for tactical decision making, and post processing of the simulator data into symbolic summaries.[5]

The artificial intelligent programs can sort the information and provide the pilot with the best possible maneuvers, not to mention getting rid of certain maneuvers that would be impossible for a human being to perform.

Haitham Baomar and Peter Bentley are leading a team from the University College of London to develop an artificial intelligence based Intelligent Autopilot System (IAS) designed to teach an autopilot system to behave like a highly experienced pilot who is faced with an emergency situation such as severe weather, turbulence, or system failure.[10]

Educating the autopilot relies on the concept of supervised machine learning “which treats the young autopilot as a human apprentice going to a flying school”.[10]

The Intelligent Autopilot System combines the principles of Apprenticeship Learning and Behavioural Cloning whereby the autopilot observes the low-level actions required to maneuver the airplane and high-level strategy used to apply those actions.[11]

When students sit at their desk, their devices will be able to create lessons, problems, and games to tailor to the specific student's needs, particularly where a student may be struggling, and give immediate feedback.

As a whole AI has the power to influence education by taking district, state, national, and global data into consideration as it seeks to better individualize learning for all.

An example of a revenge effect is that the extended use of technology may hinder students’ ability to focus and stay on task instead of helping them learn and grow.[19]

Also, the need for AI technologies to work simultaneously may lead to system failures which could ruin an entire school day if we are relying on AI assistants to create lessons for students every day.

It is inevitable that AI technologies will be taking over the classroom in the years to come, thus it is essential that the kinks of these new innovations are worked out before teachers decide whether or not to implement them into their daily schedules.

Algorithmic trading involves the use of complex AI systems to make trading decisions at speeds several orders of magnitudes greater than any human is capable of, often making millions of trades in a day without any human intervention.

Automated trading systems are typically used by large institutional investors, but recent years have also seen an influx of smaller, proprietary firms trading with their own AI systems.[20]

Its wide range of functionalities includes the use of natural language processing to read text such as news, broker reports, and social media feeds.

For example, Digit is an app powered by artificial intelligence that automatically helps consumers optimize their spending and savings based on their own personal habits and goals.

The app can analyze factors such as monthly income, current balance, and spending habits, then make its own decisions and transfer money to the savings account.[24]

Wallet.AI, an upcoming startup in San Francisco, builds agents that analyze data that a consumer would leave behind, from Smartphone check-ins to tweets, to inform the consumer about their spending behavior.[25]

This class of financial advisers work based on algorithms built to automatically develop a financial portfolio according to the investment goals and risk tolerance of the clients.

An online lender, Upstart, analyze vast amounts of consumer data and utilizes machine learning algorithms to develop credit risk models that predict a consumer's likelihood of default.

This platform utilizes machine learning to analyze tens of thousands traditional and nontraditional variables (from purchase transactions to how a customer fills out a form) used in the credit industry to score borrowers.

“The major junctions of the system were to monitor premiums in the market, determine the optimum investment strategy, execute transactions when appropriate and modify the knowledge base through a learning mechanism.”[30]

It was able to review over 200,000 transactions per week and over two years it helped identify 400 potential cases of money laundering which would have been equal to $1 billion.[32]

Robots have proven effective in jobs that are very repetitive which may lead to mistakes or accidents due to a lapse in concentration and other jobs which humans may find degrading.

In the automotive industry, a sector with particularly high degree of automation, Japan had the highest density of industrial robots in the world: 1,414 per 10,000 employees.[33]

There are three ways AI is being used by human resources and recruiting professionals: to screen resumes and rank candidates according to their level of qualification, to predict candidate success in given roles through job matching platforms, and rolling out recruiting chat bots that can automate repetitive communication tasks.[citation needed]

AI-powered engine streamlines the complexity of job hunting by operating information on job skills, salaries, and user tendencies, matching people to the most relevant positions.

Machine intelligence calculates what wages would be appropriate for a particular job, pulls and highlights resume information for recruiters using natural language processing, which extracts relevant words and phrases from text using specialized software.

Typical use case scenarios include the analysis of images using object recognition or face recognition techniques, or the analysis of video for recognizing relevant scenes, objects or faces.

The motivation for using AI-based media analysis can be — among other things — the facilitation of media search, the creation of a set of descriptive keywords for a media item, media content policy monitoring (such as verifying the suitability of content for a particular TV viewing time), speech to text for archival or other purposes, and the detection of logos, products or celebrity faces for the placement of relevant advertisements.

Intelligence technologies enables coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Join Fires between networked combat vehicles and tanks also inside Manned and Unmanned Teams (MUM-T).[43]

Another artificial intelligence musical composition project, The Watson Beat, written by IBM Research, doesn't need a huge database of music like the Google Magenta and Flow Machines projects, since it uses Reinforcement Learning and Deep Belief Networks to compose music on a simple seed input melody and a select style.

The company Narrative Science makes computer-generated news and reports commercially available, including summarizing team sporting events based on statistical data from the game in English.

Yseop is able to write financial reports, executive summaries, personalized sales or marketing documents and more at a speed of thousands of pages per second and in multiple languages including English, Spanish, French &

Boomtrain's is another example of AI that is designed to learn how to best engage each individual reader with the exact articles—sent through the right channel at the right time—that will be most relevant to the reader.

Beyond automation of writing tasks given data input, AI has shown significant potential for computers to engage in higher-level creative work.

The program would start with a set of characters who wanted to achieve certain goals, with the story as a narration of the characters’ attempts at executing plans to satisfy these goals.[60]

Their particular implementation was able faithfully reproduced text variety and complexity of a number of stories, such as red riding hood, with human-like adroitness.[62]

Power electronics converters are an enabling technology for renewable energy, energy storage, electric vehicles and high-voltage direct current transmission systems within the electrical grid.

These converters are prone to failures and such failures can cause downtimes that may require costly maintenance or even have catastrophic consequences in mission critical applications.[citation needed]

Researchers are using AI to do the automated design process for reliable power electronics converters, by calculating exact design parameters that ensure desired lifetime of the converter under specified mission profile.[67]

This prospered greatly with the Digital Revolution, and helped introduce people, especially children, to a life of dealing with various types of Artificial Intelligence, specifically in the form of Tamagotchis and Giga Pets, iPod Touch, the Internet, and the first widely released robot, Furby.

A mere year later an improved type of domestic robot was released in the form of Aibo, a robotic dog with intelligent features and autonomy.

The major challenge to developing this AI is the fact that transportation systems are inherently complex systems involving a very large number of components and different parties, each having different and often conflicting objectives.[72]

Due to this high degree of complexity of the transportation, and in particular the automotive, application, it is in most cases not possible to train an AI algorithm in a real-world driving environment.

Argonne National Laboratory Argonne National Laboratory

In conjunction with the Department of Energy (DOE)’s InnovationXLab Artificial Intelligence Summit, DOE’s Argonne National Laboratory collaborated with South Side of Chicago’s Gary Comer Youth Center to host a multi-national DOE lab exhibition for local youth, showcasing the wide range of real-world ways they can use and have fun with computing and artificial intelligence (AI).

​“Through this event, students make connections with AI technologies, learn about careers in computing and AI, build their own confidence in STEM, and envision their future in STEM or even an AI field.” “I don’t know of any events on the South Side of Chicago for inner city kids to have something like this [event] .” – Marvin Evins, King College Prep High School Computer Science Teacher This momentous event drew national attention with the presence of DOE leadership, who spoke on the need for new STEM specialists.

To help the students discover the tremendous futures offered by AI research, Argonne and the other labs ran booths that offered not only information on computational science but also immersive activities that showed how computational science can be fun and valuable in the real world.

Argonne ran a computing lab where students scanned items to, with the help of IBM Watson, make a computer program that could classify recyclable and non-recyclable trash – something easier said than done, since the computer doesn’t know any differences other than the ones you teach it.

It was hard arranging all the parts, so when he got it to work, he felt ​“really surprised.” Likewise, after completing a Jenga-like block-stacking challenge that seemed to defy gravity, he gained ​“a little pride” at his accomplishment.

​“I really want to study digital media,” he shared, ​“so if I can learn something in STEM, I can transfer it to digital media.” Teachers in the community were also very excited about the stimulating activities featured throughout STEM-CON, as they are ideal for generating long-term fascination with science.

​“If you want kids to learn about stuff,” King College Prep teacher Marvin Evins asserted, ​“you’ve got to create a sense of wonder in them.” STEM outreach is especially vital in communities lacking the full resources for STEM experiences.

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;