A rigorous scientific understanding of how the brain works would transform human health and the economy by (i) enabling design of effective therapies for mental and neurodegenerative diseases (such as depression and Alzheimer’s), and (ii) fueling novel areas of enterprise for the biomedical, technology, and artificial intelligence industries. Launched in 2013, the U.S. BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative has made significant progress toward harnessing the ingenuity and creativity of individual laboratories in developing neurotechnological methods. This has provided a strong foundation for future work, producing advances like:

• 3-D microscopy and ultra-fast imaging that enables real-time observations of brain-cell activity in intact tissues: key data for understanding circuit principles underlying human behavior.
• A sophisticated genetic method that could be helpful for finding new druggable targets that could be engaged to manage pain effectively, helping avoid the risks and harms of opioid drug addiction.
• A portable backpack supporting (i) simultaneous recording from a stimulator implanted into a human subject’s brain (part of an early-stage clinical trial), (ii) measurement of other biomarkers, and (iii) recording of the subject’s positions and movements within their environment. This integrated and comprehensive dataset is helping researchers understand links between neural-circuit activity and behavior in humans.

However, pursuing these ambitious goals will require new approaches to brain research, at greater scale and scope. Given the BRAIN Initiative’s momentum, this is the moment to expand the Initiative by investing in a National Laboratory of Neurotechnology (NLN) that would bring together a multidisciplinary team of researchers and engineers with combined expertise in physical and biomedical sciences. The NLN team would develop large-scale instruments, tools, and methods for recording and manipulating the activity of complex neural circuits in living animals or humans — studies that would enable us to understand how the brain works at a deeper, more detailed level than ever before. Specific high-impact initiatives that the NLN team could pursue include:

• Developing a multibeam, large-scale electron microscope to map the wiring of neural circuits.
• Determining the molecular components of each cell of the brain.
• Fabricating a diverse array of implantable and wearable devices.
• Conducting supercomputer-enabled mining of large datasets to support neurotechnology development.

The BRAIN Initiative currently funds small teams at existing research institutes. The natural next step is to expand the Initiative by establishing a dedicated center — staffed by a large, collaborative, and interdisciplinary team — capable of developing the high-cost, large-scale equipment needed to address complex and persistent challenges in the field of neurotechnology. Such a center would multiply the return on investment in brain research that the federal government is making on behalf of American taxpayers. Successful operation of a National Laboratory of Neurotechnology would require about $100 million per year.

To read a detailed vision for a National Laboratory of Neurotechnology, click here.

Artificial intelligence places strenuous demands on current computing hardware, but the integration of semiconductors, superconductors, and optical hardware will create revolutionary new tools for AI.

Digital computers are excellent for number crunching, but their operations and architecture contrast with the operations that support intelligence. When used for AI, vast amounts of energy, data, and time are required to train new models. Meanwhile, the field of computational neuroscience relies on these digital computers to simulate cognition. Because the underlying computational hardware is poorly matched to the operations of synapses, dendrites, and neurons, the same problems with time and energy arise. We can address both these needs with advances in computer hardware.

Project Concept

We can address both these needs with advances in computer hardware. Our approach builds upon the silicon transistors of digital computing, adding superconducting circuitry to accomplish neural computations, and optical components to realize extensive communication across human-brain-scale systems. We have already made substantial progress in demonstrating key components and are ready to scale to a multiyear effort to integrate into a chip-scale cortex (see slides).

Where are we now?

You can learn more about superconducting optoelectronic networks in this slide deck.

What is a Focused Research Organization?

Focused Research Organizations (FROs) are time-limited mission-focused research teams organized like a startup to tackle a specific mid-scale science or technology challenge. FRO projects seek to produce transformative new tools, technologies, processes, or datasets that serve as public goods, creating new capabilities for the research community with the goal of accelerating scientific and technological progress more broadly. Crucially, FRO projects are those that often fall between the cracks left by existing research funding sources due to conflicting incentives, processes, mission, or culture. There are likely a large range of possible project concepts for which agencies could leverage FRO-style entities to achieve their mission and advance scientific progress.

This project is suited for a FRO-style approach because the integration of semiconductors, superconductors, and optical hardware is beyond the scope of a single academic or government research group, and this endeavor will require appreciable investment in a well-orchestrated, focused team, more akin to a startup. However, given the complexity of the technology, five years will be required to bring a competitive product to market, which is still too early for venture capitalists. Because AI hardware is well-established and continuously improving, gaining market traction will require not only superior hardware, but also streamlined software and user interfaces. An FRO is the ideal context to pursue a complete system meeting the needs of a large and diverse pool of users.

How This Project Will Benefit Scientific Progress

By realizing superconducting optoelectronic networks, we will achieve cognitive AI with vastly more computational power than has been possible with the largest supercomputing clusters of today, while consuming only a fraction of their power. Continued scaling of our technology will not come at the cost of environmental harm. Scientists, engineers, and entrepreneurs across the country will have access to a revolutionary new tool to interpret and analyze complex, multi-modal datasets. This form of advanced AI will change how we provide health care, harness energy, model Earth’s climate, and more. Superconducting optoelectronic hardware will usher the largest transition in computation since silicon, enabling powerful tools for computing and an experimental testbed to elucidate the mechanisms of our own minds.

Key Contacts

Author

• Dr. Jeffrey Shainline, NIST, jeffrey.shainline@nist.gov 

Referrers

• Alice Wu, Federation of American Scientists, awu@fas.org

Learn more about FROs, and see our full library of FRO project proposals here.

Summary

Artificial intelligence (AI) and machine learning (ML) models can solve well-specified problems, like automatically diagnosing disease or grading student essays, at scale. But applications of AI and ML for major social and scientific problems are often constrained by a lack of high-quality, publicly available data—the foundation on which AI and ML algorithms are built.

The Biden-Harris Administration should launch a multi-agency initiative to coordinate the academic, industry, and government research community to support the identification and development of datasets for applications of AI and ML in domain-specific, societally valuable contexts. The initiative would include activities like generating ideas for high-impact datasets, linking siloed data into larger and more useful datasets, making existing datasets easier to access, funding the creation of real-world testbeds for societally valuable AI and ML applications, and supporting public-private partnerships related to all of the above.

Summary

Artificial intelligence (AI) is transforming our everyday reality, and it has the potential to save or to cost lives. Innovation is advancing at a breakneck pace, with technology developers engaging in de facto policy-setting through their decisions about the use of data and the embedded bias in their algorithms. Policymakers must keep up. Otherwise, by ceding decision-making authority to technology companies, we face the rising threat of becoming a technocracy. Given the potential benefits and threats of AI to US national security, economy, health, and beyond, a comprehensive and independent agency is needed to lead research, anticipate challenges posed by AI, and make policy recommendations in response. The Biden-Harris Administration should create the Fair Artificial Intelligence Research & Regulation (FAIRR) Bureau, which will bring together experts in technology, human behavior, and public policy from all sectors – public, private, nonprofit, and academic – to research and develop policies that enable the United States to leverage AI as a positive force for national security, economic growth, and equity. The FAIRR Bureau will adopt the interdisciplinary, evidence-based approach to AI regulation and policy needed to address this unprecedented challenge.

Summary

While the United States is a global leader in Artificial Intelligence (AI) research and development (R&D), there has been growing concern that this may not last in the coming decade. China’s massive, state-based tech-investment schemes have catapulted the country to the status of a true competitor over the development and export of AI technologies. In response, there have been repeated calls as well as actions by the Federal Government to step up its funding of fundamental and defense AI research. Yet, maintaining our status as a global leader in AI will require not only a focus on fundamental and defense research. As a matter of domestic policy, we must also attend to the growing chasm that increasingly separates advances in state-of-the-art AI techniques from effective and responsible adoption of AI across American society and economy.

To address this chasm, the Biden-Harris Administration should establish an applied AI research program within the National Institute of Standards and Technology (NIST) to help community-serving organizations tackle the technological and ethical challenges involved in developing AI systems. This new NIST program would fill a key domestic policy gap in our nation’s AI R&D strategy by addressing the growing obstacles and uncertainty confronting AI integration, while broadening the reach of AI as a tool for economic and social betterment nationwide. Program funding would be devoted to research projects co-led by AI researchers and community-based practitioners who would ultimately oversee and operate the AI technology. Research teams would be tasked with co-designing and evaluating an AI system in light of the specific challenges faced by community institutions. Specific areas poised to benefit from this unique multi-stakeholder and cross-sectoral approach development include healthcare, municipal government, and social services.

Summary

The Biden-Harris Administration should launch a national initiative to bring together academic and industry researchers and practitioners in a public-private partnership (PPP) to advance, at scale, the research foundations of artificial intelligence (AI) and its application in areas of economic advantage and national need. The National Public-Private Partnership in AI (NPPP-AI) Initiative would initially create 10 coordinated national AI R&D Institutes, each with 10-year lifetimes and jointly funded by industry partners and the U.S. government through its research agencies at $10M/year each (10x10x10).

NPPP-AI would accelerate future breakthroughs in AI foundations, enable a virtuous cycle between foundational and use-inspired research that would rapidly transition into practice innovations that contribute to U.S. economic and national security, as well as grow education and workforce capacity by linking university faculty and students with industry professionals, settings, and jobs.

Summary

With about $130 billion USD, the United States leads the world in federal research and development spending. Most of this spending is distributed by science and technology (S&T) agencies that use internal reviews to identify the best proposals submitted in response to competitive funding opportunities. As stewards of quality scientific research, part of each funding agency’s mission is to ensure fairness, transparency, and integrity in the proposal-review process. Manual proposal review is time-consuming and expensive, costing an estimated $300 million annually at the National Science Foundation alone. Yet at current proposal-success rates (between 5% and 20% for most funding opportunities), a substantial fraction of proposals reviewed are simply not competitive.

The next administration should initiate and execute a plan to advance machine learning to triage scientific proposals. This proposal presents a set of actions and a vision to diffuse machine-learning across science and technology agencies to ultimately become a standard component of proposal review, while improving the efficiency of the funding process without compromising the quality of funded research.

The 2019 defense authorization act directed the Secretary of Defense to produce a definition of artificial intelligence (AI) by August 13, 2019 to help guide law and policy. But that was not done.

Therefore “no official U.S. government definition of AI yet exists,” the Congressional Research Service observed in a newly updated report on the subject.

But plenty of other unofficial and sometimes inconsistent definitions do exist. And in any case, CRS noted, “AI research is underway in the fields of intelligence collection and analysis, logistics, cyber operations, information operations, command and control, and in a variety of semiautonomous and autonomous vehicles. Already, AI has been incorporated into military operations in Iraq and Syria.”

“The Central Intelligence Agency alone has around 140 projects in development that leverage AI in some capacity to accomplish tasks such as image recognition and predictive analytics.” CRS surveys the field in Artificial Intelligence and National Security, updated November 21, 2019.

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The 2018 financial audit of the Department of Defense, which was the first such audit ever, cost a stunning $413 million to perform. Its findings were assessed by CRS in another new report. See Department of Defense First Agency-wide Financial Audit (FY2018): Background and Issues for Congress, November 27, 2019.

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The Arctic region is increasingly important as a focus of security, environmental and economic concern. So it is counterintuitive — and likely counterproductive — that the position of U.S. Special Representative for the Arctic has been left vacant since January 2017. In practice it has been effectively eliminated by the Trump Administration. See Changes in the Arctic: Background and Issues for Congress, updated November 27, 2019.

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Other noteworthy new and updated CRS reports include the following (which are also available through the CRS public website at crsreports.congress.gov).

Resolutions to Censure the President: Procedure and History, updated November 20, 2019

Immigration: Recent Apprehension Trends at the U.S. Southwest Border, November 19, 2019

Air Force B-21 Raider Long Range Strike Bomber, updated November 13, 2019

Precision-Guided Munitions: Background and Issues for Congress, November 6, 2019

Space Weather: An Overview of Policy and Select U.S. Government Roles and Responsibilities, November 20, 2019

Intelligence Community Spending: Trends and Issues, updated November 6, 2019

While many countries recognize freedom of speech as a fundamental value, every country also imposes some legal limits on free speech.

A new report from the Law Library of Congress surveys the legal limitations on free expression in thirteen countries: Argentina, Brazil, Canada, China, Israel, Japan, Germany, France, New Zealand, Sweden, the Netherlands, the United Kingdom, and Ukraine.

“In particular, the report focuses on the limits of protection that may apply to the right to interrupt or affect in any other way public speech. The report also addresses the availability of mechanisms to control foreign broadcasters working on behalf of foreign governments,” wrote Ruth Levush in the document summary. See Limits on Freedom of Expression, Law Library of Congress, June 2019.

Some other noteworthy recent reports from the Law Library of Congress include the following.

Initiatives to Counter Fake News in Selected Countries, April 2019

Regulation of Artificial Intelligence in Selected Jurisdictions, January 2019

Artificial intelligence (AI) technologies such as machine learning are already being used by the Department of Defense in operations in Iraq and Syria, and they have many potential uses in intelligence processing, military logistics, cyber defense, as well as autonomous weapon systems.

The range of such applications for defense and intelligence is surveyed in a new report from the Congressional Research Service.

The CRS report also reviews DoD funding for AI, international competition in the field, including Chinese investment in US AI companies, and the foreseeable impacts of AI technologies on the future of combat. See Artificial Intelligence and National Security, April 26, 2018.

“We’re going to have self-driving vehicles in theater for the Army before we’ll have self-driving cars on the streets,” Michael Griffin, the undersecretary of defense for research and engineering told Congress last month (as reported by Bloomberg).

Other new and updated reports from the Congressional Research Service include the following.

Foreign Aid: An Introduction to U.S. Programs and Policy, April 25, 2018

OPIC, USAID, and Proposed Development Finance Reorganization, April 27, 2018

OPEC and Non-OPEC Crude Oil Production Agreement: Compliance Status, CRS Insight, April 26, 2018

What Is the Farm Bill?, updated April 26, 2018

A Shift in the International Security Environment: Potential Implications for Defense–Issues for Congress, updated April 26, 2018

Navy Aegis Ballistic Missile Defense (BMD) Program: Background and Issues for Congress, updated April 27, 2018

China Naval Modernization: Implications for U.S. Navy Capabilities — Background and Issues for Congress, updated April 25, 2018

Russian Compliance with the Intermediate Range Nuclear Forces (INF) Treaty: Background and Issues for Congress, updated April 25, 2018

The First Responder Network (FirstNet) and Next-Generation Communications for Public Safety: Issues for Congress, April 27, 2018

African American Members of the United States Congress: 1870-2018, updated April 26, 2018

The field of artificial intelligence is habitually susceptible to exaggerated claims and expectations. But when it comes to new applications in health care, some of those claims may prove to be valid, says a new report from the JASON scientific advisory panel.

“Overall, JASON finds that AI is beginning to play a growing role in transformative changes now underway in both health and health care, in and out of the clinical setting.”

“One can imagine a day where people could, for instance, 1) use their cell phone to check their own cancer or heart disease biomarker levels weekly to understand their own personal baseline and trends, or 2) ask a partner to take a cell-phone-based HIV test before a sexual encounter.”

Already, automated skin cancer detection programs have demonstrated performance comparable to human dermatologists.

The JASON report was requested and sponsored by the U.S. Department of Health and Human Services. See Artificial Intelligence for Health and Health Care, JSR-17-Task-002, December 2017.

Benefits aside, there are new opportunities for deception and scams, the report said.

“There is potential for the proliferation of misinformation that could cause harm or impede the adoption of AI applications for health. Websites, apps, and companies have already emerged that appear questionable based on information available.”

Fundamentally, the JASONs said, the future of AI in health care depends on access to private health data.

“The availability of and access to high quality data is critical in the development and ultimate implementation of AI applications. The existence of some such data has already proven its value in providing opportunities for the development of AI applications in medical imaging.”

“A major initiative is just beginning in the U.S. to collect a massive amount of individual health data, including social behavioral information. This is a ten year, $1.5B National Institutes of Health (NIH) Precision Medicine Initiative (PMI) project called All of Us Research Program. The goal is to develop a 1,000,000 person-plus cohort of individuals across the country willing to share their biology, lifestyle, and environment data for the purpose of research.”

But all such efforts raise knotty questions of data security and personal privacy.

“PMI has recognized from the start of this initiative that no amount of de-identification (anonymization) of the data will guarantee the privacy protection of the participants.”

Lately, the US Government has barred access by non-US researchers to a National Cancer Institute database concerning Medicare recipients, according to a story in The Lancet Oncology. See “International access to major US cancer database halted” by Bryant Furlow, January 18, 2018 (sub. req’d.).