In this interview, Tom Kalil discusses the opportunities for science agencies and the research community to use AI/ML to accelerate the pace of scientific discovery and technological advancement.

Q.  Why do you think that science agencies and the research community should be paying more attention to the intersection between AI/ML and science?

Recently, researchers have used DeepMind’s AlphaFold to predict the structures of more than 200 million proteins from roughly 1 million species, covering almost every known protein on the planet! Although not all of these predictions will be accurate, this is a massive step forward for the field of protein structure prediction.

The question that science agencies and different research communities should be actively exploring is – what were the pre-conditions for this result, and are there steps we can take to create those circumstances in other fields?   

Photo by DeepMind on Unsplash

One partial answer to that question is that the protein structure community benefited from a large open database (the Protein Data Bank) and what linguist Mark Liberman calls the “Common Task Method.”

Q.  What is the Common Task Method (CTM), and why is it so important for AI/ML?

In a CTM, competitors share the common task of training a model on a challenging, standardized dataset with the goal of receiving a better score.  One paper noted that common tasks typically have four elements:

1. Tasks are formally defined with a clear mathematical interpretation
2. Easily accessible gold-standard datasets are publicly available in a ready-to-go standardized format
3. One or more quantitative metrics are defined for each task to judge success
4. State-of-the-art methods are ranked in a continuously updated leaderboard

Computational physicist and synthetic biologist Erika DeBenedictis has proposed adding a fifth component, which is that “new data can be generated on demand.”  Erika, who runs Schmidt Futures-supported competitions such as the 2022 BioAutomation Challenge,  argues that creating extensible living datasets has a few advantages.  This approach can detect and help prevent overfitting; active learning can be used to improve performance per new datapoint; and datasets can grow organically to a useful size.

Common Task Methods have been critical to progress in AI/ML.  As David Donoho noted in 50 Years of Data Science, 

The ultimate success of many automatic processes that we now take for granted—Google translate, smartphone touch ID, smartphone voice recognition—derives from the CTF (Common Task Framework) research paradigm, or more specifically its cumulative effect after operating for decades in specific fields. Most importantly for our story: those fields where machine learning has scored successes are essentially those fields where CTF has been applied systematically.

Q.  Why do you think that we may be under-investing in the CTM approach?

U.S. agencies have already started to invest in AI for Science.  Examples include NSF’s AI Institutes, DARPA’s Accelerated Molecular Discovery, NIH’s Bridge2AI, and DOE’s investments in scientific machine learning.  The NeurIPS conference (one of the largest scientific conferences on machine learning and computational neuroscience) now has an entire track devoted to datasets and benchmarks.

However, there are a number of reasons why we are likely to be under-investing in this approach.

1. These open datasets, benchmarks and competitions are what economists call “public goods.”  They benefit the field as a whole, and often do not disproportionately benefit the team that created the dataset.  Also, the CTM requires some level of community buy-in.  No one researcher can unilaterally define the metrics that a community will use to measure progress. 
2. Researchers don’t spend a lot of time coming up with ideas if they don’t see a clear and reliable path to getting them funded.  Researchers ask themselves, “what datasets already exist, or what dataset could I create with a $500,000 – $1 million grant?”  They don’t ask the question, “what dataset + CTM would have a transformational impact on a given scientific or technological challenge, regardless of the resources that would be required to create it?”  If we want more researchers to generate concrete, high-impact ideas, we have to make it worth the time and effort to do so.
3. Many key datasets (e.g., in fields such as chemistry) are proprietary, and were designed prior to the era of modern machine learning.  Although researchers are supposed to include Data Management Plans in their grant applications, these requirements are not enforced, data is often not shared in a way that is useful, and data can be of variable quality and reliability. In addition, large dataset creation may sometimes not be considered academically novel enough to garner high impact publications for researchers. 
4. Creation of sufficiently large datasets may be prohibitively expensive.  For example, experts estimate that the cost of recreating the Protein Data Bank would be $15 billion!   Science agencies may need to also explore the role that innovation in hardware or new techniques can play in reducing the cost and increasing the uniformity of the data, using, for example, automation, massive parallelism, miniaturization, and multiplexing.  A good example of this was NIH’s $1,000 Genome project, led by Jeffrey Schloss.

Q.  Why is close collaboration between experimental and computational teams necessary to take advantage of the role that AI can play in accelerating science?

According to Michael Frumkin with Google Accelerated Science, what is even more valuable than a static dataset is a data generation capability, with a good balance of latency, throughput, and flexibility.  That’s because researchers may not immediately identify the right “objective function” that will result in a useful model with real-world applications, or the most important problem to solve.  This requires iteration between experimental and computational teams.

Q.  What do you think is the broader opportunity to enable the digital transformation of science? 

I think there are different tools and techniques that can be mixed and matched in a variety of ways that will collectively enable the digital transformation of science and engineering. Some examples include:

• Self-driving labs (and eventually, fleets of networked, self-driving labs), where machine learning is not only analyzing the data but informing which experiment to do next.
• Scientific equipment that is high-throughput, low-latency, automated, programmable, and potentially remote (e.g. “cloud labs”).
• Novel assays and sensors.
• The use of “science discovery games” that allow volunteers and citizen scientists to more accurately label training data.  For example, the game Mozak trains volunteers to collaboratively reconstruct complex 3D representations of neurons.
• Advances in algorithms (e.g. progress in areas such as causal inference, interpreting high-dimensional data, inverse design, uncertainty quantification, and multi-objective optimization).
• Software for orchestration of experiments, and open hardware and software interfaces to allow more complex scientific workflows.
• Integration of machine learning, prior knowledge, modeling and simulation, and advanced computing.
• New approaches to informatics and knowledge representation – e.g. a machine-readable scientific literature, increasing number of experiments that can be expressed as code and are therefore more replicable.
• Approaches to human-machine teaming that allow the best division of labor between human scientists and autonomous experimentation.
• Funding mechanisms, organizational structures and incentives that enable the team science and community-wide collaboration needed to realize the potential of this approach.

There are many opportunities at the intersection of these different scientific and technical building blocks.  For example, use of prior knowledge can sometimes reduce the amount of data that is needed to train a ML model.  Innovation in hardware could lower the time and cost of generating training data.  ML can predict the answer that a more computationally-intensive simulation might generate.  So there are undoubtedly opportunities to create a virtuous circle of innovation.

Q.  Are there any risks of the common task method?

Some researchers are pointing to negative sociological impacts associated with “SOTA-chasing” – e.g. a single-minded focus on generating a state-of-the-art result.  These include reducing the breadth of the type of research that is regarded as legitimate, too much competition and not enough cooperation, and overhyping AI/ML results with claims of “super-human” levels of performance.  Also, a researcher who makes a contribution to increasing the size and usefulness of the dataset may not get the same recognition as the researcher who gets a state-of-the-art result.

Some fields that have become overly dominated by incremental improvements in a metric have had to introduce Wild and Crazy Ideas as a separate track in their conferences to create a space for more speculative research directions.

Q.  Which types of science and engineering problems should be prioritized?

One benefit to the digital transformation of science and engineering is that it will accelerate the pace of discovery and technological advances.  This argues for picking problems where time is of the essence, including:

• Developing and manufacturing carbon-neutral and carbon-negative technologies we need for power, transportation, buildings, industry, and food and agriculture.  Currently, it can take 17-20 years to discover and manufacture a new material.  This is too long if we want to meet ambitious 2050 climate goals.
• Improving our response to future pandemics by being able to more rapidly design, develop and evaluate new vaccines, therapies, and diagnostics.
• Addressing new threats to our national security, such as engineered pathogens and the technological dimension of our economic and military competition with peer adversaries.

Obviously, it also has to be a problem where AI and ML can make a difference, e.g. ML’s ability to approximate a function that maps between an input and an output, or to lower the cost of making a prediction.

Q.  Why should economic policy-makers care about this as well?

One of the key drivers of the long-run increases in our standard of living is productivity (output per worker), and one source of productivity is what economists call general purpose technologies (GPTs).  These are technologies that have a pervasive impact on our economy and our society, such as interchangeable parts, the electric grid, the transistor, and the Internet.  

Historically –  GPTs have required other complementary changes (e.g. organizational changes, changes in production processes and the nature of work) before their economic and societal benefits can be realized.  The introduction of electricity eventually led to massive increases in manufacturing productivity, but not until factories and production lines were reorganized to take advantage of small electric motors.  There are similar challenges for fostering the role that AI/ML and complementary technologies will play in accelerating the pace of scientific and technological advances:

• Researchers and science funders need to identify and support the technical infrastructure (e.g. datasets + CTMs, self-driving labs) that will move an entire field forward, or solve a particularly important problem.
• A leading academic researcher involved in protein structure prediction noted that one of the things that allowed DeepMind to make so much progress on the protein folding problem was that “everyone was rowing in the same direction,” “18 co-first authors .. an incentive structure wholly foreign to academia,” and “a fast and focused research paradigm … [which] raises the question of what other problems exist that are ripe for a fast and focused attack.”  So capitalizing on the opportunity is likely to require greater experimentation in mechanisms for funding, organizing and incentivizing research, such as Focused Research Organizations.

Q.  Why is this an area where it might make sense to “unbundle” idea generation from execution?

Traditional funding mechanisms assume that the same individual or team who has an idea should always be the person who implements the idea.  I don’t think this is necessarily the case for datasets and CTMs.  A researcher may have a brilliant idea for a dataset, but may not be in a position to liberate the data (if it already exists), rally the community, and raise the funds needed to create the dataset.  There is still a value in getting researchers to submit and publish their ideas, because their proposal could be catalytic of a larger-scale effort.

Agencies could sponsor white paper competitions with a cash prize for the best ideas. [A good example of a white paper competition is MIT’s Climate Grand Challenge, which had a number of features which made it catalytic.]  Competitions could motivate researchers to answer questions such as:

• What dataset and Common Task would have a significant impact on our ability to answer a key scientific question or make progress on an important use-inspired or technological problem?  What preliminary work has been done or should be done prior to making a larger-scale investment in data collection?
• To the extent that industry would also find the data useful, would they be willing to share the cost of collecting it?  They could also share existing data, including the results from failed experiments.
• What advance in hardware or experimental techniques would lower the time and cost of generating high-value datasets by one or more orders of magnitude?
• What self-driving lab would significantly accelerate progress in a given field or problem, and why?

The views and opinions expressed in this blog are the author’s own and do not necessarily reflect the view of Schmidt Futures.

One of the main goals of Kalil’s Corner is to share some of the things I’ve learned over the course of my career about policy entrepreneurship. Below is an FAQ on a thought experiment that I think is useful for policy entrepreneurs, and how the thought experiment is related to a concept I call “shared agency.”

Q.  What is your favorite thought experiment?

Imagine that you have a magic laptop. The power of the laptop is that any press release that you write will come true.

You have to write a headline (goal statement), several paragraphs to provide context, and 1-2 paragraph descriptions of who is agreeing to do what (in the form organization A takes action B to achieve goal C). The individuals or organizations could be federal agencies, the Congress, companies, philanthropists, investors, research universities, non-profits, skilled volunteers, etc. The constraint, however, is that it has to be plausible that the organizations would be both willing and able to take the action. For example, a for-profit company is not going to take actions that are directly contrary to the interests of their shareholders. 

What press release would you write, and why? What makes this a compelling idea?  

Q.  What was the variant of this that you used to ask people when you worked in the White House for President Obama?

You have a 15-minute meeting in the Oval Office with the President, and he asks:

“If you give me a good idea, I will call anyone on the planet.  It can be a conference call, so there can be more than one person on the line.  What’s your idea, and why are you excited about it?  In order to make your idea happen, who would I need to call and what would I need to ask them to do in order to make it happen?”

Q.  What was your motivation for posing this thought experiment to people?

I’ve been in roles where I can occasionally serve as a “force multiplier” for other people’s ideas. The best way to have a good idea is to be exposed to many ideas.

When I was in the White House, I would meet with a lot of people who would tell me that what they worked on was very important, and deserved greater attention from policy-makers.

But when I asked them what they wanted the Administration to consider doing, they didn’t always have a specific response.  Sometimes people would have the kernel of a good idea, but I would need to play “20 questions” with them to refine it. This thought experiment would occasionally help me elicit answers to basic questions like who, what, how and why.

Q.  Why does this thought experiment relate to the Hamming question?

Richard Hamming was a researcher at Bell Labs who used to ask his colleagues, “What are the most important problems in your field?  And what are you working on?” This would annoy some of his colleagues, because it forced them to confront the fact that they were working on something that they didn’t think was that important.

If you really did have a magic laptop or a meeting with the President, you would presumably use it to help solve a problem that you thought was important!

Q.  How does this thought experiment highlight the importance of coalition-building?

There are many instances where we have a goal that requires building a coalition of individuals and organizations.

It’s hard to do that if you can’t identify (1) the potential members of the coalition; and (2) the mutually reinforcing actions you would like them to consider taking.

Once you have a hypothesis about the members of your coalition of the willing and able, you can begin to ask and answer other key questions as well, such as:

• Why is it in the enlightened self-interest of the members of the coalition to participate?
• Who is the most credible messenger for your idea?  Who could help convene the coalition?
• Is there something that you or someone else can do to make it easier for them to get involved? For example, policy-makers do things with words, in the same way that a priest changes the state of affairs in the world by stating “I now pronounce you husband and wife.” Presidents sign Executive Orders, Congress passes legislation, funding agencies issue RFPs, regulatory agencies issue draft rules for public comment, and so on. You can make it easier for a policy-maker to consider an idea by drafting the documents that are needed to frame, make, and implement a decision.
• If a member of the coalition is willing but not able, can someone else take some action that relaxes the constraint that is preventing them from participating?
• What evidence do you have that if individual or organization A took action B, that C is likely to occur?
• What are the risks associated with taking the course of action that you recommend, and how could they be managed or mitigated?

Q.  Is this thought experiment only relevant to policy-makers?

Not at all. I think it is relevant for any goal that you are pursuing — especially ones that require concerted action by multiple individuals and organizations to accomplish.

Q.  What’s the relationship between this thought experiment and Bucky Fuller’s concept of a “trim tab?”

Fuller observed that a tiny device called a trim tab is designed to move a rudder, which in turn can move a giant ship like the Queen Elizabeth.

So, it’s incredibly useful to identify these leverage points that can help solve important problems.

For example, some environmental advocates have focused on the supply chains of large multinationals. If these companies source products that are more sustainable (e.g. cooking oils that are produced without requiring deforestation) – that can have a big impact on the environment.

Q.  What steps can people take to generate better answers to this thought experiment?

There are many things – like having a deep understanding of a particular problem, being exposed to both successful and unsuccessful efforts to solve important problems in many different domains, or understanding how particular organizations that you are trying to influence make decisions.

One that I’ve been interested in is the creation of a “toolkit” for solving problems. If, as opposed to having a hammer and looking for nails to hit, you also have a saw, a screwdriver, and a tape measure, you are more likely to have the right tool or combination of tools for the right job.

For example, during my tenure in the Obama Administration, my team and other people in the White House encouraged awareness and adoption of dozens of approaches to solving problems, such as:

• Sponsoring incentive prizes, which allow agencies to set a goal without having to choose the team or approach that is most likely to be successful;
• Making open data available in machine-readable formats, and encouraging teams to develop new applications that use the data to solve a real-world problem;
• Changing federal hiring practices and recruiting top technical talent;
• Embracing modern software methodologies such as agile and human-centered design for citizen-facing digital services;
• Identifying and pursuing 21^st century moonshots;
• Using insights from behavioral science to improve policies and programs;
• Using and building evidence to increase the share of federal resources going to more effective interventions;
• Changing procurement policies so that the government can purchase products and services from startups and commercial firms, not just traditional contractors.

Of course, ideally one would be familiar with the problem-solving tactics of different types of actors (companies, research universities, foundations, investors, civil society organization) and individuals with different functional or disciplinary expertise. No one is going to master all of these tools, but you might aspire to (1) know that they exist; (2) have some heuristics about when and under what circumstances you might use them; and (3) know how to learn more about a particular approach to solving problems that might be relevant. For example, I’ve identified a number of tactics that I’ve seen foundations and nonprofits use.

Q.  How does this thought experiment relate to the concept that psychologists call “agency?”

Agency is defined by psychologists like Albert Bandura as “the human capability to influence …the course of events by one’s actions.”

The particular dimension of agency that I have experienced is a sense that there are more aspects of the status quo that are potentially changeable as opposed to being fixed. These are the elements of the status quo that are attributable to human action or inaction, as opposed to the laws of physics.

Obviously, this sense of agency didn’t extend to every problem under the sun. It was limited to those areas where progress could be made by getting identifiable individuals and organizations to take some action – like the President signing an Executive Order or proposing a new budget initiative, the G20 agreeing to increase investment in a global public good, Congress passing a law, or a coalition of organizations like companies, foundations, nonprofits and universities working together to achieve a shared goal.

Q.  How did you develop a strong sense of agency over the course of your career?

I had the privilege of working at the White House for both Presidents Clinton and Obama.

As a White House staffer, I had the ability to send the President a decision memo. If he checked the box that said “yes” – and the idea actually happened and was well-implemented, this reinforced my sense of agency.

But it wasn’t just the experience of being successful. It was also the knowledge that one acquires by repeatedly trying to move from an idea to something happening in the world, such as:

• Working with the Congress to pass legislation that gave every agency the authority to sponsor incentive prizes for up to $50 million;
• Including funding for dozens of national science and technology initiatives in the President’s budget, such as the National Nanotechnology Initiative or the BRAIN Initiative;
• Recruiting people to the White House to help solve hard and important problems, like reducing the waiting list for an organ transplant, allowing more foreign entrepreneurs to come to the United States, or launching a behavioral sciences unit within the federal government; and,
• Using the President’s “bully pulpit” to build coalitions of companies, non-profits, philanthropists, universities, etc. to achieve a particular goal, like expanding opportunities for more students to excel in STEM.

Q.  What does it mean for you to have a shared sense of agency with another individual, a team, or a community?

Obviously, most people have not had 16 years of their professional life in which they could send a decision memo to the President, get a line in the President’s State of the Union address, work with Congress to pass legislation, create a new institution, shape the federal budget, and build large coalitions with hundreds of organizations that are taking mutually reinforcing actions in the pursuit of a shared goal.

So sometimes when I am talking to an individual, a team or a community, it will become clear to me that there is some aspect of the status quo that they view as fixed, and I view as potentially changeable. It might make sense for me to explain why I believe the status quo is changeable, and what are the steps we could take together in the service of achieving a shared goal.

Q.  Why is shared agency important?

Changing the status quo is hard. If I don’t know how to do it, or believe that I would be tilting at windmills – it’s unlikely that I would devote a lot of time and energy to trying to do so.

It may be the case that pushing for change will require a fair amount of work, such as:

• Expressing my idea clearly, and communicating it effectively to multiple audiences;
• Marshaling the evidence to support it;
• Determining who the relevant “deciders” are for a given idea;
• Addressing objections or misconceptions, or responding to legitimate criticism; and
• Building the coalition of people and institutions who support the idea, and may be prepared to take some action to advance it.

So if I want people to devote time and energy to fleshing out an idea or doing some of the work needed to make it happen, I need to convince them that something constructive could plausibly happen. And one way to do that is to describe what success might look like, and discuss the actions that we would take in order to achieve our shared goal. As an economist might put it, I am trying to increase their “expected return” of pursuing a shared goal by increasing the likelihood that my collaborators attach to our success.

Q.  Are there risks associated with having this strong sense of agency, and how might one mitigate against those risks?

Yes, absolutely. One is a lack of appropriate epistemic humility, by pushing a proposed solution in the absence of reasonable evidence that it will work, or failing to identify unintended consequences. It’s useful to read books like James Scott’s Seeing Like a State.

I also like the idea of evidence-based policy. For example, governments should provide modest amounts of funding for new ideas, medium-sized grants to evaluate promising approaches, and large grants to scale interventions that have been rigorously evaluated and have a high benefit to cost ratio.

The views and opinions expressed in this blog are the author’s own and do not necessarily reflect the view of Schmidt Futures.

Summary

The United States should establish a testbed for government-procured artificial intelligence (AI) models used to provide services to U.S. citizens. At present, the United States lacks a uniform method or infrastructure to ensure that AI systems are secure and robust. Creating a standardized testing and evaluation scheme for every type of model and all its use cases is an extremely challenging goal. Consequently, unanticipated ill effects of AI models deployed in real-world applications have proliferated, from radicalization on social media platforms to discrimination in the criminal justice system. Increased interest in integrating emerging technologies into U.S. government processes raises additional concerns about the robustness and security of AI systems.

Establishing a designated federal AI testbed is an important part of alleviating these concerns. Such a testbed will help AI researchers and developers better understand how to construct testing methods and ultimately build safer, more reliable AI models. Without this capacity, U.S. agencies risk perpetuating existing structural inequities as well as creating new government systems based on insecure AI systems — both outcomes that could harm millions of Americans while undermining the missions that federal agencies are entrusted to pursue.

Summary

The future of computing innovation is becoming more uncertain as the 2020s have brought about a pivot point in the global semiconductor industry. We owe this uncertainty to several factors, including the looming end of Moore’s Law, disruptions in semiconductor supply chains, international competition in innovation investment, a growing demand for more specialized computer chips, and the continued development of alternate computing paradigms, such as quantum computing.

In order to address the next generation of computing needs, architectures are beginning to emphasize the integration of multiple, specialized computing components. Within this framework, the U.S. is well poised to emerge as a leader in the future of next-generation computing, and more broadly advanced semiconductor manufacturing. However, there remains a missing link in the United States’ computing innovation strategy: a coordinating organization which will down-select and integrate the wide variety of promising, next-generation computing materials, architectures, and approaches so that they can form the building blocks of advanced, high-performance, heterogeneous systems.

Armed with these facts, and using the existing authorization language in the 2021 National Defense Authorization Act (NDAA), the Biden Administration and Congress have a unique opportunity to establish a Manufacturing USA Institute under the National Institute of Standards and Technology (NIST) with the goal of pursuing advanced packaging for scalable heterogeneous computing. This Institute will leverage the enormous body of previous work in post-Moore computing funded by the federal government (Semiconductor Technology Advanced Research Network (STARnet), Nanoelectronics Computing Research (nCORE), Joint University Microelectronics Program (JUMP), Energy-Efficient Computing: From Devices to Architectures (E2CDA), Electronics Resurgence Initiative (ERI)) and will bridge a key gap in bringing these R&D efforts from the laboratory to real world applications. By doing this, the U.S. will be well positioned to continue its dominance in semiconductor design and potentially regain advanced semiconductor manufacturing activity over the coming decades.

Summary

Online disinformation continues to evolve and threaten national security, federal elections, public health, and other critical U.S. sectors. Yet the federal government lacks access to data and computational power needed to study disinformation at scale. Those with the greatest capacity to study disinformation at scale are large technology companies (e.g., Google, Facebook, Twitter, etc.), which biases much research and limits federal capacity to address disinformation.

To address this problem, we propose that the Department of Defense (DOD) fund a one-year pilot of a National Cloud for Disinformation Research (NCDR). The NCDR would securely house disinformation data and provide computational power needed for the federal government and its partners to study disinformation. The NCDR should be managed by a governance team led by Federally Funded Research and Development Centers (FFRDCs) already serving the DOD. The FFRDC Governance Team will manage (i) which stakeholders can access the Cloud, (ii) coordinate sharing of data and computational resources among stakeholders, and (iii) motivate participation from diverse stakeholders (including industry; academia; federal, state, and local government, and non-governmental organizations).

A National Cloud for Disinformation Research will help the Biden-Harris administration fulfill its campaign promise to reposition the United States as a leader of the democratic world. The NCDR will benefit the federal government by providing access to data and computational resources needed to combat the threats and harms of disinformation. Our nation needs a National Cloud for Disinformation Research to foresee future disinformation attacks and safeguard our democracy in turbulent times.

Summary

Algorithmic governance concerns are critical for US foreign policy in the 21st century as they relate intimately to the relationship between governments and their citizens – the very fabric of the world’s societies. The United States should strategically invest resources into the principal multilateral forums in which digital technology regulation is currently under discussion. In partnership with like-minded governments and international organizations, the Biden-Harris Administration should set clear priorities championing a collective digital rights agenda that considers the impact of commercial algorithms and algorithmic decision-making on both American citizens and technology consumers around the world.

These investments would build substantially upon initial forays into national AI regulatory policy advanced by the National Security Commission on Artificial Intelligence (NSCAI) established by Congress in August 2018 and the Executive Order on Maintaining American Leadership in Artificial Intelligence issued in January 2019. Both policy moves featured broad approaches focused on national security and competitiveness, without seriously engaging the complex and context-specific problems of international governance that must be squarely addressed if the United States is to develop a coherent approach to AI regulation.

We suggest the federal government pay special attention to impacts on people living in regions outside the geographic focus of the most prominent regulatory deliberations today – which occur almost exclusively in Washington and the developed world. Such an inclusive, global approach to digital policymaking will increase the potential for the United States to bring the world along in efforts to develop meaningful, consumer-first internet policy that addresses the economic and social factors driving digital disparities. At a time when the risk of a global “splinternet” increasingly looms, this clarified focus will help establish effective rules toward which jurisdictions around the world can converge under U.S. leadership.

Summary

As artificial intelligence (AI) applications for public use have proliferated, there has been a large uptick in challenges associated with AI safety and fairness. These challenges are due in part to poor transparency in and standardization of AI procurement protocols, particularly for public-use applications. In this memo, we propose a federal framework—orchestrated through the Office of Federal Procurement Policy (OFPP) situated in the Office of Management and Budget (OMB)—to standardize and guide AI procurement in a safer, fairer manner. While this framework is designed for federal implementation, it is important to recognize that many decisions on AI usage are made by municipalities. The principles guiding the federal framework outlined herein are intended to also help guide development and implementation of similar frameworks for AI procurement at the local level.

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.