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Executive Interview: Dr. David Bray, Director, Atlantic Council




The Atlantic Council offers programs related to global economic prosperity. David Bray, director of the Council’s GeoTech Center, recently spoke with AI Trends about the mission. (NASA via Unsplash)

COVID-19 Pandemic Has Accelerated the Rate of Change

Dr. David Bray is the Inaugural Director of the new global GeoTech Center & Commission of the Atlantic Council, a consulting forum for international political, business, and intellectual leaders founded in 1961. Headquartered in Washington, DC, the Council offers programs related to international security and global economic prosperity. In previous leadership roles, Bray led the technology aspects of the Centers for Disease Control’s bioterrorism preparedness program in response to 9/11, the outbreak response to the West Nile virus, SARS, monkey pox and other emergencies.

He also spent time on the ground in Afghanistan in 2009 as a senior advisor to both military and humanitarian assistance efforts, serving as the non-partisan Executive Director for a bipartisan National Commission on R&D, and providing leadership as a non-partisan federal agency Senior Executive focused on digital modernization. He also is a Young Global Leader for 2017-2021 of the World Economic Forum.

David Bray, Director, GeoTech Center & Commission, Atlantic Council

Bray is a member of multiple Boards of Directors and has worked with the U.S. Special Operations Command on counter-misinformation efforts. He was invited to give the 2019 UN Charter Keynote on the future of AI & IoT governance. His academic background includes a PhD from Emory University; he also has held affiliations with MIT, Harvard, and the University of Oxford. He recently took a few moments to speak to AI Trends Editor John P. Desmond about current events, including the geopolitics of the COVID-19 pandemic.

AI Trends: Thank you David for talking to AI Trends today. We will start with the Coronavirus since it’s so topical today, then expand out. What role do you see AI playing in the fight against COVID-19?

Dr. David Bray: With AI, we are dealing with something that really is historically unprecedented. The last pandemic that we had was the Spanish influenza [of 1918-1919] and that was, obviously, before all the advances in computers and data and AI.

So, what we need to do first and foremost is assemble good data on what we are seeing, because especially with machine learning models and trying to train the machine, this really is unprecedented territory. We currently lack sufficient data sets to train on that can help predict the future given the unprecedented nature of this pandemic. What we are discovering is that when it comes to bringing together data about COVID-19 and the pandemic, different countries, different regions, even different sectors are either unintentionally, or in some cases intentionally, biasing their data sets. That may just be because they are waiting on when they report the numbers, or what they consider a case versus not a case of COVID-19.

This absence of good quality data is making it really hard right now to use any type of machine learning or algorithms to inform both the immediate response, and even more importantly, how do we rebuild? Yet this absence of good quality data is an area where statistical techniques and some AI techniques might be able to help to at least begin to identify and curate better data sets. For instance, are we seeing anomalies in the data that look like spikes, but in fact may be because some regions might report every seven days? For COVID-19, before we can use AI, the first step is getting good quality data.

We are also seeing where AI is playing a role in trying to make sense of all the scientific literature about the virus. I have seen statistics that say we need to wade through 25,000 different articles, not all of them peer-reviewed yet, on what we know about the virus. I’ve seen others that say it’s upwards of 50,000 or more; either way, the number of articles are way more than any one human or even a team of humans can wade through. AI approaches can help us inform a novel therapy or a novel intervention that might help us address what is going on with the pandemic.

How should an AI-related global effort to fight the coronavirus be taking shape? How do you envision it?

We are seeing a fragmentation of systems in place that we had a high degree of confidence would have been able to respond to this. That is partly because a lot of these institutions involving how nations relate to each other and how nations coordinate, were put in place after World War II.

After all, back in the 1970s we eradicated smallpox. Yet now in 2020, we are discovering that, unfortunately, funds for states in the United States to have available capacity to characterize and respond to outbreaks appear to not have been sufficient. Leaders may have convinced themselves with past responses to outbreaks of Ebola, HIN1 influenza and other things, that we could handle this and subsequent public health budgets focused on pandemic preparedness waned, resulting in a loss of expertise, equipment, and experience in responding to outbreaks.

Beyond internal coordination challenges in the United States, the COVID-19 pandemic is shining a light on the reality that ways of coordinating globally have broken down. We are seeing every nation sort of turn inwards and even within the nations, fragment regionally.

COVID-19 also may be illuminating that the world may be in this era in which things that used to be done by government cannot solely be done by government anymore. There are global activities that used to be done solely by government that now need to be done by industry as a partner. Yet industry does not really know how to make a case beyond increasing ROI, increasing profits, and increasing shareholder value. So for open societies that do distinguish between the work of their public vs. private sector, what we need to do is find some way to bring together both industry and governance mechanisms that involve the public to respond to address global challenges such as the COVID-19 pandemic.

I will put these concerns in a pragmatic context with regards to data and AI. In some countries responding to COVID-19, all the data about their people belongs to the nation-state. I’m glad that, in the United States, we don’t take that approach and that we are not a surveillance state. Yet at the same time the different levels of government and differences between data that the public vs. private sector has fragments the ability of the United States and other open societies. We must find better ways to bring good data forward, with consent and via a decentralized method that is not surveillance state, to inform the COVID-19 response.

In the United Kingdom, back in 2017, the government identified a potential solution called Data Trusts. The United Kingdom proposed it to overcome the challenge that the UK does not have as much data as China does to train machines. Data Trusts would involve transparency, auditability, and a framework to involve individuals and companies for a time-defined, focused effort to share data for a specific purpose.

With Data Trusts, it is clear why the data is being brought together; it was not to make any profits or to inform the surveillance state, but for a specific purpose. In this case, a Data Trust could be informing on what we should do with COVID-19.


Unfortunately, while the UK government was starting to consider doing pilots on this in 2020 before the virus hit. With the Atlantic Council GeoTech Center, we have been emphasizing Data Trusts are needed to bring together data and inform the long-term COVID-19 recovery. Open societies need to work together to figure out a way to bring together data that is neither surveillance state or autocracy. There are several questions that need answers that require better quality data sets, such as is there going to be a second wave of COVID-19? How do we best pursue the long-term recovery needed for the world?

Some good thoughts there. Can I ask, the recent posting of the Atlantic Council suggests that the COVID-19 pandemic might lead to more adoption of AI: as complex supplier networks are restructured, as AI use by online retailers accelerates, and as spending is targeted to revive the manufacturing base and reskill employees. Could you expand on any of these?

We have been having conversations with experts as well as polling different experts in how technology and data intersect with geopolitics. What we’ve heard and what we found is that COVID-19 has accelerated trends that previously many people thought would take another 10 to 15 years. Before COVID-19, we were inching towards more digitization and modernization, remote work, and autonomous ways of manufacturing. COVID-19 has accelerated these trends. What would probably have taken 10 to 15 years will probably now be more like two to four years as the pandemic creates a digitization imperative.

For example, anyone who right now has a manufacturing factory that is heavily dependent on having people present at the factory, such factory owners or investors are probably looking at a future where they will at least pursue some semi-autonomous, if not full autonomous, manufacturing so the factory can continue to work if the humans aren’t there.

This pandemic’s acceleration on autonomous manufacturing is going to move us to more distributed ways of doing manufacturing that are not dependent on large global supply chains. We will see advances in additive manufacturing and in 3D printing. Moving to embrace distributed and autonomous ways of manufacturing will also raise questions of how do you do quality control? How do you make sure what is done in region A is as good as region B or C?

With the GeoTech Center, we are monitoring advances using computer vision and AI to watch each step of what a person does or what a machine does. If the machine in step three or four does something that’s slightly different or slightly that’s not in keeping with consistency, the machine can recommend a corrective step. Or if the manufacturing error was so egregious, the machine can label what was being produced as defective and put it aside.

As societies, post-COVID-19 we may shift to a more localized, distributed, resilient production enabled by AI, while having the same level that centralized quality assurance would provide or better. A company that’s leading in this space is Nanotronics. [Ed note: The company is a nanotechnology startup in Cuyahoga Falls, Ohio, with an office in Brooklyn, New York at New Lab, and manufacturing operations in California.]

The GeoTech Center also is monitoring the future of work and the future of education. COVID-19 has accelerated online learning in response to the new skills required for jobs of the future. Higher education is likely to have a strong online component going forward that may also be experiential-based or team-based with some in-person component as well. For the decade ahead, individuals will be comparing the value proposition of spending four years at an expensive location for college, versus interning in a work setting and gaining experiences while learning remotely.

In addition, for delivery of education, it is likely to be tailored to deliver materials online based on how a student learns. Data and AI can help identify if a student is more visual or prefer to hear things via podcasts.

Shifting to data, how much of a challenge is it to aggregate the disparate data needed to train AI systems?

For a COVID-19, because it is unprecedented, aggregating the disparate data needed to train AI systems is hard. I have talked to colleagues at different companies as well as different governmental organizations. Part of the challenge is sometimes the data does not even exist yet that is needed to train AI for the pandemic. Or if the data does exist, it has a fast rate of decay.

We are dealing with a very fast-moving, turbulent environment in which the data may not exist or it may decay quickly. There may be concerns about intellectual property or proprietary information. Some companies do not want to put their proprietary information at risk and some individuals do not want to put their personalized information at risk of misuse.

This is why I really think Data Trusts are interesting because, essentially, they can help bring together people towards a common purpose associated with data. Data Trusts should have transparency about the audit mechanisms to ensure the effort is protecting and treating the data appropriately. Data Trusts also help address proprietary information concerns, because while they can involve open data, they can also involve closed data such as personal data or proprietary data as well.

We have seen some early signs of good approaches to bringing together data across sectors and nations. Microsoft does have an open source data effort {Ed. Note: See The Economist.}, which is, I think a really great example. Since such an effort is open source, it won’t assemble personalized or proprietary information. If this open source data effort were to pursue a Data Trust approach with transparency, audit mechanisms, and bringing in people from whatever localities are being impacted by how this data is informing what we should do – it could be expanded to incorporate data sets of value to inform the long-term COVID-19 recovery.

I think it is important to involve members of the public as part of the oversight of any Data Trust. This is so the representatives can say, “I see you’re using that data for this purpose, but that’s not actually right or that’s not fully representative of us.” Maybe the data is skewed towards a certain demographic. The public can also encourage the Data Trusts to consider the time-horizon of such an effort. For example, after 30, 60 or 90 days, the data can be forgotten and the Data Trust can ensure its efforts are not used for other purposes beyond the transparent purpose of the effort.


What would be the best way to collect data about the COVID-19 virus? Where would the data come from, in your opinion ideally?

Ideally, data would come from people opting in and not having to do too much to provide the data. With the COVID-19 pandemic there has been a lot of effort towards contact tracing. We have seen Google and Apple launch contact tracing. I think that is well-intended. Based on my background with public health and bioterrorism preparedness, a concern I have is contact tracing really depends on good testing. We know that testing still remains a challenge here in the United States. Even when tests are available, we have some false positives and false negatives. When you get to larger than 50,000 people, and you have false positives and false negatives, the data analyses risk getting confused as a result.

I wish we could have a way where individuals could opt-in to answer a few questions on their phone or Alexa or Siri or similar device during the day, which could help inform the COVID-19 response. I do not know if anybody has explored using voice interactions to collect data to inform the COVID-19 response. That said, if anybody is listening to this and wants to pilot or pioneer something with us, the Atlantic Council’s happy to partner with them.

Nice offer. Shifting again, in 2019 you delivered a keynote at the UN on the future of AI and IoT governance, What is the update in that area?

Since the time that I gave the UN Charter Day keynote in 2019, the Internet of Things continues to accelerate, more rapidly on the industry side than on the commercial side. On the industry side, we are seeing an acceleration of IoT-enabled computer vision and other sensors being applied to the manufacturing process paired with AI. IoT is being used for distributed manufacturing, accelerating the automation of what a factory does by putting in an industrial Internet of Things along the assembly line.

And I’ve seen in Sweden a company called Unibap, in partnership with Intel, that is advancing the ability to have a machine watch what a human does over a period of three to four weeks, which is a period in which the machine is taught based on what the human accepts or rejects with quality control. By the end of three or four weeks, the machine can do what a human does, freeing the human up from doing the more rote, repetitive, or in some cases even monotonous or even dangerous work. A human worker can then focus on unique exceptions or the more creative work that needs to be done?

In the commercial Internet of Things, we hit two roadblocks. The first roadblock was identifying the user interface for the commercial Internet of Things. In 2015, the assumption was the user interface was going to be a computer or smartphone screen. Since then, we have discovered that the probable user interface to the commercial Internet of Things is going to be voice combined with gestures.

Obviously, we’ve seen devices similar to what Amazon, Google, or Apple can provide in your house where you can have voice-enabled interactions with a machine. Yet the use of these devices, beyond the core apps really, still needs to happen to result in always-on computing.

The second roadblock to IoT governance that we need to overcome is how do we make sure that we do not become either a surveillance state or surveillance capitalism as a result? We are seeing a lot of pioneering IoT applications in China, but that’s also because the Chinese people have acquiesced and accepted a surveillance state with the belief that that’s how control is done, potentially also producing better stability as a result.

With the GeoTech Center, part of what we’re doing is having conversations with officials in China, but also officials in Europe, India and Africa, about different perspectives on data and AI primarily to understand where China is going; not just within China, but also with global partners and their observations of China in terms of its exports globally with data and AI.

At the moment, we have a choice of two paths for the world. The first path uses these technologies and it is either surveillance state or surveillance capitalism. This is not a path I support as I have concerns about what happens in such a future to individual freedoms.

The second path provides each of us with the ability to choose when and where our data is shared and maybe also our data’s forgotten after a certain point in time. This second path makes Internet-enabled interactions ubiquitous for people using the Internet of Things and computing and AI, but at the same time, we each have a locus of choice.

This is a harder path. Unfortunately, right now, there is so much turbulence going on, there’s so much misinformation and polarization also going on in the world, that open societies are behind the curve right now on using data and AI and the Internet of Things for more free choices as opposed to ones that are more closed ones.

It’s a little discouraging.

It is. At the same time, we as a world have had challenges before and rallied before. Imagine if you were in England in 1939 or 1940. It looked discouraging, too. Part of the human condition is every time we make some advances in terms of technologies, we also have challenges that go with it. Navigating through this current set of challenges is dependent upon positive change agents who are willing to press through with resilience and with grit and creativity to get through the challenges, even if these change agents are facing adversity at the time.


The GeoTech Center of the Atlantic Council had the noble mission to ensure new technologies and data empower people, increase prosperity, and secure peace. How’s it working out? How does the organization envision delivering on the mission? You might’ve been talking about it a little bit, but maybe you can expand.

It was back in December 2019 when I received an offer from the Atlantic Council to incubate a new center focused on the geopolitics of new technologies and data. This is core to what GeoTech is, with a side note that we focus on how new technologies and data impact geopolitics (we’re geospatial tech). New technologies and data impact the choices we can make with data and AI. Each of us can choose a future that is more open, more free, and more empowering of people or a future in which that does not happen.

And it’s not just data and AI, at the GeoTech Center we recognize that we have waves of technologies that are going to come out in parallel over the next decade.

For example, personalized medicine. In the future, a doctor is going to be able to work with you to tailor therapies and health interventions and therapeutics to you that actually make you healthier than sort of generic therapies. This will involve data and AI to understand your biomes, to understand your DNA and who you are. At the same time, all those technologies, if used poorly, could become personalized poison that target certain individuals, which would be awful. Or these technologies could become available only to the rich and not everybody else, which would also be unfair. We are focusing on these issues with the GeoTech Center.

As another example, consider 3D printing and additive manufacturing. The great news is you will be able to manufacture just certain parts or equipment almost anywhere probably in the next 15 to 20 years. The challenge is you will be able to manufacture these parts or equipment everywhere. So, what does that mean for intellectual property? What does that mean for markets? What does that mean for physical security? What happens when 90% of the population, 95% of the population uses 3D printing and additive manufacturing for good or mundane purposes, but a small few do not? How do companies and societies respond to this?

Technology always has been empowering people to do things that are new. This newness prompts important questions, such as what are the choices that encourage what we call good tech choices versus what are not so good ones and what do we mean by good? If you go to different countries around the world, you talk to Russia, China, Iran, the United Kingdom or Canada, or Australia, you’re going to get different definitions of good. I do not think the United States has ever sat down and said, “When we say tech for good or AI for good, what do we mean?”

With the GeoTech Center, rather than making that sort of an abstract or theoretical debate, we have a Commission that includes four members of Congress. We have two senators, Rob Portman and Senator Mark Warner, one from each side of the aisle. We also have Representative Suzan DelBene and Representative Mike McCall, from the House of Representatives from  each side of the aisle as well.

For the Commission we also have other luminaries such as Vint Cerf, who I have known and worked with for several years. Sue Gordon, the former Principal Deputy Director of National Intelligence, Dr. Shirley Ann Jackson, the Dean at Rensselaer Polytechnic Institute, and others who are working with our center. Ideally, by the end of October, we’ll have identified pragmatic initiatives that the United States can work on with our allies and with other nations too, that embody what we want to have be our values when it comes to new technologies and data that empower people, prosperity, and peace.

For example, with the GeoTech Center we recognize we need to use these technologies to make sure we are aware of future pandemics earlier and faster. We can build essentially an immune system for the planet that uses data and AI, but also biosensors, genomic sequencing, and high-speed performance computing to figure out what a virus or a bacterium actually looks like. Then we can access a database of different interventions that might be able to be applied to it rapidly.

There is an importance of working on efforts that inspire hope amid our turbulent times. When the United States went to the moon, it demonstrated both a source of hope and aspiration, and it also rallied technology around an ambitious effort. We need the new moonshot that embodies the values we want to have when it comes to people, prosperity, and peace using technology and data to uplift lives.

Well said. Good luck.

Thank you. It is going to be hard and we will encounter a lot of adversity. The GeoTech Center literally launched the same day COVID-19 was declared a pandemic. Even without COVID-19 we are in a period in which, unfortunately, there was increasing polarization, increasing misinformation. Yet one needs to remember, the hard things are the things worth doing and it does require you to assemble different networks of positive change agents that are willing to be resilient, show grit, learn together and persevere with creativity.

The Atlantic Council has advocated for the creation of Data Trusts – focused on food supply, logistics and trade during the COVID-19 pandemic. Can you describe what these trusts are? How do you get them to come about?

Currently we are focused on three key initiatives. The first involves “Data Trusts for Good” to inform how we deal with COVID-19 and then what does recovery look like? The second involves what I mentioned about creating an immune system for the planet using data and AI, to include semi-autonomous or fully autonomous biosensors and genome sequencing to alert us earlier.

The third involves creating a Food Supply Chains and Assurance. The GeoTech Center did an analysis on the food supply with the GeoTech Center in late March and published it on April 1st, identifying that we will see regional and possibly even worse food crises as a result of disrupted supply chains associated with COVID-19. The initial analyses are that the countries to be the worst hit regarding food shortages are countries that were not very stable to begin with. Several of them happen to be oil-producing nations. As we know, oil has dramatically dropped in price. They are already experiencing turbulence and now they are going to have food challenges as well. It could get bad.


We have got to get a handle on feeding these people that may not have food during the crisis, including here in the United States. We do produce more food as a country than we need, but there are cases where we cannot even get the food to the right markets. There are cases of farmers destroying food or pouring away milk because they can’t get it to market, which is a double tragedy given we also have people that need the food but cannot get it.

No one government can solve all these hard challenges. The United Nations World Food Programme has assets that can help with the immediate response to the food crisis, but this is bigger than the UN. This is going to require industry to step forward beyond their individual self-interest.

We need an industry alliance or coalition that says, “We don’t want this crisis to get worse because of the famines that may start to happen,” because, at least by our analyses and similar analyses by the United Nations, the food crises associated with the impacts from COVID-19 could be as bad, if not worse, in certain parts of the world.

With the GeoTech Center, we’re seeking to galvanize a coalition around these issues, recognizing that it is core for us to get better data for a handle on the most challenging parts of the coming food crisis. Then, we want to work with partners to use technology to help those that have produced food and cannot get it to market. This includes addressing the logistics, making sure we keep ports and trains running. This also includes addressing the distribution questions for those places that need food or more food for their region.

Finally, what do you see as the impact of AI space?

With satellites, we can do massive amounts of computation on the system itself when compared to what we used to do in space. I have seen a device a little bigger than your hand that can provide 10 petaflops of computing power and is powered by solar panels. That is impressive and it means you can have space satellites CubeSat size [10 cm, about 4-in., cubed] collecting visual images, readings about weather or other information. They can do processing on the satellite itself as opposed to having to send everything back down to a ground station for analysis, which was the paradigm for the 1990s and even in the 2000s.

The limited power of previous satellites was limiting, because your bandwidth was limited and you had to wait till the satellite passed a ground station. With processing on the satellite, we now can have computation on the satellite itself and send data back to Earth only if it’s something relevant. For example, the computer on the satellite could be constantly scanning and looking for early precursors of a hurricane forming and sending an alert only when conditions look bad. In the near future we will be able to do  really fascinating things that benefit localities, with the computation happening in space as opposed to having everything happen here on the ground.

Thank you again for taking the time. Is there anything you’d like to add David?

I would add the future really does depend on the choices we make involving data and AI. Many of us feel like these issues are somebody else’s role, somebody else’s responsibility, or it is bigger than us. What I would say is these issues are not bigger than us. In fact, what we each choose to do every day will determine whether the world is more free or not. If there is one message I could leave people with is: We Are the Cavalry. We are the ones that determine whether the future we face in 2030 is one that is more open and free.

I want to encourage your readers to help start those conversations and help carry the banner about addressing these challenges of making sure we remain open and free as a society. As we roll out new technologies, all of us need to address the challenges of misinformation, disinformation, and polarization. We must work across sectors and nations for shared understanding and collaborations. We need to work together to illuminate a better way forward.

Learn more at the Atlantic Council’s page on the post COVID-19 world and long-term recovery.


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