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  21 janvier 2020

  Trustworthy AI: A Conversation with NIST's Chuck Romine

  By: Charles Romine Artificial Intelligence (AI) promises to grow the economy and improve our lives, but with these benefits, it also brings new risks that society is grappling with. How can we be sure this new technology is not just innovative and helpful, but also trustworthy, unbiased, and resilient in the face of attack? We sat down with NIST Information Technology Lab Director Chuck Romine to learn how measurement science can help provide answers. How would you define artificial intelligence? How is it different from regular computing? One of the challenges with defining artificial intelligence is that if you put 10 people in a room, you get 11 different definitions. It's a moving target. We haven't converged yet on exactly what the definition is, but I think NIST can play an important role here. What we can't do, and what we never do, is go off in a room and think deep thoughts and say we have the definition. We engage the community. That said, we're using a narrow working definition specifically for the satisfaction of the Executive Order on Maintaining American Leadership in Artificial Intelligence, which makes us responsible for providing guidance to the federal government on how it should engage in the standards arena for AI. We acknowledge that there are multiple definitions out there, but from our perspective, an AI system is one that exhibits reasoning and performs some sort of automated decision-making without the interference of a human. There's a lot of talk at NIST about “trustworthy” AI. What is trustworthy AI? Why do we need AI systems to be trustworthy? AI systems will need to exhibit characteristics like resilience, security and privacy if they're going to be useful and people can adopt them without fear. That's what we mean by trustworthy. Our aim is to help ensure these desirable characteristics. We want systems that are capable of either combating cybersecurity attacks, or, perhaps more importantly, at least recognizing when they are being attacked. We need to protect people's privacy. If systems are going to operate in life-or-death type of environments, whether it's in medicine or transportation, people need to be able to trust AI will make the right decisions and not jeopardize their health or well-being. Resilience is important. An artificial intelligence system needs to be able to fail gracefully. For example, let's say you train an artificial intelligence system to operate in a certain environment. Well, what if the system is taken out of its comfort zone, so to speak? One very real possibility is catastrophic failure. That's clearly not desirable, especially if you have the AI deployed in systems that operate critical infrastructure or our transportation systems. So, if the AI is outside of the boundaries of its nominal operating environment, can it fail in such a way that it doesn't cause a disaster, and can it recover from that in a way that allows it to continue to operate? These are the characteristics that we're looking for in a trustworthy artificial intelligence system. NIST is supposed to be helping industry before they even know they needed us to. What are we thinking about in this area that is beyond the present state of development of AI? Industry has a remarkable ability to innovate and to provide new capabilities that people don't even realize that they need or want. And they're doing that now in the AI consumer space. What they don't often do is to combine that push to market with deep thought about how to measure characteristics that are going to be important in the future. And we're talking about, again, privacy, security and resilience ... trustworthiness. Those things are critically important, but many companies that are developing and marketing new AI capabilities and products may not have taken those characteristics into consideration. Ultimately, I think there's a risk of a consumer backlash where people may start saying these things are too easy to compromise and they're betraying too much of my personal information, so get them out of my house. What we can do to help, and the reason that we've prioritized trustworthy AI, is we can provide that foundational work that people in the consumer space need to manage those risks overall. And I think that the drumbeat for that will get increasingly louder as AI systems begin to be marketed for more than entertainment. Especially at the point when they start to operate critical infrastructure, we're going to need a little more assurance. That's where NIST can come together with industry to think about those things, and we've already had some conversations with industry about what trustworthy AI means and how we can get there. I'm often asked, how is it even possible to influence a trillion-dollar, multitrillion-dollar industry on a budget of $150 million? And the answer is, if we were sitting in our offices doing our own work independent of industry, we would never be able to. But that's not what we do. We can work in partnership with industry, and we do that routinely. And they trust us, they're thrilled when we show up, and they're eager to work with us. AI is a scary idea for some people. They've seen “I, Robot,” or “The Matrix,” or “The Terminator.” What would you say to help them allay these fears? I think some of this has been overhyped. At the same time, I think it's important to acknowledge that risks are there, and that they can be pretty high if they're not managed ahead of time. For the foreseeable future, however, these systems are going to be too fragile and too dependent on us to worry about them taking over. I think the biggest revolution is not AI taking over, but AI augmenting human intelligence. We're seeing examples of that now, for instance, in the area of face recognition. The algorithms for face recognition have improved at an astonishing rate over the last seven years. We're now at the point where, under controlled circumstances, the best artificial intelligence algorithms perform on par with the best human face recognizers. A fascinating thing we learned recently, and published in a report, is that if you take two trained human face recognizers and put them together, the dual system doesn't perform appreciably better than either one of them alone. If you take two top-performing algorithms, the combination of the two doesn't really perform much better than either one of them alone. But if you put the best algorithm together with a trained recognizer, that system performs substantially better than either one of them alone. So, I think, human augmentation by AI is going to be the revolution. What's next? I think one of the things that is going to be necessary for us is pulling out the desirable characteristics like usability, interoperability, resilience, security, privacy and all the things that will require a certain amount of care to build into the systems, and get innovators to start incorporating them. Guidance and standards can help to do that. Last year, we published our plan for how the federal government should engage in the AI standards development process. I think there's general agreement that guidance will be needed for interoperability, security, reliability, robustness, these characteristics that we want AI systems to exhibit if they're going to be trusted. https://www.nist.gov/blogs/taking-measure/trustworthy-ai-conversation-nists-chuck-romine

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  17 décembre 2020

  How DoD can improve its technology resilience

  Mark Pomerleau WASHINGTON — The Department of Defense must bolster its resilience in mission platforms in order to stay ahead of threats, a new think tank report says. With the military's shift toward great power competition, or conflict against nation states, its systems and platforms will be under greater stress than technological inferior adversaries battled during the counterterrorism fight of the last decade-plus. Systems and networks are expected to be contested, disrupted and even destroyed, meaning officials need to build redundancy and resilience in from the start to work through such challenges. In fact, top defense officials have been warning for several years that they are engaged in conflict that is taking place below the threshold of armed conflict in which adversaries are probing networks and systems daily for espionage or disruptive purposes. “Resilience is a key challenge for combat mission systems in the defense community as a result of accumulating technical debt, outdated procurement frameworks, and a recurring failure to prioritize learning over compliance. The result is brittle technology systems and organizations strained to the point of compromising basic mission functions in the face of changing technology and evolving threats,” said a new report out today by the Atlantic Council titled “How Do You Fix a Flying Computer? Seeking Resilience in Software-Intensive Mission Systems.” “Mission resilience must be a priority area of work for the defense community. Resilience offers a critical pathway to sustain the long-term utility of software-intensive mission systems, while avoiding organizational brittleness in technology use and resulting national security risks. The United States and its allies face an unprecedented defense landscape in the 2020s and beyond.” This resilience, is built upon three pillars, the authors write: robustness, which is the ability of a system to negate the impact of disruption; responsiveness, which is the ability of a system to provide feedback and incorporate changes on a disruption, and; adaptability, which is the ability to a system to change itself to continue operating despite a disruption. Systems, the report notes, are more than just the sum of its parts — hardware and software — but rather are much broader to include people, organizational processes and technologies. To date, DoD has struggled to manage complexity and develop robust and reliable mission systems, even in a relatively benign environment, the report bluntly asserts, citing problems with the F-35′s Autonomic Logistics Information System (ALIS) as one key example. “A conflict or more contested environment would only exacerbate these issues. The F-35 is not alone in a generation of combat systems so dependent on IT and software that failures in code are as critical as a malfunctioning munition or faulty engine — other examples include Navy ships and military satellites,” the authors write. “To ensure mission systems like the F-35 remain available, capable, and lethal in conflicts to come demands the United States and its allies prioritize the resilience of these systems. Not merely security against compromise, mission resilience is the ability of a mission system to prevent, respond to, and adapt to both anticipated and unanticipated disruptions, to optimize efficacy under uncertainty, and to maximize value over the long term. Adaptability is measured by the capacity to change — not only to modify lines of software code, but to overturn and replace the entire organization and the processes by which it performs the mission, if necessary. Any aspect that an organization cannot or will not change may turn out to be the weakest link, or at least a highly reliable target for an adversary.” The report offers four principles that defense organizations can undertake to me more resilient in future conflicts against sophisticated adversaries: Embrace failure: DoD must be more willing to take risks and embrace failure to stay ahead of the curve. Organizations can adopt concepts such as chaos engineering, experimenting on a system to build confidence in its ability to withstand turbulent conditions in production, and planning for loss of confidentiality in compromised systems. Improve speed: DoD must be faster at adapting and developing, which includes improving its antiquated acquisition policies and adopt agile methodologies of continuous integration and delivery. Of note, DoD has created a software acquisition pathway and is implementing agile methodologies of continuous integration and delivery, though on small scales. Always be learning: Defense organizations operate in a highly contested cyber environment, the report notes, and as the department grows more complex, how it learns and adapts to rapidly evolving threats grows in importance. Thus, it must embrace experimentation and continuous learning at all levels of systems as a tool to drive improvement. Manage trade-offs and complexity: DoD should improve mission system programs' understanding of the trade-offs between near-term functionality and long-term complexity to include their impact on systems' resilience. https://www.c4isrnet.com/cyber/2020/12/14/how-dod-can-improve-its-technology-resilience/

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  30 mars 2022

  Accélérateur technologique canadien - Technologies numériques à Silicon Valley - Startup Montréal