YouSaid · the spoken record

H. I. Sutton

lines on the record: 49
first: 2018-05-25
most recent: 2018-05-25
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“Which is a pretty wonderful thing when you think about it. A lot of the legacy defense companies, they sort of have two competitive moats. One is their experience in aircraft or boats or whatever. And the second is their familiarity with the government contracting process, which historically is incredibly painful. China just announced that they were investing $2.1 billion to open up a new AI research center that is all consistent with their strategy of military civil fusion. That's very different than in the United States where the Department of Defense and the National Security Community generally has had a very hard time persuading big Silicon Valley tech companies that they should devote the time and effort to help the DOD think through AI. That's why the DOD is so excited to work with startups because they don't have the legacy that some of these bigger technology companies do.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“I think there's recognition that this is a new paradigm And so I think that's why you've seen DAUX is probably the most predominant example of the DOD attempting to respond to the new dynamic. What makes B2G feel like B2B”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“One thing that I think is very interesting about AI in contrasting with previous technological revolutions is that the source is very much in commercial industry. I am not breaking some security clearance or classification rules to tell you this here, but there is no super secret government lab with advanced AI way better than commercial industry. The government, the military, they are behind commercial industry.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“United States has the software talent to make a difference in this conversation. So I do think it's important that we keep in mind that it's not all doom and gloom and there's actually real protection that this technology can offer.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“The most recent defense budget basically said, let's buy a ton more weapons, of weapons that have already been designed. And politically that's incredibly popular, right? Because those weapons are built in congressional districts all over America. It's very easy to say, let's just spread the money around. But in my view, that's the equivalent of Kodak in 1991 radically increasing its investment in film cameras. Right. You're buying a lot of stuff that is probably going to be obsolete in the not too distant future. And so what I wish the military was doing was thinking more on the modernization question, investing more in research and development, and preparing themselves for the AI revolution.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“It's a whole new paradigm for national security. And if you think you can get by with the old rules, I mean, the old approaches that led to success in the Cold War, those just aren't going to apply here.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“It's actually pretty easy to policymakers to say, oh, AI is quite interesting. I think the challenge is persuading them of the scale, of the importance of this technology. They're hoping that they can mostly do things the same way they've always done them with a few tweaks here to update for the new technology. No, this is a complete revolution. It will take decades to unfold, but it will be on the same scale as the invention of aircraft for the early.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“Not at all And not everybody is functioning under the same conceptual framework there. So, how is the policy community responding to this? How is it actually perceived right now? You do see this concern among the policy community. Will the folks that we are up against have the same ethical framework? And I do think that is a question that we'll be facing policymakers in the future.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“Fires. And so right now I would say the military is, in my view, doing the right thing, which is its early use cases of AI are far removed from the use of force and in fact are a non-safety critical applications such as data analysis. We're not so certain that all other countries on Earth are going to abide by that. There was a recent headline in Defense One, Russia to the United Nations Don't try to stop us from building killer robots, and I wish that was a joke headline, but that's actually a pretty accurate summary of what Russia said at the most recent UN meetings on autonomous weapons. So we're doing this within a global security context of renewed great power conflict, and other countries see artificial intelligence as a way to close the gap between their militaries and that of the United States.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, they might be labeling the training data, but they are not in as many words programming the system in the traditional sense. So the military has this whole series of verification and validation procedures that it has developed for traditional software. How do we know that our automatic systems, our autopilots, or our heat-seeking missiles or anything that we do that uses software is going to do what we want it to do? Well, we have evaluation procedures for such software. But that's for traditional programming architectures, right? Machine learning is a new programming architecture. And we are pretty optimistic overall that we think that this can actually enhance safety. But that's not an inherent feature of the technology. Today, electricity is by far the safest way to light your home, far safer than using candles. But that's not an inherent feature of electricity. It's very easy to start a fire using electricity. And in the early days of electricity, they started a lot of fire.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, absolutely. But I think what's interesting is that this is all with traditional software programming architectures with a very long list of if-then statements, ultimately all of which were typed in by some human. And what's different now is the use of machine learning.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“Hear the immediate way that AI is changing that power dynamic on the ground, going into unfamiliar environments, are there bigger ways that it will shift the relationship between developed or undeveloped countries or different players in conflict?”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“And we just did that here Talking about performance guarantees in the context of collecting information, then immediately it jumps to oh my goodness, AI is this terrible thing when these machines are just learning to collect information better, which will save lives.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“And I think that's often lost in the discussion about AI and national security. Right, when we go to a place of Terminator, you're not thinking about the information. Right, it's Hollywood version versus the battlefield reality.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“I think that's a critical point. I think people want to go to what robots are going to be able to do tomorrow, but I think we really come back to what can we do to protect lives today. The conversation at Chield AI is really always about the idea of getting the best decisions, getting the best information, and making sure that you're creating the most knowledge. I think it's really important when people think of artificial intelligence systems that they also think of them as information and intelligence gathering tools.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“Given environmental constraints, given its own health, and so on. And it's able to transfer that learning to every other robot in the fleet immediately, and it's also able to transfer that learning to machines that have different computations, sensing, and actuation constraints, and each of those machines are able to introspect, identify the differences between themselves and the learning machine and only take lessons that are relevant to them. If you have performance guarantees or boundaries for the system and you know that all the learning will take place within the performance guarantees, people can anticipate everything that it will do ultimately. They might not know how it's going to get there, but they know the behaviors will be bounded.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“We believe in the role of having humans in the loop and allowing them to learn particular skills within performance boundaries, but still having people there with final authority on what they actually do is a key concept. But also it's finding its own boundaries in terms of what's physically possible.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“And our chief science officer Nay took a quadrotor that doesn't know how to fly, that has the most advanced controllers designed by people ever, and in a period of a few days the quadcopter just through its own experiences learned to reach boundaries of performance that far exceeded what could be realized by a controller designed by humans. And this was notable for a couple reasons. One, the learning was lifelong, and it was doing it unsupervised. A lot of times the challenges with these machine learning approaches are you worry about them learning the wrong thing and therefore you have people kind of in the loop cross-checking whether or not the machines are learning the right thing and was able to do this and continues to be able to do this to learn basically forever from its experiences. And the things that it learns are within performance boundaries.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“I think it's reasonable to believe that certainly there will be far more machines in the future than there are today. And so, therefore, this data represents the opportunity to train these machines to reach the level of capability that they need in order to protect national security and global stability. We think a lot about taking that data, not only deriving human insight, but deriving machine insight so that it can continually evolve and advance its performance.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“So, the question about we're collecting all of this data, what do we do with it? I think that there's absolutely huge opportunity to improve human understanding to enable the best possible decisions. And we should view that as kind of the critical use of the data.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“I think tying together this whole idea of current conflicts and future conflicts, as we look at great power conflict, there is a sense that the post-war rules-based order is facing increasing threat. Secretary Mattis calls it the greatest gift of the greatest generation, is this rules-based order we've all lived in. And now kind of take for granted. And I think we all think it's free. We think that it is possible to ignore it. And we also think that it's permanent. And the truth is it's none of those three things.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“The shift in tactics from the early days of the Iraq War to the more counterinsurgency strategy that we saw really throughout the sort of second half of that conflict was all about the United States saying that we believe we need to take the higher risk and endure higher casualties because winning the support of the local population and showing them that we absolutely care about their lives and quality of life as we are engaged in this conflict is crucial. And so what I think is very exciting about artificial intelligence is can we still make the hard choice to not call in an airstrike or not call an artillery, but can we use technologies such as robotics, such as AI enhanced sensors in order to reduce the risk when we make that hard choice of loss of life on our side, but also again unintentional loss of life among the civilian population?”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“And that's often used against the United States. So if you look at Syria, the last stand of ISIS in the town of Raqqa, you had ISIS really using human shields. So you cannot leave floor two of this building. We have floor four of this building, and we know that that will keep you here and that will protect our lives because we're endangering yours”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“The opportunities there are really interesting for applying AI to enhance our training and simulation capabilities because we can learn more about the truth of the types of situations that we encounter and then create simulations based on that truth, upon which to train, and also to think about our strategy and tactics and our organizational efficiency. That goes from the full spectrum of military logistics to enhancing fuel efficiency, all the way down to getting into the nitty gritty of combat operations and thinking through how do we reduce casualties and loss of life on our side. And then how do we also reduce unintentional casualties and loss of life on the other side? Right now, when we see a building and U.S. troops are receiving fire from that building, we have to make the decision, do we take the easy way out, which would be to call it an airstrike and topple the whole building? Or do we recognize that there might be non-combatants in that building that”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“Satellite imagery or drone based imagery. But there's also this whole diversity of data of what is going on within the mission, within the platforms that we are using. For instance, any kind of flight scenario, what occurred with the airplane”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“An awful lot goes on in a conflict zone, and there's a ton of different diverse types of machines in the environment and sensors in the environment. The United States military has outfitted itself with an extraordinary diversity of sensors, and they are collecting an unimaginable amount of data. But most of that data just goes into cold storage, never to be seen again because there aren't enough people to analyze it and to drive insights from it. Now with advanced machine learning, we are for the first time really seeing an opportunity to make use of data sets that historically would lie dormant. So the archives are suddenly newly useful. There's two types of data here. One would be the sort of data that the United States military knows that it wants to collect, which might be like intelligence or reconnaissance imagery.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“It feels like a very granular immediate level of all this large scale AI and machine learning playing out into moment by moment. I'm walking through a door. What am I going to see right before I get there? But you're gathering this incredible amount of information about the spaces, about the context, about the environments, about all kinds of things that humans aren't even picking up on. Are there other uses that are less immediate when you're doing all this information gathering that you can see this information playing out in sort of longer term ways in terms of either on the ground conflict like this or national security ways that you can see that information being used beyond on the ground decision making?”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“And I need to solve this better. And so adaptation is given my awareness of my capabilities, how can I change what I'm doing or change something about the circumstance in order to improve the likelihood that I succeed? And finally, evolvement is that given an encounter with a situation enough times, the machine then becomes very good at it.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“It could be. I know that if I try to fly through a doorway that is 24 inches across, I know that I can do that very well. If I know that I need to coordinate the exploration of a village with 100 other robots and the communication network is going to be jammed the whole time, I might not know how to do that well today.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“Correct. So perception is this notion of here's where everything is in the world. Cognition is given my prior experiences and what I want to achieve, this is what I should do. And then action is just I affect the world or move myself in the way that I need to in order to take whatever step I decided to do. And we just go through this perception cognition action loop over and over and over again as people and machines do the same thing. Now in order to achieve really advanced levels of performance, there's another component, and we call this loop the introspection adaptation and evolvement loop. So introspection is the notion of what are my capabilities or what is my health. And so this could be something as simple as what's my battery life, or it could be something more complex, such as I know that I'm good at doing X and therefore I can behave optimally in these circumstances, and I know that I'm bad at Y, and therefore I'm going to spin up millions of”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“How does that work? There are chemical sensors, there are hyperspectral cameras that can see things beyond what our eyes or normal cameras would be able to see. And so they actually do have superhuman sensing capability. But the key is to turn that from pixels and data into actual understanding that the machine can use. Because for a long time, we've actually had the ability to apply these sensors, but it always came back to humans needing to assess the information in order to determine courses of action.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“No, we use a combination of cameras, LiDARs, and radars and many other sensors to help machines navigate the world, to get into the areas they need to in order to collect the information. The environments that our machines operate in are relatively challenging in the sense that there's a lot of dust, there's a lot of unstructured obstacles. Battlefields are dynamic at the same time. There are people moving around and so on. And so you need a lot of complementary sensors in order to ensure reliability.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“So, for a long time, we've had machines that could significantly outperform people in their ability to execute mechanical functions, provided they were repetitive or well-constrained. What AI represents is now the ability to allow machines to apply them to a much larger spectrum of activities. And so for us, the way that we think about the tech stack is in terms of a decomposition of intelligence and what does it mean to be what we would consider a resilient intelligent machine. That breaks down into two buckets of things. So the first is what we call perception action and cognition. And the second we call introspection adaptation and evolvement. Let's start with the first, perception, action, cognition. Perception is the ability to look around the world and understand what you are seeing. And it could be through a machine, a camera, but it doesn't necessarily need to be looking at the physical world. It could exist in the cyber domain. It could exist.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“In uniform. So that's an interesting point that it's bringing you back to a very immediate, and this is the information that they need right now. Can you break down the technology that makes that possible that wasn't before?”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“And you really can make a difference in terms of mission effectiveness, right? I think we have to remember the stakes. When you think about the consequences of not having that information, we have seen over and over people walking into booby-trapped compounds or compounds where people who understood when U.S. forces were coming and evacuated but left things for them, namely explosives, for them to find. And what happens when you don't have that information ahead of time is, you know, you can have really tragic loss of life.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“That's a good question because clearing buildings of threats has been one of the most costly missions for U.S. forces in terms of human life and one of the most costly missions for civilians in terms of human life since post-9-11. We've applied an artificial intelligence to a drone that's able to fly through buildings and basically in a completely autonomous manner, it looks for people inside of those buildings. And so rather than an 18-year-old or 19-year-old being the first person through a door, you can throw a robot inside, which will provide a very clear picture about what's going on on the inside, similar to people listening has seen the movie Prometheus, where they've got these robots that explore caves.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“There's some amount of analysis that is conducted ahead of time to try to give the best possible picture to these people before they go on their operation. But in many circumstances, when people are asked to conduct operations, there is still a good amount of uncertainty. And so brave men and women are asked to, for instance, go into buildings, not knowing whether or not they're booby-trapped, whether or not they're walking into an ambush or a number of other possible risks. There is a huge opportunity to improve the gap of uncertainty that exists.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“You're really talking about providing mission critical information at mission critical moments, and how do you get the right information to the right person at the right time in a moment when it can really make a difference? So if we're seeing rooftops, how does that play out right now?”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“Exactly. And so just getting higher quality information in the first place can dramatically accelerate or reduce the amount of information that you even need to go through to make a positive identification of that event or object or thing that you're looking for.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“Two ways that I think artificial intelligence can really make a big impact. One is just helping pre-process that information before it's presented to people. And there's a great opportunity there, and there's some programs within the DoD that are focused on doing that. The second is actually just getting better information in the first place. So when you don't have somebody on the ground and you can't, so let's say, for example, you're looking for a person and you cannot actually see that person because the satellite is so far away.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“Once it gets robbed, you can look back. Exactly. So this analysis shortage is really a human bottleneck within the US military. There are literally thousands of people whose primary job is to watch drone footage and analyze it for information that is relevant to the conflict at hand or U.S. national security. But there's far more data than you could ever hire enough individuals to go after.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“I would add to that a distinction between the amount of data that we collect and the amount of data that we analyze. Right now, for instance, on just drone platforms alone, more than 95% of the data that is collected is never viewed by anyone, ever. And that is simply because we are collecting far more data than we have humans that are able to analyze it. So there's one sensor in particular that is capable of observing and basically an entire city at one time. But the problem with this sensor is that there's not enough humans to be watching it all of the time. And so really its primary use cases as a time machine once a IED improvised explosive device goes off, we then look at that footage and rewind it to the day before and say, okay, who must have planted that explosive? Because we don't have enough people to watch the sensor to see the explosive being planted. But once it's gone off, then we know where to look and we can security footage.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“So, just a very simple example is what's indoors versus what's outdoors, right? People spend the vast majority of their time, first of all, their macro trends towards urbanization. And then on top of that, people live inside. They don't spend their day sitting out in the middle of a field with all of their activities in open view. And so kind of at a very basic level, we can't see the vast majority of the world in terms of where people and things are located.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“The number one challenge is getting eyes and ears in the right places. So we have very advanced technologies in terms of satellites, high-altitude platforms, and we also have very brave young men and women that are willing to get very close to the information order. But the challenge with these approaches is that the quality of information is not at a standard where you can say with certainty what's happening on the ground, either because of the way that it's collected or because the limited amount that might be available”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, let's talk about what that really looks like as it plays out what we know, what we don't know from both kind of mission control and also immediately on the ground. Like what's the information flow like currently and what are the tools that are providing that information like?”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“Even in these new or potential conflicts of the future, we're seeing the same uncertainty. So if you look at Russia moving into the Ukraine in the past couple years, there was a huge amount of deception and uncertainty in terms of what was actually happening on the ground.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“The most recent U.S. national security strategy anticipated a shift in posture for the United States military from the type of wars that we have been fighting, and we have now a lot of experience fighting from the types of conflicts that they want to focus on preparing for. For the first time in a long time, the United States did not name terrorism as the top national security threat facing the nation. Instead, great power conflict is now large scale. Exactly. Okay. So the primary sort of named competitors or potential adversaries in the national security strategy would be China and Russia. And if you look towards the preparations that the militaries are thinking for in their long-term strategy, both for organization and for acquisition, that's really where they are gearing towards. But at the same time, we are still into war.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“Operations has been Than 5 Up in these conflicts, and I think that technology has tried, but not necessarily really succeeded in catching up with what is happening on the battlefield. So if we sort of telescope out, how is that reflected in the overall U.S. national security strategy for dealing with conflict? What's the relationship between on the ground missions to the overarching military strategy?”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source
“Hi and welcome to the A16Z podcast. I'm Hannah and today we're talking about AI and automation in the context of national security, given the nature of today's conflict situations. How do these technologies change how we protect lives in those conflict situations and also how is AI shifting power dynamics around the globe? Joining me is Gregory Allen, fellow at the Center for New American Security and Co-Author of the Belfer Center report on AI and national security, Gail Laman, Chief Marketing Officer of Shield AI, and the author of The Dressmaker of Kerkana and Ashley's War, both of which dealt with post nine hundred eleven conflicts, and Ryan sang, the CEO and co founder of Shield AI. So what are today's conflict situations actually? What do they really Look like.”
2018-05-25 · a16z Podcast · a16z Podcast: Autonomy in Service · IDENTIFIED FROM THE TRANSCRIPT · source