(exciting music) - Welcome to "From the Crow's Nest," a podcast on electromagnetic spectrum operations or EMSO. I'm your host, Ken Miller, Director of Advocacy and Outreach for the Association of Old Crows. Thanks for listening. In this episode, I sit down with Dr. Joseph Guerci to discuss the evolution and outlook for cognitive electronic warfare systems.
Dr. Guerci is an internationally recognized leader in the research and development and transition of next generation sensor and cognitive systems. He has over 30 years of experience in government, industry, and academia, including a seven-year term with DARPA, the Defense Advanced Research Projects Agency, where he concluded his service as the Director of the Special Projects Office. Before I get to him, just a couple quick items.
First, believe it or not, Congress just recently this weekend completed its long overdue work on the Fiscal 2024 federal budget by passing a funding agreement before embarking on a two-week recess, or what they call district work period. The spending package is $1.2 trillion, and includes 824.3 billion for the Pentagon and defense related activities. That's about 26.8 billion more than Fiscal Year 2023, or a 3% increase.
I'm in the process of putting together some of the summary details that we'll put that out in the form of an article in AOC's weekly eCrow Newsletter probably next week, and so be on the lookout for that. It'll have a little bit more detail. If you're a regular listener to the show you'll know, we've been critical of Congress in the past.
We can talk all day about total funding, spending increases, percentages greater than last year and so forth, but the reality is that when you don't pass a budget for six months into the fiscal year, it really becomes less about the total money and more about the delays in program development, missed milestones, the loss in testing and training, et cetera. So it goes without saying that it's important for Congress to figure out a way to get at least the defense appropriations done on time.
You would think it shouldn't be that hard. You know, the total federal budget is $6.3 trillion, but only about 1.7 trillion of that is discretionary, and that's the part that Congress funds through its annual appropriations process. So that's only about 27% of the federal budget. And of that, about 50% of that is the defense bill. So if Congress figures out a way to just pass the defense bill, you're talking close to 87%, 90% of the federal budget basically done and running on time.
So, of course, you know, defense being the largest pot of discretionary funding opens it up for being used for a lot of political leverage, but, you know, this is a critical piece of legislation that Congress has to focus on each year. That being said, we already know that don't expect anything in 2025, This agreement that just passed will fund the Defense Department through September 30.
This being an election year with Congress and the President, we can expect CRs to continue, so Congress will pass CR on September 30 to fund probably until after the election, and we'll see what happens after that. But hopefully Congress can figure out a way to do a better job at getting the defense bill done on time.
I saw an interesting chart in the news the other day, and it actually highlighted the problem that we face on this front, because you would think that... I always was under the impression that Congress was a little bit better at getting bills done on time than it actually was, but if you look at over since 1977, Congress has only passed all of its appropriations bills on time four times since 1977, and that was in '77, '89, '95, and '97.
Other years, various bills have passed and others have not, the defense bill being the most regularly passed bill, but still, the trend since about 2007 has not been good. So for the past 15, 20 years, it's been quite abysmal. So, you know, Congress has its work cut out for it, but hopefully, you know, they can figure out a way to, you know, focus their energies on that bill moving forward.
The only other quick item I wanted to mention is, as you know, we've been releasing bonus episodes every two weeks on the off weeks of our regular "From the Crow's Nest" episodes. These are going to form our subscription episodes available to only paid subscribers and AOC members. Now, we are in the process of putting that subscription together behind a paywall, but until it does, they are free for everyone, so feel free to download 'em and listen.
The added benefit of this is that if you are a subscriber or an AOC member, or until we get it to the pay wall, it's open to everyone, if you want to listen in on the live recording of that episode from the audience and actually participate, you can comment, ask questions, and so forth, you can do that. So if you're interested in listening or participating in the live recording of those bonus episodes, please go to crows.org, contact us through that, and we'll get you the link to participate.
Our next recording for the bonus episode is next Tuesday, I believe it's April 2 at 1:00 PM Eastern Standard Time. And with that, I'd like to introduce or welcome my guest for the show today, Dr. Joseph Guerci. He is an internationally recognized leader in research and development and transition of next generation sensor and cognitive systems. I had the pleasure of having Dr. Guerci on AOC webinars sometime last year to talk about cognitive radar, cognitive EW. It was a fascinating webinar.
As we were going through it, I realized that this is a topic that for those of you who are familiar with our AOC webinars, it's a little bit more of a technical discussion.
We have presentation slides and so forth that you can kind of go in deeper, but I really felt like there was a need to have this as a topic of conversation on our podcast, where you can kind of look at it at the 30,000-foot level, kind of come to a better understanding of what we mean when we talk about cognitive systems, because we hear about them a lot, but I don't think we really dive into what they mean. I know I don't.
I use the word and I don't really understand the depth of what those terms mean. So I reached out to Dr. Guerci, asked him to be on my podcast, and Joe, it's great to have you here on "From the Crows Nest," and thank you for taking time out of your schedule to join us. - Thank you, Ken. It's great to be here. - All right, so just to get started, you know, as I mentioned, this is a topic that mystifies a lot of people.
We use the terms, we think we know what we're talking about, but oftentimes we're kind of mixing up definitions. You've been in this field for decades, and it's really not a new field. We've thought about cognitive systems and been working on them for what, 20, 30 years, but recently, within the last 10, it's kind of picked up speed.
So could you give us a little bit of insight into why cognitive systems are picking up pace in terms of their opportunity being in the spotlight today and kind of where we came from over the last 20, 30 years? - Thanks, Ken. That framed the question really well. I will answer it directly, but let me just first talk about the definition of cognitive.
Unfortunately, the word cognitive is not that well known, I would say, or defined by the general populace, and that's especially true when interacting with our international colleagues. You know, translation, many things get lost in translation. So cognition is actually a very well-defined scientific term in the biological and psychiatric sciences. So it relates to all of the things we think about of a person who's awake and functional, like problem solving, memory, things of that sort.
And in fact, the very first page, the first chapter of my book on cognitive radar, which was first written back in 2010, I actually used the National Institute of Health's definition of cognition, and then you can very clearly see how that translates into engineering parlance. Now, a lot of the confusion is because people think the word cognitive is synonymous with consciousness. Science does not have a definition of consciousness, but it absolutely has a definition of cognition.
And so to your question, you know, why... It's been around for a while. That is absolutely true. We didn't exactly call it cognitive radar and EW back in the early 2000s, but that's what we were doing, especially during my time at DARPA. And why were we doing it and why is it so popular now? And that's really because of a couple of factors. One, the electromagnetic spectrum environment is ever more congested and ever more contested. Lots going on.
If you turn on your antenna, you're gonna see all kinds of things. And if you transmit, you're gonna elicit all sorts of responses. And it's becoming ever more increasingly difficult to sort that out. The problem is even further exacerbated by the fact that we have lots of flexibility now globally to transmit arbitrary waveforms, to transmit signals that hadn't been seen before.
You know, and back in the old days, 30, 40 years ago, especially radar transmitters were much more constrained in what they could do in terms of agility, and they were much more easily identifiable.
But today, that's all gone, and that's due to solid state front as replacing tubes and magnetrons and klystrons and things like that, and of course, lots of embedded computing, digital arbitrary waveform generators, and so you need to actually make decisions on the fly, and you can only make decisions on the fly if you understand what you're looking at.
And what really where cognitive really comes in and really separates itself is this idea that you don't just sit there passively and listen and sort things out, but you actually interact with the environment, you probe it, and that probing can give you a lot of information. And that idea of probing and enhancing your understanding of the complete radar or jamming channel is at the heart of cognitive waveform. - And so we've seen this, you know, particularly over the last 10 years.
You know, we talk about the spectrum being, you know, contested and congested, and the complexity of trying to figure out how to transmit and receive the signals.
And so if I'm understanding you correctly, it's, we oftentimes, I think when we talk about AI, we talk about it in terms of speed, but it's not just in terms of speed, it's in terms of understanding and breaking down that complexity and being able to more effectively or adapt to a certain environment to transmit or receive or collect that signal. Is that correct?
- Yeah, so in the old days, you know, not that long ago, you know, one would go out with a database of what you expect to see, and that database would, you know, provide a of prescription of how you're supposed to behave in response to that. As I mentioned before, due to all of the advances that I mentioned and other issues, that just is obsolete. You know, you literally have to get on station, see what's going on, and do a lot more of that analysis that used to be done offline, online.
So it's almost like you need to take all our subject matter experts down at the EW program office and have them with you right in the plane, making decisions in real time. But obviously that's not practical for a lot of reasons, because sometimes the speed involved needs to be faster than humans can process. But that idea is basically what the whole point is, right?
It's basically to embed subject matter expertise, brain power into the embedded computing so that on the fly you can reprogram and update your databases and respond. I mean, that's the best way to look at it. The old OODA loop, if you will, was we go up, we fly, we see things, we come down, we update the database, so next time we go out, we're ready. That's all gone. I mean, you have to have that OODA loop on the plane.
Now, I could get into a lot of technical detail about, well, how on earth do you do that? And that gets into the weeds obviously, of the nature of the signals you're observing. And, you know, at least today we have to obey the laws of physics as we know it, and we also have to obey the laws of information theory. Just because you have a high power transmitter doesn't mean necessarily that they have the waveform that would really help them do tracking or detection.
You know, there's physics and information theory, and those are the anchors that you start from when trying to do this OODA loop, if you will, on the fly. - So could you go into that a little bit more, because I feel like anytime that there's kind of two forces at play, we tend to confuse what we're focusing on when you talk about, you know, information and so forth, and the waveform and what capability an adversarial threat it has.
Could you talk a little bit about how that has shown itself in terms of some of the development that you've done, how we've adapted to that, and kind of where that's leading us? - In the context of electronic warfare, a very key question is what is the nature of that signal that you're observing? So a good example would be, let's say it's a spoof jammer, like a DRFM, a digital RF memory type of thing, right?
So on a radar scope, you would see, let's say targets, right, that look like targets you may be interested in, but are they real? As you know, smart jamming can spoof you, leak you fake things that are not there.
And so while I can't get into all the details on how you might address that for obvious reasons of sensitivity, it does lend itself to a proactive probing approach where you don't just sit there passively and accept the world as it's presented to you, but you actually then design probes to answer questions.
Is it a real target or is it a false target is a really good example of probably one of the most advanced cognitive EW functions, for example, you know, in the context of sorting out jamming signals. And of course, and if you flip the coin around and you're the jammer, then you're also playing Spy v. Spy, right? You're trying to figure out whether or not what you are doing is having an effect.
That's a very difficult thing these days, especially, you know, if you're anything other than an extremely high powered jammer, understanding the effect that you're having on a radar is not easy, generally speaking. And so you have to get very sophisticated in the way that you probe and then listen and then update your knowledge base as to what you're dealing with.
You know, again, what enables all that, highly flexible front ends so that you can transmit arbitrary types of waveforms that have interesting properties and elicit interesting responses, coupled with lots and lots of high power embedded computing, and that's where the intersection of AI and cognitive EW takes place, right? Because that's a lot of heavy lifting, it's a lot of pattern recognition, for example, a lot of inference.
And of course, as you know, the deep learning techniques certainly have a play in that arena. And that's why I think you're beginning to see more and more of this intersection of AI and cognitive EW. But again, I must stress that cognitive systems have existed, as you pointed out, long before the modern deep learning AI has sort of become all the rage. - I wanted to ask you a little bit more about the relationship between AI, deep learning, and cognitive systems.
Before we got on the show here, you had mentioned that, you know, cognitive systems are a consumer of AI, and I was wondering if you could go into a little bit more about discussing how those tie in, because the two, they're not synonymous. And so talk a little bit about that relationship and what we're maybe getting wrong that we have to rethink or re-understand to truly grasp what we're dealing with when we talk about cognitive systems.
- Well, thank you for that question, because this is really, really important. A lot of people think cognitive is synonymous with AI, and it is not. Again, I went through great pains to do scientific definitions of cognition. In Dr. Karen Haigh's book on cognitive EW she spends a lot of time also trying to clarify that. But unfortunately, you know, it's kind of easy, 'cause both sound like, you know, intelligent machine learning cognition, that sounds like AI.
All right, so again, cognition is basically performing a subject matter expertise function, but on the fly. Think of it as having the world's best EW analyst brain encapsulated on the plane. And that's a form of expert systems, you know, that is a form of AI, and traditionally that's what what we use. But cognitive systems is definitely, as you said, a consumer of AI. It's not synonymous with it. You can do cognitive radar and EW without any of the modern deep learning AI techniques. Absolutely.
But however, incorporating modern AI techniques, would that make it even better? Potentially, yes. The answer is probably yes. A good example would be looking for certain features, right, in a signal. That's one thing that we care a lot about. There's certain features we look for to be able to sort it out, identify it. And I think you're probably well aware that modern AI is very, very good at pattern recognition when given the appropriate training data or training environment.
And so rather than writing out thousands of lines of C++ code to do pattern recognition, feature recognition, the AI algorithm can implement that in a deep learning network. And by the way, blindingly fast, especially if you implement it on what are called neuromorphic chips, that we talked a bit about that previously. I mean, that's... - We'll get to that in just a moment. I knew that's where we were heading. - Yeah. But again, you're right.
Thank you for making the point that they're not synonymous, cognition and AI, and thank you for making the point that cognitive systems are a consumer of AI. They don't need AI to be cognitive, but as I just mentioned in that example, they actually can be quite empowering. - So with the notion that AI can empower cognitive systems, and you mentioned that it would probably be better, and I would tend to agree, but in what ways might it not improve cognitive systems?
What can you do without AI today in terms of cognitive systems that really AI doesn't really help you achieve your goal? - I think it's best summed up with the old computer science adage, "Garbage in, garbage out." A lot of people don't understand that this revolution AI is all about deep learning. These are convolutional neural networks, and how good they are depends on, and this can't be emphasized enough, how well they were trained.
By the way, it shouldn't be surprising because we are neural networks. Our brains are neural networks, and if you weren't brought up properly and educated properly, well, you get what you get, right? So the analogy is exactly the same. And so the danger with AI is, well, how do you know that that training data really represents what it's gonna see when the shooting starts?
(exciting music) - I wanted to touch on the training aspect, because we talk a lot in EW about we need to have realistic threat environments. We need to, you know, really train like we fight. And that's becoming increasingly hard. We opened the show, we talked about, you know, how crazy the spectrum is these days.
You can't base your AI on realistic, you can't get maybe that realistic data from training without kind of rethinking how you conduct training in general, and that's where it gets into modeling and simulation. And I think this touches, then this gets into the neuromorphic chip concept.
And that's where I wanna kind of go to, like how do we model and simulate a training environment that gives us the data that we need, that we know it's accurate, our systems can learn from, so that when we do go into the real fight, we are actually using the knowledge that we've built through the training. - Yeah, that is the key to the whole problem. You put your finger right on it. So AI is very good at recognizing faces, for example. We all have iPhones and what have you, and why is that?
Because it's had plenty of controlled training data. This is a face, this is a nose, these are, you know, so sometimes we call that annotated training data, whatever. And the same goes with ChatGPT, right? It's had access to terabytes of plain text. So there's copious amounts of training data, and the quality of the training data was good enough, as you can see in the results, right?
The conundrum for military applications, and especially for electronic warfare, is of course, wait a second, where am I gonna get terabytes of training data that is absolutely reflective and realistic of the environment I expect to see when I'm fighting, including all the potential unpredictable things that may pop up, new, you know, they're called war reserve modes, for example. Well, I can give you an answer.
It's called digital engineering, and that's both a cop out and an answer at the same time. Digital engineering is all about replicating digitally, including modeling and simulation, all the physics, all the nuances, all the technical constraints associated with your system. And as, you know, we've discussed in the past, B-21 bomber, next generation air dominance, have taken advantage of digital engineering to speed up their acquisition cycles.
Well, those same types of tools can be co-opted to train AI systems, since if you have such an accurate synthetic digital environment, why not immerse the AI into that environment and let it learn and train it, you know, to what it would expect to see in the real world. And I think as we've mentioned previously, in our previous discussions, you know, we have done that. In my own research, we have advanced radio frequency design tools that we use where we can really replicate...
By the way, there's site-specific too, so you can pick a part of the world where you wanna operate, put emitters down, create that complex, congested contested environment, and create copious amounts of data. It is synthetic, admittedly, but it is definitely next generation modern syn capability.
So if I had to give you an off-the-cuff answer, were passing each other on the elevator, how do we solve the training problem for military applications, the simple answer would be digital engineering, as I've sort of outlined a little bit. - And you mentioned it is being used, and I've referenced it. I read an article about the next generation air dominance fighter, I think that's what it's called.
- Yes. - And they were saying it's gonna start replacing the F-22s around 2030, and I'm sitting there, I'm like, "How in the world is that gonna happen?" And I was not thinking digital engineering at the time. I was like, "They need at least another 15 years." I mean, this is DOD. - Yeah. - And when we were talking, I'm like, "Oh, that's exactly what they were using."
But digital engineering is not a panacea, or it hasn't been fully used to its potential maybe is a better way of doing it, 'cause there are short... 'Cause you mentioned, you're not able to maybe digitally replicate every aspect of a design. And I'm thinking particularly of, you know, you might be able to do an environment, but certain components within that environment might not, like the avionics on a jet, you might have more of a challenging time using digital engineering for that purpose.
Could you talk a little bit about some of the limitations, and then also when you mentioned this, you said it's both an answer and a cop out. So how is it a bit of a cop out? But what are some of the limitations of digital engineering? - All right, let's unwrap that question there. So... - Yeah, I like to ask very complex questions that give you wide range to answer any way you want. - It's great. So why was digital engineering so successful with B-21 and the next generation air balance?
Well, think about it, right? Aerodynamics codes, all the major airframe manufacturers have been perfecting for decades and decades aerodynamic codes so that when they put a CAD model into their digital wind tunnel, it actually very, very accurately represents what you're gonna see when you actually fly it. The same is true with stealth codes, decades of perfecting the digital MNS codes for stealth.
So, and engines, you know, the major components of airframes have wonderful tools that have been vetted over decades. And guess what? When they utilize those tools, make predictions, those predictions are very accurate. As you point out, though, and this is very important, there's a whole lot of stuff that gets stuffed inside those airframes, call 'em avionics, radar is all communication systems, and then there's antennas stuck on there. That is the Wild West.
There is no uniform level of quality in the digital engineering tools for avionics, for example. There are certainly pieces, and some companies are better than others, but it's nowhere near the level of maturity and uniformity as there is for, you know, aerodynamics and things of that sort. So it's a very important point. So that's why it's sort of an answer and a cop out, right? If you have digital engineering tools that are accurate, it's an answer.
If you don't have them, it's a cop out. (chuckles) So, you know, I know that's the first part of your question. I think I forgot the second part of your question. - Well, I mean, I think you actually did answer both parts of the question, 'cause I was talking about, you know, why it would be a cop out. But, you know, with regard to the challenges of avionics, do we embrace that shortfall, that challenge of digital engineering when it comes to avionics?
Do we really truly understand that when we embark on the overall design of a system, an aircraft or what have you, it would seem to me that, you know, in today's fighting age where spectrum dominance is paramount to success, avionics, what you have on whatever system, whether it be an antenna, a radar, a jammer, that's gotta be your starting point in terms of your final system.
Is it a matter of simply needing to spend a lot more time maybe adapting digital engineering to this field, or is there something that we can do to just kind of make progress in this area? - I would like what you just said etched in stone somewhere, because that's it. We have to embrace digital engineering in the electronic warfare world. There's no other answer.
We're never going to be able to go to a test range and faithfully replicate what we would see in a true, especially peer-on-peer engagement, without lots and lots of help, which means a lot of synthetic signals injected into the tests, synthetic targets. Some people call that live over sim, so accommodation simulation and live, you know, effects.
But we have to, and by the way, I think, you know, this kind of went unnoticed by a lot of people, but OSD came out in December, I think it was, and mandated that all major defense programs must employ digital engineering unless they get a waiver. And I thought, "Wow, I mean, that's really quite something."
Now you just pointed out, even on the B-21, they can point to all the digital engineering for the avionics and what have you and the stealth, but they can't do that for all the avionics inside. So, but the DOD wants to go that way. I think you know, there's a digital engineering czar out of the office of Secretary of Defense. I think when people call it digital engineering, some people call it model-based systems engineering, MBSC is basically a synonym. We don't have a choice.
And let me just add one more wrinkle to it, which is, well, wait a second, who's creating the scenarios that are used for training, right? If I said before that cognitive systems were invented because we don't know exactly what we're gonna be facing, and yet you're saying, but humans are creating the training data, then there's a built-in flaw.
And the way that I've seen to get around that, and we're actually implementing it on some of our research here that we're doing for AFRL, is let's let AI help create scenarios. And why is that so powerful? Well, DeepMind from Google is the best example, right? The original chess playing DeepMind was trained by subject matter experts, just the way we would do with digital engineering tools using humans to create the training data.
But when they let it play on its own, they just created a training environment, not data, and let it, by trial and error, making dumb move after dumb move, eventually it learned. And guess what? There's no human that can beat it now. So using AI to help with the training portion of another AI system is ironic, but it seems to be the answer to, well, how do you prepare for the unknown unknowns?
- Well, if you can use AI to train another AI to design an environment to train against, can you use AI through digital engineering to really almost design your next system, even though that's not what, you know, like, it can anticipate kind of here's what you're gonna need, here's a design of what you're going to need, not based on any sort of real human modeling or human outline. It's just let AI run over and over again, all the mistakes made in the past, all the successes made in the past.
Here's exactly how your next system is gonna have to look in this threat environment that we're training against, and almost take it, in some ways remove us a step further from the design of the next generation systems. So is that already being done? Is that how it's so fast today with the next generation air dominance? - So once again, I'm gonna give you a mealy answer. Yes and no. So yes, we need to let AI, I'll say run amok and come up with all these scenarios on its own.
A good example might be instead of coming up with the next generation air dominance, why don't we just send it a million stupid drones and just overwhelm our adversary with dirt cheap millions of drones, right? That would be something that, I mean, an AI system wouldn't be constrained, right? It would just come up with that answer. So, but here's what I will say about that.
The good news is while we can let AI run amok and come up with scenarios that we wouldn't have thought of, we can then look at those scenarios, and we can say, "Is that really something we need to worry about?" A good term for that, a colleague of mine came up with a great, we call it black swan. You know, you might recall the whole point about black swan was there's no such thing as black swans until one day we actually found a black swan.
And so we like to call this the black swan analysis stage, where you let AI come up with things that no human would've thought of, but are physically allowed and technologically allowed, and then you look at that. So now is where humans come back into play. Because look, we don't have unlimited resources, we don't have unlimited time.
We can't spend trillions of dollars on a single aircraft, for example, so a subject matter has to look at these black swan events and decide whether we really care about that. And like I said, launching a million dumb cheap UAVs is certainly technically possible, but are we really gonna, is that how we're gonna base our whole defense posture is on something like that? So it's a yes and no answer, right?
We're not letting go of the reins completely, only we're letting go of the reins when we're asking AI to use its imagination to come up with things, but we pull back on the reins when we see what they've come up with and decide whether or not that's something we should care about. - So we're talking about how many things we can do with AI. I wanna talk a little bit more, kind of take a step back, and continue talking a little bit about how AI works.
And you had a slide in your webinar presentation that we were talking about the relationship with AI, and there's an aspect to AI that's using neuromorphic computing and neuromorphic chips, and we were talking about this. This concept just blew my mind, because I really never heard the term before. So I wanted to kind of, I wanna ask you to talk a little bit about this.
What is this piece of the puzzle, and what does it it hold in terms of the future for artificial intelligence, and then feeding into cognitive radar and EW? - So cognitive radar, EW, live and die by embedded systems, right? They don't have the luxury of living in a laboratory with thousands of acres of computers, right? They have to take all their resources on a plane or at UAB or whatever platform and go into battle.
And so to really leverage the power of AI, you need to implement them on efficient embedded computing systems. Right now, that means FPGAs, GPUs, and those things are, when all is said and done, you know, all the peripherals required, the ruggedization, the MIL-SPEC, you're talking kilograms and kilowatts. And as I pointed out, there is a rather quiet revolutionary part to AI that's perhaps even bigger than all the hullabaloo about ChatGPT, and that's neuromorphic chips.
So neuromorphic chips don't implement traditional digital flip-flop circuits, things like that. Essentially they actually, in silicon, create neurons with interconnects. And the whole point of a neural network is the weighting that goes onto those interconnects from layer to layer.
And the interesting thing about that is you've got companies like BrainChip in Australia, right, that is not by any stretch using the most sophisticated foundry to achieve ridiculous line lists like conventional FPGAs and GPUs do. Instead it's just a different architecture. But why is that such a big deal? Well, in the case of BrainChip as well as Intel and IBM, these chips can be the size of a postage stamp.
And because they're implementing what are called spiking neural networks, or SNNs, they only draw power when there's a change of state, and that's a very short amount of time, and it's relatively low-power. So at the end of the day, you have something the size of a postage stamp that's implementing a very, very sophisticated convolutional neural network solution with grams and milliwatts as opposed to kilograms and kilowatts. And so to me, this is the revolution.
This is dawning. This is the thing that changes everything. So now you see this little UAV coming in, and you don't think for a second that it could do, you know, the most sophisticated electronic warfare functions, for example. Pulse sorting, feature identification, geolocation, all these things that require, you know, thousands of lines of code and lots of high-speed embedded computing, all of a sudden it's done on a postage stamp. That's the crazy thing.
And by the way, in my research we've done it. we've implemented pulse, the interleaving, we've implemented, you know, ATR, specifically on the BrainChip from Australia, by the way. So really quite amazing. - So where is this technology? You said we've already done it. We have a pretty good understanding of what it can do.
And like you mentioned, you know, a scenario where whether it's a UAV or whatever system, I mean, something the size of a postage stamp, it completely changes size, weight, power, all those considerations, and makes almost anything a potential host for that capability. - Yeah. - What are some of the next steps in this, call it a revolution or rapid evolution of technology?
I mean, because we obviously, you know, a couple years ago there was a CHIPS Act, you know, trying to make sure that we, in the development of a domestic chip production capability, Congress passed a CHIPS Act to kind of help spur on domestic foundries, domestic capability to produce chips. And does this kind of fall into kind of the... Is this benefiting from that type of activity? Is this part of the development that's happened through the CHIPS Act?
Is there something more that we need to be doing to spur on this innovation? - Well, the CHIPS Act is a good thing domestically speaking. And by the way, part of the CHIPS Act, it is focused on neuromorphic chips, by the way, so that's good to know. However, the real culprit is the age-old valley of death, bridging the valley of death.
And by the way, I spent seven years at DARPA, and even at DARPA with the funds I had available to me, bridging the gap between S&T and Programs of Record is still a herculean maze of biblical proportions. And so while you'll hear lots of nice-sounding words coming out of OSD and other places, saying, you know, "We gotta move things along. We gotta spur small business. We gotta..." it's all S&T funding.
There still is an extraordinary impediment to getting new technologies into Programs of Record. And I, you know, I'm not the only one saying that, so don't take my word for it. I can tell you lots of horror stories, and I've done it. I was successful while at DARPA. So my stuff is on the F-35 and F-22, for example, and other classified systems. I mean, I know what it takes to get it done.
Unfortunately, though there's a lot of lip service about overcoming that barrier, it still has changed very little in the 20 years since I've been successful at DARPA in transitioning. So I'm sorry, but that's biggest impediment.
And I know it's not a technical thing, and I know there's lots of- - But here's what concerns me about that, is, you know, the valley of death, I mean, that's been in our terminology, in our lexicon for decades, like you say, going way back, you know, even before we even under, you know, back in the nineties and eighties when the technology, while advanced at the time, pales in comparison to what we can do today, the process hasn't changed.
And so like if we had a valley of death back then, how are we ever going to bridge it today with as fast as technology is moving, as fast as the solutions we need to prototype and field. I mean, you mentioned it's herculean. I mean, it's almost beyond that it seems, because our system hasn't really changed that much over the past 20, 30 years.
- Yeah, so maybe it's ironic, I don't know the right word, but on the S&T side, OSD, the service labs, you know, I would say that they're pretty forward-leaning and they're making good investments. The problem is getting into a Program of Record is where the rubber hits the road, and where things get fielded. And so you look at the S&T budgets, you look at the number of small businesses getting DOD S&T funds, and you could almost say, "Well, they're a success," right?
I mean, we're giving small businesses, they're coming up with great things. But then look at how much of that actually ends up in a Program of Record. And let me just be clear. I don't blame the Programs of Record, because the game is stacked against them. They, very often, especially if it's newer technology, they are having lots of problems with getting the baseline system fielded.
There's cost overruns, there's scheduling issues, and so they're already with 2.95 strikes against them, and now all of a sudden you want to on-ramp and entirely new capability when they're already behind the eight ball. That's just impossible, unless the whole culture of Programs of Record changes where, for example, you structure it so that every year you have to answer how are you dealing with obsolescence? How are you keeping up? Where are the on-ramps?
How successful were you with these on-ramps, these upgrades, all of these things? Because until you fix that, I don't care how much money you spend on S&T, you're not gonna get fielded. - From a technology standpoint, let's just, you know, assume for a second that we make some progress in the policy side of the equation as it pertains to acquisition and the valley of death. From a technology perspective, you've been following this for 20 years.
You know, where are some of the opportunities that are before you that you're like, this is the direction we need to go in, this is something that excites you or keeps you awake at night in a positive way, of like this is promising and it's gonna be your next pursuit? - Well, we definitely have to embrace cognitive systems for sure. I mean, I don't think there's anyone out there that would say we don't need that kind of flexibility and adaptability on the fly.
Now, we can argue over just how much cognition we need and the flavors. That's fine. So there's that, right? Let's all just accept that.
And then I think you touched on this earlier, you know, there's a big push across all the services on what's called the JSE, which is the Joint Simulation Environment, which is this grandiose vision for having multi-user, multiplayer, high fidelity training environments, synthetic environments, which, by the way, can include live over sim, so that our systems become much more realistic and reflective of what they're really gonna see when they get out into the real world.
Again, I come back to lots of good things going on on the S&T side. You almost can't, you know, you really can't argue with it, but that transition to field its systems and Programs of Record is still very much broken, and that's just a fact. And it's not just me saying that. You can ask anyone who's in the business of trying to transition technology to the Department of Defense, and they'll tell you the same thing.
So, you know, again, S&T community, doing a great job, I think, generally speaking, your DARPAs, your AFRLs, all of these, but that transition piece is just continuing. And by the way, do our adversaries have the same issues? Some do, some don't, you know? And this technology I'm talking about, neuromorphic chips, that's available to the world. I mean, BrainChip is an Australian company. There's no ITAR restrictions, so.
- Well, and also I think it speaks to the multidisciplinary approach to technology today. I mean, the neuromorphic chip, I mean, it has military applications you can obviously use it for, but, I mean, you're gonna find this in all various sectors of an economy and society and what we use in everyday life, and so, you know- - So Ken, let me just say that the neuromorphic chip that BrainChips makes from Australia had nothing to do with electronic warfare. It's designed to do image processing.
So one of the things we had to overcome was take our electronic warfare I/Q data, in-phase and quadrature RF measurement data, and put it into a format to make it look like an image so that the BrainChip could actually digest it and do something with it. So you're absolutely right. I mean, these chips are not being designed for us in the electronic warfare community, but they're so powerful that we were still able to get it to work.
Imagine if they put a little effort into tailoring it to our needs. Then you have a revolution. So, sorry to interrupt you there, but I just want... You made a point and it's very valid, you know. - It's valid. It's valid, it's important. I mean, it goes to just the possibilities that are out there.
- Well, and to amplify that point, all the advanced capabilities that we have in our RF systems, radar and EW, most of that is driven by the wireless community, the trillion-dollar wireless community compared to a paltry radar and EW ecosystem. So, you know, what's happening in the commercial world is where, and leveraging, you know, commercial off-the-shelf technology is a gargantuan piece of staying up and keeping up, and by the way, addressing obsolescence as well, right?
If you have a piece of proprietary hardware from the 1980s, good luck, you know, with obsolescence, right? - Well, that, and also hopefully, you know, as we move down this path on standards and open systems and so forth, some of that will work its way in. We can adapt some of that so that as we struggle less with obsolescence in the future than we do now. - We hope. - Hopefully, yes. I mean- - Again- - We'll see. But, I mean, I would think that's the idea.
- I mean, look at the day-to-day pressures that Programs of Record are under. So I'm not gonna get into all kinds of details here, but we had a capability that was vetted by the program offices and was developed under SBIRS, and went all the way through to a Phase III SBIR. We have flight-qualified software to bring this much-needed capability to the war fighter. This is all a true story.
And all of a sudden the program ran into scheduling and budgetary constraints, so they had a jettison the on-ramps, and so a capability that was vetted was a really important capability, just got thrown to the curb because of the everyday problems that Programs of Record run into, and that's not how they get judged, right? They're judged on getting that baseline system over... Look, the F-35 was just recently declared operational, what, a month ago? You gotta be kiddin' me.
- Well, Joe, I think this is a good spot to, I mean, I feel like if we keep talking we can keep going in layer and layer and layer, and I don't wanna put our listeners through that, but I think a good consolation prize is to have you back on the show in the future, and we can go a little bit deeper into this, but I do really appreciate you taking some time to talk about this, 'cause this is a topic as of, you know, really 24 hours ago, I realized how often I just use the word,
and I never really understood the depth of the definition of what the words I was using, so I really appreciate you coming on the show, kind of helping me understand this better, and hopefully our listeners as well. - Thank you, Ken. You had great questions, great interview. And let me give a shout out to AOC. Great organization. I'm personally, and my company's a big supporter of AOC and what you guys are doing, so you're part of the solution, not part of the problem.
- We appreciate that, and, you know, appreciate all that you've done for us in terms of helping us understand this really complex topic. And really I do say this honestly, I do hope to have you back on the show here, and there's no shortage of topics of conversation for us, so I appreciate you joining me. - Thanks again, Ken. - That will conclude this episode of "From the Crow's Nest." I'd like to thank my guest, Dr. Joe Guerci, for joining me for this discussion.
Also, don't forget to review, share, and follow this podcast. We always enjoy hearing from our listeners, so please take a moment to let us know how we're doing. That's it for today. Thanks for listening. (exciting music) (uptempo music) - Voxtopica.