The Future of AI: Determinism, Security, and Beyond

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Thanks for having me on the show. My name is Savak Avakian. I'm the founder and CEO of Intelligent Artifacts At Intelligent Artifacts, we have completely solved AI ML's black box problem So that's our company. We have a platform that we use to replace underlying neural networks with this specific transparent technology and we could talk more about that as we go

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Right.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Right, yeah, let's get into it a little bit deeper because the public has a misunderstanding of what the scientists are working on. And usually, you know, the public believes that the scientists have it all figured out and everyone's in agreement on everything. And the reality is that's not the truth, right? So traditionally, there have been two different ways of approaching artificial intelligence.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) there was a bottom-up approach using what's called connectionist theory. And that approach is what we now know of neural nets. Like if you've heard of machine learning today, chat GPT, Dolly, like all of those technologies that have machine learning, today they are all built on top of neural net technologies. So these are open source techniques. They've been around since the 1960s, even before with something called the perceptron.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) But they've evolved. They've had a huge lift recently because of a breakthrough in deep learning. So these are very large models, neural net models, that have been able to automatically do really incredible things that were not possible in the past. People knew that at some point we could get there, but they weren't able to do with what was around at the time.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) So those bottom-up approaches, all you do is you send the data and it automatically identifies and extracts features from that data that it thinks are important in order to make the predictions that it does.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Okay, so the nice thing about this is, you know, any novice engineer, anyone who's an amateur could go in there and start building a neural net without having to understand the data in any way, right? You just throw the data at the neural net, you keep iterating on this until your outputs look like what you want them to look like, right? And that's called the training period.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) And once you're getting the metrics that you want, that's done with that model and you freeze it and you put it out into production and then all hell breaks loose and we can talk about that in a little bit.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) The other approach was a top-down approach, and that was called symbolic AI. And over the years, different terms have been thrown out, and that's mostly because of the way funding had dried up and people tried to pivot the technology and remarketed to make it sound appealing again or slightly different. And that happens in AI in both camps all the time. We've had several AI winters. I'm hoping we don't have another one because of all the hype. But the top-down approach, the thinking was this.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Look, we don't need to simulate what brains are, right? So the neural net approach, you're trying to simulate the structure of a brain, but we don't know enough about how brains actually work. And neuroscientists will tell you, the ones that have looked into these artificial neural networks, that this is not how brains work at all. So those models are completely wrong as far as cognition science goes, like as far as neuroscience goes.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) The symbolic AI people were saying, look, we don't build airplanes by simulating birds, right? We don't have flapping wings with feathers on it and all that other stuff. What we've done is we've identified the functions that are relevant for the case.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) For example, the Bernoulli equation allows us to calculate the lift over a wing, right? So as air travels across the top of the wing, it'll move faster than the bottom, and that creates a pressure differential generating lift, right? So the symbolic AI people would say, look, let's just figure out the functions of cognition and write code that represents those functions.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) This top-down approach is 100% explainable. You could go in there. You could look at the code. You could understand if this data came in, what the output should be. And if the output's not that, you go back into your code and bug fix. Completely transparent, 100% traceable, too. So you could identify the prediction came all the way because of this specific function or rule or data point.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) They had actually huge success in the early 80s, but the problem with their solution was that it doesn't scale, right? You had to have a lot of really smart subject matter experts writing these rules by hand so that these systems could produce the outputs that they expected.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) The ultimate goal of it though wasn't to write these rules for every domain, it was really to find the rules or the rules set for general thinking in the first place, general intelligence. And that turned out to be a really hard problem to solve, right? Not impossible, but really hard problem to solve. So both of these solutions have their advantages and their disadvantages.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) In fact, some of the advantages are also the disadvantages. For neural nets, the fact that it extracts the features that it thinks are important actually introduces biases into the solution. If it believes that, for example, I'm sure your audience knows about redlining. So if the data set had house pricing or

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) it was about getting loans and they used your zip code. Right? Well, there's bias inherent in there, even though you're not seeing that specific data point as being biased, right? It's a zip code, you think. Well, this shouldn't be a matter of bias, but we know about redlining and how that has introduced bias into that same exact data set.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) On the flip side, the top-down approach is they demand to explicitly put in the features that are relevant. So an advantage to that is that we know exactly what goes into it. Disadvantage to that is for lazy people like myself, we have to do additional work. So it would have been nice if those systems could also learn from data.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) And that's where we come in. So we basically audited the entire field, looked at the different ways things work and things that didn't work, came up with a list of things that we demand of our artificial intelligence and machine learning systems. And we created a whole new category from scratch, bottom up. And we call that Gaius.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Originally, a little bit of history around Gaius, originally it stood for general artificial intelligence using SOAR. SOAR was a cognitive architecture from that symbolic camp. My intention was to have a plug-in to kind of prove out that this alternate path is viable. So let's start using it instead of all these other things that are failing.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Over the years, that changed and we had to build guys again from scratch to make it more general because we bumped into some issues from those cognitive architectures that are inherent within them. Just like the black box issue is an inherent intrinsic problem of neural net technologies that there's no way to solve, right? You can't solve it. You have to try a different technology for it.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) So today, you may have watched some of the congressional hearings on AI, and people are talking about, well, we're going to put some guardrails around these things, we're going to make them safer, we're going to make them this and that, or whatever. That's very problematic, right? In fact, you're always going to be behind the eight ball if you go down that path.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Trying to put guardrails around these complex systems that are black boxes that you can't fix, it's just increasing the complexity of the system without addressing the underlying issue.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Right? So it's kind of like if you went to the doctor and you had all these, you know, aches and pains and your doctor's just throwing all these different medications at you. And maybe he's taking care of some of the symptoms, right? But the underlying cause isn't solved, which means you're still diseased. You're still sick. Right? So that means at some point, those roosters are going to come home to roost.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Right?

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) I know that for sure. Absolutely, and cyber is actually a great analog to what's happening in AI today because what happened with cyber in the past, and my background is also I used to work as a cyber security analyst for the Federal Reserve, doing it for the entire Federal Reserve system and the US Treasury. So we were embedded in this.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) And in the past, people developed software and they weren't concerned about the cyber implications of that software. They figured, you know what? We'll put up some guardrails later. We'll tack on stuff later down the line. And nowadays we know that is the wrong way to develop software. Security has to be part of the very beginning of that whole development process. So we've learned lessons from the cyber world.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) And I feel like sometimes we're not applying the same lessons learned in the AI world. Although I do have hope because at the last congressional hearings, one of the congresspeople had mentioned the, so in aerospace, there is something called DO178C certification.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) This is actually a software certification. It's independent of aerospace. It's independent of, I think they use it in a few other transportation areas. And this specific certification guarantees a safety of software that's used in these, you know, life or death mission critical applications. So for example, we just went through the whole Boeing 737 MAX.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) issue again, right, another one. This time it was a hardware issue and that airplane has to get recertified before it's FAA approved to fly again. There's a hardware certification, and I forget what the acronym is for that, but there's also the software side of it. And that's what impacted the previous disasters with the...

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Right, the throttle thing. That was a software issue, right? And the reason why we were feeling safe to fly on that aircraft after that was resolved was because we knew that they had to go back into the code, identify the bugs, fix it, and that guarantees that will never happen again. Maybe a different failure mode would happen, but that failure mode would never happen again, right?

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) You can't do that with traditional machine learning using neural nets. They are black boxes. There's no way to introspect inside of them. There's no way to bug fix. And then there's other privacy issues as well.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Thank you sir.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) That's right, you're always gonna be behind the eight ball with neural nets. And you just described the fact that it's non-deterministic, right? We need software, especially safety, mission critical, privacy, legal, you know, software that is deterministic. We need to know that it will always do this thing given that input, not change it randomly, right? And with neural nets, you start off with a random seed. That's, you know, step one.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) random seed. So everything that follows is based off of that and it doesn't matter if you lock down that random seed and you say okay I'm always going to use this number. What was the intent behind using that number? Why not a different number? So it's because of that you actually have this black box problem that it manifests itself in so many different ways.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) One of them being what you just said, which is different outputs, the non-deterministic outputs of it, right? Another way it manifests itself is as hallucinations. It just makes things up, right? Another problem, especially in the privacy space, is that there's no way to prove that neural net model didn't use your personal data.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Again, there's no way to introspect inside that model and say, ah, here's Aaron's data. This is Aaron's record. Aaron asked us not to use this anymore, so we have to remove it. You actually have to throw that model out, go back to your original data, remove your record from that data, then retrain the whole system again.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) And estimates of how much it costs to train ChantGPT and range everywhere from like $4 million to $12 million just for training. So every time you have to retrain it, every time there's another errand that says, no, don't use my data, you have to go through that process. With our system, it's a simple matter of identifying that record and deleting it. It's gone. So we could show complete traceability of our predictions

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) records used at training time that generated that prediction and if somebody says I don't want you to have my record anymore all we have to do is delete that.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Hahaha.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Right. Aaron, you're a lot braver than I am. I would have not stood in front of that car. So one of the great things about what Chad GPT has exposed for society is this very specific thing about hallucinations.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) So again, that's because of the underlying neural network. Now we understand it because we've been playing with it. So as a society, we've now played with chat GPT, we put in prompts and we've gotten different outputs and whenever we're asking you to actually give us information, I always go and Google it afterwards anyway, so I have to confirm it. But, you know, the fact that...

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) we are now able to see these things in action and intuit that, hey, wait a second, this really isn't what people are telling us it is. It's not that magic genie that everyone's talking about. Now translate that over to an autonomous car, like the one you were standing in front of. The prompt is the environment around the car, including you.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) And I don't know how many times that car cycled through that same prompt. Was it every second, every millisecond, whatever. But that's basically putting in the same prompt over and over and over again. And you're hoping that it's not going to run you over, right? You hope, right? And then there's that hallucination part.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) that hey, it might think that, you know what, that's not a person standing in front of me. That is a floating ghost or something, you know, whatever, right? And I could go right through it. And this is what happened with, and I believe it was Tesla, the car that killed a pedestrian crossing the street with a bicycle and there was a bag hanging from the handle. And...

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) After the tragedy, everyone was looking for an explanation. Right? And the engineers weren't able to find an explanation. They came up with theories. Right? We don't need theories of why something went wrong. We need to identify what went wrong and fix it to guarantee it doesn't happen again.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Right? So that's my concern when it comes to neural nets. Great for chat GPT. I love using it. It helps me reword paragraphs and sentences and get my grammar correct, because that was wrong. I'm from New York. Our grammar's supposed to be wrong. We've invented our own here. But those tools are great for some things.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Like you mentioned, you know, video games and social media. I would say no to like, I wouldn't think that they're actually good for social media because they sway people. Right. And then we don't know why they're swaying people. Are they swaying people because someone has trained it to make you a little bit more towards the left, a little bit more towards the right or extreme in whichever direction? Or is it just, you know, the algorithm is doing what it thinks?

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) is supposed to be doing because it's extracting the futures out of things that are important and maybe they're not, maybe they're biased, right? So we need to be able to identify all those things along the way before we put these things into production environments.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Yeah, great question. So our technology is a base technology. So the framework itself can be deployed in any use case. Any use case that requires artificial intelligence, machine learning, or computer reasoning. So we let our customers decide what those use cases are. Our ultimate goal is to put our platform out for public use. So right now, it's in private beta. We work with some specific customers.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) they're able to create these agents. We've taught them how to build these agents and deploy them. Our system uses four API calls to get all of machine intelligence into your application layer. And because we separate out the data layer from the intelligence layer from the application layer, the customers are able to control their data. We don't have to integrate it into the agent.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) So they own their data, they don't have to expose it, and that allows them to do a lot more things that they weren't able to do with neural nets. So basically, they come up with their data, and they don't need tremendous amounts of data because our system's analytical, not statistical, right? So you don't need a tremendous amount of data. Yeah, great question. So statistics require...

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) a shit ton of data. You can't have a few samples of something and say, hey, we're done with this. Polling results, for example. Let's say you walked around our New York City office here and polled 10 people. You're going to get a very skewed political viewpoint.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Right. Exactly. And you can't extrapolate from that and say, well, the entire country is now whatever we are here. Right? So for statistical systems, you need a tremendous amount of data across the entire population size. So you have to sample.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) large amounts and the more data you have the better the results should be right Well Analytical is completely different right Think of Nunez law F equals MA That's analytical we found a function We plug in the mass we plug in the acceleration. We know what the force is going to be right we don't have to

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) have millions of examples with different masses and different accelerations. You know, in the beginning, this was done to like, not millions of examples, but like dozens of examples. And a model was created, an analytical model was created to prove out the formula. But then you don't have to keep doing that every single time. You have the formula, right?

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) That's exactly right. So these form different cohorts. Each cohort has their own pattern. You only need to see a pattern once in an analytical system, right? In statistical systems, you need to see variations of that pattern millions, millions of times before it becomes useful. Yeah.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) That's right. So our art.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) We call them information models, not data models, right? Because what they're doing is they're extracting the information from the data and they're processing that information. So it becomes more general. We could apply it to multiple use cases. So those information models or configurations of our agents can be static, right? And the only thing that needs to change is that data. So as the data changes from one domain to another or from one customer use case to another, that same agent...

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Now with the different data it's filled this knowledge base with is not capable of solving that customer's problems.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) year.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) So one thing that's near and dear to my heart is in cybersecurity, actually. So security operations centers require a team of analysts to sit there and watch hundreds of millions of records flying by their screens.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) And these records are often generated from signatures, which are, back to those expert systems, hand-generated rules to identify specific types of patterns. But as a SOC analyst, you're not sitting there and looking at each signature that fires and saying, ah, that's malicious activity, let me escalate this. What you're doing is you're looking at a pattern of signatures that fire.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) and you're looking at that pattern within the context of everything else that's around it and saying, ah, this looks malicious, let me estimate this, or nope, this is the IT guy doing their daily job, right? So you have to have that context in that too. The thing that I'm most excited about right now is applying our Gaius platform to cybersecurity, and we've come out with a product called CyberSoc.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) that allows different environments, so different network environments, to just stand this up, have the system baseline their network in real time, so it actually can learn in real time. We don't have to go through an expensive, long, dragged out training period. So it baselines their network in real time.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) and then identifies anomalies that are occurring there, because those are the patterns that it hasn't seen before. Unlike with, let's say, a neural net, if we were to try this with a neural net, we would have to train it, and this is what people have been trying to do, train it to identify anomalies. But by definition, an anomaly is something that you haven't seen before, so you wouldn't have the data for it. It's something new. It's the black swan event, right?

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) With our system, we can identify those black swans because of the fact that it's not already in its knowledge base. So it says, hey, this is a pattern I haven't seen before, which means that our system is capable of catching zero days. So that's one of the exciting things. Another thing that we could do with this is train it on the MITRE ATT&CK framework.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) So for those customers who want a little bit more sophistication in their results and not just, hey, this is telling us that this is malicious activity, which they could click on and trace through and understand exactly the whole picture, right? 360 degree view of that event or series of events and sequences and identify whether it's malicious or benign activity. But.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) But you could do a lot more with something like the MITRE attack framework, right? You could have this system tell the analyst, hey, this is the specific type of attack that's occurring. And this is how to defend against it. So we could also include actions to the system that an analyst or the business decides they would allow the system to automatically take on their behalf to mitigate those threats in real time, to defend against them in real time.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) And this plays into the whole self-healing networks and self-healing computers and all that as well

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) That's exactly right. So our customers want human in the loop and human on the loop, right? So our platform allows both as well as fully autonomous. So the way I see AI and ML playing out over the next few years would be people need to start feeling comfortable with these systems before they can fully trust them.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Right? Having human in loop and a human on the loop.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) is an absolute necessity. We can't just go straight to fully autonomous because we need to be able to trust these systems. And to build that trust, you have to have a whole bunch of things in play, right? You have to have those kinds of certifications that we spoke about that DO-178C, which proves out the deterministic nature and that you could go in and fix errors that appear and that it's also cyber secure, right? So those are important bits to it,

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) over time, right? We have to know that this system is not going to hallucinate and it has to be guaranteed, right? And we can guarantee all of that with certifications and paperwork and everything. But you also have to have people who aren't, you know, in the tech world, they're not the experts. Just like when they bring in someone, you know, I...

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) I don't know if your kids are old enough to drive, but if they're old enough to drive, you're not just going to hand them the keys to the car and say, okay, good luck. You're going to be in the car with them for a while, right? You're going to watch what they do. You're going to see where they're looking, and you're going to provide that advice back before you have enough trust in them to allow them to go on their own. So similarly with autonomous applications.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Ha ha!

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) This way.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Right, right. Very cool.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Yeah.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) So let me start with the concerning part first because this is a very real issue and a possibility. So right now there's a group called the G34 which works out of the SAE and they are looking at ways of allowing some sort of certification process for neural networks to be able to fly on commercial aircraft.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) And they're thinking is and I understand the thinking if there weren't alternative Possibilities or alternative technologies that could do the things that we need done, but they're thinking is you know we have to progress We need autonomous aircraft and We have these neural nets that allow us to get there

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) let's figure out a way, because they cannot pass that DO-178C certification. It's just not possible. All the safety regulations forbid these non-deterministic black boxes. So they're trying to figure out ways of allowing that to fly on civil aviation, right? I am extremely opposed to this.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) So let me bring up adversarial attacks as an example. And I think your audience working in cyber would appreciate this.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) You've seen maybe the adversarial attacks on a picture of a panda where by changing just a few pixels, they were able to confuse a neural net into thinking that panda was a thing like a cat or something. Now imagine a vehicle, you know, whether it's a car driving down the street and someone puts a sticker on a stop sign, which we see here all the time, that tricks the neural net into thinking that's not a stop sign, right?

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) It doesn't look suspicious. It's just some small sticker or a few stickers that doesn't change the whole view of a stop sign, but changes some of those pixels that the neural net observes, thinking now it's something else, right? Or similarly for an aircraft, and this is a nightmare scenario for me, where possibly a terrorist...

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) mows down alone somewhere out in the field that is underneath a flight path and they mow a pattern into the grass there that tricks the aircraft into thinking that it is pulling up. And suddenly that aircraft is doing a nose dive just like the 737 MAX issue that we saw a couple years back.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) So that's my fear. It translates also into other domains, right? And I think when Congress looks into this matter further, they will understand that there is already a precedent for.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) software safety and that is DO178C standard, right? They just need to apply that across the board for all AI ML that is in any way affecting life, liberty or limb, you know?

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Yeah, on the positive side, now that we've gone through the doomsday scenario of it, on the positive side, I see an acceleration of these technologies, but there is a large trend of people looking at doing this properly.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) right, not just throwing this out there and hoping for the best. So I do see an acceleration of technology in the right direction. We should be able to work alongside of these machines to get our jobs done, not to replace our jobs, but to make ourselves more efficient, more performant, right? So for example, if you had that cybersock agent

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) sitting at that screen and it's guiding the analyst, hey, look at this pattern, right? I think this is malicious because of this, that, whatever. Or this other pattern I think is benign because of this, that, whatever. Or the analyst finds a pattern and they ask the AI, give me a little bit of information about this so that I can understand what's possibly happening behind the scenes before I escalate this up and becomes a full-time analyst.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) So we could elevate everyone's standards, everyone's performance by using systems like this working alongside us. So the human in the loop, on the loop, that human becomes better along the way.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) I love that. I love the whole wargaming aspect of it. Perfect.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Sure, so our company is called Intelligent Artifacts, so they can go to inte They can also hit me up on LinkedIn. I post very frequently about all these issues there as well. So it's LinkedIn, forward slash in, forward slash Svaka Vakins, and I'm sure the link will be in the link below. Here we go.

Sevak Avakians | Intelligent Artifacts (NaN:00NaN) Absolutely, thanks Aaron.

Aaron Crow (NaN:00NaN) Hey, welcome to the show. Why don't you introduce yourself? Tell us who you are and a little bit about your background and, uh, the company that you guys run.

Aaron Crow (NaN:00NaN) Yeah, man, let's dig in. So AI, everybody wants to talk AI, right? So I work in cybersecurity in both the IT side and the OT side, OT being like critical infrastructure and power plants. Everybody sees this AI thing coming. And lots of us are wanting to use it and benefit from it. Obviously there's pros and cons, there's risks, there's things that we need to be aware of. So let's dig into that. Like talk about, so you talked about, you solved this thing underlying, like dig into that. Explain a little bit, you know, remember some of the audience,

Aaron Crow (NaN:00NaN) may have a brief understanding of AI and their level of AI may be chat GPT. Right? So that's different than true AI and all the other underlying things that you can use in an enterprise type environment.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Okay. Sure.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Great. Yeah.

Aaron Crow (NaN:00NaN) Yeah, well, and that's true in so many levels, right, is we've got, especially in that analogy, right, is the people that are trying to set up the guardrails don't understand the technology. You know, you've seen that in the congressional hearings around like Facebook and social media and these congressmen and senators that are asking questions about something as simple as social media and how it works. Just don't get it. So...

Aaron Crow (NaN:00NaN) How can we expect, and I've spent time on Capitol Hill and talking with congressmen and senators, there's no way they can understand, they're not technical people, right? They're not AI scientists, they're not technologists, that's just not what their role is, right? So these are the people that are responsible for setting those boundaries. And yes, they have advisors and people are giving them guidance and they do bring experts on to help advise them, but still, it's hard to grasp

Aaron Crow (NaN:00NaN) the magnitude and it's also the same type of problem. Like it's a difference between, you know, whitelisting and blacklisting, you know, antivirus technology, like it can only block the things that it knows are bad as opposed to, I only want you to do this and this, anything else assume no, right? Unless I tell you otherwise, you can only do, I only want you to, you know, swing the hammer. Like I don't want you to do anything else. If anybody asks you to do anything else, the answer's no. And you can only hit.

Aaron Crow (NaN:00NaN) these nails, these 12 nails that are in front of you. That's the only thing you can do. Anything else is a no. And you have to explicitly give it better permission or different permission. It's like my child, you know, you can't do these certain things instead of saying, you know, trying to blacklist every possible thing they could come up with of things that they're gonna try to do as a toddler. Like that would be a never ending loss. Ha ha ha.

Aaron Crow (NaN:00NaN) Ha ha

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) throttle up thing, right? Yeah.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Yeah, I mean, you know, I use chat GPT and other, you know, language models quite often. And it's, I've done some testing and playing around with it and I can give the same prompt to the same, you know, chat GPT at two different times, really changing nothing and get different results, right? With no other prompts, no other differences other than this time it spits something else out, trying to get repetition. And you know, I've built some, some AI tools in the background to integrate with other stuff.

Aaron Crow (NaN:00NaN) And same thing, like sometimes, you know, 90% of the time it's great. And then it just throws you this random curve ball. It's like, where did that come from? Like, and this is not public chat, GPT stuff. This is local models, right? Running in a very small environment. I've only given it my data. So like, but it still just comes out of randomness and it just guesses and you're not like, no, not, that's not right. Like, why, how did you get there? And there's no way to track its logic from, okay, it.

Aaron Crow (NaN:00NaN) did this and then it did this and then it did this. And that's how it got to here. So I can fix that last step, like you said, there's no way to do that. I just have to continually go back through it and try to retrain it better. But there's no logical path to tell it to stop doing that. That's not right. Don't give me that answer again. It's not the right answer.

Aaron Crow (NaN:00NaN) Sure.

Aaron Crow (NaN:00NaN) Right. And that's, that's a lot of the concern around, you know, if you look at operations, if you look at, you know, critical infrastructure, you talked about TSA, you talked about, you know, trains and airplanes and power plants and, you know, all this type of stuff, we can't deal in, in what ifs, like we can't have, it has to be predictable. It has to be reliable. And I have to be able to troubleshoot it down to exactly where it failed so that I can fix it, or it doesn't make sense that the.

Aaron Crow (NaN:00NaN) cons and the negatives far outweigh the wins of the value added and efficiencies in a power plant or in an airplane. When you're flying 30,000 feet above the air, you can't risk one little thing going wrong and crashing the plane. It's just not worth the risk. It has to be in something that you can guarantee repeatable, traceable, bug fixable, all of those types of things. To your point, I mean, I'm...

Aaron Crow (NaN:00NaN) I'm a novice at best in this AI stuff, but even just a little bit that I've played with it, I know there's no way I could take this to a power plant and have it run a power plant or have it run, you know, look at the AI driving, you know, Tesla and the self-driving cars and you know, you see things all the time. Like if you want to fail it, just put a cone on the hood or, you know, I was, I was rocking in downtown Austin and one of those self-driving taxis was there and I just stood like he was

Aaron Crow (NaN:00NaN) the car he I called it a he I don't know why the car was turning right on the street that I was crossing and I just stood there just to it was like four o'clock in the morning nobody around it was just me and the car just sat there and it did what I was just wondering if it was going to freak out or just go straight like I had no idea what it would do I just wanted to see what it would do so I stood there for like five minutes waiting to see what it would do and it just sat there with its blinker on and just sat and sat and sat and then as soon as I got to the sidewalk it went so

Aaron Crow (NaN:00NaN) There's some things about it that they figured out, but what happens if that thing went haywire and it decided I've waited long enough for, you know, whatever, like there's way too many variables in most of these scenarios to be comfortable to use these in production where safety, lives, criticality is important. Yeah, it's cool to run it for marketing and to post a YouTube video or something like that, but where stuff is mission critical, it's just not there. And I just don't see a path to all the reasons that you just pointed out.

Aaron Crow (NaN:00NaN) how that general chat GPT version of AI could ever get to a place where I would feel comfortable trusting my life, my kids life, my community, my country's life on. It's scary.

Aaron Crow (NaN:00NaN) Yeah, right. Right.

Aaron Crow (NaN:00NaN) It gets the same result every time.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Yeah.

Aaron Crow (NaN:00NaN) Mm-hmm. Ha ha ha.

Aaron Crow (NaN:00NaN) That's right.

Aaron Crow (NaN:00NaN) So we kick this off with development of code and how we create applications. And I 100% agree with you. Unfortunately, I still think that code that's being created today is not done with necessarily security in mind. Maybe it's a line item, but it's still functionality. First, we'll figure out how to secure it after the fact. It's whack-a-mole. It's why there's so many vulnerabilities. It's why there's so many.

Aaron Crow (NaN:00NaN) you know, uh, unknowns, you know, software bill of materials is coming out. That should, you know, hopefully help at least shine a light on some of those things, but still like it really starts with when I create the code. So what is the, the use case, uh, talk through that with, you know, how does, how does a product like what you guys are talking about, how does that help to, you know, bring AI into these environments, do it in a safe way, do it in a secure way where I know I can try to get down.

Aaron Crow (NaN:00NaN) How does that happen? Like is that, is that you guys building it? Is that integrating with other software developers? Like, what does that look like?

Aaron Crow (NaN:00NaN) Mm-hmm. Okay.

Aaron Crow (NaN:00NaN) So explain the difference between the two there real quick.

Aaron Crow (NaN:00NaN) Sure. Like gobs and gobs of it. That way I can, right, right. Okay.

Aaron Crow (NaN:00NaN) based on where you're at, the demographic, the time of day, a lot of things are gonna skew those numbers. Sure, okay.

Aaron Crow (NaN:00NaN) Right, right, right.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Right. So flip it on the head there. If I go ask those same 10 people and I have that equation, then that equation can help me to see if this is accurate. If the equation is right, because I can go ask those 10 people, then I can go to another state and ask 10 people. I can go to another country and ask 10 people. And in theory, if the formula is accurate, it should play out no matter who I ask and when I ask them.

Aaron Crow (NaN:00NaN) Going back to the car, me standing in front of the car, a smaller human, it may not have seen me as a human if I was too short or maybe thought it was a dog or any number of scenarios, it has to understand all of those different things. If I was super tall, maybe it thought I was a tree. I can see where that can be a problem, whereas a statistical would say, A plus B equals C every time, right? It doesn't matter the height or the differences is there. So,

Aaron Crow (NaN:00NaN) How does that help? So I love that. So the local, especially in OT, data is important, right? So there's all sorts of regulations around, how do I secure my data? Can I put stuff in the cloud? Like, I don't want my proprietary, you know, if you think about Coca-Cola, like their secret recipe for Coke, they don't want to put that in chat GPT because obviously then Pepsi and Dr. Pepper and all these others can get it.

Aaron Crow (NaN:00NaN) So if they can use a local model that they're guaranteed and it's statistical and it doesn't, they own their data. It sounds like your agent basically just accesses a call. Hey, there's, I need to request this data. Okay, there's the data lake. I go grab or whatever it is. I go grab that data from the repository, bring it down in, crunch the data, and then I'm done with it, right? I don't hold it, I don't house it. I'm just accessing it to fill a variable in my equation. And then I get an output and then I'm done with it. I don't need to keep it anymore. Is that?

Aaron Crow (NaN:00NaN) fairly accurate.

Aaron Crow (NaN:00NaN) What are some example use cases you're excited about? And they can be theoretical. I'm not asking for specific client stuff, but what's some scenario type things that you're excited that you see this really being a differentiation in?

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Yep. Right.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Right.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Yeah, and I can even see a use case because you know, one of the one of the struggles that we have and again, I keep going back to OT and, you know, manufacturing and all that kind of stuff. Maybe I don't want, I don't want it to take control of the airplane, but I can at least pop it to screen up on the pilot's thing and say, Hey, this is going on what you should do procedure ABC. Right. Um, and give the operator that way. They're not having it. Oh, what's going on? I'm having to figure out which procedure should I use, you know, uh,

Aaron Crow (NaN:00NaN) but more quickly, you know, be able to differentiate, you know, look at the sock analysts you talked about. Like I'm looking through all of this code or all these alerts, trying to find the needle in the stack of needles. If I can see this pattern and pop it up and say, hey, it looks like ABC is going on. You've got a run book that says to do this. I think you should initiate this run book because of these reasons. Or maybe there's two options. I'm not exactly sure. It could be either A or B. The scenario is different, but having that pop up.

Aaron Crow (NaN:00NaN) That's a one way and I see that as a path, especially in critical infrastructure and OT and transportation and aerospace type things where you want them to get that thing up quicker so they can respond. Maybe I don't want it to automatically respond for me, but at least I want it to pop up and say, A, this is what we think it's gonna do. We got a 95% probability that this is right. This is what we think you should do, but you're the analyst, you click the button.

Aaron Crow (NaN:00NaN) I'm not gonna fire the missile, but I at least want to say you should think about firing the missile Yeah

Aaron Crow (NaN:00NaN) right. Good luck.

Aaron Crow (NaN:00NaN) Yep.

Aaron Crow (NaN:00NaN) Sure. Yeah. And I do have a 15 year old that's in the process of getting his learners permit, all that kind of stuff. So that one hit very close to home. I definitely am not going to let him drive on his own, although he's a good driver. He's he's safe. He's still he's just an experience. So, you know, it's just a matter of it's not that he's overly aggressive or, you know, he's got a seat belt on. He's not paying attention to his phone. He's he's 10 and two, you know, the whole nine yards. But still, he just hasn't driven enough. Right. And in the back seat or even in the passenger seat.

Aaron Crow (NaN:00NaN) You're not, you just don't get it. It's different than being behind the wheel and making those splits of decisions. And like you said, it's the same thing of there's so many scenarios and the only way you can go through those scenarios is to be in them. Right? So, you know, what happens when you come to this intersection? Is there a four way stop? Like which directions do I look? And there's a car and, you know, and, and the other piece of that is, you know, I can be in the right and still get in an accident, right? I can do all the right things and somebody else can not be paying attention and hit me. So I have to be.

Aaron Crow (NaN:00NaN) Defensive and pay attention to others that aren't doing following the rules and all of those things It's no different than in these AI spaces and in my cyber world You know grow doing operations and things like that in these spaces when I brought in these new technologies The way I've been successful is doing exactly what you talked about right is I would have them be in parallel So back in the day when we were implementing, you know next generation firewalls, so application aware firewall rules, right?

Aaron Crow (NaN:00NaN) everybody uses them now, but 15 years ago, we weren't using them in OT. So when we brought them into the plants, I would set up two rules. I would set an old school port and protocol rule and, and right above it or right below it, I would set a, you know, application aware rule, right? And or above it, sorry. So I would want it to flow through the application rule and never get to the port rule and I would let it run for X number of months, maybe even a year to prove that this rule was passing all the traffic it needed to.

Aaron Crow (NaN:00NaN) It wasn't having to get to that next, you know, port protocol rule because it was passing it there. And then after six months, a year, whatever, where there was zero hits on that, that port protocol rule, then I could disable it and everybody was comfortable. Right? So the same scenario would probably be, I'd want to have this thing running on the side and compare it to an operator said, I should do X, Y, Z. What did the AI system say? And assuming that they mirror or what, you know, they're at least close and they're not.

Aaron Crow (NaN:00NaN) way out of left field. I don't have any ghosts in the system, right? I don't have any, oh, you should, you know, shut the plant down in the middle of the night for no reason. And I feel confident, then I can start transitioning some non-critical systems, prove it in a non-critical system, and then expand it until everybody is comfortable with it running and making either, you know, a human in the loop or, you know, trying to get to an automated, autonomous type scenario, which,

Aaron Crow (NaN:00NaN) I believe will happen. It's just a matter of what is that timeline, especially in some of these critical environments. Yeah. So awesome. So we talked a little bit about it, but next five to 10 years, what is something that you're really excited about coming up over the horizon? And maybe something that's a little concerning that we need to adjust or shift or really look towards to make sure we don't impact us in a negative way.

Aaron Crow (NaN:00NaN) Uh huh.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Right.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Sure. Yeah.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Yeah, absolutely. And the positive, what? On the good side.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Uh huh.

Aaron Crow (NaN:00NaN) Mm-hmm.

Aaron Crow (NaN:00NaN) Oh yeah. I see huge implications, positive implications from a training perspective. I'm working with a group right now that's doing some, you know, normal old school tabletop exercises, you know, cybersecurity type things, but using AI to help drive those. So you're not having to have these static boring, you know, um, uh, tabletops, but integrating using AI local models, that kind of thing. Don't need to solve world hunger. We're just giving it some basic data and being able to be.

Aaron Crow (NaN:00NaN) you know, have a, a not static tabletop. So you can ask it questions. You can give it responses. You can have it, you know, do a command line and, and really do a lot more things. And it does a lot of things. I can do the same exercise multiple times and get different results. So I learned the first time, Hey, I should have done, I should have responded quicker or done this quicker so I can do the same thing again. And because I did earlier, I took a different action.

Aaron Crow (NaN:00NaN) It takes me down a different path, right? And it's not a, it's not a static, you know, old school, you know, storybook adventure game where, you know, I get to question one, there's three choices. And then, you know, there's a, there's a finite number of answers I can get to when I use AI to expand that amount of options and the flexibility, I can turn it into a game. I can have more fun with it, but I can train my people better, right? I can have them see more scenarios in a very easy

Aaron Crow (NaN:00NaN) safe environment. So when that sock analyst two weeks in where used to, it took years to train a sock analyst to get from, you know, entry level to that next level. Imagine the ability to throw all these scenarios at them in a real world type scenario and have them respond and how much quicker they can get. Because when they see it in real world, Oh, I've seen that. Like, I've seen this exact command come up in my, in this, you know, tabletop game or whatever, right? And, and be able to accelerate the effectiveness of all of our people.

Aaron Crow (NaN:00NaN) that much faster because of AI. So to your point, it's not taking away from, it's enhancing and making all these people more efficient and more capable to do the things that you want them focused on.

Aaron Crow (NaN:00NaN) It's a lot of fun. I have a lot of fun and excitement around AI and I have a lot of concerns around it too, but it's more around bad implementation than it is the technology. I believe the technology is amazing and incredible and can do a lot of things. It's like, you know, the car or the airplane, like, you know, yes, there's some risks, but if done well, then it can really enhance all humanity and make us all better at our job. Now there's also some risk because the bad guys have it too. So we have to be watching out for those things too. So.

Aaron Crow (NaN:00NaN) Well, awesome. Thank you so much for your time today. What? How do people get a hold of you? Like what, what is your call to action for anybody listening wants to know more about AI and what you guys do.

Aaron Crow (NaN:00NaN) Yeah, we'll put all that in the show notes for sure. Absolutely. Well, awesome sir. Thank you very much for your time today. It was an awesome conversation. Let's do it again sometime, maybe in the end, six to nine months and see what's changed because of this stuff I know is changing super fast and lots of new developments coming in. So I appreciate your time today, sir. All right.

Transcript lightly edited for readability.