Practical AI

This episode is a special crossover between the Practical AI podcast and The Changelog podcast. Chris was recently invited by longtime friends Jerod Santo and Adam Stacoviak, cohosts of The Changelog, to join them on the show. They discuss AI, drones, robotics, swarming technology, and the rise of high-performance edge computing with Rust.  Chris points out that open source software, small AI models, and affordable hardware are making home automation and local AI accessible to everyone. From automating household functions to experimenting with drones and single-board computers, Chris describes how hands-on maker projects are shaping a bright future for physical AI, on small budgets and right from the comfort of your own home.

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This week we have extended show notes below from Chris!

Swarming & Fully Autonomous Multi-Agent UxV Systems

Chris’s Definition of Swarming (anchor link in show notes)
Chris’s definition of Swarming
“Swarming occurs when numerous independent fully-autonomous multi-agentic platforms exhibit highly-coordinated locomotive and emergent behaviors with agency and self-governance in any domain (air, ground, sea, undersea, space), functioning as a single independent logical distributed decentralized decisioning entity for purposes of C3 (command, control, communications) with human operators on-the-loop, to implement actions that achieve strategic, tactical, or operational effects in the furtherance of a mission.”
© 2025 Chris Benson
Conceptual Foundations
Open Research & Multi-Robot Resources (Stepping-Stones Toward True Swarms)
Getting Hands-On: Consumer Robotics, ROS 2 & Gazebo

ROS 2 (Robot Operating System 2)
Gazebo Simulation
micro-ROS (ROS 2 on Microcontrollers)
Open-Source Drone & Robotics Platforms
AI Agents, Swarm Reasoning & Learning Resources
Rust for Robotics, Swarms & Infrastructure

Core Rust
Embedded & Concurrency
Web, Data & Protocols
Rust ML & Simulation (Candle, Burn, Bevy, Rapier)
Home Automation, Matter & Open Smart Homes

Open Standards & Controllers
Devices & DIY Firmware
Affordable GPUs & Home AI Inference

Creators and Guests

Host
Chris Benson
Cohost @ Practical AI Podcast • AI / Autonomy Research Engineer @ Lockheed Martin
Guest
Adam Stacoviak
Guest
Jerod Santo

What is Practical AI?

Making artificial intelligence practical, productive & accessible to everyone. Practical AI is a show in which technology professionals, business people, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs, MLOps, AIOps, LLMs & more).

The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you!

Jerod:

Welcome to the Practical AI podcast, where we break down the real world applications of artificial intelligence and how it's shaping the way we live, work, and create. Our goal is to help make AI technology practical, productive, and accessible to everyone. Whether you're a developer, business leader, or just curious about the tech behind the buzz, you're in the right place. Be sure to connect with us on LinkedIn, X, or Blue Sky to stay up to date with episode drops, behind the scenes content, and AI insights. You can learn more at practicalai.fm.

Jerod:

Now onto the show.

Chris:

This is Chris Benson, co host of the Practical AI Pod cast. I was recently invited by my good friends, Jared Santo and Adam Stokoviak to be their guest on the Chainplog Podcast, which is one of the most popular open source and software development pod in the world. Though independent now, Practical AI used to be part of the Changelog family of podcasts and we remain very close. We have reproduced that Changelog episode to be this Practical AI episode. And in this episode, we're gonna cover a wide variety of topics.

Chris:

AI, drones, robotics, swarming, home automation, and the Rust programming language. I hope you enjoy our conversation as much as I did.

Jerod:

Today, have Chris Benson, Practical AI co host and longtime friend. Welcome to the show, Chris.

Chris:

Hey. Thanks a lot. It's great to be on the on the guest side of the equation here.

Jerod:

Yeah. You've been interviewing folks for a long time, but now you, sir, are being interviewed, so to speak.

Chris:

Mhmm. Indeed.

Jerod:

Does that make you nervous?

Chris:

Well, I got it. You know, you guys taught me everything I know. So like, yeah, a little bit. It's kinda like,

Jerod:

We have back and forth tricks. We're gonna unleash them on you on this shit.

Chris:

Oh my God. Okay. But yeah, like, you know, you guys were the OG originals. Daniel, Whitenack and I learned everything we know from you guys.

Jerod:

Well, you guys are good at what you do. So I'll take that as a compliment. Yeah.

Chris:

Well, thank you.

Adam:

What's funny is how back well, how far back we go. I think there's some context to give here. Jerry, just for an exercise here, I went and searched the name Benson because Chris's last name is Benson Correct. In my calendar just to see if the history was there. And literally, April 3 at 10:30AM, Chris Benson on Skype.

Adam:

That's how far that's how

Jerod:

far What year?

Adam:

What's the year? 2018. Did I say the year? My bad.

Jerod:

No, you didn't.

Adam:

04/03/2018 Chris Benson 10:30AM Skype. That's what you're doing.

Chris:

That was way back when we use Skype, you know.

Jerod:

That's right. We had to.

Adam:

That was so wild.

Jerod:

It was our only option.

Adam:

And that's what the that was the original conversation that started the host co host Practical AI. I think it was a data show back then even. I'm not even sure if it had a name.

Jerod:

It didn't have a name yet.

Adam:

The beginnings of Practical AI and and this long history of relationship.

Chris:

It was funny because I know I had reached out to you guys. Then so you guys had go time, and there was this changelog family that was already there. I wasn't part of it yet, but Daniel Whitenack and I were both the data AI people in the Go community at the time. And so I was thinking, I was listening to Changelog and stuff, and thinking, boy, maybe these guys need to start an AI focused podcast or something. And I was thinking, but I'd like to do that.

Chris:

But I was thinking, but I need somebody to do it with. And I was thinking, I got to reach out to Daniel. He's the other AI data. So I reached out to Daniel and he's like, Oh, by the way, I just started talking to Jared and Adam about this. I was like, Perfect.

Chris:

I just sent them a message. So the timing, yeah, it all just came together. The timing was perfect.

Jerod:

You guys were so far ahead of the curve.

Chris:

Yeah, well, was very clear. If you were really plugged into the AI world at that point, it was very clear that this was going. Where it was going, things change all the time. But it was very clear by that time that the gas pedal was on and sky was the limit and there was some kind of journey ahead. And at that point, Daniel and I, we wanted to be steering that journey for everybody.

Chris:

And that was how you know, and you guys were awesome in terms of saying, this would be fantastic. And we'd love to do it. And that was back in 2018. And here we are in 2025, late twenty twenty five.

Jerod:

Yeah, things have changed, but have stayed the same as well. Here's a funny story that you might not know, Chris. I've given you credit for this before, but I don't think I ever told you this, which is at some point, the four of us were on a call and this is like post launching Practical AI, but pre ChatGPT moment. And you were lamenting that we like missed NVIDIA or something like you you we were talking about the run up. I think NVIDIA had just had a huge run up with regards to first it was gaming, but then also, you know, machine learning was kind of starting to take off.

Jerod:

And you were like, man, I can't believe like, look at Nvidia, it's crazy the hockey stick growth on that stock. You're like, but we're too late now, we're too late. And this is 2019. I was

Chris:

so wrong.

Jerod:

Yeah, here's

Adam:

the funny

Jerod:

part Chris, I thought to myself, are we though? I said, I was like, are we? And I actually left that call and I went and I bought a little bit of Nvidia stock thinking, if Chris thinks we're too late, this guy's always ahead of everything. So I think he's ahead. So I have to thank you for a stock tip that has paid off nicely.

Chris:

You're welcome, you buy contrary to my advice, that's probably probably the

Jerod:

Right, so I need to talk to you more often and kind of do the opposite thing.

Chris:

Yeah, there you go.

Jerod:

So yeah, thanks for that. That was cool. Unfortunately, I didn't buy enough to like just quit everything else and retire. You know, I'm happy that you thought we missed it.

Chris:

I'm glad I was wrong on that. They've done amazing things. I think it's kind of funny, just in AI in general, AI has been around at some level. Even the modern form of AI has been around for decades. It's not a recent thing, because I got introduced to it by my parents who were actually technical people, Georgia Tech and Lockheed and things like that.

Chris:

And they were doing stuff back in the late '80s and early '90s and stuff. And my dad introduced me to neural networks, which is still the basis of all this stuff, in 1992. And I think it was funny. The tie in here to NVIDIA is we went through another AI winter. There's been a series of where everyone gave up on AI for a little while and then circled back around.

Chris:

They're called AI winters. And so the last AI winter kind of happened at the end of the '90s going into the 2000s there for a few years before the modern era, if you will, picked up. But I think the difference is that the notion of modeling and the software basis of AI was there, and there were a lot of great ideas, and a lot of the stuff we're doing today originated back then conceptually, but we didn't have the hardware. We couldn't actually do the thing. We didn't have these GPUs and now other types of chips that enabled all this to happen.

Chris:

And so it was really like the hardware side of things had to catch up so that the software And when people say, Well, why did we have an AI winter? And I think to a large degree, it wasn't the lack of amazing brainpower to solve these problems and create the models. It was the fact that you didn't have the hardware infrastructure to do the things that people were envisioning were possible. And it wasn't until NVIDIA came along and became really the AI hardware company. I know they do a lot of software stuff, but that made the difference.

Chris:

And Google came along eventually with TPUs and lots of other players jumped in, but both sides had to be there. So a little journey down memory lane there. It's the benefit of being old.

Jerod:

You've seen it all, Chris. You have seen it all. I've been around. I'm old as dirt. From your purview, this is not stock advice, but from your purview, here at the 2025, and you have Nvidia, you have AMD, you have Google, you have Meta, you have these large players making huge investments.

Jerod:

OpenAI, of course. I mean, the list goes on and on and on. Which single entity do you think is best positioned to like succeed over the next ten years? If you had to pick one of the top contenders like, is it Google? They seem like they've really turned the corner, but I'm not sure if their capital investment on their own infrastructure is going to be the big win that some people are saying that I don't know.

Adam:

What do you think?

Chris:

I think there are so I'm going cheat a little bit. I don't really have a one. For a long time, people would say OpenAI. And before that, were saying Google. There is a top group, and they are certainly doing well.

Chris:

And I think at the risk of getting slightly in terms of social issues, there's growing inequality between those group of haves and a lot of others that are have nots in that way. But I know one of the things I think is that I really think that open models are becoming increasingly important because the difference If you go back a few years and it wasn't coming out of OpenAI, there was a big performance difference in what you were able to do. And if you look at the closing of the gap between what's possible I mean, are millions of open models out there, and there are hundreds of them that are kind of like they are nipping at the heels of the leading ones. That gap between the latest, greatest thing from one of these big name companies and what's possible in the open world has narrowed dramatically. And what that's really doing is pushing model creation into something of a commodity area.

Chris:

And I think you've seen that in terms of what some of these big companies, they've built services, and they're building separate businesses, and they're going into verticals and things like that. But that's because just the model generation is not going to be the profitable thing for years and years going forward. And so they're turning from being AI providers explicitly into AI service providers now that are specific to different types of businesses. And I think they'll do quite well. I don't know.

Chris:

I'm afraid, especially after pointing out my horrendous- Oh,

Jerod:

you drilled it last time.

Chris:

Yeah, I was gonna say after my horrendous NVIDIA prediction, the last thing I'm gonna go do is pick a winner here. But yeah, I mean, they're making a lot of money, by by pivoting, you know, within the scope of what they do, and they have that the expertise. And I mean, like meta or as we're talking now, meta is just like, just purely buying the AI talent. I don't care what Google's gonna pay you. I'm gonna pay you 10 times more, and there's no way you're gonna go any place but us.

Chris:

And trying to kind of catch up to that OpenAI, which is still, as we speak, probably still of the gold standard there. But with a few others such as Google,

Adam:

as

Chris:

you mentioned, and several others that are kind of nipping at the heels there. So it's interesting times.

Adam:

So a long winded answer is OpenAI? Is that what you're saying?

Jerod:

You have to go back and analyze what Chris said and tease out the truth of it.

Chris:

Oh, I tried to escape that. Adam, that was not fair. I worked really hard for five minutes to kind of square my way out of your question there. Yes. So very close.

Chris:

When you say OpenAI, very close. You got the word open, right? How's that?

Adam:

Okay. All right.

Jerod:

Chris's answer is the open models will catch will commoditize the frontier model, so to speak. And these people that are just buying all the GPUs and just training, training, training, and then of course inference as well.

Chris:

I mean, what you can do, it's requiring you, we're seeing this progression where we're building out frontier models is costing less money. There's a ton of money in some of them, but the efficiencies that are now built into training from some of the latest research has made it to where you can build some amazing stuff with not quite as much as you might have expected a year or two ago in terms of relative performance against the hardware that

Jerod:

you need

Chris:

to support that. So it might be, who knows, where the research is taking.

Jerod:

Is there such thing as peak parameter? I mean, think I read that XAI's next model coming out whenever is going to have a trillion parameters or something. And it's like, how large is large too large? Or is there no such thing?

Chris:

So, yeah. I mean, one of the things that we've we've been talking about for a while now is the fact that like, it used to be in the early days of the GBT series from OpenAI that you saw distinct capability differences as you went from three to 3.5 and to four and that kind of stuff. But there's also been We've seen kind of plateau. It's almost like you're seeing that a lot of the It's not just a model thing, but also some of the infrastructure that's being built around it has made it much more accessible in terms of its productivity and its usefulness, and there's less of a friction when we're trying to use models at this point. So I do think that there is no infinite rides on terms of the number of parameters you have to do.

Chris:

I think that that does level out. And also, if you're going to have that many parameters, being able to use that productively from an inference standpoint, the world is turning out to be a mini model world instead of a giant model world. And I'm not sure that a lot of people in the general public that aren't people like us that follow this closely really realize that. I think when they think AI, they're thinking chat GPT, because it's what they know. And one model to rule them all, one model to bind us.

Chris:

And I'm not at all That's not what I think is the world. I think the world is many, many models contribute to solving a problem in various ways. Here we are in 2025 deeply into the age of agents. So it's no longer just models, but now agents with models that are acting on your behalf. And I think the reality is it's a mini it's a mini agent future that we're talking about here.

Adam:

Before we go there, I gotta ask you, because we're talking about companies and predictions and potential here. Have you tapped into or heard of the next Jeff Bezos thing Prometheus and the startup he's cheering, co founding, etcetera? Are you are you tapped into that?

Chris:

I'm not I'm not up to date on the details.

Adam:

That's like

Jerod:

hot off the press, isn't it? They announced

Adam:

Yesterday's news basically. Today, today's news.

Jerod:

I think there's like a perpetual Bezos Musk pissing contest that goes on. And this seems like the next one. He's like, you have x AI? I've got this thing.

Adam:

According to TechCrunch, Jeff Bezos reportedly returns to the trenches as co CEO of new AI starter Prometheus. Project Prometheus. So he hasn't done anything from a CEO aside from shareholder, you know, etcetera behind Amazon. He's been just, you know, getting swell essentially. Just getting swell and going to space.

Jerod:

On his yacht.

Adam:

Yeah. Yeah. As you would if you were

Jerod:

But he's been doing the space stuff. He's been doing Blue Origin.

Adam:

That's what I said, like, getting swell and going to space. Oh, that's what he's been doing. Yeah. Yeah. So this is kind of cool that I suppose the next big thing could be from him.

Adam:

So maybe the next time we talk Chris, you can give us your non prediction prediction.

Chris:

I can slide out of that one too.

Jerod:

Yeah. Do we go by Amazon right now? That's what I wanna know, Chris.

Chris:

So I'm probably the wrong person to talk to about this, not only because of the prediction that we just talked about, but also I wanna point out, am honestly This may sound really counterproductive as Practical AI co host on this, but I think Daniel's the same way. We're less interested in the big, big names coming out with their latest big things, because there's so much amazing work being done by real people out there. Take that, Bezos. There's the chat Yeah, plastic Jeff Bezos, like, Hi, I'm Jeff Bezos, and Elon Musk and all these guys. I'm just like, they're always one upping each other and they do some big things.

Chris:

But I think 99% of the press is going to these people.

Jerod:

Six people.

Chris:

But I think 99% of the real productive work in AI is going to all these invisible masses of amazing people that are doing this stuff every day. And if I could get the mainstream press to refocus, I'd be like, look around. There's just astounding, amazing things that are happening, but they're not happening by these famous figures. And these guys, yes, they have tons of money, they're super, super ultra wealthy beyond imagination. And they can throw their money around and stuff.

Chris:

But you kind of mentioned, it's kind of the pissing contest, for instance, between some of them.

Adam:

And

Chris:

I just like, there's so much cool stuff out there that's the latest, you know, the latest Bezos, know, Yeah, Elon massive thing.

Adam:

I mean, 6,200,000,000 behind this thing is

Jerod:

quite It's like crazy.

Adam:

Quite an investment in there that he's raised for. It's $6,200,000,000.

Jerod:

What are they doing?

Adam:

What's their deal? It's only speculative at this point. It's only got a name. Project Prometheus, Jeff Bezos, co founder I believe is Vic. Would only mess up the last name.

Adam:

B a j a j is the last name of Vic.

Chris:

Can you imagine being able to throw $6,200,000,000 at something that you don't really know what it is yet?

Jerod:

Right. Well, I think he knows.

Chris:

I'm just saying.

Adam:

I don't know if we know. I think you've already checked for 6.2 bill or you even raised those funds.

Jerod:

The reason he announced it is to get better raises.

Adam:

That's right. Some version of more money. Get people interested.

Jerod:

So Chris, you probably can't convince the mainstream media to ignore the 800 pound gorillas, but you can convince us. So here we are, we're ready. What's cool? What's underneath the covers? Or what's the invisible stuff that people are doing that you and Dan and we should be interested in?

Chris:

So I think it's funny. I'm going to say something that I said the other day, and I'm starting to say it more and more. But I think people easily look around wherever they are in the world politics are, and it feels like a difficult moment. And it feels like there's all these things you can point at and say, we're going through a really tough time, and it's tough, and everyone's trying to figure out. But I want to offer a counter narrative to that.

Chris:

We're also at this moment where this stuff, the AI, and there's a hardware revolution going on, and there's a robotics revolution going on all together, and they're all connected, they're powering each other. And I think we live in the coolest moment in human history right now, like we are sitting in it as we speak today.

Adam:

So I agree.

Chris:

What's happening right now is with all of these different relevant capabilities, the robot people and the AI people and the software people and the hardware people, it's all coming together, and you can do amazing stuff today that even a year ago we couldn't do. I mean, it's like if you think about before now, we'd kind of have several years of little software eras when we were getting into certain ecosystems with a language or whatever, and they'd kind of run for a few years. But right now, it's changing so fast, and the capability is coming so fast that aside from the big 800 pound gorilla types and stuff, everybody can get into this stuff. And so I think we're at a moment right now where it's really going to start being pervasive in everyone's life in a bigger way than it has been. Not just like, I'm gonna open my phone up and talk to chat GPT kind of way.

Chris:

Because that was unimaginable if you think about it just a few years ago. It hasn't been long since. That was an unimaginably amazing thing to do. But we don't even think about that now. We do it all the time.

Chris:

Don't even think about it now. But like physical AI and the fact that robotics have come so far in the last few years now and there are In addition to NVIDIA, there are many other chip makers that are coming on scene to support AI, and some of them are doing more of the dedicated AI chips, and others are doing more like combining different types of chips so that you have that. And some are great for data centers, big cloud data centers, and others are great for edge devices and tiny little constructs. And I think you're gonna see so much happening in the marketplace right now that are coming from startups. They're not coming from the 800 pound gorillas.

Chris:

They'll have their fair share. At 6,200,000,000.0, they better.

Jerod:

Yeah, better do something with that.

Chris:

Yeah, you're going to see amazing capabilities coming out of fairly small companies. Speaking back again to Daniel Witenack, my co host and part of our family in this, he started his own company, which is kind of supporting that. And that's what I like seeing. He has Prediction Guard, which is kind of supporting open model approach. And I think that in general, that approach of anybody can go, whether you're using a cloud environment or startup like Daniel's or something like that, you can go productively pull down models from Hugging Face, which I liken to GitHub for the way GitHub has always been for software, combine a bunch of different, fairly sophisticated open source software packages and do some amazing things without 6,200,000.

Chris:

You can do it as a college student in the dorm, figuratively speaking. And that's the thing that really excites me is that, is the ability to everyone becomes a maker, if you will. Everyone out there can become once upon a time, were kind of like, hey, we have the internet. Everyone can be a software developer. All the stuff you need to learn is online.

Chris:

There's all these resources. A lot of it can be done for free. It doesn't matter where in the world you are. Well, now everybody can become a maker. Everybody can access these different things and go do something great.

Chris:

And I think that's the fact that we all have these Roomba type things, these vacuums in our houses, everybody is now completely used to that. But I think we're right on the cusp of having lots of little devices like that in our houses and our businesses that are doing all these things, which eventually will get us into this notion of that we're going to talk about.

Jerod:

Ready for the little robots. I don't want the big scary robots, but I like the little robots that help you

Adam:

do

Jerod:

things. The neo thing is weird. We don't have to talk about that. But that was kind of strange.

Adam:

Was it neo? It wasn't neo. John Mnemonics. You think Johnny Mnemonics?

Jerod:

Yeah. What's Johnny Mnemonics?

Adam:

Well, Johnny Mnemonics was like he had man, I can't remember this one, but it was the same actor, Keanu Reeves. And I believe he had, like oh, he had something in him and he was carrying data. And it was Vaguely, I recall this.

Chris:

Yeah. It's been a while.

Adam:

The idea of a mule, but not drugs.

Chris:

Yeah, it was That was back when he was young.

Adam:

Yes. Yes. I thought you were talking about Johnny Mnemonics.

Jerod:

You jumped right to the matrix, which makes sense, Adam, because most of my references are the matrix. But I was actually talking about this new robot in your house that cost $20 and it's controlled by a human currently.

Chris:

I saw that but I still don't think that's going to be the thing.

Jerod:

No, don't think I would say that's kind of weird at this phase like that's it's a general purpose like it does laundry, it does your dishes, and it's like a humanoid full size similar to what the optimists think they're building. And yet it's at this point because they need data to train these models better. It's not at all autonomous. It's controlled by a human with what I imagine is like a sophisticated joystick, probably overseas.

Chris:

It's kind

Jerod:

of creepy when you think about it. Super creepy.

Chris:

Your grandma's in there with a stranger in the form of a robot. The Wall Street

Jerod:

Journal did a great video about it. Joanna Like, Stern told it to do the dishes or something. It took like three minutes to load a cup into the dishwasher, which is a fifteen second task. Anyway, let's not do that. Yeah, it's not there yet.

Jerod:

I feel like that's being too big in general purpose. I feel like more specific, small, the Roomba, it's gonna vacuum.

Chris:

The Roomba is the future. That was an early thing, but it's purpose built for a very specific thing. And there's a whole bunch of them on the market, a bunch of different makes and manufacturers and stuff on the market. And we can go through and debate what's better and all that kind of stuff. But I think you're seeing that times many, many, many things across all sorts of tasks.

Chris:

And they're cheap. And even Roomba type vacuums are too expensive right now. Think with the cost of robotics coming down and accessibility, then it's like if you think outside this and just walking into a retail store or getting online to Amazon or whatever and just buying something that once upon a time might have been expensive, and now it's $30. And I think that in this day and age, that $30 purchase, I think that getting a robot that'll do this and that and the other, and the fact that they have eventually, you have families of robots that can do different things, And you can put it in swarming mode and just say, Auto my house in swarming mode, as we'll get into. And they just coordinate and do all the stuff.

Chris:

They're sensing you. They're moving around you. You're doing the thing. And that's real life. Aside from just the vacuum, lawn and garden care is getting taken care of, your security around your house, your roof and gutter inspections.

Chris:

Nice. It's integrated into your smart home stuff. You don't have to worry anymore about where your packages were left by the delivery driver because those robots or the swarms that are managing your house are just doing that. And and it's not insanely expensive. People are like, yeah.

Chris:

Yeah. Where am I gonna get the 6,200,000,000.0 from bases to buy my swarm for my house?

Adam:

And I'm

Chris:

like, no. No. It's not. You're gonna have the Christmas deal. You know, we're coming up on the holiday time.

Chris:

That's right. You're going to get online, and you'll have all the different packages about what level of swarming do you want. This one is an 18 accessory swarm package that you can come. It's gonna handle your It's gonna do this. And you're trying to choose.

Chris:

You're like, Well, I don't know. I'm gonna spend more for my kids on that. But there's great aunt Louise, and we only talk to her once every five years. And I send her kind of a token thing. So I'll send her the four item swarm package that she can add into whatever she's already using because it's all open stuff.

Chris:

And that's that's gonna be normal, and we're not that far from the opportunity, and it's not the 800 pound gorillas that are gonna bring It's gonna be the billions of startups out there. They're each doing a little piece of it, and their swarm components and stuff we're able to communicate. That's the future that we're gonna build.

Adam:

Well, I'll tell you one thing. You've definitely put a lot more pressure on the idea of HomeLab. That's for sure. Because that's all HomeLab. Those are ton of DNS queries out there.

Adam:

Probably a ton of telemetry being tracked. A lot of

Chris:

Yeah.

Adam:

A lot of things you may or may not be concerned. Those are things I think about when I think about adding more and more devices to my home. Gosh, man.

Chris:

So separate, I have a slight side story, but it contributes to that. So about a year ago now, almost exactly a year ago, we bought and moved into the house that I'm in now. And the guy that we bought it from, he and his wife, he was a fanatical home automation person. And so we moved in, not because of the automation, that was incidental, but it's helped me move from just more of a professional kind of thing. We're talking AI in a professional kind of, to thinking about stuff around the house with all the sensors and the cameras and stuff.

Chris:

And we have all the various types of home automation stuff that you see out there combined, CASAS here. We have many, many, many dozens of Casa's devices all over the place.

Adam:

And Casa's the brand from Lutron. Is that right? Am I picking that up?

Chris:

Right. It's from TP Link, actually. TP Link. But that's just one. There's a whole bunch of them in Apple Home and Google Home.

Adam:

Was thinking Casita. Casita's from Lutron. Those are the light switches.

Chris:

But yeah, the Lutron does light switches, but there's some common protocols that they all work on. And I'm starting to see, because I didn't have to go start it from scratch and because I inherited what this guy had already kind of put together and then had to figure it out and make it work, and suddenly I'm like, Oh gosh, it would be really easy to add this And when we're talking about this robotic future, even in our homes, not just a commercial or industrial or whatever thing, but in our homes, it's so easy for me to see that now. Because I realize I already have a good bit of infrastructure here, and it's not expensive and it's not. It just takes a little bit of effort. And if they can make that easier for people to get into, it's a done deal.

Chris:

We already have Wi Fi and all the other things. And then you start adding things to plug in. It's like Legos. It's like home automation Legos in your home.

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Jerod:

Speaking of Legos and home automation, IKEA just announced a whole new set of like 27 smart home things coming from IKEA.

Chris:

I saw that. Talk about

Jerod:

bringing it to masses like that's the kind of thing that IKEA brings to the masses now is they make it very simple and straightforward and Lego esque in order to and it all runs on Matter, which is I think the open standard for communication between these things.

Chris:

And so like Matter's in an interesting place and that like, I only buy things that that are mat that have Matter integrated in. And for for listeners and viewers, the Matter is a protocol that allows different makes and models of automation to work together over a common protocol, and it's local based instead of cloud based. But not everything does it yet, so it's still kind of working. It's been very slow. It took a long time to kind of come into play, but it seems to be having a second wind right now because of all this new capability that's coming about.

Chris:

And so every new thing I buy, whether I'm using Matter yet on that or not, I have to have Matter so that as I go forward, I can integrate into that. But yeah, everything is local, it's Matter. And I'm finding with today's craziness out there that I'm moving more local and a little bit more out of the cloud. And so Matter is becoming increasingly important from my standpoint.

Jerod:

Well from the startup perspective and the swarming perhaps at least the droning perspective, you'll be happy to hear Chris that we do have a startup coming on soon zipline who are now moving delivery drones into production. They actually have a delivery drone system that is started off delivering medical needs in Africa, vaccines and stuff like that. And now they're moving into The States and they're doing food delivery, small item delivery, small package. So you think your Chipotle burrito, that kind of thing. Yeah, your you know, pounds eight

Adam:

or less.

Jerod:

Yeah, eight or less. It's super cool stuff. And they've got it to where they're actually rolling out into into commercialization now. So startups are making moves in this direction. And now there's our I assume in each city they have a fleet of these delivery drones.

Jerod:

Obviously each drone is operated on its own. I assume eventually autonomously. It actually seems like a simpler problem than autonomous cars because the airspace is just pretty open, right? Like you got problems like wind and snow stuff but like that, it's gotta be easier than cars.

Chris:

Yeah, generally. And so it's a different problem. So it's easier. It's a little bit of both. It kind of depends on how you're looking at it.

Chris:

With cars, and we were just talking to Waymo again a few weeks ago in Practical AI about this, so this is very top of mind for me. With cars, yeah, there are a lot of challenges, and you have the notion of the child running out or the ball bouncing out. There's a lot of stuff that's right there, but also how you're navigating is very well defined in terms of the streets and stuff like that. Air becomes more three-dimensional, and so the challenges are different. But so long as it's not highly congested, I would agree with you that it is generally easier that you can kind of move from here to there, and so long as you have good collision avoidance and some other capabilities for navigation there, then you're probably doing Okay.

Chris:

Though that changes with swarming, because swarming brings in close collaboration.

Jerod:

Yeah, so define swarming then, because I think of killer bees when I hear swarming. And I assume with drones, you're talking about a bunch of drones nearby each other then.

Chris:

You are. It's not just a physical distance thing, because is physical distance is a relative thing, depending on what it is you're trying to do. But it also, it's really more about behavior. And so we can dive into that. But before you say that, I think that's a line of thought we should go down is that, as you guys know, I'm really into animals.

Chris:

We were making jokes earlier about bazillion dogs and stuff like that. I'm a licensed wildlife rehabber, and I study animals. And in the context of swarming, mother nature has perfected not just swarming, but there are many different types of swarming from different species. And so I have a set of species that I tend to look to for swarming purposes and say, if I want to swarm with this type of technology or this type of platform, how do we get started on that? How do we get inspiration or look for some insights on the technology?

Chris:

Well, you can look to certain species that are similar to the technology platforms you're interested in terms of how they move around and do stuff and say, Well, how has nature solved it there? And I definitely do that a lot. It's not uncommon for me to go into tech meetings and start off with lots of pictures of animals and stuff and people like, What's going on with this?

Jerod:

Who is this guy?

Adam:

Are you thinking like fungus, bees, and Some bees, bats,

Chris:

birds, starlings, those huge, what are called murmurations of starlings. Answer awesome. Ants are awesome. When I'm thinking about robotics on the ground, meaning what we would call a UGV, which is an unmanned or uncrewed ground vehicle, Ants are amazing in what they can do. And so they're an awesome thing to look at.

Chris:

But I'll start with the definition that I use. Given the fact that I work in the military intelligence space, My definition sounds It uses that jargon, but it really Don't get caught up in that. Can be applied to residential. It can be applied to commercial. It can be applied to industrial.

Chris:

So don't get caught up in this specific wording. So I'm going to read it in front of me. It's one really long run on sentence that's very specific in what it's trying to imply. It is, Swarming occurs when numerous independent, fully autonomous, multi agentic platforms exhibit highly coordinated locomotive and emergent behaviors with agency and self governance in any domain, which could be air, ground, sea, undersea, or space, functioning as a single, independent, logical, distributed, decentralized decisioning entity for purposes of C3, which is command, control, and communications, with human operators on the loop to implement actions that achieve strategic, tactical, or operational effects in the furtherance of a mission. So long, long, long sentence, but it hits a bunch of very precise concepts and integrates them in together.

Jerod:

I can tell each word was selected there.

Chris:

Yeah, a mission might be, instead of thinking military, a mission might be getting a package to your house. That might be the mission. And that does have command, control, and communications involved. So it doesn't have to be the military esque jargon that

Jerod:

we're talking Yeah.

Chris:

Applies to any these commercial, industrial, residential, military, whatever. So

Jerod:

that's a lot.

Chris:

It's a lot. It's a lot. And if you want, I can kind of break down a high level what some of those mean.

Jerod:

Yeah, I think my broad takeaway, we can talk about the individual words, because I know they're very specifically chosen, like independent, logical, distributed, decentralized, decisioning entity, like stuff like that. Can tell each word was selected for a reason.

Adam:

Yes.

Jerod:

But I think my grand takeaway of a swarm is kind of the E Pluribus Unum. It's like, okay, all these things are individual and autonomous, but they're all acting as one. They're acting with one purpose.

Chris:

That's a fantastic insight that you have. And that is the key to it is Swarm is such a buzzword. We always have buzzwords in this AI and software space. There's always the buzzwords of the year. And Swarm is certainly a huge buzzword right now.

Chris:

And almost without exception, I will turn around and tell I can go back to my definition, assuming that you want to accept that as the definition of swarming, and I can defend that fiercely.

Jerod:

I cannot attack it. Can you attack it, Adam?

Adam:

I wouldn't

Chris:

try. You're the expert But here, I would say, all you people who are talking swarming, no, you're not. It's not swarming. What you're describing is all sorts of things that lead to swarming. There's a whole bunch of incremental capabilities that would eventually, as you add all those capabilities together, they culminate in swarming.

Chris:

But the chances of somebody saying that what they're doing out there is consistent with Chris Benson's definition of swarming So is pretty what you said was right on. And that is that just as you see in nature with those ants, every little ant has its neural capability, shall we call it, what it's doing. But at the end of the day, they're functioning to get a mission done, a job done, something productive for the colony. And they are all lending themselves to that greater good, even if some of them may not survive that kind of thing. They are functioning as a single entity, and it is the entity that's trying to get the thing done, not the individual ants.

Chris:

The individual ant may be like we have a crack in the ground, and we have to get from this side to that side, and they build an ant bridge. We've seen pictures of that. And that one little ant may have the job of, I'm holding onto the ant on this side, and the ant below me is holding on there. And then they have that going on as well. And we're all creating this ant bridge over a chasm that none of us individually could span.

Chris:

But by working together for that swarm approach, which is make that accessible, they are doing something well beyond what any of the individuals can do. They are super ants in that way. And that's what I'm getting at is that ability to give up your individual identity as a member of a swarm for the purpose of the overall swarm's intent. And that swarm itself has an intent that is a swarm level thing, kind of to your point, Jared. It's like, that's not the thing on any one brain.

Chris:

But when you put all those brains together, or technology that represents that, there's a thing that the overall thing is trying to do as a single entity.

Adam:

Its power is a number. It's like, I saw my kids love ants, animals, you know, all the things essentially. Venomous plants that kill things, you know, that stuff entertains them dramatically. Venus flytraps, things like that. And we watched this show.

Adam:

It's kind of a documentary, but it's also kind of dramatic. And John Cusack was the narrator, and it's a movie called The Besieged Fortress from 2006. And there's an ant type that I wanna mention to you. It was actually I ruined the plot a little bit, but it was ants versus termites essentially. And it was very, very well done.

Adam:

If you've seen it, Chris, obviously say something.

Chris:

I have not, but I'm gonna check it out now.

Adam:

It's it's a 100% worth it. It is phenomenal. It's probably gonna visualize for our entire entire audience the things you're talking about because the particular ant, I guess you call it the name of the ant, I suppose, is is how you describe it, were driver ants. And these driver ants are are so swarm like. You know, they don't think little.

Adam:

They they they create rafts for themselves. The entire colony can can float. I mean, you can put them underwater, they won't die. They will like create this bubble. They are just basically resilient to to the nth degree.

Adam:

And if you're in their path, you're dead. Like, no matter what you are, snake, a rat, a bug. They're gonna overwhelm you. Oh, yeah. They they drive in numbers.

Adam:

They're called driver ants, and they are truly truly incredible. And this whole entire dramatic documentary narrated by John Cusack is phenomenal. The Besiege Fortress, I would highly recommend it. 2,006, amazing. But these drive rants probably elicit a lot of the a lot of the qualities and and characteristics that you're mentioning because they act like if you're in their path, it's not as if they're one, it's they're many, and they act together, and Yeah.

Adam:

It's

Chris:

I mean, it brings a whole capability. Whether you're talking to ant or whether we're humans with our technology doing this, you're basically inventing a whole new category of what's possible by introducing this. And because of conflict of interest and I stay away from my employer, Lockheed Martin, generally I'm delicate on defense and intelligence stuff anyway when we're talking in public. But the notion of, if you were to look on the military side for just a second, at a high level, there's the notion of mass. And if you go back and warfare, many people would say, Okay, let's build up mass to win against an enemy.

Chris:

And then as things progress forward, we learn that maneuver could kind of out You could go around mass and you could hit it from different ways. And so maneuver as a capability started trumping what was possible with mass. But swarming becomes a whole new thing, is that you're kind of getting the best of mass at individual small scale, but you're getting mass and you're getting hyper maneuverability, and so it's able to trump that. So in that domain, in that kind of military world, it brings about a whole new capability that never existed before. And similarly, when you move into commercial and industrial, and we talked about this super automated house a few minutes ago, you're bringing about things that just were not possible before.

Chris:

You could have little pieces of it that were possible discreetly from a source, but the notion of this integrated solution that would just kind of go attack a real world problem and overcome it, kind of going back to your driver ants, is a new capability that the world will enjoy going forward across all different types of domains. And so I think that's the magic of swarming right there, is it's different from a fleet. I think a lot of the times where people throw up a whole bunch of things like drones, so that's the thing everyone knows, will throw up a whole bunch of drones in the air, but it's not really a swarm. It's a fleet of drones. That's what it is.

Chris:

And each one requires individual programming to go do this or do that. There may be some communication between them potentially, depending on what they're doing, but they're not thinking almost like a brain, like an abstract brain themselves. They're not looking and dynamically handling what's happening in the real world in real time and saying, this is changing right here, right now. As a swarm, I'm going to go do that. They can't do that.

Chris:

They're fleets. They can respond, but it's going to take inputs. It's going to take some collaboration between them, but it's going to take a lot of guidance from afar to make that happen. And that's the difference in mass numbers in a fleet versus what a true swarm would be, is that that capability and that intent and that emergent behavior is really key to identifying a swarm. And you do see that in mother nature.

Jerod:

So let's take a recent phenomenon, which is the drone light shows, you know, where they go out and let's say they're making a dragon. Is not

Chris:

a swarm, but yes.

Jerod:

Well, I was gonna ask depends on the intensity.

Chris:

Not a swarm. See how I did that? Just say I did that. Not a swarm.

Jerod:

I Well, was going to ask, it depends on how it's implemented, isn't it? Couldn't you swarm to accomplish a dragon?

Chris:

Absolutely could, but nobody has. So these days, what they're doing is you may see these light shows where they have thousands of drones in the air, but each one of those is following a preprogrammed path. There might be some limited communication when they're very close in case of their winds and things like that in terms of anti collision. But what I would say is if you were to do the big dragon that you talked about as a swarm, the swarm would figure out how to do it in real time. It's actually using that decisioning entity that we talked about in the definition and saying, my mission is to produce a dragon over this area for people to watch.

Chris:

And it would go do that. It would go figure out where all the pieces need to be for that dragon to come about. That's true swarm behavior. If you think about animals that are getting out and doing something, they're not producing dragons, but they're going out and doing something in a swarm, There's no external thing saying, Swarm of bees, I'm telling you to go do this. And you need to make an adjustment there and all.

Chris:

They figure it out in real time in the swarm and make whatever it is that those species are trying to achieve, it happens. Emergent behavior that's real and in real time that supersedes the individuals. And that's what I'm saying. The light shows fleets of drones that are being provided instructions, often essentially a three-dimensional vectoring trajectory on what me as an individual drone would do, regardless of what all these others are doing.

Jerod:

Okay. So even inside of emergent behavior, let's say in an ant colony, you have roles, you have leadership, there's some sort of like, there's some sort of mission that comes from somewhere.

Chris:

There is.

Jerod:

And I assume now we're getting to the part where it's like, okay, how do you make these things? Because as a guy who's makes fancy websites his entire life for a living, this sounds really hard. I just feel like, if I had a new job, hey Jared, your new job is to build a swarming technology of autonomous whatever, I'd be like, nope, not going to even try that. Because that just sounds very, very, very difficult. Where do you start?

Adam:

Like, how do you do it?

Chris:

That's a great question. And not only that, but you've identified the thing that you just said in your vulnerable moment there in terms of like, I don't even know where to go. That's what almost everybody That is why it is a problem yet to be solved. And there many groups, companies, individuals out there working on it, including me. This is my passion.

Chris:

And all of us, at some point, some of us might have had the benefit of coming from robotics. But just like many other skills, that also carries some baggage with it that you have to unlearn to do it. And that's one of the pros. So when I talk to people, I've been doing drones for twenty years. I know everything.

Chris:

And I'm like, well, I'm like, that's good in some ways, but-

Jerod:

Not a swarm.

Chris:

Not a swarm. And not only that, but sometimes it's

Adam:

the- Let

Jerod:

me get

Adam:

you a T shirt says not a swarm. Ask me anything, not a swarm.

Chris:

That fresh learner's mind though often does it. And so it's a complex problem and you have to break it down into its constituent parts. And there's a whole bunch of layers, because there's things that have to happen at the member. Like if you talk about the individual ant, at the member level, there's a whole bunch of things. It's got to navigate.

Chris:

And that's kind of like where we are on drones today, in the sense of if you go buy one, we're going go out, you go out to the toy store and you buy a drone today, or order one online these days because toy stores are not so common anymore. So we ordered the drone online, and that has basic navigation. And there's a whole bunch of tasks associated with that, and that's where most of the robotics world has been, obviously, over the years. But as you move into communication between them and what kind of tasks happen, you kind of move up to a level. There's a local drone level in a larger swarm.

Chris:

And then there is the how do all those locals operate together. So you kind of steadily move up in abstraction till you have that notion of this emergent thing, which is really It's really quite a challenge of because there's not a master member. There's not the boss. Some You may have a queen bee.

Jerod:

Would it be easier if there was a boss, though?

Adam:

It would.

Chris:

So it depends on what you're trying to do. I would say that's the step below. If you're doing almost everything a swarm can do, but you still have some centralized control, there's a couple of levels below that. While I can't

Jerod:

Not SWORM.

Chris:

Yeah, I can't share it today. I invented I created a document that helps people at my company evaluate these technologies at different levels. It's called a maturity model towards swarming. And they can look at anyone else. Somebody has put something out there, and we can evaluate it based on that criteria about what exactly it does.

Chris:

And I need to see if they'll let me release it publicly because I think it would be useful.

Adam:

Let me see if I can maybe break down an idea. And I I don't have your depth, but if I were thinking about this problem and and obviously when we compare ants, so in the case of the driver ants, just because that's my example that I have some clarity on at least, they do have a queen and the job is to protect the queen. It's like if the queen disappears, they will elect or attempt to elect a new queen, but there's always somebody in charge essentially. But if that's not if that's not a swarm, then the way I might try to create a boss would be through consensus. Because if you're a controlling entity that's connected and so you know all your parties in this connected mesh network or whatever you want call this one, you know player b versus z over here has new information the swarm needs to know to consensusly, if that's even a word, to have consensus on the next decision.

Adam:

And so we may, as a swarm, elect a new, not so much boss, but a primary information source that changes the way the swarm acts as an entity. And so it's sort of self evolutionary.

Chris:

You're hired because you're on the right track. That's it.

Jerod:

So aren't they just making their own boss then, basically?

Chris:

So that's the thing. So the queen, like in the case of the queen, yes, there's a queen who is the general, the one in charge, but at the same time, she's actually not making all the decisions. A lot of it is instinct that is being played out.

Adam:

It's preservation in that case, right? The queen is not the boss in terms of leadership and knowledge because the drones have the knowledge, The drone ants out there doing the work.

Chris:

That's right.

Adam:

She is the preservation system for the It's a necessary component of many.

Chris:

So she's not a master, like a master direction giver. That's not her. Her role is, as you said, perpetuation of the colony versus she's not driving the specific actions of drones. Those are built in. Mother Nature has imbued the members with that, and they understand how to do that.

Chris:

But to your point, Adam, that notion of kind of consensus, are different approaches to it. We can use some different words because there are different algorithmic approaches of consensus, election, things like that in terms of saying, well, we have a distributed compute grid that is our swarm, that is imbued in our swarm, And how do we arrive at single overarching directives that perpetuate themselves downward through the swarm and which change as they go down? Because this is the overall, this is what we need to do. There's a mission. There is a high level sense of abstraction about, well, to accomplish the mission, you must do A, B and C.

Chris:

But A has 10 steps to it, you know? And some of the swarm members are going to take the assignment of doing those, and others are going to say, well, I'm going to go off and do these other things that are part of that, that might have been part of the B category. And so they have to self organize in the way to do that in real time, because this is a physical technology. So it's one of those. And there are sensors coming in.

Chris:

Things are changing constantly without. And so you are with your sensors, whether you're a biological being or whether you're technology, you're having to take all that new information in. You're having to do distributed computing and decisioning through algorithmic approaches and select members to accomplish all the things as part of that overall mission that you're doing. And it's quite complex. I mean, it's a very complicated thing as we sit here in 2025.

Chris:

I think we'll nail it gradually. I think we'll nail it in iterations. And I think somebody a century from now will be like, yeah, well, of course we did that. But today, it's a tough problem to solve.

Jerod:

So at what level do the humans interact? So let's imagine that you've created a swarm of vehicles. And it's a legit swarm. It's not swarm swarm.

Chris:

A legit swarm.

Jerod:

Yeah. I was thinking about that as you said that, you remember the old Jeff Fox where you're thinking like, you might be a redneck, we could do a whole line of like, might not be a swarm. Like if you've got a boss, you might not be a swarm. If you've got a path they gave you to fly, you might not be a swarm. But let's say you have one and this is like, Chris approves.

Jerod:

And it's a bunch of drones, let's just do that. At what level does the drones receive their mission from the humans? Like, is it very generic? Or is it very specific?

Chris:

It can be either. It depends on what you're building toward. And swarms have different purposes. So remember, a swarm is not a generic thing. They're purpose built for certain capabilities.

Chris:

And you do have that C3, which is command, control, and communications, that's inherent to that. And one of the other phrases I use, which people outside of the military context may not be as familiar with, is human on the loop.

Jerod:

Not in the loop, but on the loop.

Chris:

Not in the loop, but on the

Jerod:

Does that mean?

Chris:

Are two different things. An in the loop is where a human is controlling a technology directly, and they're making it. So a human in the loop may say, make a choice for a task. So they may say, yes, I'm going to now have you drop that package on that person's front door. And yeah, it's clear.

Chris:

We've looked at it. It's safe. There's nobody in the way. And we're going to have you put the package on the front door because it's safe. And we did not want the drone to do that until me as a human verified that that was Okay for us to do so that we didn't hit people or hit things.

Chris:

On the loop, you are essentially tasking that. It's kind of a the human has a supervisory role and maybe a mission giving role. Like your mission swarm is to deliver the package to that. Or maybe more, it might be, here's a bunch of packages to the swarm, and I want you to go to this neighborhood and deliver all these packages to the right houses. And that is the mission.

Chris:

And then the swarm understands that geographic layout. It understands the real world environment it's in, and it figures out which member they each pick up a package, and it figures out how are they going to do that. Some of the packages are more than the eight pounds that Adam talked about. Some of them are 60 pounds, and it takes multiple swarm members to get that package airborne and collaborate. And so as they go into that environment and they're looking, I've to get this package, to that address, and oh, by the way, that address might have been reachable by a four pound package on one sworn member acting alone, but it's now 60 pounds.

Chris:

We have multiple sworn members. And even with all those sworn members, it's outside of our range. So how do we address getting it outside the range, given the fact that we have other concerns that may be limiting that? The Swarm would work that out through its distributed computing and collaboration that we just talked about, that, you know, where kind of comes to that consensus on how it's going to collectively solve the problem. Does that make sense?

Jerod:

Yes, I think that it does. I'm wondering if maybe I'm sniffing danger eventually, because

Adam:

Oh, go for it.

Jerod:

Well, because at a certain point. You give a directive and maybe that directive is completely benign, like you have a swarm of cleaning bots, you know, your house and you say, Okay, bots, you know, clean the bathroom. And that's as far as you get into it, you're on the loop, but you're not in the loop. And so they go about doing that. And we've accomplished Chris Benson level swarming.

Jerod:

So I now have numerous independent, fully autonomous, multi agentic platforms in my bathroom exhibiting highly coordinated locomotive and emergent behaviors with agency and self governance. Right? So at a certain point, couldn't they just say, this toilet's really dirty.

Adam:

What if we just removed it?

Jerod:

Wouldn't that be the bathroom would be even cleaner? And then they all decide that yes, that's a great idea. I come back, I don't have a toilet.

Adam:

That

Chris:

is just Adam had a great moment a moment ago, and you just had a great moment there, Jared. Oh, thank was Most

Jerod:

of my great moments in those toilets.

Adam:

One is all I get, just one for sure.

Chris:

You're cleaning up your act, man. But yeah, so that's a great thing, and that comes down to you're not giving What you're really telling about in swarms is when you get down to the task level, then you're talking maybe not about the whole swarm making a decision. It might be a few of them that are addressing a task and figuring out at a more logistical level, operational level, And how am I going to do that is one of the things, is that when you're doing We're back to AI safety and AI training

Jerod:

on this,

Chris:

is that maybe removing the toilet in most cases is not an acceptable thing. So we need some technology based guardrails there, but that's also where, depending on the circumstance you're looking at, that human on the loop needs to be able to go, no. There's kind of a kill button, if you will, figuratively speaking, meaning killing of the swarm, not killing a person, just to be very clear so that nobody misunderstands me.

Adam:

Super clear. No killing here.

Chris:

No, no. We're not talking about killing people here.

Jerod:

That's why I use a bathroom and a toilet as my example.

Chris:

In this context, it might be don't kill the toilet kind of thing. That's where the human on the loop, an oversight, where we still have these amazing, capable human brains that have they can't do everything that digital technology can do, but digital still hasn't yet arrived. It will, but it hasn't yet arrived at what our capabilities are. And we can look at it and go, taking the toilet out is not acceptable to the homeowner. We're not doing that.

Chris:

Maybe if you're Jeff Bezos, maybe instead of cleaning the toilet, you just remove it every time. Maybe Jeff Bezos will have the toilet removal every day. The drone swarm goes into Jeff Bezos' bathroom and it just takes the toilet out and it puts the new

Adam:

one It's a sea of toilets back there. It's like a pile of them.

Chris:

There you go. Well, it cleans it all up because you know what? When you can throw $8,000,000,000 at something you haven't really identified yet, you can probably afford to have your toilet

Adam:

6.2.

Chris:

Oh, 6.2. Made such a big difference in my head.

Adam:

Yeah, sorry about that. 8,000,000,000 is close, but let's just round up to 8,000,000,000.

Chris:

Let's just round down to see Okay, we're there. But yes, so other than Jeff, I don't want the drone swarm taking my toilet away. That would get rather

Jerod:

I'm with you.

Chris:

Yeah. It's a it's a little bit too much.

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Adam:

I think it was the first no. First one was to he tricked the genie to get him out of the cave. We'll we'll skip that one. And then the second one, technically, the second wish was make me a prince. And he didn't really make him a prince.

Chris:

That's great memory. Gosh. Like, I've seen the movie, but like, you're really bringing it back to me.

Adam:

I'm Well, got a good brain, you know, over here.

Jerod:

My brain is That's a second great moment

Adam:

right here. That's right.

Chris:

Two in one moments here with me. So okay. Keep going.

Adam:

He doesn't really make him a prince. He just clothes him as a prince. He mimics a prince. He doesn't really give him royalty. He doesn't really give him lineage.

Adam:

Mhmm. And I guess I'm sort of sidetracking to some degree just to be accurate about my Aladdin reference. But point is is there's times when he adds With it. Or I guess in in all of the lore around the Aladdin figure and a genie figure where you ask the genie for something, but you have to be careful. That's where this term comes from.

Adam:

Be careful what you wish for because you wish for something without the full awareness of the agency behind the genie or behind the swarm, and so you might get your toilet removed. Is that a concern? Like how

Chris:

Totally.

Adam:

How are you guarding against that? How do you guard against that without the QML loop or the kill switch? Is there an OS? Like, I don't know. How do you how do you guard against this genie issue?

Chris:

I know. I think there's a lot, and I think it's at many different levels. It's a real thing that we talk about in real life today without having achieved full Chris Benson level drone swarming. And that we talk about that in terms of AI safety all the time now. That's a huge part of the AI world, is what is AI safety?

Chris:

How do you keep unintended consequences from coming to pass? I think anyone who's reasonable will recognize that some of those will still come to pass out there. You can put guardrails around things, and you can even ask AI to put guardrails around other AIs as we're doing because we're using the tool to build the tool. But we will have bad outcomes across the board, just as we always have with software and always will. And so I don't have the magic bullet on that, but there is training the distributed swarm brain, this abstraction of computing, of grid computing, where they're all doing this and using their algorithms.

Chris:

That will Where it goes wrong may happen different places. We often talk about today's LLMs coming out with inferences that are suboptimal. Sometimes it's quite funny, sometimes quite tragic, actually. But that will continue to happen. We have software issues.

Chris:

We're also moving into the physical world where if you have these physical agents that are imbued with a whole bunch of AI agents that are doing stuff and they're acting as a member of a larger swarm, there's a lot of places where things can go wrong. So there's going to be a learning curve on that, and we're going to have problems along the way. So I certainly wouldn't want I know for a lot of listeners and viewers, they probably think a little bit pie in the sky. Not everyone's going to believe that this is probably sooner than they would otherwise expect. But we'll get through it and stuff.

Chris:

And we'll try it. We'll do the best we can. And the responsible people will put a lot of safety around it the best they can. But we will make mistakes.

Jerod:

Where do we stand? Where are we in this initiative to create this thing or these things?

Chris:

So I think like many things, you'll see it coming from specialists. There's a whole area of expertise that you develop around trying to solve these problems. And some companies are specializing that. And just like other things, you'll see that. But I think over time, especially given the fact that it's not one industry, it's many industries, there'll be many players.

Chris:

I think one of the things to make this happen isn't just can we get there? Because if you think about it, once you can get there, almost everybody kind of does a close copies of that. Once we had our first chat GPT, it wasn't long before that we had competitors and other models that were nipping at its heels. And I think you'll see that here as well. But I think it really comes down to getting organizations and motivated individuals into it so that they are producing some level of whatever's productive in what they're doing.

Chris:

In their industry, in their world, what's productive and costs will drive down. And I think as those costs drive down, that's where you see it really pushing out into lots of different places in life. So a lot of it isn't just a technology question. It's an economics question. But I think the pervasiveness of it will drive that.

Adam:

Let's get since we helped create a show called Practical AI, let's get practical. Yes. You'd mentioned this is obviously burgeoning. You're coining this. I I kinda feel like swarming is the protocol.

Adam:

Maybe this is a specification or somewhere, and the implementation is more of a product potentially. But take us into the practical nature of, let's just say, over the next three years. Will we see swarming of any sorts in a consumer level, home lab, put it in my home level? And if we do, like, be realistic, practical, if you can. Like, what will it be?

Chris:

At the level of the definition that I provided, which is a very high bar, I think you'll see lots of things that are calling themselves swarming things developing within that two to three year horizon. I don't think many of them will rise to that level. There'll be quasi swarming capabilities that you're starting to see in consumer and commercial products and stuff. I do think, however, there are so many really smart minds around the world working on swarming because by opening up an entire new category of capabilities that don't exist today, that people already have productive use cases in mind for, there's a lot of money to be made there. You have not only commercial entities and motivated makers, but you have nation states that are highly motivated to do that.

Chris:

And it's a it's a big scientific topic of research. I think you'll see it probably first in areas where people can throw lots of money at it. And if we do talk about in the commercial space, our 800 pound gorillas, you're more likely to see it in a narrower case of use cases there. I think in the military space and intelligence space, you're likely to see it there because you have the economies of nation states that don't want to be left behind. If we're not able to produce a swarm first are very closely following whoever is first, then we have a national security issue here in terms of what's possible.

Chris:

And so I think you'll see nation states prioritizing that probably in very close collaboration with commercial entities, which is really common today. I mean, if you look at certainly how both the US government and most of our allies, as well as the Chinese government, there's a lot of overlap between nation state resources and commercial entities that have special knowledge and skills working together to produce that stuff. So I think those types of collaborations are likely to be the first ones, largely because they can throw resources at the problem until you get there. I think the key is it's thinking about the problem the right way. And I think that's where people struggle is breaking down that complexity that we were talking about earlier, you know, that Jared pointed out, and saying, how can we discreetly address those points of complexity in a way that you can then pull those many solutions together to achieve the grandiosity of the definition that I provided?

Adam:

Let me see if I can not predict, but this is where I would

Chris:

because I'm not gonna predict anything. Yeah. Yeah, we both We know that

Adam:

think the two of you, I think will agree with what I'm gonna say here. I think the area where I like to see this type of swarming is in energy conservation. And so I think there's multiple devices in my house that consumes energy from a HVAC system above me that both heats and cools my home to the lights that power my house to, let's say, a kettle that is electrified, all the things. I wanna give my home the task of being energy conservative.

Chris:

Right?

Adam:

This this swarm. I wanna have a swarm of devices that help me be that. And it can it hey, Adam and the family are not here. It makes sense as an agency to be to be conservative with our energy use because there's no one here to do it. And that's where the you can do like individual device level smart home automation, which is here today.

Adam:

Matter supports that. It's not a swarm though, right? It's not a swarm.

Chris:

That's right.

Adam:

So I would like energy conservation to be my first swarm tactic. The next would be I live in Dripping Springs, Texas just outside of Austin, Texas, and we always have water challenges. Right now, we're always in some version of a drought. There's actually a big bet on the wall on Wall Street against Texas running out of water. Like, there's a bet essentially shorting Texas running out of water at some point.

Adam:

I just heard this headline. That's a headline only. I don't know what the truth is behind that, but I heard it so it must be true. Okay. So the next thing is water conservation.

Adam:

Help me as a household, maybe even help me as a neighborhood, a sworn neighborhood, be conservative when it comes to water conservation. So my child goes to flush the toilet or yeah, don't know, some sort of action tries to take place, but the swarm is like, hang on a second, we're in a conservative nature. We're gonna use the one or the point five gallon version flush versus the 1.2 because it's a, you know, it's number two. There are some reason, right? But for whatever reason, like we now have new tech in my household that gives me things that really matter energy, water, and then the last one for me is food.

Adam:

There is so much food waste in America, tremendous amount. I know I for sure buy some chicken once, twice a month and I'm killing chickens constantly because I'm wasting my chicken not making it. So I don't know if that's a problem that's me, but at some point my tech, my swarm tech can help me solve those three key things, energy, water, and food. And I think you start there because that's what matters. My laundry, kind of a me problem.

Adam:

Maybe my washer can say, hey, you put a white in with a darks, probably not smart, eject it or alert me. You know what I'm saying? But like, I don't need help with laundry. I don't need I mean, I like my iRobot and vacuum. That's cool.

Adam:

But I think that the thing I would wanna conserve on is those three things. That'd helpful.

Chris:

I I and I think I think you'll see that. I don't think it'll just be the swarm doing that though because, like, even today, you know, if you start with where we're at right now and talk about the fact that energy monitoring is really common within a lot of these existing devices.

Adam:

Not a swarm.

Chris:

Not a swarm. Not swarm yet, but we're getting there.

Adam:

We're getting

Chris:

keep saying

Adam:

it. Sorry.

Chris:

So bear yeah. Bear with me for a second. So we we have the we have what we can already do at the individual device level. And then as we really start viewing our homes with AI agents, which is gonna happen even before the swarms are hitting.

Adam:

So soon. Yeah. That's next.

Chris:

You're gonna have AI agents doing lots of different things, including the monitoring, and those AI agents will be monitoring your matter driven devices thinking, Oh, we need to make some adjustments. They'll be communicating with the devices that are being governed by that. And so they're able to get you a great deal of the way down that use case that you just talked about. But there are also going to be things in your home where things like for energy, the energy conservation thing, you mentioned things like airflow and temperature, where it's not an explicit device that's matter enabled and has the energy monitoring built in, but it may be like that corner of the room is cold. And in that case, that swarm that's monitoring the house and maybe it has other functions that aren't just monitoring.

Chris:

Maybe it's doing a cleanup. It's doing the cleaning job, but it also notes that, hey, this corner is not getting good airflow. The temperature's changing. To your vision, Adam, that you just talked about, that's where the swarming capabilities of having different devices work together will do it. Now, an individual robot could also detect that device.

Chris:

It doesn't have to be a swarm. So you really get it for a swarm to be effective there. You're really going to be looking for how does a cluster of members working dynamically together get me something I don't already have? And I think that's the question to answer in that use case, if you're actually wanting to introduce the swarm to it.

Jerod:

Well, we humans have our own form of swarming. It's called open source software. And I'm curious if there's a place where people who are as passionate or maybe even just potentially interested in this initiative, this movement, this I don't know, this next big thing of swarming tech. Is there a place they can gather? Is there a framework?

Jerod:

Is there a conversation? Is there anything in the world of open that people could gather around?

Chris:

There are. And I probably should have brought a list maybe in the show notes, we can add some stuff in. Some of the things that I often tell people to start off on is robotics has been a big part of this, kind of robotics role being part of developing to the swarm is ROS2 exists. ROS stands for ROS2. ROS is the Robotic Operating System, which is open source, and it is the most widely used robotic software technology out there.

Chris:

It's not the only one. There are many, and some of them are closed and some of them are open. But there's tons of books now on Ross. And so I often, when people are interested in this and they're like, but how do you do Aside from the swarm, I can't even make a single robot work, what do I do? Well, there's tons of information about that.

Chris:

Start off maybe not solving the overall swarming problem that we were describing as remaining a hard challenge, but start with something more accessible. You get on we've mentioned Bezos so much, Amazon and others, and there are a lot of maker kits that you can get that are open maker kits. You have Ross. They're very similar in terms of But if you want to not do robots and you want do drones, There's a whole bunch of open source drone stuff. And then the thing that I love doing, I do this all the time, is diving in on GitHub at different software communities that support open specs and stuff.

Chris:

There's tons of repositories on GitHub that are designed to do this that just interested people said, want to go scratch an itch. I want to solve a problem. And I go there, and I'll then also go to Hugging Face and look for small models that may, if I need AI in the mix, can contribute because really, you know, small models are where the future is, you know, it's not we talked to the very beginning of the conversation about the giant versus the small, go for small stuff you have you there's very likely that you have a GPU in at home, it may be in your laptop or something that you can buy for a couple of $100 that they can do all sorts of cool inferencing with an existing model that you can then go do some of the stuff with. So with open source, that's the place to go. That's where I think that's where I think the majority of innovation is really driving from.

Chris:

And it's a good place to start and figure out what is interesting to you. And even that area, I'm really into, I'm gonna also pitch a language that I'm into, which is Rust. I mentioned Go beginning of the show. Love Go. I and I use that in a in a lot of environments, but I've been using Rust as a replacement for c c plus plus because and it's great for embedded, you can use it with no operating system at all.

Chris:

And it's fast as can be. And so I've been that's been like when I go play on my own, aside from like work work stuff in this area, I'm always I mean, I'm every day I'm looking at all the innovation in the Rust community, to do small little projects that I can do for fun, that drives my own passion forward. It doesn't have to be a giant 800 pound gorilla, or defense industry or whatever kind of thing. Can be something that the kid in you or maybe the kid in your house can go do on their own.

Adam:

Give some shout outs to, I guess, some crates or some projects out there in the rest of world. Think probably Tokyo or Tokyo probably is one of them. Saturday is probably one of them. What else should you play with?

Chris:

Tokyo is really good because it allows you to kind of multi threaded things, many things happening at once, which is really important in robotics. And so that's really taken off. Is I'm trying to remember the name Embassy is the name of it. Was trying to remember. For embedded, it is a runtime in Rust that allows you to do a whole bunch of embedded capabilities without writing everything from scratch.

Chris:

It kind of gives you this framework. And so you can go get a Raspberry Pi, even one of the small ones, I think, the Nano and stuff that isn't there supporting the OS and use Embassy to create an executable that runs on something that's too small for an OS. And so I like exploring all these different possibilities in terms of how you're gonna And when I said Tokyo being multithreaded, it wasn't multithreaded. It was concurrency. I said the wrong thing, so I just wanted to correct that before we get too far.

Chris:

But being able to do highly performant, concurrent things on very small pieces of hardware out on the edge is a real thing. Like five years ago, it just wasn't possible to do anything like what we're doing now. But the beginning of the show, we talked about the revolution of all these different areas coming coming together. Well, now anybody can go use different languages, but in my case, Rust, and find small bits that cost me $10 out there and put some unique software and do something that scratches my itch that no one in the world has done. And it's no longer out in the cloud or out on some computer.

Chris:

It can be something that I'm carrying around on my body or is literally a robot. This is all reachable now. And so that's really what I would encourage people to do is the future I get asked all the time about the future of AI, and I really think the next big revolution in AI is going to be physical AI, is AI imbued in all these things in our life that we've been talking about, that we refer to as on the edge in the software world. But that's going to be the new normal. And now you can do that without any real budget on your own, anytime, from any place in the world.

Chris:

So this, if you want to go create the future, and I said this is the coolest time we've ever lived in, boy, you can go create that right now no matter where you're living and no matter what your budget is. So that's what go do it. If you're

Adam:

tinkering with Rust right now so let's say you're done with this podcast, you're off for the day. Let's just say magically, have nothing to do. You're gonna go pick up your next or your current Rust project. Maybe you've got a new model you wanna play with. Where are the places you're going?

Adam:

You mentioned Hungry Face. What are some of the stack that you're tapping into?

Chris:

So there's the swarming stuff that we've talked about and trying to figure out robotics and all that. We've talked about home automation. Think that feels, for answering this question, that's an accessible thing that I like to do now. So as I've picked up this kind of home automation stuff, I'm trying to figure out what can I do? I go get some Raspberry Pis or I can use a slightly larger, like a mini PC to do something in the house.

Chris:

None of this costs much. And I'm now, on my day to day, when I'm just at home and I'm not thinking about the day job, if you will, I'm looking at all the things that I do with my family and thinking, wow, I can go pick something to handle that. So like almost all the lights in our house are automated. A lot of the appliances are automated. We have voice command from anywhere in the house where we can tell a particular assistant, Go do this, and it happens.

Chris:

I've been starting to integrate AI agents into that workflow. Now that that is becoming super accessible with all the There's so much open source that have made agents very easy to do, and you can get small models off Hugging Face and run it off compute that you have in your house already. And so that's the kind of thing that I like to do. And I think it's amazing because it's gotten people in my family who are like, Oh my god, Chris is doing technology again. Like, have the family members.

Chris:

They're like, Yeah, yeah, I don't want hear it because you're talking about that with everyone else all the time. But now they're like, they're using that and they're getting interested in like Yeah, they're like,

Adam:

Tell me more, Chris.

Chris:

Yeah, they'll start

Adam:

Not a swarm, though. Tell me more.

Chris:

Not a swarm. But my wife will say, How could we automate this to make it better? And I couldn't get her to think Yeah, of she didn't want to get was my thing, and just stop talking about it, Chris. My daughter is starting to get really She's 13, and she's really starting to think about what can we do. And it just sparks the imagination because it's real and it's tangible.

Chris:

So that's why I get to go do something. Just decide today you're a maker. Go get some cheap stuff. Have a vision. Recognize that every part of it is either free or only a few bucks, and just go do something in your imagination.

Chris:

If you can't think of anything, there's tons of websites with maker projects out there, and find something that you go, Oh, god, that's cool, and just go

Adam:

do it.

Chris:

Even if it It doesn't have to be the greatest thing in the world. Just go do it. And then you're helping push all this stuff forward. You are diving into the future and making this stuff happen. And that's why this is the greatest moment in the history of the It really

Adam:

is. I mean, we went from photography or from painting photos to photography in a blink of an eye. And now we're thinking, gosh, I just wouldn't like paint the picture that way ever again. I would just take the photo because that's the way. Yeah.

Adam:

It's a cool moment in life. I'm super curious about one one particular area that you mentioned. You mentioned voice. Are you leveraging Alexa or leveraging the behemoths or are you home assisting in it and you're doing something with home assistance?

Chris:

So I am moving. We have been for a while, Alexa all over the place. And given the fact that I am increasingly concerned about privacy, just in terms of surveillance is so built into everything now that I am generally moving from cloud based systems into more private systems that are completely under my control and local and stuff. And I realize that may not be for everybody. I think part of that is because I work in a world that is obviously touching on intelligence, and I'm more aware of what's possible from a surveillance standpoint than probably most people are, and how pervasive it is.

Chris:

And that makes me obviously want to kind of protect our own privacy a little bit. So I'm keenly interested in automation that's not specifically commercial cloud dependent.

Adam:

We should circle back in the new year for a deeper conversation. I'm sure you'll have some time away, maybe new progress, new projects, and new insights. Because these are things I'm about to go into in my curiosity is I haven't automated anything in my house. They're like, Adam, you're such a nerd, you care about Home Lab. I'm like, yeah, I don't care about that part of the Home Lab.

Adam:

It's a different area of the Home Lab that I'm trying to conquer.

Chris:

I didn't either. For me, the kick in the butt was buying a house that already came with a lot of automation in it. And it's not just catching up on that and learning. There was a certain ramp, I had to level up. But then there was also the It starts getting your imagination going.

Chris:

You knew in the back of your mind you could do this, but now you're living it. And then you're thinking about the next five things after that. And I think that's it. Once you do a little bit, it wets your appetite, and you start seeing all the possibilities. And that's what it took for me, professional technologist, but I wasn't really doing it until a year ago.

Chris:

And now this last year has just take off.

Adam:

Being able to host models locally, have that privacy, the fact that Home Assistant is so pervasive and so massive as an open source project that they have a you could tap into via the API, you know, whatever local, you know, models you have running for inference, They have voice capabilities. There's just so much happening there. Why give that data to, you know, to Amazon? It's not that they're bad. It's just that I have preferences, and the preferences don't involve me telling what I want, and then now I get hit with ads for x, y, and z as I scroll the Internet.

Chris:

People often complain about how creepy it is that you're almost just thinking about something and then it shows up in your Amazon cart kind of thing, you know, or Google or whatever. And and like but you're do you're doing that. You're giving them that power over you. And so to some degree and it's not happened all at once, but I'm taking responsibility for the fact that that's been my choice because it was the easy way to go, because they were providing this ecosystem. I didn't have to do much.

Chris:

It just happened. All I had to do was let them was say yes every time they send the updated terms and conditions, and they would take my data and do whatever they wanted, and there they are. And I've kind of gotten to that point where I'm done with that, and to some degree and and turning around.

Adam:

Just gave me an idea, Chris. They you know, somebody should I don't if this is actually a good thing or not, like, AI is great at scanning an entire document, like a terms and conditions. There was a documentary, I think, on Netflix about this that if you try to read all the terms and conditions you would agree to in modern society, you would spend more than your entire life just reading terms and conditions.

Chris:

It's a lot

Adam:

of terms updates and or literally scrolling them to say, yes, I accept, is not is not possible. It's not realistic

Chris:

Yeah.

Adam:

Of a request from the people. So we're agreeing to a lot of things just out of the nature that we don't have the time to do it.

Chris:

And you're not going to if you're trying to get something done. And now you have to do through terms and conditions to get something done. They do it at that moment, because they have you, they know you have to get something done. And what are you gonna do? Go, well, I had to do the thing.

Chris:

It was really important, but now I can't do it because I'm not gonna do terms and conditions.

Adam:

Correct. You're correct now.

Chris:

Yeah. So I'm I'm starting to invent my own world.

Adam:

Whereas the kids say cooked, you're cooked.

Chris:

Yeah. I'm starting to invent my own world where I I'm not bound in that little prison, if you Well,

Adam:

that was cool. Thanks for deep diving on the swarm, not a swarm, Rust, all the things. Make sure, if you don't mind, some of the things that you can link us to in the show notes, I'm sure you got lots of links. Just spam us with all your links. We'll put them

Chris:

in the

Adam:

show notes for everybody.

Chris:

Fantastic. Thanks for having me in, guys. It's been great catching up with you and a fun conversation.

Jerod:

Tons of fun. Go listen to practicalai. Practicalai.fm. If you want more, Chris, that's where you find it.

Chris:

Thank you, Jared. I'm so glad you did that because we would love for people to join the conversation. And we all It's one big happy family, as people can see here. I love Changelog, and I hope some of the Changelog people who haven't given us a shot will give us a shot and join our conversation. So

Adam:

you go. Practicalai.

Chris:

That's s it.

Adam:

You go. And be square as they would say in the eighties or nineties, which is cool now. It's cool. It is cool now.

Jerod:

Yeah. The eighties

Adam:

and nineties are cool again. Good stuff, Chris. Bye, friends. Bye, Chris. Bye, friends.

Chris:

Thanks, guys.

Jerod:

Alright. That's our show for this week. If you haven't checked out our website, head to practicalai.fm, and be sure to connect with us on LinkedIn, X, or Blue Sky. You'll see us posting insights related to the latest AI developments, and we would love for you to join the conversation. Thanks to our partner Prediction Guard for providing operational support for the show.

Jerod:

Check them out at predictionguard.com. Also, thanks to Breakmaster Cylinder for the beats and to you for listening. That's all for now, but you'll hear from us again next week.