Practical AI turns 100!!! 🎉

Well, I think AGI is a lot of people just think of some type of singularity and Terminator incident where like now we cease to exist because AI has crushed us into the ground and we're no more sort of thing. Which I think is to me, inconceivable. But whatever.

He's so bullish on being inconceivable, though. Like, he's dead set. He's sure of it.

Challenge him on it, Adam. Challenge him.

I still know, man. I don't know if I agree with that. I'm not on the inside, but this.

Is my own opinion, right? It's at least, I think inconceivable that it would be sometime in our near future or my lifetime. But to me it's inconceivable.

But it depends so much, though, on how you're defining that, which I think is important when we define what AGI is. Because you can define it a couple of different ways, and I'll give you completely different answers about what my opinion is.

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Welcome to Practical AI, a weekly podcast that makes artificial intelligence practical, productive, and accessible to everyone. We've teamed up with our friends at Pachyderm for an awesome giveaway in celebration of the 100th episode. Check the link in your show notes and enter to win an Nvidia Jetson Nano Intel's Neural Compute Stick or Google's Coral Dev Board. Plus Practical AI and Pachyderm Swag.

Welcome to another episode of Practical AI. This is Daniel Whitenack. I am a data scientist with SIL International and I'm joined as always by my co host, Chris Benson, who is a principal AI strategist at Lockheed Martin. How you doing, Chris?

I am doing very well. I think it is a lovely day to talk about AI.

Oh, it is. It is a beautiful day to talk about AI. And I was thinking we've kind of done a lot of these Practical AI episodes, haven't we?

I think we Have. I think we're actually hitting a milestone, possibly at some point here. Hey.

Hey.

Who is that?

Wait, did we not lock down our zoom?

Oh, who is this?

Surprise. Guys, we're here.

Ah. Hey, what's going on?

What's going on? Oh, my gosh.

Busting in on this party.

Yeah.

Oh, it's Jared Santos and Adam Sokoviak of the Changelog, who are kind of our bosses, you know, Dan.

Oh, gosh, please don't call us that.

Are we in trouble?

We gotta be careful what we say.

What's going on?

Well, we're here to say happy 100th episode, guys. Yes.

What a milestone it is.

Can you believe we've done that many episodes?

Yeah, 100 episodes. Talking about AI. Who would have thought there'd be that much to talk about?

True. Well, podcasting is hard. Doing 100 episodes is harder. So, seriously, congratulations for sticking out. Not just sticking it up, but, like, thriving. Congratulations.

Thank you very much.

Yeah, it's been a lot of fun.

It's been a long time. So listeners who've been listening all the way since the beginning may recall that Adam and Jared actually interviewed Daniel and me in episode number one as we got going, and they finally come back. They've been here all along, but now they're back.

Yeah, we're back, baby.

So the team is all online together.

We've gone full circle.

We're silent behind the scenes.

Yeah, yeah, you're always around. But what was the date of that first episode? 2018. July 2nd. So it's been a little over a couple years.

What's funny, though, is the recorded date is April 20, 2018. So I guess we took a little while to ship.

That first one's the hardest.

Is that accurate, Jared, do you think?

Yeah, probably.

Wow. What would happen there?

Maybe we just didn't know how to tag them correctly at that point in time.

That could be the case as well.

I don't know.

Well, there was a lot of stuff to figure out, like how to do the intro, what music to use, etcetera, Et cetera.

Yeah.

Plus we were also launching other things at the time.

Yeah, true.

Regardless.

And we had to figure out if this whole AI thing was just a fad or not, you know, that's true.

Well, so what's the consensus? Is it a fad? Is it over?

Oh, it's definitely a fad.

Definitely a fad.

Yeah.

Yeah, it's going away.

Yeah, I think it's sticking around. I think. You know, I don't know. One episode that we talked about this and maybe I mentioned it a couple times, I'm kind of thinking of it at this point like another layer in the software stack. So just like, you know, developers don't necessarily have to be like into all the weeds of DevOps and like CICD and that sort of thing. But if you're going to be a developer these days, like you're going to sort of interface with DevOps and CICD at some point. So I kind of started viewing it that way where if you're a developer these days, even if you're not an AI practitioner, you're going to sort of interface with AI related something at some point.

What are like the most bucketized or verticalized aspects of AI that are like in products today? I know there's computer vision, there's natural language processing, sentiment analysis. What are the ones that people are using where it's like, yeah, this is kind of a little niche inside AI. This little niche or a product that people are actually putting into play.

Yeah, well those are certainly the biggest two right there. And in terms of commercialization, you know, showing up all over the place, there's.

Definitely a lot of areas even of computer vision. So I would say like certain things like facial recognition or object recognition are fairly commoditized in terms of like major, major categories of detection and that sort of thing. So my wife and I just bought a building for her business and it's. There's no Internet there yet, so we bought these like little deer cam things like trail cams.

Oh yeah.

That like takes snap a picture if something walks by. That's my security system right now. And they have an option, it's like AI integrated. Like if a deer walks through the like zone of the camera, it'll identify that it's a deer and like ping your phone versus like a turkey or I don't know, other things that people shoot, I guess they're great.

I use those to catch lost dogs around here because, you know, we do all this little animal stuff.

Ah, interesting.

That's a better use maybe.

Yeah.

I hate to burst your rebel, Daniel, but deer are not dangerous, man. You don't need to be protecting yourself from deer.

Yeah, I've even so far caught other things that I wish, wish wouldn't have been in our building, but that's another story.

I'm just envisioning the deer coming in from the skylight in the Tom Cruise fashion, you know?

Yeah, Done doing so.

Yeah, yeah.

Oh yeah.

I should mention my side project then.

Yeah, go for it.

Please do.

So I live In a cul de sac and I've got some cameras outside my house. So I've fed all my neighbors faces into a little system and then I can detect whenever there are people in my cameras that are not those people.

Yeah, exactly.

Interesting.

I'm kidding. I want that. I can't make that, but that would be cool.

Interesting.

Oh you could totally make that.

Do your neighbors know you fed their faces into a system?

Jared's like dang dude, you're awesome.

I thought you were saying you had done it and I was like I'm not telling him. Keep me quiet. I'm on a cul de sac too. But you know, I don't think any of them are listening to this so.

Wouldn'T that be so cool though?

That would be pretty cool.

Would you have to get your neighbors permission?

I mean it's not, it's not that hard to.

To build.

Yeah, you should go for it.

It doesn't take that many photos to do it well.

So I'm just looking at for example like, I mean Google Cloud aws. Like all these platforms have different things so they have like categories like in Google Cloud they have site language, conversation structured data and cloud automl. So site like these object detection sort of things. Language of course there's translation is you know, very much used these days. But also like transcription, speech recognition and then there's like conversation type stuff like you know, chatbots and like you were saying, sentiment analysis.

Right.

So those are all definitely pre built solutions for all of those things out there for sure.

Can you tell that Daniel is a natural language processing expert? You know he just whipped those things right off the top of his head like it wasn't even. He didn't have to think about it.

Let me flip this question on its head then. What are some untapped areas or some emergent areas where people aren't quite putting this into play yet but it's going to be big once it reaches developer the masses.

Yeah, first thing that comes to me is adversarial networks, you know, which I know we recently had an episode on. But there are so many uses for adversarial situations and you know, GANs, Generative Adversarial Networks.

GANs. So what are some uses of GANS that we could look forward to?

Deep fakes.

Deep fakes, yeah, that's the most famous.

Aren't those here though? Are they just not that easy to do?

I think what Chris is meaning is good applications of GANs.

Probably I was going for good applications but you went right to it. Jared, you went right to the, you know, the nefarious thing right there. So, and we're certainly, we're certainly seeing.

That there's some cool things though. There's like people are using these generative networks to like generate almost in a data augmentation sort of way. So particularly like for example in healthcare, if you need to generate a bunch of example like medical imagery of tumors to help you train other models that discriminate between cancer and non cancer, it's hard to get your hands on that data because it's all, you know, very private. There's simple laws, all those things. So if you have a small amount of it and then use one of these networks to sort of augment your creation of other data, that can really be a benefit. I'll also say, you know, just because I'm always the language related person, you know, that even though language and conversation is on this sort of list on the major platforms, you know, almost like 98% of languages of the world have no support. So I think also, you know, as the benefits of AI extend to emerging markets in the developing world, there's going to be like new applications or new challenges in language. But also there's going to be other new challenges that are probably hard to anticipate because most of AI development has been fairly Anglocentric. So when you're solving problems in other contexts, I think there's cool stuff going on in agriculture. Even though a lot of times we think of AI in the US we might think of driverless cars or some cool tech thing like that. But for a lot of the world agriculture is, is a big deal. So cool new stuff in agriculture or other areas as AI kind of gets hopefully democratized and the benefits of it extend to more than just the US and Europe.

Is there anything like a Hippocratic oath for those in AI? Because I'm thinking like a GAN could be both good and evil. Right? So is there anything more compass when you think, okay, I'm going to be a practitioner or an expert or you know, somebody doing these things, using these for good, Like I can see that tumor detection totally a good reason to manufacture, for lack of better terms, data to support medical, you know, research and whatnot, given the privacy of medical records and individuals and things like that. But like is there anything out there like a Hippocratic oath for AI?

There is a rapidly developing entire professional field that is addressing that which is commonly called AI ethics or you know, responsible. AI is another term for it. Several terms that, that lend themselves to it and they're tied into other terms like explainable AI. And so yeah, there is definitely the recognition, you know, going back, Adam, to your, your determination to capture all your neighbors images and, and, and enter them into the.

Yeah, I will do that with their knowledge. I will literally go to them with my iPhone and say I just play from my neural network to detect you. And not the in quotes, bad guys or bad people that come into our cul de second. Like so some realness behind that. Our neighbor down the street, like we haven't. We live in a decent neighborhood and our neighbor down the street got their tires stolen two days ago.

Wow. Legit.

Like he went out and his truck was on blocks.

That's disturbing.

Yeah.

You know what I mean? Like we have these things happening and I think it's just a result of the economy. Honestly, I don't think that people are generally that bad. I think that people get desperate and these are things that happen in desperate times and the economy is definitely suffering. So you have people that are willing to do things that maybe they're not more, more willing to do otherwise. But I'm not saying thieves don't exist any of the day, but I think it might be a normal occurrence or a more common occurrence. And I live in Houston, so it's a pretty well populated city.

Yeah. You know, if you kind of take that condition and you put it in the context of AI, we don't have safeguards. So we have this field that's developing called AI ethics. But you know, across the world we really have very little law or regulation that people are required to follow. So for the most part it is, it is other laws that are not specific to this that have applicability. You know, GDPR is one thing that comes to mind in Europe and that is really the only, you know, national level law that I can think of off the top of my hand. Am I forgetting something, Daniel? I know California has some laws. Yeah.

There, there's some other regional things and I think probably there's some others that exist now. But I think in general people are still reliant on developing good like principle statements and how that trickles down to the actual development workflow. Like Google and Microsoft, IBM, all these companies have developed their sort of AI principles and that may or may not trickle down to the actual development work. Also I think there's still a perception that like any sort of like when developers hear governance or like, you know, that sort of thing, it's just an assumption that it's going to slow down all work until like we can't do anything. And so there's still like that feeling I think exists there, although people would acknowledge that it's like there's some important problems to address.

You know, it's funny, I was on a call where I was having a conversation with some folks from the World Economic Forum about a week ago and we were talking about this a little bit and there's so many companies putting out so many principles that it's becoming cluttered and stuff. And a comment was made that there are enough really good examples out there of AI ethics. And maybe this is a moment where before you go create your own, you do a little soul searching in your organization and go pick one of the 100 great examples that are already out there just to make it a little bit easier for people to keep track and stuff. I mean, it's definitely a growing field, but it has a long way to go to mature.

It's funny, Daniel, I was listening to your guys AI ethics episodes, which was just recently, and I very much identified with you when you said that because Chris, you light up on this topic. I can tell you're into this. This is like where you play thinking at this level. And Daniel feels as a listener and the producer of the show, I think more like boots on the ground, like putting the, slapping the code against the data kind of a person. And he's like, when I hear governance or I hear these things, he kind of resonated what he just said. And I was with him. I'm like, my eyes rolled, they glaze over. I'm just like, oh, here we go again.

Yeah, I think it's what people don't realize though is, and I don't think it's immediately obvious that. And O'Reilly, I forget who wrote the post, maybe I can find it and put it in the show notes, but they wrote a article about doing good data science, quote, unquote. Like ethical data science actually allows you to do better data science. I think that is generally true in the sense that part of the whole governance aspect is understanding what data produce, what AI model that produce, which predictions at what time. And if you actually know those things, your development workflow can be supercharged because you're not duplicating your effort. You know, what you did in the past, you can do some analysis to figure out what parameter space you've explored and what like issue popped up when you did what. And so there is like a lot of benefit there. And I think that's why certain of these practical Tools plug in. Like we had an episode with Allegro AI recently and they have open source project called Trains which helps you track a lot of these things. And there's other projects like Pachyderm which is open source project, helps track some of these things. So I think there is developing some tooling around it. It's still not like super streamlined. So there is like still when you, when you're, you know, on the ground doing the development, like it's not just like there's no road bump when you try to integrate these things, right?

Everyone has their own, their own approach to it and their own solutions. There's no standardization yet, we're way before that. You know, it's in TensorFlow, but once again they have, you know, that team has done, and I haven't used it, but presumably really good job. But it's their thing, right, that their community is for. So it'll be interesting to see how this rolls out in the years to come.

It's kind of like a code of conduct, similar concept, right? Like you need one, you should have one. If you don't enforce it, it's useless, right? If you don't have the tooling to help you enforce, listen, enforce it, then it's difficult to enforce, they're hard to write. So you usually start with somebody else's. But if you don't internalize that somehow, then you just cargo cult in somebody else's values and they're not yours. And so there's lots of things there. I'm curious how the state of explainability has moved since you guys started the podcast. Because that's one big aspect of this, right? The scary hard thing about machine learning and whatnot is the fact that you're basically putting data into a black box, training a model. Then out comes your response. Whereas there's not necessarily a policy that created that response. It was a bag of bias, or not bias or whatever you put in with algorithms. We can write an algorithm and you can go back and say, who wrote this? And you're like, I wrote that. Why did you write that? Well, because my boss told me, why did he do that? Oh, here's the policy. This is a bad policy, right? And it had these bad effects with these things. It's non deterministic. You're like, I don't know how we got this result, it just happened. But as you talked about, there's, there's progress on explainability, which is great, both as an end user. Why are you showing me this ad?

Daniel, you interviewed Darwin AI recently yeah, you know, what was your takeaway?

Yeah, that's definitely a good episode to listen to. There's some. Some things happening there in terms of explainability, but I think that there has been some progress. I am hesitant to say that, like, I feel like there's been a huge amount of progress. There's still a lot of open challenges and open questions. There is more like organized information now, though. I think I'm thinking of. There's a book which you can actually read online. It's called Interpretable Machine Learning by Christoph Molnar. And you can just read through the entire book and he talks about, like, all sorts of things from, you know, counterfactual explanations and adversarial examples. And, you know, we just had a previous show on adversarial examples. And so there is, like, gradually more tooling and organized information out there. But I don't think there's a consensus on this subject either, like, how to approach it. But there's just like a series of tools in the toolbox that you can use and maybe not, you know, people are still developing those and still actively researching them. And it depends too, a little bit on the, like, type of data and model and such. So when certain things happen, like, you know, there's like a model that enhances an image and you put in like, you know, Barack Obama's image, and then the enhanced image turns out like he's a white guy and there's like this, like, blows up on Twitter and stuff that motivates a lot of, like, in computer vision. I think they've been struggling with this for quite some time in other areas, like natural language processing. It's probably a little bit newer.

Foreign.

Jared Santo here, your humble producer. I'd like to tell you about something new. We're beta testing around Practical AI. It's a membership program which we think could be really valuable for the whole community. We call it changelog, and it's the best way to directly support practical AI and all the podcasts, videos, and other stuff we create here at Changelog. We have big plans and ambitions for this, but we are experimenting for now to make sure there's interest. That means when you sign up today, you get practical AI and whatever changelog shows you listen to now, accept no ads. I guess that means this part you're listening to right now, it'll be gone. We also have some extended episodes planned, bonus content, merch store discounts, and a lot of ideas. But since it's such early days, we are offering memberships at a 40% discount for early adopters that disappears at the end of August. So head to changelog.com to join today, lock in that discount, get closer to the medal and make the ads disappear. Once again, that's changelog.com we'd love to have you supporting us as a member. So let's talk about what has changed since that 2018 April to July timeframe, episode one through episode 100. I've been listening, I've been producing alongside, so I do know one thing. I'll cue you up, Chris, because I'd love for you to say this again or say it to me. One thing you've seen and you've said, which I'm not quite sure I know what you mean by it is that you think that we're moving into a post deep learning era or not post, but beyond deep learning era of AI. Can you say what you mean by that and explain it to me?

Yeah, and it's actually on my mind a lot these days. It's been on my mind today before we started talking about this, you know, before we did the show because I'm having conversations with other people about the same topic and to set it up. If you look at these last couple of years and when, when we started the show we were still in kind of the ramp up stage. We're deep learning. I mean there were stuff, there was stuff just coming out every day because everybody was finally setting their focus to it, it was being funded and we were getting some pretty amazing stuff coming out on a day to day or weekly basis. And in some of those early shows, you know, Daniel and I, we would struggle to figure out which news items to include in the show and which ones to just not mention because there just wasn't the time. So it was very exciting, very wild west. You never know what's going to happen next kind of moment. We've matured since then and so it's definitely, we kind of went through the kind of the rise of computer vision and all its various things and there was for a while there was a new algorithm every, every week coming out in that space. And then we transitioned into the NLP period where we had tremendous progress there. And Daniel has been right in the middle of that. I've learned so much from him. But we're also, we've matured quite a long way in a fairly short amount of time. And one of the things I'm noticing is we're still having things like, I mean GPT3 just came out recently and that was a big deal. But I don't see the, like every week things happening. And so because we're seeing kind of an evolutionary progress, there's a lot of people in this space that are starting to say, have we mined this for all the, the big new things? Or maybe most of them? I don't think we're there yet. But are we getting to a point where we've had some maturity and we're having lots of kind of just evolutionary improvements in the, in the current space of deep learning? And because of that, a lot of people are turning back to kind of AGI, you know, which is artificial general intelligence. The idea of the, the AI that can kind of do multiple complex things instead of this simple AI that we have the narrow AI that is very good at doing one particular task. And so there's talk of something that. If you had said this to me two years ago when we started, I would have laughed. I would have, you know, like, there's no way. And that is the idea of an AI winter at some point down the road. I don't think it's quite that because it may be that the research being in a more, in a slightly more mature area, is focusing on lots of evolutionary things, but we probably have quite a ways to go before AGI. So I think we might have a lot of commercialization over the next few years about what's already out here, because there's tons of industries that can use what's already been created and discovered. But how far are we from the next major step up, revolutionary wise? That's not just an evolution of the existing, but a new thing coming out.

I always, and Chris knows I kind of cringe a little bit when we talk about AGI and like general intelligence, because I think what a lot of people mean by that doesn't, like, it doesn't mean what they think it means.

What does it mean and what do they think it means?

Well, I think like, AGI is like the. A lot of people just think of like some type of singularity and like Terminator incident where like now we cease to exist because AI has like crushed us into the ground and we're no more sort of thing, which I think is to me inconceivable, but whatever.

He's so bullish on being inconceivable, though. Like, he's dead set. He's. He's sure of it.

Challenge him on it, Adam. Challenge him.

I still know, man.

Like, I just. Yeah, I don't know if I agree with that.

I'm not on the inside but this.

Is my own opinion, right? It's at least, I think inconceivable that it would be sometime in our near future or my lifetime. But to me, it's inconceivable.

But it depends so much, though, on how you're defining that, which I think is important when we define what AGI is, because you can define it a couple of different ways. And I'll give you completely different answers about what my opinion is on those.

And I think this comes back. We had a conversation about the NeurIPS, which is a big AI research conference, and in the keynote, they were talking about AGI and they were talking about it in much more terms, like I could grasp, like certain things, like the architectures that we've been seeing in, in natural language processing that involve attention and self attention and models that actually go beyond sort of fitting parameters, but actually paying attention to certain pieces of data more than other pieces of data. And they tied that into saying, okay, well that's an advancement to generalization and I can latch onto that. That seems like some interesting steps there. And so, yeah, I think it does depend on how you define it. And to Chris's point as well, I think that, you know, people are going to start trying new sorts of things. And I just looked up Sasha Rush. We had him on the podcast from Hugging Face a couple of weeks ago, and he was one of the organizers at iclear, which is another one of the huge AI research conferences. And he posted a graph like he graphed over the last two years. So while we've had the podcast, the keyword growth for certain topics in research over that 2018-2020. And I was actually somewhat shocked. So the top growth was a thing called graph neural networks, which is a new way of constructing neural networks to work on graph structured data. And so I think that that's an indication that people are like, they've pushed the sort of architectures that people are used to pretty far and they're searching kind of for something new and exploring and new types of structured data, new types of data, maybe multimodal types of data where multiple different types of data are linked. So that's really what I think of when I think of people pushing the boundaries a bit.

Yeah, but it's deep learning, specifically. If you think of AI as a broader collection of technologies that are advanced, these are more deep learning things. And I think some of the luminaries in this field talking more about what AGI means to them and you know, what they think about that and stuff and then you kind of have the whole commercialization thing going. So there's a little bit of divergence in some areas between the research community and the commercial interests, the commercial community, because, you know, we're seeing deep learning models and architectures deployed in many different areas. But that is separate from, you know, what, what leading researchers at Google Brain or OpenAI or something like that would be focusing on. So you're seeing people kind of cash in on where we're at, which is good. And I think we'll see it everywhere, I mean, just pervasively. But that's different from where things are going in the future. And you know, it also going back to the AGI point, one of the things I think that I've Learned in this 2 year period is I think I was unclear on the idea that if you were to look into this, somewhere down the road, future about, you know, AGI comes about and you know, obviously we're a long way from that. But if at some point I think people confuse whether or not consciousness is part of AGI or not. And so I think we'll get to tremendous levels of intelligence that has no sense of self or consciousness many years before any breakthroughs on, on the ladder on consciousness. So, you know, we're already seeing models that in very specific areas can outperform any human in a particular task. And so, you know, if you extend out that computation capability, then if you're defining intelligence as a form of computation, being able to solve a problem, then we're kind of, we're much farther down that pure computational road than we are. I don't, you know, you can have an amazing model that can outperform any human, but that doesn't mean that it's thinking, wow, I wish I had a cup of coffee right now.

For sure.

That's a big leap right there.

Self awareness is a key ingredient missing.

I'm not sure that's clear in most people's minds, because if I have five different conversations on this topic with five different people, their idea of what they're starting from, all five can be very, very different.

Yeah. And maybe a good way to put it in. One of the reasons why it's inconceivable to me is because the way that we're approaching this path to advancement in AI, we're going to destroy the planet way before an AI singularity happens. There was a study, it takes like we release into the atmosphere as much carbon as five cars during their entire lifetime. When we train one of these state of the art NLP models just once.

Wow.

So I think if we were to say, oh, let's just like keep rolling with this, like, we're going to have to be living on Mars by the time the, you know, AI singularity happens. So that's like a whole nother strain of things where it's like, can we actually solve some type of the computational issues that are happening with AI and we're gonna have to like, we can't just keep doing things status quo. We have to figure out like more efficient and like creative ways of doing what we're wanting to do.

I think, you know, to your point there, when we do get to that point where the singularity does come about and it's self aware, it's gonna start off depressed as hell, you know, we'll have destroyed the planet. It'll be like, why did I bother?

No, it'll turn itself off.

Why did I wake up to this?

I'll just turn itself off and wait too. I'm using way too much energy here.

And has anyone noticed we have a Princess Bride thing going here? We keep saying the word inconceivable.

That's true.

And, and we were saying, I don't think that thing means what you think that thing means.

I was hoping that someone would do some other reference after I said that, but I wasn't.

There is a Princess Bride theme going in this episode.

Anybody want a peanut? No, that was too non sequitur. Let's talk about GPT3 for a minute. Just to completely. It's not actually a hard shift because I think it plays into this concept that you're referring to where it's like a plateauing and a question that I have, because what happens with us who aren't deep in the field like you all are, is every once in a while we just get impressed with the results of some sort of new technique or model. And it has been a while. So I've definitely seen the slowing for me where it's like, I feel like I've seen a lot of the things and it's like, yep, I've seen that before. That's cool. Like different applications. I think the color transformation stuff is really cool, right. With the style transfer kind of things.

Super cool. Like de oldify.

Yeah, exactly. So you're just seeing like, well, let's take that and apply it to X, Y and Z. And then every once in a while you get super impressed. GPT3 just made a splash this summer on kind of like the tech Twitter and the VC Twitter and like in our space by generating some pretty realistic and even tricky sentences and phrases and blog posts even. And I don't know what GPT1 or GPT2 were. I don't know what GPT3 is. I know it's OpenAI. I know it's the first time I've signed up for an AI related beta. I'm like, I want to play with this. You know, it's, it's that. But is that like, is GPT3 kind of like the last or the next phase? Is it an evolution of what they've been doing? Is it a brand new thing? Tell us your guys take on that thing.

Daniel's our NLP expert, so I'm going to throw this to him.

Okay, so I think it's an amazing achievement and like incredible results and also like people just being really creative in their use of it. I don't think it's like a fundamental paradigm shift in terms of like how we're doing modeling necessarily. And one of the reasons that they listed in not releasing the model publicly was the fact that like it requires an extreme amount of like compute to make it run like efficiently and good, which people just don't have access to. Like normal people, maybe like us on the call. I don't know if I want to call this normal, but speak for yourself. And OpenAI people as like other than everyday people. Yeah, it is an amazing achievement. I think it's an amazing achievement both computationally and scale wise and what they had to do to achieve that scale and all of that. But I think it isn't a sort of paradigm shift in the way we're going about things. I guess it still fits into, I think this model of bigger and bigger sort of general language models that people are training on huge amounts of data using sort of task multitasks or arbitrary tasks to help the model learn about language like filling in words or you know, question answering or sentence completion or character completion or predicting next sentence and like these sort of tasks that people don't really care about that much but are used to train these like large models that are then able to be fine tuned for very many tasks that may be unrelated, like you know, translating code from one programming language to another or you know, generating like understanding queries to generate, you know, front end components and that sort of thing. I know those are a couple things that were shared in the changelog Slack.

So yeah, could you maybe hypothesize about what's after this first magic trick?

You mean for GPT3?

Exactly. I say that's the first magic trick to sort of introduce this new generation of language prediction model and what it can do. It's very impressive that that blog post was generated. And then I think on some ethical level, do I trust that blog post less because it wasn't actually generated by a human. So why do I just trust humans so much more than, let's say, anything else? That's an aside, but I think like, okay, this is the initial magic trick. What are the actual applications of it? What's beyond this blog post for GPT3?

Yeah, for me, I'm mostly interested in how people go about interacting with this model because the standard has kind of been in the past that, you know, people train a model and then at some point like a serialized version of that model is released and you can load it into your own code and do like nifty things with it. That's, you know, not going to happen in this case for reasons that were on purpose that they are releasing this via API. They're running it internally, they can shut it off when they want to. There's interaction patterns that are governed by that API, which aren't just kind of you can do whatever you want. And so I think it'll be interesting to see how that influences how people are using it. Obviously, as you've already seen, there's a lot of creative uses already, but I think that I know why these reasons why they did this, and that's within OpenAI's set of AI principles, but it also somewhat constrains or puts some boundaries, I think, on how you're going to use it. And so I'm mostly actually interested, not so much in the application because I think it'll be things similar to what we've seen in the past, maybe just leveled up a notch, but more how people figure out ways to use the API or develop workarounds or creative uses and that sort of thing. The workflow wise, it's very different.

Back to the inconceivable part though. This shows some level of fear from humans to non human. In this case, like the machine, so to speak, there is some sort of fear. Maybe it's a fear of how other humans will use this.

Yeah, that's it.

But the governance and the API and the restrictions definitely shows either responsibility or fear. I'm not sure, maybe it's both.

Well, it's a tool, right? And any tool can be used for evil or good.

Yeah, and we've had this conversation a bunch of times on the show in that, you know, like Every technology, people always take technologies and most people use them for, for good ends and there's always a handful of actors that use it for bad ends. And the worry here obviously is especially if you combine this, you know, like we're talking about GPT3 and first it's been done. So you know, people know even if they don't have the details, they now have seen it, it will eventually get out there. Whether it comes from it's point of origination or whether it comes from some other group that says, oh, I think I could do that as well. I've seen the results. I have a pretty good sense that's possible down the road. And we have seen that with previous models coming out and then other people kind of piling on. But if you combine that with obviously other aspects like gender, data, serial networks, and we're talking about deep fakes and stuff like that, then there is room for bad actors to do stuff. And so that is a concern. So you know, when you're detecting that, I think it's less about, you know, it's not that we're worried that we're about to hit the singularity and you know, what do we do about these machines? I think it's more a question about what do we do when the criminal mind out there or something decides that it's commoditized to the level they can use it effectively and they, they decide to deploy it and we have to, we have, it's part of the process. We got to sort that out.

I mean that fear element, to me it comes down to a couple different pieces. It's one that like this is trained on so much data in a sort of programmatic way that like when you scrape 80 million websites like you don't always know like what's going to happen if you train a model on that and what your output's going to be. So there is this element of like, how do you probe all of the unexpected things that could be output from this model? That's a hard piece. The other thing is just like I think, you know, yeah, any tool can be used for good or bad, but like if you think of different tools, like, like we can all go get a hammer from the hardware store, right? And I can choose to like, you know, hang a picture on my wall with that hammer. I can choose to hit someone over the head with a hammer, but everyone can go get a hammer.

Don't do that.

Like that's, that's like democratized. I can go get a hammer, I can go get something else to hit you with, right?

But with.

With these sort of AI technologies, if, like, for example, China is using these sort of computer vision technologies to detect a person's ethnicity from a street camera based on their walking gait, to determine if they're part of a minority population called Uyghurs, and so that they can track them and put them in concentration camps, and those Uyghurs, they can't go out to some store and get that same technology. Like, there's such an imbalance with this technology and who has access to it and who has the compute, who has the money to run the compute, who has the facility to get the data. That the imbalance of power is really emphasized for these technologies in particular, I.

Think, and there's a good point there when you talk about scale, is that to your point there, it's a powerful tool that is powerful enough to where nation states are very, very interested in this, and they put a lot of money and effort to. Into this, and that just amplifies how things can go off the rails. You know, it's. It's one thing if I get a, you know, a mean streak in me, and I go out and use a tool to go do something, but when people do it at that level, it's one of the challenges of our time. So, I mean, I think it's not unique to AI, it's any tooling, but it's going really fast. In general, if you look at the fact that over just a decade, we've made profound advancements in this area that are usable. So this is part of society, this is part of culture. We have to get solutions to this, and it's not going to stop anytime soon. So rather than be afraid of it, we just need to be focused on. On answering it effectively. We need great minds to put their attention on it.

So y' all have done a hundred episodes, and I've been curious to ask you this for a while, which is that when we set out to do this show, we called it practical AI. And that first word has really been kind of a primary focus of the show, and maybe a guiding light to a certain degree. I remember hearing Daniel oftentimes saying it is practical AI, and he'll use that as a way of kind of turning the conversation into the practical aspects of deploying and using and et cetera, these technologies. But I'm wondering if you've felt like that's limited you or made the show go in directions that you haven't wanted it to, or if there's Any sort of. Maybe an inkling of like a regret of being pigeonholed into the Practical AI podcast?

No.

Okay.

Dang it.

I don't know. I guess you could interpret practical as being like, practitioner, like, you know, in the sense of like, tutorials and implementation, tooling sort of things. But we have gone into things like ethics or, like, the use of AI for good or telling stories more so than just like, highlighting methods, I guess. And to me, that still fits within, like, the practicalities because it's, you know, the same reason why you want to see, like, case studies or whatever when you're looking at a particular tool or product. You know, something like that where, like, you want to get a sense of how people are using a thing. You want to get a sense of how people are thinking about a thing that maybe you haven't thought about as much. So to me, I don't necessarily feel pigeonholed. I think we have gone into certain of those things, but, you know, it's kept us away from. To me, it's like, brought a bit of focus so that we're not always talking about Terminator and those things.

And I'll actually, to be honest, since you asked, I hadn't thought of it that way, but I'm going to actually say occasionally, just to give a different perspective. I would imagine that anyone who's listened to us for a while has heard me. If between the two of us, I tend to be the one that gets out there into the speculative realm more often.

Yeah, I'm the downer that always is. Like, I don't know how to implement that from my terminal. So I'm out.

I'm already step by step directions.

Daniel's like, if I can't deploy it to kubernetes, it doesn't count. You know, I definitely have an interest in looking out there into what is essentially speculative, you know, even philosophical realms. Part of that is probably not because of my job, because that's not what we do at my company, but just as a person myself in the defense world and if I go give talks, Daniel and I have talked about all these talks we've given over the last few years, and I commonly am asked about these speculative things because people make the assumption incorrectly that being in defense and doing AI, that I must be thinking about Terminators all the time. So it's something I do think about that idea. It's not real life. It's not real life in the defense industry or at the dod, but I will confess that I can't Help wondering about a future like that and putting some thought into it. So it occasionally creeps out in the podcast. And I think Daniel does the right thing. Is he quickly, he goes, well, it is practical AI.

It's good to have the balance.

Exactly. And he pulls me back to reality on that.

As you can tell, probably tell from 100 episodes that I'm not, uh, maybe not the speculative, philosophical type.

Right.

He whips me into shape immediately.

Yes, I do struggle in those, like, planning strategy meetings at certain points in, like, organizations where it's like, well, how does this fit with our vision? How does this, like, you know, what are our transformational things that are going to happen in the world based on the stuff? And yeah, so it's good for me to be pushed into those areas. It's definitely good for me to put. Be pushed into areas where I'm looking like beyond like my, my VEM window. But, you know, I also always like to, you know, go back there.

I kind of see the name as more of a. Less of a restriction and more of a. A North Star, rather than like, this is the direction you're going. You can go into the fringes to. Because it's always good to entertain possibility. But maybe a better way to define it might be what you think practical actually means. I think practical means possible and useful.

I do too.

What's the lore to the word practical for you both?

I think that we keep it for the most part. And even me, despite my confession a moment ago, I think this is real life. So when we were starting, AI was still very young and cool, and most organizations didn't have that as a capability in house, and that's still developing. But right now it's. It's no longer the thing where it's the strictly aspirational intent. It's now something that a lot of organizations are incorporating and they have really practical problems to deal with. Like, okay, well, we now know how to produce, to address an architecture and produce a model that can be deployed. And how do I get that into our deployment methodology? How do we get it out to our customers? How do we do that? And I think that's where the bulk. That's where 99% of AI is and should be, I think for most people, if you're not strictly a researcher. And so I think my sense is that we've given a platform for that, for people to talk about it and to come on the show and to help others. And I think one of the things that might have set us apart is that we're always thinking about is this going to mean meaningful for listeners who are trying to do it themselves out there? And I think that's where Daniel's. But this is practical AI Drawing us back on the line is really important because it's fine to dream a little and it's fine to speculate a bit, but for the most part, people are trying to figure it out and get work done, and I think that we actually help them get there.

Yeah, I think useful and meaningful are two words that have been mention in the past couple minutes that are, to me, important. So there are useful and meaningful things that are not code or implemented on GitHub. Right. There's things that influence our workflow and the problems that we solve that are still useful and meaningful, but not talking about a framework or specific open source project or something like that. So, yeah, I agree with that.

So over the course of a hundred episodes, surely there's been highlights and lowlights, there's been successes and struggles. We didn't prep you guys, like bring your favorite episode or anything like that, but if you could just think back and what have been some of the struggles of the podcast and what have been some of the successes for y'?

All?

You want to go first, Daniel?

Yeah, sure. Put me on the spot.

I did see how I did that.

I think one of the successes for me is the sort of. And we're always like, I think we can always do better in getting a sort of variety of guests. And we're always like, trying to cover different things that we haven't covered. But I do think that we've had guests on sort of a range of like, from students just like getting into AI and giving their perspective all the way up to like, you know, people like, like Stuart Russell and others that have been like luminaries in the field. So I think it's really cool that we've got that diversity of perspectives and, you know, a whole lot of different topics covered. So looking back, I think that's one of the things that I'm. I'm pleased with is that kind of variety of perspectives that we've brought, all kind of bringing their unique spin on whatever they're talking about. Also, I don't know if this was specifically a thing that we set out to do when we started. I don't think it was. But even from the very start, I forget it was one of the first episodes where we Talked about a TensorFlow project that was helping African farmers with this app where they took pictures of their cassava plants and Helped them.

That was really early. Yeah.

Identified as Eve. That was like one of the first episodes. I forget, like, ever since that point, it seems like we've always kind of had in the back of our mind like specifically highlighting AI for good episodes. So I think those are really the ones that stand out for me. There's a one with, you know, representing a project from datarobot where they were working, trying to detect like waterline issues. Also in Africa, there were recent ones about like Covid related data and question answering systems related to that or like getting scientific knowledge out there to scientific researchers about COVID So those AI for good ones are really some of the ones that stand out to me.

Yeah, I think I would agree. And I think going back to what you were saying before, some of these folks that have come on are just luminaries in the field. You mentioned Stuart Russell a minute ago.

Wo check. Zaremba.

Oh, yeah, a lot of different people. Bill Dally from Nvidia, you know, we had Anima Anand Kumar. I've probably just messed up her name and I apologize for that on the show from Nvidia and just, I mean, just these people that have really wowed me and to some degree been heroes for us. But at the same time, then we bring on people that have never been out there at all, but they have great ideas and they have great insight and they're hungry and they're doing really amazing things and it's been a platform for them to come out and share that. And so I think it's been from my standpoint, a really good balance that we've struck in terms of not just going with superstar people or just going, you know, whatever, but being able to get all those different perspectives into the show. I think that's good for the AI community. I wish that there were many channels within the AI community to do that. There's going back to actually one of the things that I admire from another changelog show, which is GO time for those who don't know that's for the GO programming language, is the GO community has really rallied around that podcast and I love that sense of community. And I think that's been one of our aspirations here is to give the AI community a place to rally, to be real, to have this chat like what we're doing today and what we've been doing all these episodes. I think I really would like to see us be more successful, you know, in the next hundred episodes for trying to bring people on board and make it a conversation and recognize that AI is just now part of life, like so many other things, and that there's room for every voice involved.

We've seen great success there, especially with JS Party and gotime, in terms of community representation. Diverse voices, diverse perspectives, polite disagreements. We love that. We think that's a welcome place for good dialogue like that and conversation. And those two shows are great examples of us iterating towards that. In quotes, greatness. I don't want to call us Great Jared, but we're doing a great job there. Those shows are very representative of two diverse spectrums in software. JavaScript, Go, of course, and all the ways those two languages and communities tend to go out to do what they do. And I'm happy that both of you are inspired by that, impressed by that, and desire that too.

Yeah, definitely. And I think one of the cool things that I've seen is there are people that are active on our Slack channel, on LinkedIn, on Twitter, and are interacting with both Chris and I, you know, a good bit. And like, even today I was getting, you know, show suggestions or like, hey, I was looking for a show about this, but it doesn't look like you covered that yet. And so all of that's really cool. I think there's been a lot of things throughout the episodes where people have suggested something, we've tried it, or we've covered a gap that people wanted to talk about. And so one of the things that I maybe didn't quite expect was that sort of ongoing, continual throughout the week outside of the podcast, conversation with a lot of people out there that aren't guests on the show but are involved in the community. And that's really cool to see, like, people, you know, having discussions that are useful for them, even outside of just listening to the podcast on these different, you know, channels.

The reach, I think, you know, Daniel and I came into this not having ever done this before. And, you know, Adam, you and Jared have been doing this for years and you're. And you're real pros at it. I think it was very surprising to me when, you know, it wasn't a big deal kind of thing, but like, when I joined my current employer and I'd meet some people and they'd say, oh, I really enjoy your podcast, by the way. Just wanted to let you know that as we're meeting and it was a quiet thing and no big deal, but it was like, wow, the reach is really, you know, really there. And the conversation that that creates is really there. And I've ended up away from the podcast having A lot of conversations about topics that we address, because you'll meet people out there, and they just want it. They're like, by the way, you did this episode on such and on this topic, and I'm. It just really struck a chord with me. And you'll end up spending, you know, 15 or 20 minutes just kind of redressing it. It might have been an episode for many months or ago, and that's had a fairly profound impact on my awareness that that sense of community, it's not always completely apparent, but we have developed that, and AI is getting there. It's following other technical communities in that way, and the need was recognized, and people are choosing to opt in. And that's something that's truly gratifying from my standpoint.

So my interview skills failed me once again. I asked two questions, allowing you to slip the second question. But my persistence pays off, so I'll ask that one again. Struggles doing podcasts is hard. Getting to 100 is hard. Most people fade, and there's a lot of work involved. What has been some struggles along the way? We know I've had life problems, guests problems, scheduling problems. You know, it's not all roses and rainbows. So what have been some of the struggles along the way?

I felt like for quite a while when we were starting, when we were reaching out to guests, I mean, I knew certain people in the community, and certain of them were easy to kind of make connections. But when you're starting out a podcast and you don't have a long history of things, it's like, I feel pretty much like a creeper reaching out to these people on, like, Twitter or whatever. Like, the name of a random message, like, you have no idea who I am. Thankfully, people are mostly gracious, but there was a lot of, like, no, like, no response. Like, there is that time period where you feel like, is this really going to, like, pick up? I know some people are listening, but, like, you know, every. Every time I reach out to people, it's like explaining this thing we're trying to do, and, like, I'm kind of coming out of left field. So I think that was, like, a bit of a hard, hard period for me. And of course, anytime I've learned over time that anytime I listen to myself, which I try to do not, you know, like, 100% of everything I say, but I try to listen to what I'm saying. I think it was, like, various things that you do that, like, make you cringe, and then you replace those things, like, you say, all right, I'm gonna, like, work on that. And then there's various other things that you just replace that thing with that also make you cringe. And so it's like, yeah, there's a little bit of that too. And, you know, life happens. Chris and I, I think, have both had times where I tell Chris, I'm like, I need you to, like, push this forward for a couple weeks, because I am like, I'm not gonna be able to do it. Thankfully, I don't think either one of us had had one of those times where we both needed it at the same time, but there have definitely been times like that where we flip flop back and forth.

Yeah, I would agree. And we rely on each other. Daniel said that about me. But without going into all the specifics, listeners who've listened know we've had some challenges this year that have certainly affected my family. And Daniel stepped in, stepped right in and just took care of things, and that was good. And you really have to, you know, all of us that are doing this, we have day jobs. So we need our. The people that we work with to make allowances and accommodate to some degree. We have families who are supportive. We're all doing these podcasts out of our houses and, you know, families having to recognize that you need a little time, a little quiet space to get some stuff done. And. And I know for ours, when we have a house full of dogs and a young daughter, and that can be pretty challenging. And then, frankly, there's a burden that I didn't appreciate back when I was listening to the changelog and go time and jsparty before we started this. I don't think I understood the burden of trying to provide a Great hour or 45 minutes of content week after week after week. I've learned to really appreciate that when I listen to other is I listen to other podcasts, that it is hard work to do that. And so I've developed as still an amateur in this compared to you guys, who are the real pros. I've really developed an appreciation for the amount of effort it goes in to. At the bottom, what it comes down to is serving your audience and trying to help people get what they need out of it. And they're choosing to listen to you for a little while, and they should rightly expect something from that. And so I think that's what I would finish with is just, I. There's always the burden for Daniel and me to make sure that we have content that is good for people to listen to. And so for Those of you out there listening, please keep coming at us with social media, all the places that we like to engage you. We are listening. We are welcoming your ideas. You are giving us our ideas for our future shows, and we want to serve you.

And finally, after, like a year of doing the podcast, I got to meet Chris in real life and he's a real person, so sort of want to.

Take your word for it.

We hadn't actually met in person before we started the podcast, and that happened way later, which was kind of funny.

It was actually, I was about a year, and it was only in the beginning of January, right before the COVID stuff set out. So most of these episodes, of these hundred episodes were done. Daniel and I had never been in the same place at the same time before we became friends through the podcast.

We just started our Covid life early.

Yeah, we did. But it's worth mentioning. It's remarkable that it is through these episodes that everyone listens to that Daniel and I have become very, very dear friends. And we had never met until January of this year. So in person, it's amazing.

Well, these struggles illustrate why we're here today, which is to celebrate.

Yes.

One, Jared and I are both proud of you guys. You guys have done a great job with this show. It takes, as you have said, a lot of work to produce on all sides. So, one, congrats on 100 episodes. Two, we're proud of you guys. You guys are doing a great job. Keep doing it. Don't stop.

Yeah, thanks for believing in us and keeping us on track. It's been awesome, truly.

And yeah. And you guys have been right there. Even though you're not there from the listener's perspective, you're invisible to them in most episodes. You're actually there every episode. And I cannot count the number of times that Daniel and I have thought, oh, at least we have a great post production team that can clean up whatever mess we just created during that. And so that's really important.

Yeah. And do cool promotions on Everywhere and. Yeah. Keep the wheels moving and it's awesome.

Yeah, it's a team effort. Totally.

Well, I'm not just a producer. I'm a happy listener. And I fall into the category of the AI curious, which we know that practically AI listeners kind of fall into a couple of categories. You have the practitioners, then you have the curious. And I'm very much in the curious. I've never used any of these things in a useful context. What was the other word? Useful and valuable?

Meaningful.

Meaningful. Yeah. I've never Used it in a useful or a meaningful context.

Oh, possible. Possible is a good one, too.

That's inconceivable.

Yeah, I've definitely used it in some inconceivable fashions, but I know more about this stuff than I ever thought I would just by osmosis of listening along and producing. And I'm very much a conversational AI enthusiast at this point. I can talk to anybody about it and trick them into thinking that I know what I'm talking about. And that's a valuable skill in life.

Yeah. Whenever you need a large salary, you can go out and really score high on that interview. I think I probably could.

So what's next for the podcast? What can people expect soon? Where's it headed?

Yeah, you know, for me, I was thinking about this as we were coming into this, and one of the things that I really would like to hear more about is how people are incorporating these AI technologies into their lives and into their work. We hear that to some degree and we know the breadth that this stuff is just going to be pervasive in every industry. And I'm really curious to learn more. I think partly that may be because I spend, you know, I'm in my own job and I spend so much time completely absorbed in trying to get my own work done. But people are using this stuff for really cool, cool applications. And I think I would like to go out there and understand where it's being applied and what are some of the things that I never would have thought about. So I'm pretty excited about that.

Yeah, I mean, there's still a lot of gaps that we, that we haven't covered. Even like today I was talking to a listener who saying, hey, let's cover something about drug discovery and pharma with AI sort of thing. There's just tons of areas that we haven't covered yet. So I think getting some people on from there and I think also developing more good relationships with the kind of leaders in the field and hearing their perspective over time is something that you can continue to expect along with those up and comers. And I think there's really cool stuff as well that's happening all around the world outside of the US and Europe, with AI both in Southeast Asia and Africa and Latin America. And one of the things that I really have a passion for and would love to see on the show is us continuing to try to get those guests who are really innovating in those areas and bringing the benefits of AI to the whole world, really.

So one of the Best ways to circle back is to invite the listeners. We mentioned community a lot. So one easy way if you're, I'm sure if you listen this, you may get the outro and occasionally we'll see it in the outro, which is, hey, there's a community. So changelog.com community but there's a slack. There's lots of people in there. If you want to share your story, these stories, the ways you're tinkering, the way the ways you're practitioning, etc. In AI, you can do that there. So the invitation is there. Changelol.com community the practically I channel hit that up. Daniel Chris they're always hanging there. We're in there, all that good stuff. And there's lots of other people there too. Come and hang out.

You know, another one of the things that I wanted to mention was I think we're at a point now where it's been commoditized enough to where it's not all about business. There's a lot, you know, going back, you know, in, in the spirit, Adam, of you talking about, you know, your cul de sac, camera identification. There's so many places. I believe you. I believe you. There's so many places where we can bring kids into this and do weekend projects. We've talked about some of that at the beginnings of some of our episodes where you can bring people in, you can take it into your schools. As schools are opening up, as Covid gets under control, there's just so many opportunities to do this with it. You don't have to be a top scientist, you don't have to be a data scientist professionally. You can do it at your house. You can. I've heard so many little home projects that people have gotten into over the last year or two. Actually, I would like to highlight some of those. So if you're a listener and you're doing something cool over the weekend, especially if your kids are involved in it, please let us know about that. I'd really like to share those ideas. I have an 8 year old daughter and you know, it's a perfect time for kids to get into this and it's cheap enough and you can get the equipment cheaply these days. So, so yeah, let's bring, bring your family endeavors. It's not always just about work.

It's like you said though, it's real life. It's not going to go anywhere. So it's not going like, hey, let's teach our 8 year olds because we have to but more like because it's interesting and it's curious fun, but it's really fun. Who to thunk it to actually make AI practical to everyday users. I mean, that's the thing, right? Like, I literally want to learn about AI to the point where I can do something to help defend my cul de sac from nefarious people who are trying to steal our wheels. Like, come on, there's got to be an easier way than that. And I have no idea.

And there's tools out there.

Pie and stuff like that. So maybe I can arm myself with an R pie and a camera, of course. And different detection. And maybe my neighbors will let me take their picture and put them into my model. Who knows? But that's. That's shooting for the moon there. But it could be fun. And I have no idea where to start with, really.

If you're protecting everyone's tires, why not, you know?

Exactly.

That's what I'm thinking.

It's like Neighborhood Watch, only with no people.

Exactly. I like it.

Well, guys, thanks so much for sticking with it, having an awesome podcast, and thanks for letting us crash.

Thanks for interrupting.

Yeah, we're happy to join you for this one. Celebrate with you.

Gosh, it was a fun crash. I love that.

If you hadn't interrupted, who knows what this episode might have been.

Yeah, wait and see. I guess I'm curious.

That's right.

Don't forget we have a giveaway going on in celebration of episode 100. Enter for your chance to win some awesome AI hardware from Nvidia intel and Google plus, practical AI and pachyderm swag. We're giving away three bundles so you have a good shot at them. Check your show notes for details on entry. You have until the end of the month. Speaking of Pachyderm, a little birdie told me they have a big announcement coming soon, and you should join their Slack channel to stay tuned. Learn more about that@pachyderm.com thanks to our longtime sponsors for their continued support. Shout out to Fastly Linode and Rollbar. And to the mysterious Brakemaster Cylinder for these awesome beats. That's all for now. We'll talk to you again next.

Did y' all hear my kids there for a second? I guess something fell out there and my son was like, no, I heard it. I was just chuckling because I'm like, I know it's. You guys got to hear it because I can't stop it.

I'm half blind and deaf anyway.

So I dropped the marker, though. I mean, you could totally see in the timeline. Yeah, I heard it. That happens occasionally. That gets edited out. Thankfully, no one gets upset, but, hey, that's what works.