Meet your Practical AI hosts

In this inaugural episode of Practical AI — Adam Stacoviak and Jerod Santo sit down with Daniel Whitenack and Chris Benson to discuss their experiences in Artificial Intelligence, Machine Learning, and Data Science and what they hope to accomplish as hosts of this podcast. Sponsors: Hired https://hired.com/practicalai – Salary and benefits upfront? Yes please. Our listeners get a double hiring bonus of $600! Or, refer a friend and get a check for $1,337 when they accept a job. On Hired companies send you offers with salary, benefits, and even equity upfront. You are in full control of the process. Learn more at hired.com/practicalai https://hired.com/practicalai. Rollbar https://rollbar.com/changelog – We catch our errors before our users do because of Rollbar. Resolve errors in minutes, and deploy your code with confidence. Learn more at rollbar.com/changelog https://rollbar.com/changelog. Fastly https://www.fastly.com/?utm_source=changelog&utm_medium=podcast&utm_campaign=changelog-sponsorship – Our bandwidth partner. Fastly powers fast, secure, and scalable digital experiences. Move beyond your content delivery network to their powerful edge cloud platform. Learn more at fastly.com https://www.fastly.com/?utm_source=changelog&utm_medium=podcast&utm_campaign=changelog-sponsorship. Linode https://linode.com/changelog – Our cloud server of choice. Deploy a fast, efficient, native SSD cloud server for only $5/month. Get 4 months free using the code changelog2018. Start your server - head to linode.com/changelog https://linode.com/changelogFeaturing: Daniel Whitenack – Website https://www.datadan.io/, GitHub https://github.com/dwhitena, X https://x.com/dwhitenaChris Benson – Website https://chrisbenson.com, GitHub https://github.com/chrisbenson, LinkedIn https://www.linkedin.com/in/chrisbenson, X https://x.com/chrisbensonAdam Stacoviak – Website https://adamstacoviak.com/, GitHub https://github.com/adamstac, LinkedIn https://www.linkedin.com/in/adamstacoviak, Mastodon https://changelog.social/@adam, X https://x.com/adamstacJerod Santo – GitHub https://github.com/jerodsanto, LinkedIn https://www.linkedin.com/in/jerodsanto, Mastodon https://changelog.social/@jerod, X https://x.com/jerodsantoShow Notes: Symbolic artificial intelligence https://en.wikipedia.org/wiki/Symbolic_artificial_intelligenceDeep learning https://en.wikipedia.org/wiki/Deep_learningMachine learning https://en.wikipedia.org/wiki/Machine_learningNVIDIA’s GPU Technology Conference (GTC) https://www.nvidia.com/en-us/gtc/ - the premier AI and deep learning event from NVIDIA. Upcoming Events: Register for upcoming webinars here https://practicalai.fm/webinars!

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Baiting for change log is provided by Fastly. Learn more at fastly.com. We move fast and fix things here at change law because of roll bar. Check them out at rollbar.com and we're hosted on Lino servers. Head to linode.com slash change log. This episode of practical AI is brought to you by hired. One thing people hate doing is searching for a new job. It's so painful to search through open positions on every job board under the sun. The process to find a new job is such a mess. If only there was an easier way. Well, I'm here to tell you there is. Our friends at hired have made it so that companies send you offers with salary, benefits, and even equity upfront. All you have to do is answer a few questions to showcase who you are and what type

• 0:44Speaker 00

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• 1:38Speaker 00

  So I think Jared, for me, this show goes back a while because I met Daniel several years ago at GopherCon. He was in data science and wowed me with his super awesome ways and like over, I think a while after that he came on go time and we kind of struck a conversation of like, hey, we'd love to eventually do something around data science regarding a podcast. And then now we're here practically high. Here we are. And we're not just joined by the Daniel. We also have Chris with us. Hi guys. Hello. Hey there. So we're going to talk about the show. This is going to be very much a getting to know what this show is going to be like and what it's going to be about. But we do know it's going to be hosted by you two. So let's get to know the hosts and

• 2:20Speaker 00

  then we'll get to know what, you know, why we're here and why we're even thinking this is a good

• 2:27Speaker 01

  Uh, since Adam started with Daniel, I guess I'll start with Chris. Chris, why don't you give us a little bit of your background and what makes you want to do a show that we're doing?

• 2:36Chris Benson?

  Well, I've been a longtime change log subscriber. I follow change log, follow go time in particular. And one of the things that that that I have loved is being part of this community. And so as a as a member of the AI community that's, you know, racing along and developing, this has always been a missing part of that community. I've listened to other podcasts about AI, but it's never had the same dynamic and the same feel. And I, you know, having gotten to know Daniel a while back, we were talking about that. And it was really clear that this kind of of podcasts, this kind of community really

• 3:19Chris Benson?

  needed to come into being for the AI world, so that people have a go have a place to go and have a home. And that has gotten us both very excited. And so that is, that is what I really want is I want to have a change log style community where every one of us, we know each other, we welcome each other, we help each other along, and we're able to bring this amazing topic of artificial intelligence and machine learning and deep learning to the world at large and make make it possible for people to start participating in it and understand what it means and integrate into their lives because it's it's a truly, it's a it's a technology that's going to affect every every person on the planet.

• 3:58Speaker 01

  What was your particular angle into this community and these topics and this work that you're doing now? How'd you get here?

• 4:04Chris Benson?

  Well, that's a little bit of a roundabout way. I actually got exposed to it many years ago when I was I was in college. And believe it or not, my my parents were in computer science. Mom taught some computer science classes at tech, and they were both engineers at Lockheed, and they worked on the the F 22 project, which is the, you know, the stealth fighter is still the the leading air to air stealth fighter in the world. And they were part of that core team. And when they were, they had these two prototypes that were out at Edwards Air Force Base going Through their their maneuvers and kind of proving it out as this new plane. It was in 1992 and one of those prototype F 22s. It was called the Y F 22 at the time.

• 4:48Chris Benson?

  In the test flights, it was low down and they were going through some maneuvers where I believe the pilot was trying to kind of crash the plane onto the runway and the avionics were supposed to prevent it. And unfortunately, the avionics did not do that. And they hit the ground and the pilot was was fine. There's YouTube video, you can go out and see about the event in 1992. But my dad was one of the people tasked to figure out what happened and how to solve this and such going forward. And so as a college student, I came home and they came home from work and that was our evening talk for about a year as dad was getting into neural networks back in, you know, way back in the day. And that was when I first learned about it.

• 5:30Chris Benson?

  And so I kind of got exposed to it very intensely for about a year in our conversations and developed a passion for it. And eventually the problems got solved and they moved on and it fell out of my life for a long time. But then as we've had this resurgence in deep learning, which is, you know, neural network technology has come back. I've come back into it initially as a hobby and then I turned my whole career toward this. And so over the last, last few years here, this is where I've arrived at and it's, it's become, it is my career going forward.

• 6:01Speaker 00

  That's what I love about these intro shows, Jared, because like that shows that Chris, and I can't wait to hear what Daniel has to say, but that shows that Chris has.

• 6:09Speaker 01

  Top that, Daniel.

• 6:10Speaker 00

  He's got this, you know, he's got this. Stealth fighters for me. Sorry. Well, it just shows that it goes back generational, you know? It's like legacy, you know what I mean? Like it's part of the family, which makes it even cooler.

• 6:24Chris Benson?

  This will sound really corny when I say this, but honestly, my dad is since passed away, but I actually feel closer to my dad, working in the space because it brings up memories of us talking through this in years past. So there, there really is actually an emotional attachment, a family attachment in this topic for me in that sense.

• 6:41Speaker 01

  It reminds me of a change log episode that we did and I can't remember who we were interviewing. Maybe it was TensorFlow, maybe it was somebody else, but you know, they mentioned this thing, which you just brought it back to me, Chris, is how that for a very long time, these particular methodologies and the mathematics and this was like. They aren't new. They were in the land of academia and research and in the labs. But there wasn't like a commercial viability to them for so long. And then all of a sudden it exploded and everything changed overnight. And all of a sudden, the place of of R and D became Apple commercials and you know, Google products. And that's such a dramatic shift.

• 7:23Chris Benson?

  Yeah, it's really amazing what's happened. When I, when I think back, one of the thoughts I've had going back to when he was doing it, and I was still, you know, you know, this was what 20 years ago, basically. And he had some advantages that I don't think most people had at the day. And that he worked for a company that was invested in emerging technologies. And he had the compute power available for what it was that day. He had the background and the data set that came off the plane that they were trying to do is they were using the other prototype and generating data. They were flying around. I remember my dad telling me that they were basically pulling data off all these sensors on the, on the remaining prototype.

• 8:05Chris Benson?

  And that generated a massive amount of data, you know, on a per second basis. All these sensors are sending it down. And that's what he was using for neural network training. But he was in, in retrospect, he was in such a unique position to have both the compute, the big data necessary and the algorithmic expertise to, to bring that together in 1992. And that was such a rare case. And that's why it didn't really take off in the world at that point, other than these exception cases. And so we're in a very different world now where we all have this compute power, which we, I'm sure we'll be talking about at length. And we have lots of big data around that we're using and lots of algorithmic, both the expertise and it's continuing to develop on a day to day.

• 8:47Chris Benson?

  So something that he was in a very unique opportunity to do back then is now available to all of us today. And that's one of the reasons for me that I wanted to have this podcast is that this is now available to everybody.

• 8:59Speaker 01

  That's definitely what this is going to be about is the exploration of all the missing pieces and all the pieces that are there now to make it something that has become such a

• 9:09Speaker 00

  Mind blowing and crazy growing industry that just wasn't quite there before. Now it's it's like the magic moment, the special time for it. It really is. We're in the middle of a revolution. So Daniel, what about you? What's uh, what's your story? Let's go. How did you get into AI? I know you were in data science, met you years ago at GopherCon. Kind of give the backstory to you and and data science and your perspective.

• 9:32Daniel Whitenack?

  Yeah, definitely. And it's just super awesome to be be here with you guys. I know we've like you mentioned, Adam, we've been talking about this for

• 9:40Speaker 00

  A long time.

• 9:40Daniel Whitenack?

  Quite a while. So it's

• 9:42Speaker 00

  I like that though.

• 9:42Daniel Whitenack?

  Pretty exciting to, yeah. I mean, it, we went through, you know, various iterations of what this should be. And I'm really excited that, you know, Chris, Chris is kind of coming along here and has, has the same passions. And this is becoming reality. So I'm super excited about it.

• 9:59Speaker 00

  It's been at least, I want to say at least a year, maybe a year and a half in the making.

• 10:03Daniel Whitenack?

  Yeah, I think it was, um, we might have met, I was on GoTime, probably like one of the first few episodes.

• 10:10Speaker 00

  And the first one, I would say.

• 10:11Daniel Whitenack?

  Yeah, something like that. So yeah, it's been been a while.

• 10:14Speaker 00

  I think it was around that time or not long after it. I was like, hey, you did great on that show. I know you're in data science and that we're expanding different things we're doing here. We should do something like, there's no one really covering data science that well. From a podcast perspective, like the stories and the nuances and the, the news and the changes and the people and the projects and all the things that are coming from it. I mean, I learned about Pachyderm and data lakes that day on that podcast. And I was like, I had no idea there was even a data lake. That's so cool. And that's, that's was like, we should do a podcast about this. And here we are now, it's evolved into not just data science, but also AI and machine learning and neural networks and all the in betweens.

• 10:54Daniel Whitenack?

  Yeah, definitely. It's, it's exciting. And I think it's, I'm excited to just learn from, from everybody on the show. It's, it's gonna be great. So yeah, I can kind of give a little bit of, of my backstory. I actually started out in the physics world. So I did my undergrad and graduate work in physics and started out doing, you know, academic research. In atomic and molecular physics, basically trying to calculate. The properties of of atoms and molecules from first principles. So basically, what that means is like from first principles, there's kind of two camps in this world. There's the camp that says, hey, we're gonna like figure out the equations that describe nature.

• 11:36Daniel Whitenack?

  And just using those equations, calculate properties about these things. And then there's the other side, which is like, well, we're just going to like create these incredibly complicated functions and parameterize all of their various parameters based on experiments. And then they'll do good at predicting properties. So I was in the camp of like using all like from first principles, just using the equations that describe nature. Which now I see as incredibly ironic, because if you look at what AI is, it's basically the the other camp from which I was in, creating these these complicated functions and and models and parameterizing them to describe reality.

• 12:20Daniel Whitenack?

  Right? So I find that incredibly ironic. And it was during this time that I was actually exposed to machine learning to begin with. Because if you think about that problem, you know, people are trying to like come up with all of these complicated models to describe how to how to calculate the properties of electrons. And eventually, someone, you know, a group in Berkeley, they they said, well, hey, let's just let the machines do it. And so they started using machine learning methods to actually learn kind of the exact solutions for these functionals to to describe to describe the properties of atoms and molecules.

• 13:01Daniel Whitenack?

  And I was basically, I mean, it was kind of sad in one sense, because I was like, well, then, you know, all like, that's basically the way we should be doing it, right? Because but at the same time, I'm doing a whole PhD on not doing things that way. So it's it's just kind of funny in that sense that that was my first first exposure to it. But then I I went into industry after after academia. I really didn't know why, you know, what jobs were available for a theoretical physicist in industry. It's not really that clear. And that was kind of at the beginning of the hype of data science. So like pre AI hype, but like data science hype.

• 13:44Daniel Whitenack?

  And just the beginning of that. And so I found my first job working with this IP firm, basically, you know, helping lawyers understand math is essentially the job, which is incredibly interesting in certain ways.

• 14:01Speaker 00

  And I In which ways?

• 14:04Daniel Whitenack?

  Well, I mean, it's, I think it was incredibly valuable for me because it, in some ways, it helped me try to like really articulate complex things to people that without, you know, without as much of a math background, right? And so I think that's a hugely valuable skill in

• 14:24Speaker 00

  You had to make it practical.

• 14:26Daniel Whitenack?

  Yeah, exactly. So like making it practical, being able to help people flesh out the flow of all the different things they were doing. I think all of that, I mean, I think it built a lot in me that has really helped me since then. So yeah, I think it was, it was, it was great. Also improved my writing skills greatly because it turns out lawyers are are rather particular about their grammar.

• 14:50Speaker 00

  Yes, words mean a lot. Words, the right word in the right place or the wrong word in the wrong place could be very, very detrimental or very good.

• 14:57Daniel Whitenack?

  Yeah. So eventually kind of how I got into data science was I kind of worked with all these people and helped kind of explain their stuff. And I started to realize, hey, there are these data science people out there, data scientists who are doing things that are like quite familiar to me, even though I didn't know people were doing them in industry. Like they're creating these models that are very similar to some of the like mathematical and computational techniques that I've done, but applying them to different problems in business. And so that's where I, it kind of hit me, hey, like I should be one of those people because that sounds fun. So I got a job as a data scientist at a startup and have, have been one since. And now I'm working with a great, on a great project called, called

• 15:41Daniel Whitenack?

  Pachyderm, which I'm sure we'll talk about more on the, on the show. But yeah, just really excited to kind of take that full circle from initially being kind of trumped by machine learning in my, in my academic research. To kind of coming full circle and participating in the revolution, as Chris was saying. But no stealth fighters at all in my in my past that I know of, although I'm sure I've been spied on. But I haven't been participating.

• 16:10Speaker 01

  So the tagline for this show, for this podcast, is that it makes artificial intelligence practical, productive, and accessible to everyone. So why don't you guys help the audience understand, first of all, exactly what that means? And then, secondly, how we're going to achieve that? Like, what are we going to go about doing here?

• 16:29Daniel Whitenack?

  I would love to hear Chris's definition of AI. And maybe that will lead into to, you know, make it accessible.

• 16:37Chris Benson?

  It's kind of funny, that has evolved for me over time. And when I, when I look at, you can look at different people, how they relate, you know, AI and define it. And with machine learning and deep learning. And I've evolved as well on that. And where I am right now is I think of AI as almost a, a holding term for whatever is the current technology that you're defining that with. So if you were to go back and when, when my mom was studying AI back in the like 19, early 1980s at Georgia Tech, the things that she was working on, which is, you know, no longer exists today. It was called symbolic AI.

• 17:20Chris Benson?

  And that is, that is essentially a field that is no longer with us. But, you know, that was what people meant when they said AI. And today, as we are, as we are recording this, AI is almost exclusively in most people's minds, deep learning, and that's how people refer to it. And that realization made me understand that AI was a term that evolves over time and evolves with us, with the technology advancements that are occurring. And so it made me realize that AI in 2030 may be something that is different from deep learning as we define it today. And so I've almost disconnected what it means.

• 18:01Chris Benson?

  It's kind of like if you're, if you're thinking about it as AI being a variable and you have, you know, and you're setting that variable to a value, that that value changes over time. And it's not, it's not a constant. So in my view today, and this is certainly, it's not the truth, it's just the way I see it. I, I really largely associate deep learning with what AI is today and, and deep learning being a, a subset or sub-discipline of machine learning. And that is, that is, so if I say AI, I'm almost invariably referring to deep learning. There, there is debatably a few other machine learning technologies that you might define in that category, but that is it for me. But I also go forward

• 18:45Chris Benson?

  with that expectation that how I'm defining AI is going to evolve. And so what, what, what we talk about down the road may be a bit different as new technologies arise and, and are put into, into practice.

• 18:57Speaker 00

  So do you have to define artificial intelligence based on the definition of deep learning?

• 19:02Chris Benson?

  Yeah, I think right now it is, but I think overall, you know, we're, we're in this, this long-term trend over decades of trying to bring more intelligent solutions into being. And, and that brings the word intelligent in, which is itself in this context, a little bit, a little bit mushy in terms of how we're defining it. But we're trying to get to where our computing capabilities are becoming ever more sophisticated and require ever less explicit programming from us. And so I kind of think of today, the place I've arrived is I kind of think of AI is the current state of the art of, of

• 19:44Chris Benson?

  how you get there and, and largely associated with self-learning in terms of how you would approach it. And it's funny, there, there's a, I was at the Nvidia GPU.

• 19:58Chris Benson?

  Conference a few weeks ago and the CEO of Nvidia Jensen Huang had some had a line in his presentation that I've really taken to and that is that AI is the new source code and and he really sees it as redefining not just data science but specifically software development itself and and that it is a it is a new way of getting where you want to go where you're letting your systems find that best path forward instead of a programmer explicitly using if then or case statements you know to to to get to that same place and they have to think of every edge case.

• 20:36Chris Benson?

  So AI is is to me forward thinking that that statement has colored the last few weeks how I'm looking at is is it's it's the next generation of software. It is the revolution in software programming that we're going through and it's not just something that that belongs to the data science world. It's a fusion of them.

• 20:55Daniel Whitenack?

  I'm really glad to hear that perspective from you, Chris, and I expected the reason why I asked that because I think it kind of leads a lot to how we answer like Jared's original questions around the show and that sort of thing. And the fact is that, you know, AI and this space is kind of constantly evolving and there's so much, you know, happening happening and it will happen over time that will will change how that's even defined. And so I think part of the show is really kind of bringing people along with us on that journey and kind of bringing them into the current state of the community, introducing them to what the new things are are coming online and and how you

• 21:37Daniel Whitenack?

  know, how we're kind of navigating this constantly changing thing called called AI. At the same time, I was kind of, you know, poking poking Chris because if you ask that question at, you know, some AI conference, it's guaranteed to get you like two days of intense discussion and and argument.

• 21:57Speaker 00

  The answer was longer than I expected, that's for sure, but I like that.

• 22:00Daniel Whitenack?

  Yeah, no, I think I actually just listened to the Changelog show on Elastic Search, or Elastic, I guess is the company now, but you were talking about Elastic Search. And I think a great comment was made in that show about no one wants just data, right? They want insights from the data, which has led, you know, the Elastic Search team to create, you know, these great tools around full text search. And they kind of have implemented that as a way to get insights. And I would say like, you know, no one wants data, they want insights from the data, right? And in so many cases, either we, we don't want to, or we can't define the relationships

• 22:42Daniel Whitenack?

  between the data and the insights in an easy way, right? And in my mind, AI is filling that gap, right? It's filling the gap between data and these complicated, complex relationships that eventually lead to some insight, whether that's detecting objects or, or whatever it is. And I, I think that's why, like Chris mentioned, it changes so much over time is those insights that we want might change, but our data also might change. And we might, you know, develop new methods for, you know, defining those relationships. And now we have, you know, examples like using machine learning to figure out how to do machine learning and define those relationships. So it's, it's a lot of exciting stuff.

• 23:26Speaker 01

  So touching on a little bit, you know, looking down the show, let's say we've been doing this for six months, what do you guys imagine are some of the topics that have been covered, or maybe even some of the guests that you've had on? What are some conversations that you just can't wait to have and that people can expect from Practical AI?

• 23:45Daniel Whitenack?

  That's a great question. I kind of view, you know, multiple different categories of shows that we'll have and interviews that we'll have. So we will have kind of guest interviews that will be focused on certain applications of AI, right? To, to really interesting things like, like detecting new planets and, you know, Identifying cancer and other things like that. So that's something I would expect. I would also expect us to have guests on that will help us pick apart these complicated topics because we we really want people in the more people in the AI community. We we welcome you and we want you and we as well want to learn about all of these complicated topics in a very accessible way

• 24:28Daniel Whitenack?

  and don't want it to be intimidating, which, you know, AI might be to to certain people. So we'll have kind of some learning shows like that. And then, you know, finally, we'll just kind of have some topical shows. So there's a lot of kind of general areas in in AI. So Chris mentioned deep learning, which if you're not familiar, has to do with these very deep neural networks. So in in essence, a very, very, very complicated, you know, nonlinear function that you parameterize to do something like object detection. And so well, I'm sure we'll talk about that as as a topic.

• 25:07Daniel Whitenack?

  But there's a lot of other things and really interesting things like, you know, AI ethics, you know, what if what if your self-driving car, you know, runs over someone in malicious AI and other things. And then, you know, what what infrastructure do people use for AI? How do you do AI on a phone? So there's all sorts of interesting topics that have to do either with the application of AI or actual technology around the, you know, practical implementation of AI.

• 25:40Chris Benson?

  Yeah, there there are so many different levels to to answer that question on. There's in terms of what I'm looking for to cover a couple real fast, there's I I will. I know that Daniel and I will have joy when we see people that maybe are intimidated, that don't necessarily know how to how to enter into the field. Understand that the tools are accessible, understand how to get started in it. And so, you know, that data scientist or that software developer who says, wow, it seems like this might I might have a really good problem to solve. If I only was able to do that and and to help that person get started, understand where to go,

• 26:22Chris Benson?

  what the resources are, how to how to think about the problem and and just a very pragmatic sense of there, which is which is just how all of us have entered into so many other technology areas, you know, and that this is just another one at that level, because the the availability of tools and and the the explosion in terms of resources out there, tutorials and stuff is just it's just overwhelming in terms of what I see coming across my newsfeed and to help find really good material and and push it toward people and say, hey, I think this is a useful thing. That's that's one. And then beyond that,

• 27:02Chris Benson?

  one thing that's unique about this field is that as AI does evolve beyond where it's at today, someday, this is potentially a technology that takes on characteristics that no other technologies have ever achieved. You know, there there is the speculation on self-awareness and then superintelligence and consciousness that are parts of the of just everyday language. You don't have to be in AI to be discussing these. And and there are questions that that I get asked all the time. And and they're fascinating to think about. And and Daniel mentioned the ethics concern. I think this is this is a huge area where you have a toolset that is so powerful, but we're just starting

• 27:46Chris Benson?

  to understand how it can be used. And there's so many things that we don't know about where we're going to go with it. And so these questions of using it responsibly is going to be a really big topic going ahead. And so there you don't just have the the technical side, you have the the societal impact, you have the ethical concerns that are associated with it. And and you have it as how do you merge this technology into humanity in the years ahead in a productive way, in a way that helps people rather than hurting them? And and just is there's so much opportunity for conversations on all of these different levels that I'm really

• 28:26Chris Benson?

  Excited to welcome people into this conversation and have that kind of diversity of thought on this. And some shows I would imagine are very hands on and others are debating these these larger issues.

• 28:40Speaker 01

  Well, as you all can tell, we are very excited, all four of us, as we continue on. I'm excited to listen because this is very much an area of software development and the industry that I'm on the fringe of. And I think podcasts are an excellent way to learn these things in a non-pressure filled, right? No, you don't have to perform. You can do it while you're mowing the lawn or while you're commuting or what have you.

• 29:08Speaker 01

  And so I definitely intend on listening every week and accessing myself some AI as well. So looking forward to it.

• 29:17Speaker 00

  That kind of leads into maybe what we can expect. So if you're a listener out there listening to this, obviously you're like, hey, what can we expect from the show? And I think, you know, we're, we have some ideas, obviously those ideas may change. We're all about iteration here. So the rough idea is obviously sharing news, resources. And as Daniel and Chris mentioned, making them very practical, you know, maybe even demystifying things that are very complex or hard to understand, or giving people inroads who are intimidated. But you've also got this other side where you want to do, you know, either topical interviews or straight up interviews, or having people that are really innovating in this space, sharing what they're working on and how they're working on it. So I think that that's something that the listeners can expect.

• 29:59Speaker 00

  This is definitely going to be a weekly podcast, so expect something in your ears every single week. If we're not doing that, then we're dropping the ball. We're not going to do that. So any closing thoughts from Chris or Daniel? Anything I just want to leave on the floor here before we close out this first intro show?

• 30:15Chris Benson?

  Yeah, I certainly do. I want, first of all, I want to thank both Adam and Jared for, for kind of believing in, in this idea of, of, of the community around AI and giving Daniel and I this opportunity to engage the community. I think I'm really looking forward to what we can build ahead. And then on top of that, I'm really hoping that The listeners engage us. Daniel and I are both very active on social media and Twitter. And and this this is a dialogue. This is something that the reason we wanted to get this going is to have a community that you can come to feel welcome in, take a risk in. You don't have to know everything and explore it with us as we go forward. And so I'm looking forward to that feedback and that

• 30:59Chris Benson?

  conversation with our community as we build that up. That's I'm tremendously excited to do that.

• 31:04Speaker 00

  On that note, I also want to mention, since you said the word community, you can go to changelog.com slash community. And, you know, that dialogue can happen in Slack. So if you go to that that URL, you'll see a sign that says, join the community. Click that button. It costs you nothing. You can get in Slack. You can talk with us in the AI channel, or any other channels we have going on there about certain topics. But I'm sure Daniel and Chris will be in there. But if if that's something you're interested in, you should definitely do that like right now. And

• 31:33Chris Benson?

  Absolutely.

• 31:34Speaker 00

  What about you, Daniel?

• 31:35Daniel Whitenack?

  Yeah, I kind of will echo Chris's. Thanks to everyone. And also, I just reiterate, you know,

• 31:44Daniel Whitenack?

  we we view kind of the development of practical methods in AI and the practice of AI as something very important as as as this trend grows. And we want you to be part of things. And I would just encourage you if you're if you're starting out in AI, or maybe you you feel like you need to level up in in AI, you know, get your get your hands dirty. And we're here. We're here to help, you know, just hack on something and get something working or not working. And then come grab us in in Slack or on Twitter and reach out. And, you know, the that's, that's how we all got started. And and I certainly hope that you'll just try to get hands on and try some things.

• 32:28Daniel Whitenack?

  And and we can't wait to see what you build. And on that note, I'll also emphasize, I mean, we're going to have some great guests on the show from all sorts of places like, you know, like Google and and other places. But we also want to hear from from people, you know, in the trenches, working in in startups, trying To build products around AI or maybe just doing amazing side project work that's that's open source and we want to hear from you. So if you if you've got something really interesting going and an interesting project and maybe you're doing something, you know, for, you know, not not for profit, but just for your own interest or for some greater good,

• 33:11Daniel Whitenack?

  you know, these are the things that we would love to hear about and we'd love to feature. So we at least like you to come and share those with us and and hope to discuss those things. So.

• 33:21Speaker 00

  There's two URLs I can suggest for that. Actually, one, sorry.

• 33:27Speaker 00

  One's an email, one's a URL. So you can email us at any time for things like this. So show suggestions [email protected]. Our preference is our open inbox, which is on GitHub. It's a repo called Ping. So you can go to github.com/thechangelog/ping. And we have labels in there for all of our different shows. So if you want to mention a particular suggestion for us to key in on for practical AI, go there. Or if it's something you just want to email us and say, hey, email [email protected]. We'll get that in the right place. And so if you've got ideas like Daniel's mentioning here, just reach out. So that's it. Well, fellas, thank you so much for,

• 34:10Speaker 00

  you know, this is gonna be a fun ride. I think that's probably what I'm thinking before. It's like, this is gonna be an adventure. It's gonna be an exploration to you, the listeners. You're gonna come on this ride with us and just hang on. It's gonna be fun.

• 34:23Daniel Whitenack?

  We're psyched, man. Totally psyched.

• 34:25Speaker 00

  Yeah, it's gonna be awesome. All right, we'll leave it there.

• 34:29Speaker 00

  All right. Thank you for tuning into this episode of Practical AI. If you enjoyed the show, do us a favor, go on iTunes, give us a rating, go in your podcast app and favorite it. If you are on Twitter or a social network, share a link with a friend, whatever you gotta do, share the show with a friend if you enjoyed it. And bandwidth for change log is provided by Fastly. Learn more at fastly.com. And we catch our airs before users do here at change law because of roll bar. Check them out at rollbar.com/changelog. And we're hosted on Linode cloud servers. change law check them out support this show this episode is hosted by Daniel Whiteknack and Chris Benson editing is done by Tim Smith the music is by Breakmaster Cylinder and you can find more shows just like this at change law.com when you go there pop in

• 35:12Speaker 00

  your email address get our weekly email keeping you up to date with the news and podcasts for developers in your inbox every single week thanks for tuning in we'll see you next week