How do you make decisions as a human? How do you consider your options and then choose whatever final option that you want? This is a question I want you to ponder as you listen to today's episode of Developer Tea. This is the second part of the interview with Chris Alban. My name is Jonathan Cottrell, and my job here is to help coach you through some of the hardest parts of your career and help you level up as a developer. I want to help you cultivate the mindset of a great developer. The mindset of a great developer is not fixated on tools. It's not fixated on languages or really hyper-specific things. The great developer mindset plays the long game. That's what I want you to grab a hold of as a listener of this show. I want you to treat your development career like you would treat a long-term investment account. If you're moving too quickly or if you're changing your mind all the time, if you're buying stock and then selling it right away, then that account is ultimately going to be worth very little money in the long run. If you invest predictably, if you ask bigger questions instead of get-rich-quick questions, if you ask questions like, how can I become a great developer and continue to build my career? Over the course of the next 20 or 30 or 40 years? How can I build my career not just based off of the one language that I know, but instead off of the way that I think? And that's what I want you to be thinking here as you listen to today's episode and really every episode of Developer Tea. Thank you so much for listening to this show. Thank you again to Chris for coming on the show. If you missed out on the first part, make sure you go back and listen to that. And I'm going to get out of the way. We're going to wrap up. We're going to get into the second part of the interview with Chris Alban, the co-host of Partially Derivative. Well, I'll share my first experience of actually implementing something that used machine learning. I didn't actually do the algorithms myself. I just used a service and basically evaluated pictures for the objects in the picture. And then we created a live search against those pictures, which a lot of you have probably experienced this now in Google Photos or in your Apple Photos or whatever it is that you use. You don't have to index your own photos anymore. There's something that will recognize that there's a cat in that photo, and you don't have to put cat as a tag. And so the first time I did this, I did it against a bunch of screenshots of my home movies. And basically just started typing in words that would show me the home movies that would have that thing. And it was like a rediscovery. So it was a very deeply emotional experience for me, which is unusual probably. For the average person, you're probably going to see a demo and not connect with it directly. It feels kind of businessy for a lot of people. And to be sure, business will be true. It's not going to be a game. It's going to be a game. It's driving this forward. But there are so many... I think the wrong perception that maybe the hype drives is that there isn't a human or good thing about this, that somehow it's going to kill every job, and it's out to make our world more systemized, and it's not going to make our... There's no optimistic version of that that's being portrayed. At least there's very few. There's less optimism in the world about AI and about machine learning than there is these kind of cold, calculated business narratives are being portrayed. And there's so much more to it. There's so much more power and... I guess power is kind of a bad word to use. There's so many more possibilities is the better way to think about it. And there's so many things that you can do very simply with it. You don't have to be... Like Chris was saying, you don't really have to be... You don't have to understand every single thing that's going on. And really, I guess the hope of this episode of this podcast is to kind of give people the vision for what they could do, some of the things they could do with machine learning. And I think, again, that door is opened up by the first or the second step of understanding of what it does, exactly what you said, what it is and what it isn't. Yeah. I mean, there's just so many examples of it adding more tools to people's toolkits that... It allows you to do better things like just fly a drone up and then shoot a camera down. And there was this one, I think they flew a drone over the ocean and they flew it back. And they trained it, not the drone, but they trained the image recognition machine learning algorithm to spot whales. So they would fly it out and fly it back and then they would count if there was a whale there or something like that. And that just opens up this huge range of things that people didn't think we could do before. And to me, it's one of those things where everything in our life is just going to get smart. And I know there's branding around smart items and smart internet and smart whatever, whatever. But every single product is going to be smart. And I think that's the thing that's going to make it a little bit more interesting. Because every single product around us is just going to be a little bit better. And my huge hope is that it means that it sort of takes some things off our table that we don't need to think about. I hate notifications. I think notifications are crazy. And every single app wants to notify me. Every single website wants to add themselves to my notifications, stuff like that. I want the anti-notification. I just want things to happen behind the scenes. And the example I really love is Amazon. love is is amazon's grocery store which like they're they're trying to make but they use um a neural network and a camera i guess a bunch of cameras to when you pick an item off the shelf it recognizes you because of your i guess the app on your phone and it recognizes the the item that you're picking off the shelf because they've trained it to recognize the items that are in the store and so your experience is that you walk into the store open the amazon app grab an item and walk out of the store and like that's it and like i i want that experience because i don't want to stand in line and i don't want to think if like if like i should choose register four register three or anything like that i want to be distracted by what matters in my life and just absent-mindedly you know like go like deal with these mundane parts that to me is like just the killer app part of a lot of these things and you know like i i i kind of hope it gets to the point where uh you know like down the road that the the jobs that basically people do and they don't like we've we've sort of those are probably the really good jobs to eliminate through automation because it turns out that like those jobs are mind-numbingly dull yeah and like that's the there's a problem with like the transition i think between like where we have now where we have a lot of manual jobs and where we have very few manual jobs and like i think that transition is probably going to have a lot of pain to it which sucks but i hope that we get to the point afterwards you know like a generation through or something like that where it's like no no like i like i there's still jobs and there's still people working and stuff like that of course but they're just you know like there's different jobs because uh you know ai and automation opens up new areas and stuff and and and new jobs that we didn't think before that existed and now of course they exist and it's just it's a shift and like i'm of course worried that that shift is going to be painful yeah and there's lots of like things around universal basic income and that kind of stuff to like to alleviate that and it's something that like i wish the government thought about more but at the same time i really do want to get to this part where where we as as human beings get to focus on spending time with our family and focus on you know say cooking if we like to cook and you know whatever we want and like all the little mundane things in our life are just sort of like you know like you know like you know like you know like you know like you know sort of handled by something else yeah and it's not one great ai right it's like the amazon store has a neural network so it means you don't have to like stand in line with the register and then your fridge has a camera in it and it sort of you know whatever you set you you always want to have milk and so whenever it sees that the milk is low it just orders more milk and more milk arrives and like that kind of stuff where it's just like i don't need to think so much about all those parts of it right would be beneficial to me because like i do a you know like me and my wife split all the chores evenly and that kind of stuff and i spent a huge amount of time so does she like doing all the work and i'm like i don't know what to do i don't know what to do these mundane parts of of our lives just to sort of keep going um yeah i'd like to i'd like to get past that and like a lot of these things around you know ai are are really appealing to me like self-driving cars like i live in a in a you know really rural area um and like man like i would love a car that drove itself because i have to drive like two and a half hours to the airport it would be awesome if my car you know would just drive it or even better if like i could just you know like i could just have i don't even have a car and a car drives up and like i get in and then like i fall asleep and then i wake up at the airport and that kind of stuff like i that that would be a better life experience for me and you know ai is a huge part of of moving that forward and like it's working like i mean a car can drive itself i mean not probably amazing apparently but like we're getting there like give it five years and that's crazy like our car drives itself yeah and and relatively reliable if if every car on the road was a self-driving car then it'd be you know it would happen now um the the technology is certainly available uh to to make that happen um it's really interesting the i one of the things i heard in my master's program that has stuck with me ever since uh it was i did this um certificate it was a management of technology certificate uh at georgia tech and and basically it's kind of the intersection between big business management and technology and i think it's a really interesting thing to think about is that technology is a technology and one of the things that my professor said was technology the definition of technology is knowledge embedded into something and that has stuck so well with me because i see it over and over and over again so um watching that movie going back to hidden figures uh certainly a fantastic movie uh in my opinion i think it's really good um i don't know how accurate it is but some of the messages it portrayed particularly about technology by the way i thought were were really good but this idea that you're going to be replaced um because the knowledge that you have or the capability that you have um the the ability to you know to manipulate something in in time or space or whatever that you have can be embedded into another thing so you think about um industrial revolution this was technology taking on the job of uh the people who were effectively making the bolts right now we have machines that can make the bolts and so the people who made the bolts now they have to go and do something else and this is true for so many individual things along the way where the knowledge is no longer needed to put into a human we can put it into something else whether it whether it's a machine or it's bits or you know whatever it is we can offload some of that information uh some of that knowledge into something else and then we can put it into something else and the thing can have more predictable output the thing can be more predictably you know cost efficient and we have to we don't have to rely on um what is relatively fallible people are relatively fallible so if you think about the the taking this information out of people's heads and instead putting it into something else now we have a much more reliable kind of base level to work from yeah i mean i think it's it's you know i i really like the idea of technology is putting knowledge into something because that's you know like that's really close to what machine learning feels like and that we're like you know we talk about training models and optimizing things and stuff like that but where the paradigm is different is that you know with traditionally in like knowledge-based systems what we would say is like say we wanted to we had a bunch of photos of fruit we wanted to predict something whether it was an apple or not an apple we'd have to come up with some you know steep definition of what an apple is like it's green well some of them aren't green okay well it's certain sizes well some oranges are that's okay okay has smooth skin and it's great you know etc etc etc etc and you know the paradigm of machine learning is totally different where we as humans specialize in the the learning algorithms so just like the algorithms that teach computer stuff and then what we do is we say here's a bunch of photos of fruit um some are apples and some are not we'll tell you which ones are fruit or which ones apples and which ones are not and then we'll tell you which ones are fruit and which ones are not and then we'll was not and then we apply these these uh learning algorithms that that we've come up with and then we look at the output and then the computer actually builds a model for what it thinks you know what it thinks apple's like the definition of an apple is and how to predict apples and stuff like that but like we don't do it we don't have a a definition of apple and like all these algorithms like they don't have a definition of of what a you know middle-aged man looks like it's just it's learned um how to how to predict that kind of stuff and that's such a cool paradigm to be in where the teachers of these kind of things and we're the sort of given the strategies for learning and then we kind of release them to to go to go figure that stuff out you know of course there's the big problem now is that we don't have a lot of data like there's a lot of data in the world but like the data that we can use for things there's just not enough like there's not enough photos of people's fridges in order to train a really good model to predict what's in your fridge right just need more labeled photos of fridges i had this conversation actually very recently we were talking i was talking with a couple of business associates basically i can't name names at this point but basically we were talking about this this idea that uh you know what what is the value of all this information that twitter is amassing right and for someone who who is you know relatively uh uh they don't they don't know a lot about machine learning or they aren't really acquainted with that concept um me looking at it knowing something about machine learning i see this is just a ton of data to talk that just like is connected to humans and when humans create something when humans create some kind of you know informational like some something they've been just spewing out well that's that can be used and we can predict things from it i don't know what i'm not sure exactly what we're going to do but i'm not sure what we're going to do you know something behavioral can be determined from twitter right yeah something valuable is is embedded in this like this moment of somebody interacting and writing out some words we can actually use that information and predict stuff yeah no and i think i mean coming from the social science background and i'll just say it is it is much harder to predict humans than it is to to do other things even probably self-driving cars like humans are are really crafty and they they they do stuff like randomly but more like strategic randomly where they try to be random at moments that they don't think it's very like but it's like it's a nuanced relationship and and people have good days and bad days so there's internal stuff and there's extra it's just like humans are a very complicated thing um but there's like there's just there's so much stuff out there and you know i this one example i love which is that you know siri like we like constantly people mock siri for not being that smart right that not not being able to like understand what you mean and that kind of stuff and like i don't know for certain i don't know anybody working on it in apple or anything like that but like i could almost guarantee that the reason they released siri earlier than it being perfect is because they need your data yeah you trying to do stuff to make it better so like every single time you get it wrong get frustrated they're trying to detect that and they're trying to retrain it every single time you're satisfied with what it gives you it's like that data goes back to the back to apple and they retrain their universal siri model based on based on that and like this you know the big problem with self-driving cars is is not that neural networks can't drive cars it's that we just don't have a lot of self-driving cars out there compared to what one would need to cover every single situation possible um one of the examples was uh there was in pittsburgh i think they had uh like uber has self-driving cars or maybe a tesla or something like that and they put salt on the road because it was snowing and that was kind of the first time the computer had dealt with salt on the road and salt on the road like leave these lines these white lines on the road and of course you know like the neural networks never seen that before because they don't have training photos from that you know like they don't have enough but like give it 10 years of self-driving cars and like they'll start to have covered a lot of yeah a lot of stuff everything you know we're we're getting there but it is about getting that data and of course humans are like the final frontier where we have lots of this messy data and around humans but like humans are insanely hard to predict um so like it's kind of the probably like the final frontier is predicting like more than rudimentary stuff that human does um but maybe we'll get there what does it take to be a real developer well if you're like me a real developer is determined by what they can build not how they build it of course there's some interesting things with code we don't want to do away with code altogether and code can be a lot of fun but ultimately sometimes code can be a lot of fun and today's sponsor helps you write less code and have better collaboration as you're building ios and android apps today's sponsor is fuse fuse is an all-in-one cross-platform solution that works on both mac os and windows the cross-platform side of this means you can build android and ios native applications and you can build a lot of information on your own and you can build a lot of The Fuse installer includes everything you need to know to get started. They also have some really good full source code, detailed explanation examples over at FuseTools.com. If you've ever used a game development system like Unity, Fuse is essentially Unity for app development. Go and check it out, spec.fm.com. And of course, you can find the great examples over at FuseTools.com. Thank you again to Fuse for sponsoring today's episode of Developer Tea. I think it's kind of weird whenever I read, for example, I've talked about these things on the show so many times, but things like the Enneagram and other personality tests, the categorization of different types or personality types, and using the psychological side of things to inform other pieces of what we do. You know, there's a... There's a couple of APIs out there that can take at least a piece of text and cast it into these different, you know, predictive models that basically say, okay, most of the time people use this kind of language. They fit this, you know, personality type. Now, of course, you know, I can be copying and pasting from another thing and it not be me at all. But I do think it's interesting that for certain things, like, for example, purchasing. We have a relatively good ability to predict, you know, how many people are going to purchase this thing based on how many people purchased yesterday and the day before and the day before. Right. So it's as a whole, we have certain predictive abilities. But then when you're trying to say exactly what one person is going to do, that's incredibly difficult. Right. It's very hard to understand what the trajectory of a single person would be. Yeah. It's a... You know, it's this... It's the very problem that a company called Stitch Fix is tackling. So they do machine learning, but for boxes of clothes that they send you. So men and women, I guess they do both genders now. They... An algorithm, like, learns about you through, like, a conversation with a human. And then they send you four or five items of clothing that an algorithm has decided that you want. And it's algorithm based. And it turns out that... It turns out that the whole thing, like, figuring out what sweatshirt you want is insanely, insanely difficult. And I think they have something like 75 to 100 data scientists working on it. Like, it's really, really, really hard because, you know, like, people have preferences and that preference has changed. And they don't even know their preferences. Like, they can't even list out their preferences of clothing. It's literally in their natural neural network of their brain. So it's a hard problem. But, like, you know, it's one of those things that if we can... And the more we get it right, it would be really nice if clothes showed up on my door and they were just clothes that I like to wear. Yeah. It reminds me of probably the most cited AI or machine learning movie of all time, Minority Report, right? So when he walks in the gap... I'm not going to talk about the murder prediction or anything like that. But when he walks in the gap and they basically scan his eye and they're like, hey, how did you like the jeans that you bought last time you were in here? That's... Almost happening now in my inbox. You know, it's pretty amazing. It's like around the corner when I walk in now, you know, you have things that are going to recognize, at least recognize your phone. And your phone is going to recognize where it's at. And it'll pop up something that says, hey, you know, what did you think about those jeans that you bought last time? It will feel quite similar, maybe not quite as eerie as something scanning your eyeball. But certainly the same kind of information going around. Yeah. I mean... I have not seen this yet, but I could almost guarantee that in, you know, like a year or two max, you'll see in like New York and San Francisco, like ads, like sort of like the billboards on bus stops and stuff like that will be directed, not like specifically to you, but to the... The collective you. Right. The like, oh, you're a man. Here's clothes. Yeah. Like here, here's a woman. Here's dresses. And like... Like they'll just see who's around and then try to direct it towards that. I could almost guarantee that like the technology to do that is like definitely commercially out there. And so, you know, like you could go like down the crazy road of thinking that it's going to mine everything and it's mining your Twitter and it's decided like that's probably not going to happen. Right away. Yeah. Yeah. But like a basic thing of saying like, hey, like, well, we use this API and it predicts like, you know, it predicts your gender. And your age. And then we show you products of people who gender and age like, you know, like that. Okay, cool. So like when I walk around, it's like camping gear. Right. Yeah. Exactly. You seem like you might like that. Yeah. And we, I mean, we already see that like that's been going on with AdSense for like a long time. A long time. Yeah. Yeah. You know, and it's just like the goal, of course, is that the experience for the person is better, which was always the argument for AdSense is that it's customized ads. So you only get ads that like at least you... Might want to buy. Yeah. But like, it's one of those things that like, hopefully, it can be used to make our lives more enjoyable and more better and can make products more enjoyable and more better, which hopefully improves our lives. And then, you know, there's like, there's lots of really interesting conversations around now about like how to do it with, you know, like ethically. Right. And like, that's a really important discussion that's like just, just getting started. Like, I feel like it's been Wild West until now. And it'll probably be Wild West for another few years. And like, we'll finally start to get a handle on it. But there, you know, there's discussion of how to do it right. And like, even if you have a bunch of information about people, like, how do you, how do you do it in a way that's responsible? Where like, you know, you don't want someone walking down the street and like, they're being shown things that are private that they don't want. Right. Yeah. You know, like, even if you do, in fact, buy those things, that's not, you know, that's not okay to show that on a billboard while you're walking down the street. Exactly. And, you know, like, it's one of those things where this is... This is a new, you know, like a new set of tools that we're putting in and like a sort of a new discovery and a new planet of opportunity. And we're just barely at the beginning of it. And we like, don't know how to do everything with it yet. Like, there's no... Right. There's no machine learning undergrad degree yet. And I bet you in a few years there will be. And there's barely data science master's degrees. Like... Yeah. It's because we... There was no... Like, when all of us got... When all of us got started there, of course, there wasn't anything like that. It was just like, did you go to a certain meetup and were intrigued by someone's talk and then looked online and saw other things about it? Like, that's where this all came from. Yeah. And a lot of this stuff has a background that's driven in math and statistics and, you know, business analysts, that kind of stuff. You know, linear regression has been around since, I think, like the 50s, I believe. You can probably speak more to that than me. But a lot of this, you know, the fundamental ideas here are all driven... By math and similar types of things. So collecting, you know, for example, engram. You have a collection of words and every three words that would be, I guess it's called a three gram? I don't know. Anyway. Yeah, three gram. Evaluating language and evaluating clumps of data together. That kind of stuff has been around for a while, but it hasn't been, like, it hasn't been given to a machine. To, you know, generate a million iterations of a given thing to test your assumptions, right? That is where the new and powerful capabilities are coming in. So in my management of technology certificate, we did some linear regression to try to predict some demand. That kind of stuff, right? That's been around as long as spreadsheets have been around. But this new frontier of, you know, automatically, like you said, mining data. And you... Doing some of the things that AdSense has been doing since the beginning, but making that available to, like, startups and people who are brand new in the space. That stuff is new. And we're looking at, I think you said on the most recent episode of Partially Derivative, a 20-year kind of revolution in this space. I think that's spot on. I think we're looking at a huge shift in pretty much every area. Every space. Every discipline you can imagine is going to be affected by this in a pretty major way. But it is exciting. And I think the ethics question is probably one of the most important things to start thinking about early, right? Because it's kind of like thinking back to the early days of the Internet. It's easy to look at things from a perspective of that's kind of like a toy, right? That's not really going to be... It's not something to really take seriously yet. And I think we're just now, like you're saying, getting to the point where we're actually seeing data science as powerful. It's no longer just an academic thing. It's no longer theoretical. It's actually real. And the power that you have with it, you can really infringe on people's rights. You can infringe on people's privacy at the very least. And there's a lot that goes with that. And so, in my opinion, privacy is kind of the part of... It's kind of the equal frontier of the future for the next 20 years as well. Yeah. I mean, the big thing that's coming up now is... I think it's called algorithmic responsibility, which is that... So, there's a bunch of different algorithms that we use. Like deep learning versus... Like regression would be considered like a machine learning algorithm in a way. And decision trees and random force and all that kind of stuff. And some of these algorithms... I can tell you... Once it's trained, I could tell you why it made a decision. So, I could literally take your data in and say, Oh, well, you were denied a loan because you're below a threshold of age and you defaulted on a credit card or something like that. Sure. And then a lot of these or all of these artificial neural networks and deep learning, we train the model. We put the data in. We train the model. We understand how it works. We understand how it learns. But the way that neural network is structured, I couldn't tell you the one thing that made you being denied for a loan. You would just be denied for a loan. You'd be like, why? And you're like, I don't know the algorithms that you were denied for a loan. So, it almost takes on a personality, right? Well, the issue is that it becomes an issue of a large amount of trust has to be put in the algorithm itself that it's working well. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. to give to people like there's no reason you should trust your loan companies out their loan companies algorithm isn't being racist like there's no why should you believe that like it like hopefully they have tried really hard and there's a bunch of really good techniques you can actually use to make sure like for example just the simple one of not including not telling the computer about race so it can't like figure that stuff out yeah yeah um but like of course you don't know what they're doing behind the scenes of course they could be of course they could be including that kind of stuff and then you know there's a discussion of like well these neural networks are really powerful but like do we want that like like maybe we should use something like a random forest or a regression or a logistic regression where like i could tell you exactly why it made a decision or didn't make a decision and it's this real back and forth because neural networks are incredibly powerful and all of the really cool stuff that sort of is getting a lot of you know pop culture attention like self-driving cars and like you know like uh google translation which is now a neural network and that kind of stuff that's all things where it's really hard to like figure out why the computer made that decision and we're just barely getting to the point where we're starting to address this for example uh there's a research project that just came out with a paper where they trained a neural network to explain what another neural network was doing oh interesting so let's try to try to like give some kind of thing where like the second neural network was trained to do something like that and then they're like oh yeah i'm gonna do something to convert the like how the first neural network is working but into human words but like that kind of stuff so we can actually have some kind of responsibility because it's it's you know algorithms have been used to uh you know put some sort of structural violence and against people for a long time like totally you know even before computers were really in there like people used formulas that like weren't necessarily racist but clearly they were designed to be racist like that was like they just they targeted things by you know they were designed to be racist and they were designed to be racist and they were like using other variables other than race in order to do that and like that can continue under this scenario totally and that's why we need to have this conversation around these around these things because you know like it this like the black box when people talk about neural networks they talk about it's a black box because we don't know why it made a decision we just know how accurate those decisions tend to be you know is that okay and they're really powerful and we all want a self-driving car but we also want to know why it turns left and why it turns right and if it gets into an accident like why did it why did it get to next and why did it decide to swerve into the pole or not or whatever yeah um and that kind of stuff we're like we're just like it's hard to describe how recent that conversation is and like how infant infant baby steps like we're talking months ago like this kind of stuff got started like we we are just barely getting there and it's a 20 conversation that we're just in month three of or whatever it is and we're just like maybe a year of yeah thank you for listening to the second part of my interview with chris albin the co-host of partially derivative and of course the dr chris albin i guess i should call him by that name but he's such an easy person to talk to such an enjoyable person to talk to and so passionate and excited about the topics that he is discussing so we just naturally kind of hit it off i really feel like i can fully endorse him and i'm really excited about him and i'm going to keep going with him and i will keep going with him and i will keep going with him you guys as developer t listeners to go and subscribe to his show so thank you again to chris for coming on the show thank you to fuse for sponsoring today's episode of developer t write a little bit less code collaborate a little bit more and see things instantly updated on your devices in an in-screen or on-screen simulator go and check it out spec.fm fuse thank you again for listening to today's episode of developer t there is one more part to this interview and if you don't want to miss out on that make sure you subscribe in whatever podcasting app you use thank you again for listening and until next time enjoy your tea