WEBVTT 00:00:00.001 --> 00:00:04.900 If there's ever been a time in history that journalism is needed to shine a light on what's 00:00:04.900 --> 00:00:09.840 happening in the world, it's now. Would it surprise you to hear that Python and machine learning are 00:00:09.840 --> 00:00:14.320 playing an increasingly important role in discovering and bringing us the news? On this 00:00:14.320 --> 00:00:18.660 episode, you'll meet Carolyn Stransky, a journalist and developer who's been researching this 00:00:18.660 --> 00:00:25.280 intersection of tech and journalism. This is Talk Python To Me, episode 280, recorded August 26, 00:00:25.280 --> 00:00:26.180 2020. 00:00:26.180 --> 00:00:44.560 Welcome to Talk Python To Me, a weekly podcast on Python, the language, the libraries, the 00:00:44.560 --> 00:00:49.320 ecosystem, and the personalities. This is your host, Michael Kennedy. Follow me on Twitter where 00:00:49.320 --> 00:00:54.680 I'm at mkennedy. Keep up with the show and listen to past episodes at talkpython.fm and follow the 00:00:54.680 --> 00:01:01.100 show on Twitter via at talkpython. This episode is brought to you by brilliant.org and us. Before 00:01:01.100 --> 00:01:05.660 we talk with Carolyn, a quick announcement. Two of the courses that we've released in early access 00:01:05.660 --> 00:01:11.620 mode are now complete, 100% done, and they're ready for you. That's the Python memory management and 00:01:11.620 --> 00:01:18.520 tips course and moving from Excel to Python with pandas and Jupyter. Just visit talkpython.fm and 00:01:18.520 --> 00:01:24.180 click on Python courses to learn more. Carolyn, welcome to Talk Python To Me. 00:01:24.180 --> 00:01:26.300 Thank you. Thank you for having me. 00:01:26.300 --> 00:01:32.420 I'm really interested to hear about this topic. I ran across one of your presentations talking about 00:01:32.420 --> 00:01:40.080 how AI is affecting journalism, and I just was really fascinated by all of these ways that newspapers and 00:01:40.080 --> 00:01:45.460 journalists are doing really cool stuff with like machine learning and AI, and oftentimes that means 00:01:45.460 --> 00:01:52.660 Python as well. Now, before we get into our main topic with AI and journalism, let's just start with 00:01:52.660 --> 00:01:57.440 your story. How did you get interested in programming? You started out on the journalism side, not the AI 00:01:57.440 --> 00:02:05.720 side, right? Yes, exactly. So I studied journalism in university, and that was my focus. My focus was 00:02:05.720 --> 00:02:11.720 actually print journalism. I really thought that was going to be... I mean, okay, I knew it wasn't going to be the 00:02:11.720 --> 00:02:17.820 future, but I really... my skills were in writing, so I thought, okay, newspapers, solid. They're not going 00:02:17.820 --> 00:02:26.120 anywhere. It's fine. So that was kind of my specialty in a way. I started out in sports journalism and then 00:02:26.120 --> 00:02:32.620 kind of moved around after I graduated. Right after I graduated, I moved to Berlin, and I realized that 00:02:32.620 --> 00:02:39.720 being a journalist is very hard, and being a journalist in a country that you don't speak the language is even 00:02:39.720 --> 00:02:47.560 more difficult. Yeah. So yeah, it took me a while, but I figured that out. And so then I started covering 00:02:47.560 --> 00:02:53.380 things like when I first moved to Berlin, it was the refugee crisis or in the midst of the refugee crisis 00:02:53.380 --> 00:03:00.840 in 2015. So I was able to get a few freelance pieces covering that. I also was able to do more 00:03:00.840 --> 00:03:09.360 tech-related. So I started doing a bit of activism fused with tech articles, things like anti-harassment 00:03:09.360 --> 00:03:17.140 tools, like two-way sex toys, things like that. So because in Berlin, there's a big... Yeah, in Berlin, 00:03:17.140 --> 00:03:23.760 there's a big tech and startup scene here. So that was like an English-speaking community that I had access 00:03:23.760 --> 00:03:31.200 to. And eventually, I needed to get a full-time job to keep my visa. So I went into... This is long, 00:03:31.200 --> 00:03:36.920 but I went into tech marketing, went to technical writing, then learned to code. Now I'm a developer. 00:03:36.920 --> 00:03:41.100 What an interesting journey. I think a lot of people find their way into working with software 00:03:41.100 --> 00:03:45.800 in a roundabout way like that. Like for me, it was, oh, I'm going to go study chemistry and math 00:03:45.800 --> 00:03:50.600 at college. And I guess I got to learn a little programming so that I can do the math work and the 00:03:50.600 --> 00:03:54.380 math research. And wait a minute, I actually like this better than the math. What am I doing here? 00:03:54.380 --> 00:03:58.780 That was me as a technical writer because I was writing these tutorials, but I didn't really know 00:03:58.780 --> 00:04:04.980 how to code. So then I would just hand off... It would be like, put code here. I'd hand it to a 00:04:04.980 --> 00:04:08.860 developer. And finally, I was like, I can do that. Write code. 00:04:08.860 --> 00:04:12.820 That's awesome. Yeah. And you're having fun doing programming these days? 00:04:12.820 --> 00:04:18.380 I mean, it's a great way to make a living. And I think the problem-solving aspect of it, 00:04:18.380 --> 00:04:22.920 but I do, I really miss journalism. Like I would be lying. That's why I like to research 00:04:22.920 --> 00:04:26.280 topics like this because it helps me feel a bit more connected. 00:04:26.280 --> 00:04:32.800 Stay connected. Well, I do think that there's a lot of ways in which journalists can use tech 00:04:32.800 --> 00:04:38.640 or could be helped by folks with tech skills. So who knows? We may find you back in the journalism 00:04:38.640 --> 00:04:41.180 space on the tech side of the desk. 00:04:41.180 --> 00:04:42.200 That's the dream. 00:04:42.200 --> 00:04:44.020 Yeah. Awesome. Awesome. 00:04:44.240 --> 00:04:47.620 So what are you doing these days, like day to day right now? 00:04:47.620 --> 00:04:53.460 So right now I'm actually dipping back into technical writing a little bit. So I'm doing 00:04:53.460 --> 00:04:58.340 the Google season of docs, which is a three month program from Google where you're partnered 00:04:58.340 --> 00:05:04.600 with an open source organization. So I'm working with the GraphQL foundation and yeah. So not 00:05:04.600 --> 00:05:11.380 really using a spoiler alert, not really using Python day to day, not really a proficient Python 00:05:11.380 --> 00:05:18.020 developer. But in my previous job, I was using Python because we were an automated testing service. 00:05:18.020 --> 00:05:24.100 And a lot of our, you know, suites were written in Python. We had a data team, a data science team 00:05:24.100 --> 00:05:30.100 that primarily written Python, anything that wasn't written in Python, we usually had some sort of like 00:05:30.100 --> 00:05:33.180 Porter so that people could write it in Python. 00:05:33.680 --> 00:05:34.400 Right. Exactly. 00:05:34.400 --> 00:05:38.180 You know, compiled into PureScript or something like that. So. 00:05:38.180 --> 00:05:46.120 Yeah. Very, very cool. Let's start pre AI talking about journalism and just talk about data and 00:05:46.120 --> 00:05:50.320 journalism. Now, I feel like these things have always gone together. You know, if you went back to 00:05:50.320 --> 00:05:55.920 like 1920 and you grabbed a newspaper, it would probably have stuff about the stock market and here 00:05:55.920 --> 00:06:04.640 the trends and whatnot. But accessing data has become much, much easier in the last 10 years or whatever. 00:06:04.640 --> 00:06:09.760 When we have web scraping, we have APIs, we have all these different ways of accessing data, right? 00:06:09.760 --> 00:06:16.460 The internet was massive in that regard and so on. So when I think of data internalism, probably the 00:06:16.460 --> 00:06:24.200 first place that I think of is 538.com. Like that place just has so many GitHub repositories of all 00:06:24.200 --> 00:06:28.040 the data that they use and that you go there and there's all these graphs and stuff. But you know, 00:06:28.040 --> 00:06:33.200 maybe just give us a sense of like where you see data having an impact in journalism these days. 00:06:33.200 --> 00:06:38.060 You know, it's funny that you jump right away to GitHub and these data sets, because for me, 00:06:38.060 --> 00:06:42.500 I think about it and maybe it's because, you know, I studied it. And when you study something in 00:06:42.500 --> 00:06:49.700 university, you get all of the philosophy behind it. Right. Yeah. But, but I think about data and 00:06:49.700 --> 00:06:56.900 journalism, as it's always been a really integral part of journalism, like most really good quality 00:06:56.900 --> 00:07:02.540 reporting has an element of data to it. I think about things like I mentioned, I used to do sports 00:07:02.540 --> 00:07:07.940 reporting. And you think about a story like that. And would you rather read something that says, 00:07:08.080 --> 00:07:14.720 the teams played well, this person did pretty okay? Like, seems like last time we think. Yes, 00:07:14.720 --> 00:07:20.320 exactly. Or would you rather like read an article that really breaks down the statistics, you know, 00:07:20.320 --> 00:07:25.680 what the score was, what the batting average was, I don't know why I'm using baseball, but 00:07:25.680 --> 00:07:32.860 so this idea of data and especially like really well researched and well curated data, 00:07:33.080 --> 00:07:39.740 it's great because it can help like, do things like fight misinformation and it can help. There's 00:07:39.740 --> 00:07:46.320 a really great quote from Catherine Gashiro from, she's from the International Center for Journalists 00:07:46.320 --> 00:07:52.860 Knight fellow. And yeah, she said that like data can help journalists speak truth to power. And I love 00:07:52.860 --> 00:07:59.540 that because I think when you have data in reporting, it's instantly more trustworthy. We can dive into the 00:07:59.540 --> 00:08:04.740 ethics of that and whether or not that data is actually trustworthy later, but it gives people 00:08:04.740 --> 00:08:10.780 that sense of practice. It's probably more trustworthy than just, it's my opinion, or here's an antidote. 00:08:10.780 --> 00:08:15.600 I heard on Facebook that somebody said that this was true. So here's my, you know, things like that, 00:08:15.600 --> 00:08:23.000 right? Definitely. You know, one of the big ironies it feels like to me is with the, all this availability of 00:08:23.000 --> 00:08:32.300 real data we have, it seems like a proliferation of just insanity around fake data that I don't know, 00:08:32.300 --> 00:08:37.860 maybe it's because it's also on the internet and it appears like, well, here's some piece of information 00:08:37.860 --> 00:08:41.860 and we'll call those facts or opinions. And here's another one that's different than that, but they're 00:08:41.860 --> 00:08:46.880 both on the same webpage, right? They're both in the browser. And like that kind of puts on equal footing 00:08:46.880 --> 00:08:51.020 as opposed to, well, that used to be on the front page of the New York times and the front page of the 00:08:51.200 --> 00:08:57.100 require, like I didn't consider those to be equally weighted sources, but maybe people are just not 00:08:57.100 --> 00:09:03.460 distinguishing them. But it seems ironic to me that we have more access to real data and it, there's also 00:09:03.460 --> 00:09:09.320 seems to be a lack of embracing of real data or whatever. I'm not trying to put that, but people 00:09:09.320 --> 00:09:14.340 seem a little wacky. Yeah. I think there is, it's confusing. I mean, I also think it's confusing. 00:09:14.340 --> 00:09:20.080 We're living in this world where someone can be, you know, blogging on their own site and that can be 00:09:20.080 --> 00:09:28.820 almost more credible journalism than, you know, someone in certain news outlets. So we don't need 00:09:28.820 --> 00:09:35.060 to get super political in this, but it is. And, but yeah, it's, I think it's really confusing and I 00:09:35.060 --> 00:09:41.800 don't blame people who especially don't, might not understand how the data is collected or, you know, 00:09:41.800 --> 00:09:46.800 this, a lot of people aren't very transparent about how it's being presented or collected. And 00:09:46.800 --> 00:09:48.860 yeah, it's confusing. 00:09:48.860 --> 00:09:54.140 It's definitely confusing, but it's also the foundation of the real journalism, the real 00:09:54.140 --> 00:09:58.960 reporting. I think there's a lot of interesting ways in which you use it. And we're going to talk 00:09:58.960 --> 00:10:04.220 about some actually concrete, cool tools that a lot of newspapers are using, but you know, maybe let's 00:10:04.220 --> 00:10:10.800 just talk about why would journalists and newspaper journalists, freelance journalists, 00:10:11.460 --> 00:10:18.080 associated press type things and so on. Like why would those folks use AI and ML? Like why are they 00:10:18.080 --> 00:10:18.800 adopting these tools? 00:10:18.800 --> 00:10:25.200 There are a lot of reasons. And just so you don't have to hear it from me, there was a recent survey 00:10:25.200 --> 00:10:32.200 done by journalism AI, which is from Google and Polis, which is from the London School of Economics and 00:10:32.200 --> 00:10:38.000 Political Science. They have a think tank on this and they surveyed newsrooms across the nation. You'll 00:10:38.000 --> 00:10:43.620 probably, or sorry, across the world. And you will hear me reference this survey a lot because it's 00:10:43.620 --> 00:10:48.540 really thorough and it's really recent. They published it at the end of last year. And they 00:10:48.540 --> 00:10:55.940 mentioned that there were like three key motives for using AI. So the first was to make journalists 00:10:55.940 --> 00:11:04.100 work more efficient, about like 68% of the replies said that. Then to deliver more relevant content to 00:11:04.100 --> 00:11:10.960 users, about half of the respondents said that, and also to improve business efficiency. So what I've 00:11:10.960 --> 00:11:19.280 mostly focused on is that element of making journalists work more efficient, because there is so much that you do 00:11:19.280 --> 00:11:25.860 in journalism that can be automated. And you know, we're developers, we love to automate things. 00:11:25.860 --> 00:11:32.220 Once you realize you're like, I don't have to do this for four hours by hand. Okay, we're not doing this for 00:11:32.220 --> 00:11:36.000 four hours by hand. Let me do it once for two hours, and we'll never do it again, right? 00:11:36.400 --> 00:11:41.940 Exactly. So there's so many opportunities like that, especially because traditional print journalism is 00:11:41.940 --> 00:11:47.560 such, you know, a very thorough, very logical, but, you know, a bit slow moving in that sense. And 00:11:47.560 --> 00:11:52.960 especially with this quicker news cycle, you need to keep up. So there are things like, you know, being 00:11:52.960 --> 00:11:58.580 able to retrieve one of those massive data sets and comb through it and see whether or not there's a story 00:11:58.580 --> 00:12:05.380 in there. Fact checking basic articles, maybe organizing story ideas that are facilitated from 00:12:05.380 --> 00:12:12.180 the public, making initial rough cuts of videos or deciding what camera angle is the best. And there's 00:12:12.180 --> 00:12:17.540 just a lot of tedious tasks. Yeah, I used to be an unpaid intern. I know what those tasks are, because I had 00:12:17.540 --> 00:12:25.620 to do them. And I think it's great, because, and I think the reason I focused on that, is that it's really 00:12:25.620 --> 00:12:31.540 supplementary to human journalists. Right? Yeah, none of the stuff that you said, people would want to 00:12:31.540 --> 00:12:38.960 defend as like, that AI is taking my job. Like, I used to go by hand, and rename these columns to that 00:12:38.960 --> 00:12:43.700 columns, and then merge it over here. And I want to keep doing like nobody wants to keep doing that, 00:12:43.700 --> 00:12:49.140 right? They want to do it to tell the stories to find the insights to do the research, not juggle and 00:12:49.140 --> 00:12:55.380 wrangle data, or other things along those lines, right? Exactly. But I think when a lot of journalists are 00:12:55.380 --> 00:13:01.140 being presented the idea of AI and machine learning, they're not presented, like, no one says exactly 00:13:01.140 --> 00:13:04.540 what you said, they just say, Oh, we're going to introduce this new bot, we're going to introduce 00:13:04.540 --> 00:13:12.220 this new tool. And I think people immediately are very afraid, you know, very unsure about, you know, 00:13:12.220 --> 00:13:18.480 you hear about robots are going to take all of our jobs. And so I think they get a bit uneasy in that 00:13:18.480 --> 00:13:23.680 sense. And I don't blame them, because if it's not being explained in a way that like, Oh, this is 00:13:23.680 --> 00:13:28.800 supplementary to your work. And that's what most of the respondents in the survey even said, they said, 00:13:28.800 --> 00:13:36.840 like, they see AI as something that is, you know, supplementary and additional, not necessarily like 00:13:36.840 --> 00:13:38.580 transformational yet. 00:13:40.840 --> 00:13:45.680 This portion of Talk Python To Me is brought to you by Brilliant.org. Brilliant has digestible courses 00:13:45.680 --> 00:13:51.040 in topics from the basics of scientific thinking all the way up to high end science like quantum 00:13:51.040 --> 00:13:56.040 computing. And while quantum computing may sound complicated, Brilliant makes complex learning 00:13:56.040 --> 00:14:00.700 uncomplicated and fun. It's super easy to get started. And they've got so many science and math 00:14:00.700 --> 00:14:05.420 courses to choose from. I recently used Brilliant to get into rocket science for an upcoming episode. 00:14:05.420 --> 00:14:10.580 And it was a blast. The interactive courses are presented in a clean and accessible way. And you 00:14:10.580 --> 00:14:16.020 could go from knowing nothing about a topic to having a deep understanding. Put your spare time to good 00:14:16.020 --> 00:14:21.700 use and hugely improve your critical thinking skills. Go to talkpython.fm/brilliant and sign up for 00:14:21.700 --> 00:14:28.580 free. The first 200 people that use that link get 20% off the premium subscription. That's talkpython.fm 00:14:28.580 --> 00:14:31.800 slash brilliant. Or just click the link in the show notes. 00:14:31.800 --> 00:14:40.740 Who knows where the future goes? I've never ceased to be amazed by how crazy some of the things people 00:14:40.740 --> 00:14:45.300 are coming up with. Like the fact that we have self-driving cars, that seemed like pure science 00:14:45.300 --> 00:14:52.100 fiction, you know? So who knows? Maybe we'll get creepy AIs writing stuff. But for now, I don't see it 00:14:52.100 --> 00:14:57.780 that way. And I remember working at a company quite a while ago where it was not a very tech heavy company. 00:14:57.860 --> 00:15:02.840 It was like a research place, but a lot of people were researchers, not developers. So there were not a 00:15:02.840 --> 00:15:07.900 lot of automated systems. And every time we would say, you know, that thing used to do for like four 00:15:07.900 --> 00:15:13.280 hours a week, we just made that automatic and it just happens now. They're like, ah, this is like, 00:15:13.280 --> 00:15:17.720 that's what I used to do. This is going to be my job. But every single time they just got more 00:15:17.720 --> 00:15:23.100 interesting work that was less tedious, you know? And I don't remember anyone getting laid off or 00:15:23.100 --> 00:15:27.200 anything like that because we automated stuff. It's just, we could do more work and do more 00:15:27.200 --> 00:15:30.920 interesting work. Exactly. I think there'll always need to be that human component, especially 00:15:30.920 --> 00:15:36.700 in journalism because it's so based on things like storytelling and, you know, conveying emotion. 00:15:36.700 --> 00:15:42.920 I mean, not every form of journalism, but a lot of the really human centric things focus on that. 00:15:42.920 --> 00:15:44.680 And I think you'll always need someone. 00:15:44.680 --> 00:15:48.720 Yeah. Well, a really interesting semi-recent, I'm going to call it recent story, 00:15:49.180 --> 00:15:56.780 was people applying like text processing and machine learning to the data that came out of the Panama 00:15:56.780 --> 00:15:59.140 papers, right? So I don't know much about it. 00:15:59.140 --> 00:16:05.660 Yeah, I didn't either. But I recently interviewed a guy who worked on a project that was a search engine 00:16:05.660 --> 00:16:11.080 type of thing. And as they were working on it, there was a journalist who was super interested 00:16:11.080 --> 00:16:16.060 in the tech. They're like, why is this journalist so interested in this? And it turned out that they were 00:16:16.060 --> 00:16:22.780 using it to analyze and search all the data, like do OCR and types of stuff like that on the data that 00:16:22.780 --> 00:16:27.280 came out of the Panama papers to create relationships and see what, you know, like that kind of stuff 00:16:27.280 --> 00:16:32.520 would have taken a lot more people, maybe would have gotten exposed before the Panama papers could have 00:16:32.520 --> 00:16:37.580 been, you know, analyzed fully, all sorts of stuff. So yeah, a lot of interesting ways to use technology, 00:16:37.580 --> 00:16:37.900 I think. 00:16:37.900 --> 00:16:38.360 Absolutely. 00:16:38.820 --> 00:16:43.740 All these are interesting. But honestly, I think maybe the biggest boost actually might be just the 00:16:43.740 --> 00:16:48.360 automate the boring stuff story, which you were touching on is like, there's all these things 00:16:48.360 --> 00:16:53.300 you got to do, a lot of them are tedious. And if you had a little programming skill, you could automate 00:16:53.300 --> 00:16:58.300 them. But because you don't, you just maybe use Excel and find and replace or something painful. 00:16:58.960 --> 00:17:03.640 And if you could just take like 20% of the tedious work away from journalists and let them 00:17:03.640 --> 00:17:07.760 focus on the story, or getting out and getting the story, like that would be great. 00:17:07.760 --> 00:17:14.120 Exactly. I mean, then I think you can focus on all of those aspects that a computer cannot. 00:17:14.120 --> 00:17:16.780 Like what you just said, I literally just repeated what you said. 00:17:16.780 --> 00:17:19.580 Obviously, the computer can't go and interview people 00:17:19.580 --> 00:17:23.440 in a way that's going to ask the right questions and so on. Right? 00:17:23.560 --> 00:17:29.480 Exactly. So in your talk, you mentioned a bunch of newsrooms that are actually using 00:17:29.480 --> 00:17:35.400 different software applications, libraries, and so on. Some of these are open source. Some of these 00:17:35.400 --> 00:17:40.140 are just talked about, but I thought they were fascinating. So you want to go through some of 00:17:40.140 --> 00:17:40.320 those? 00:17:40.320 --> 00:17:41.400 Yeah, definitely. 00:17:41.400 --> 00:17:44.000 All right, let's start with the Washington Post and Heliograph. 00:17:44.000 --> 00:17:51.160 Heliograph is a bot from Washington Post that produces content for them. I mean, does a lot of other 00:17:51.160 --> 00:17:55.520 things, you know, like tweets, its own code snippets, identifies trends in the stock market. 00:17:55.520 --> 00:18:01.560 But at least as someone from the outside, it seems like that's the value that it brings is that it 00:18:01.560 --> 00:18:08.920 just rapidly generates articles. Like the first year it came out, it made over 800 articles in a single 00:18:08.920 --> 00:18:15.840 year. Yeah, exactly. And it's really good for at least from what I've seen is that it's really good 00:18:15.840 --> 00:18:21.920 for those kind of short, quick reports. So things like covering the Olympic Games, like in Rio, it was 00:18:21.920 --> 00:18:29.760 able to just pop out like 300 articles. Then for things like politics in the election, that's I think 00:18:29.760 --> 00:18:37.920 the main focus of it. They won an award for it in 2016, or for the coverage in 2016. But just that volume, 00:18:38.280 --> 00:18:45.960 that is, I mean, I don't know a human who can write, what is it, like five articles a day? I have no idea. 00:18:45.960 --> 00:18:51.180 Yeah, the turnaround time as well, right? Like, it seems to me like this kind of thing would be good 00:18:51.180 --> 00:18:57.760 for, oh, there's a big crash during rush hour that is shut down I-5 North. Details coming. Or 00:18:57.760 --> 00:19:02.520 something like, you know, just like those really short little, this thing has happened. We're going to 00:19:02.520 --> 00:19:06.720 write a story eventually, but we want to get it out there because we know it's a timely sort of thing, 00:19:06.780 --> 00:19:11.860 right? Definitely. And I think it also ties into the relational issue you're talking about with the 00:19:11.860 --> 00:19:17.060 Panama Papers. I think that's where, based on what I've read, where they're kind of going with 00:19:17.060 --> 00:19:23.360 heliograph, especially in politics, because, you know, a human reporter, there are tens of thousands 00:19:23.360 --> 00:19:27.900 of elections going on throughout the year, especially just in the US I'm talking about. 00:19:27.900 --> 00:19:28.240 Yeah. 00:19:28.380 --> 00:19:35.180 And let alone the entire world. And so being able to have something that can monitor all of those, 00:19:35.180 --> 00:19:41.400 and perhaps maybe even eventually find relationships between those, I think is really exciting. 00:19:41.920 --> 00:19:50.200 Yeah, absolutely. Also just alerts on like emerging trends, like this thing seems to be getting talked 00:19:50.200 --> 00:19:54.760 about on Twitter and we've pulled it out and maybe this hashtag is now all of a sudden trending. 00:19:54.760 --> 00:19:59.760 Hey, reporters, look at this and see if this is interesting. And, you know, maybe it would let 00:19:59.760 --> 00:20:03.660 them know they should pay attention sooner rather than later about this thing that's coming up. 00:20:03.740 --> 00:20:08.680 Yeah. And that's actually a really good segue to like the next example that I had that was from 00:20:08.680 --> 00:20:16.320 Forbes. So they have a CMS system called Birdie. And the reason I said it's a good segue is because 00:20:16.320 --> 00:20:21.680 part of that CMS has exactly that. It has the hashtags, you know, they recommend maybe you write 00:20:21.680 --> 00:20:26.280 articles or when you put an article together, it can read through it and say like, ah, this is trending, 00:20:26.280 --> 00:20:32.200 like put this hashtag on it when you post it or tweet about it. And it looks, at least from what I've 00:20:32.200 --> 00:20:38.060 seen in the videos, it looks pretty much the same to any sort of, you know, WordPress contentful, 00:20:38.060 --> 00:20:44.720 like whatever you have there, but it just has these extra features. So when you go to add an image, 00:20:44.720 --> 00:20:51.600 it'll have suggested images that are related to the article that you have, or, you know, you'll write a 00:20:51.600 --> 00:20:58.760 headline and it'll say, ah, okay, if you switch these around, it'll be, you know, this percentage more 00:20:58.760 --> 00:21:02.460 click, click worthy. I don't know if that's actually the term I use, but. 00:21:02.460 --> 00:21:07.180 But that's the idea, right? Is that it's, it attracts, it's more shared, like more likely to 00:21:07.180 --> 00:21:11.860 be shared. People are more likely to click on it when they see it in some kind of feed scrolling by 00:21:11.860 --> 00:21:12.400 and so on. 00:21:12.400 --> 00:21:17.500 Exactly. And I mean, even topics like SEO, like accounting for like, what is click worthy, 00:21:17.620 --> 00:21:23.560 also what will get you up there. And so, or at least for certain topics. 00:21:23.560 --> 00:21:28.700 Yeah. And so all that sounds like, oh, we're trying to just take the content and make it more, 00:21:28.700 --> 00:21:35.880 um, viral, have a higher viral potential, but it also has more concrete things like reading complexity. 00:21:36.360 --> 00:21:41.320 Yes, exactly. Because I think one of the difficult parts of journalism that I think 00:21:41.320 --> 00:21:46.360 not a lot of, I don't know, maybe I'm wrong, but I feel like a lot of people misunderstand about 00:21:46.360 --> 00:21:51.660 journalism is when you're writing, especially for, you know, a newspaper that's supposed to be 00:21:51.660 --> 00:21:57.200 accessible to everyone. You need to be writing at like sixth grade level. Yeah. 00:21:57.200 --> 00:22:05.020 So sixth to eighth grade is what I was told in university. And that's difficult if, you know, 00:22:05.020 --> 00:22:10.300 writing is what you do for a living and you're used to trying to string together these beautiful 00:22:10.300 --> 00:22:16.620 prose. And so getting things really down to that plain language, short sentences, and having something 00:22:16.620 --> 00:22:21.920 to assist with that, I think, yeah, game changer, my opinion. 00:22:21.920 --> 00:22:25.940 Right. Well, absolutely. And I'm sure people could look down on it and like, well, you're trying to 00:22:25.940 --> 00:22:30.720 dumb down your article, but at the same time, if you reach more people, if the message gets across 00:22:30.720 --> 00:22:36.000 to more people, like that's the ultimate goal is to convey the information to people reading it and 00:22:36.000 --> 00:22:39.640 get more people to read it. So it seems like a noble thing to do. 00:22:39.640 --> 00:22:44.660 Absolutely. It's making information more accessible. I mean, we even have this problem in the software 00:22:44.660 --> 00:22:50.440 community with technical writing, you know, people are, it's like, oh, if I make all of my, 00:22:50.440 --> 00:22:55.860 if I make all my documentation, like really short sentences, it sounds so whatever. And you're like, 00:22:55.880 --> 00:23:00.740 but there are so many people who maybe English isn't their first language, or, you know, maybe 00:23:00.740 --> 00:23:05.840 they have a cognitive disability, or maybe they just, you know, it's a lot to read and take on. 00:23:05.840 --> 00:23:09.020 So making your language more simple helps everyone. 00:23:09.020 --> 00:23:13.000 Even if you're really good at reading, you're trying to juggle two things in your mind. You're 00:23:13.000 --> 00:23:17.760 trying to juggle the programming ideas and what the lesson is teaching you. So you're already kind of 00:23:17.760 --> 00:23:23.520 splitting your, your mental capacity. So it, it feels to me like it should go down. And I'm always 00:23:23.520 --> 00:23:29.700 amazed and sympathetic to folks who English is their second language, because all the programming 00:23:29.700 --> 00:23:34.560 keywords are in English, which it just seems like really a little unfair, but you know, 00:23:34.560 --> 00:23:39.700 such as life, I guess, but the documentation, obviously the easier, the better in that space. 00:23:39.700 --> 00:23:43.880 I also think it's a different, you know, you have to think about it in different contexts. I mean, 00:23:43.880 --> 00:23:50.480 I think journalism can be art. No one's arguing that, but I do think that the primary purpose of 00:23:50.480 --> 00:23:57.140 most journalism is to convey information. So being able to get that as succinct and clear as possible 00:23:57.140 --> 00:23:58.900 is, I would say the goal. 00:23:58.900 --> 00:24:04.740 Yeah. Well, and there's always the Mark Twain quote of, sorry, I wrote you a long letter. I didn't have 00:24:04.740 --> 00:24:05.740 time to write a short one. 00:24:05.740 --> 00:24:07.180 Exactly. 00:24:07.180 --> 00:24:10.860 Right. All right. Let's talk about earthquakes, West coast. 00:24:10.860 --> 00:24:15.000 Earthquakes. Yes. So it's another bot. 00:24:15.180 --> 00:24:18.160 Actually, I'm hoping there are no earthquakes, but let's talk about reporting on earthquakes. 00:24:18.160 --> 00:24:24.980 Reporting. Apparently there are a lot. That's what I learned from this bot. But the LA Times 00:24:24.980 --> 00:24:32.600 has also a bot. I swear, not every AI implementation in journalism is a bot, but they are, in my opinion, 00:24:32.600 --> 00:24:40.620 some of the most interesting. And it's similar to heliograph, but it specifically focuses on earthquakes. 00:24:41.580 --> 00:24:48.760 And it is exactly what we're talking about as far as the turnaround time being so quick. So for example, 00:24:48.760 --> 00:24:55.920 like back in 2014, there was this earthquake that hit the LA area and the LA Times was the first to 00:24:55.920 --> 00:25:03.020 report on it because basically the earthquake happened. The reporter woke up, just went to his computer 00:25:03.020 --> 00:25:09.740 computer and reviewed the article and published it within three minutes, like three minutes after it 00:25:09.740 --> 00:25:10.120 happened. 00:25:10.120 --> 00:25:14.660 That is incredible. A lot of people are still trying to figure out, was that an earthquake? What just 00:25:14.660 --> 00:25:16.760 happened? They're like, I published this. This is good. 00:25:16.760 --> 00:25:23.420 Exactly. And it's because it was sitting there waiting for him because he has a, like, QuakeBot is 00:25:23.420 --> 00:25:29.240 connected to the US Geological Survey. And so when an earthquake comes in above a certain, 00:25:29.240 --> 00:25:36.200 he has, he said, the programmer who did this set different parameters. So it's within this area of LA, 00:25:36.200 --> 00:25:41.900 it has this size threshold. I think there's a few other ones, but those are the main ones I remember. 00:25:41.900 --> 00:25:50.020 And then he was able to like extract the data that was sent and throw it into a pre-written template 00:25:50.020 --> 00:25:50.780 in their CMS. 00:25:51.480 --> 00:25:57.560 Anyway, last night there was an earthquake at this time, this magnitude, you know, it was for this long 00:25:57.560 --> 00:26:03.180 and yeah, just go up there and review it and hit go. That's pretty cool. There's also a gist where you 00:26:03.180 --> 00:26:07.920 can go and the person who was working on it or wrote it talked a little bit about how it works and so on, 00:26:07.920 --> 00:26:08.100 right? 00:26:08.100 --> 00:26:13.480 Yes, exactly. There's a bit, it doesn't have like the full working code, but there are a little bit of 00:26:13.480 --> 00:26:19.000 like code snippets to be able to see like what parameters were put on it. So yeah, it's very cool. 00:26:19.160 --> 00:26:23.240 Yeah. So I'll link to that in the show notes. Let's stick with LA and, you know, I was thinking 00:26:23.240 --> 00:26:27.540 earthquakes were kind of, it made people nervous. This next one definitely will make people nervous, 00:26:27.540 --> 00:26:28.720 right? 00:26:28.720 --> 00:26:37.880 Yes. I mean, it makes me nervous, but it's the reality of it. So the LA Times also has a homicide report 00:26:37.880 --> 00:26:45.400 and it takes all of the information that is given to them. I don't know exactly what the original 00:26:45.400 --> 00:26:54.520 data resources. I would assume the LAPD or something similar. And it plots all of the homicides onto a 00:26:54.520 --> 00:27:02.360 like interactive map on their website. So you can sort it and filter it by, you know, the year, it has the 00:27:02.360 --> 00:27:09.880 name, the gender, and a few other areas that I'm not exactly remembering. But any information you would want 00:27:09.880 --> 00:27:12.120 on this homicide, you can find on this map. 00:27:12.120 --> 00:27:16.360 Yeah. And it's much like the Quake bot, it just automatically receives that information 00:27:16.360 --> 00:27:22.680 and pull it up. Okay. Yeah, cool. A good service for a sad thing, I suppose. This next one is 00:27:22.680 --> 00:27:24.740 interesting, comes out of the Guardian Australia. 00:27:25.220 --> 00:27:33.140 Yes. And this is maybe my favorite of the bots, for only the reason that it is entirely open source, 00:27:33.140 --> 00:27:34.540 which I think is. 00:27:34.540 --> 00:27:35.380 Yeah, that's pretty cool. 00:27:35.380 --> 00:27:36.500 Yeah, that's very cool. 00:27:36.620 --> 00:27:44.300 So it's called ReporterMate. And it pretty much does the exact same things we were just talking 00:27:44.300 --> 00:27:52.300 about. But instead of, no, actually, it pretty much does the same things. It reports on Australian 00:27:52.300 --> 00:28:00.700 election coverage, I think a few like weather related things, stock market. So it does like very similar 00:28:00.700 --> 00:28:07.820 work, especially to Heliograph, but it is open source. And the open source tool, in and of itself 00:28:07.820 --> 00:28:14.700 is pretty cool. It uses, I this is a Python library or package that I do know pandas. 00:28:14.700 --> 00:28:16.220 Uh huh. Nice. 00:28:16.220 --> 00:28:23.100 Yes. So it uses that it uses handlebars, and a bunch of helper functions. 00:28:23.100 --> 00:28:28.200 Yeah. And it looks really cool. Like you just pip install it off you go. And yeah, it gives you all 00:28:28.200 --> 00:28:35.060 that automation, which that's kind of an interesting contrast back to like the Washington Post, doing 00:28:35.060 --> 00:28:39.940 its own sort of private thing versus here's the open source equivalent that a bunch of people can jump 00:28:39.940 --> 00:28:44.820 on. And it seems to me like this would be really helpful for smaller newsrooms, you know, 00:28:45.100 --> 00:28:50.620 small town city that doesn't really have like lots of money and maybe doesn't have much of a, 00:28:50.620 --> 00:28:54.020 they might have an IT person, but not a, like a software development team. 00:28:54.020 --> 00:28:58.780 Oh yeah. That's a big, like when we talk about this field, that's something I definitely, 00:28:58.780 --> 00:29:01.760 I don't know if you want to talk about now or we can talk about it later, but that's. 00:29:01.760 --> 00:29:06.660 Let's talk about a little bit. So there's this, I suppose there's this really big challenge between 00:29:06.660 --> 00:29:11.720 like take the top 10 newspaper organizations and they have paywalls, they have tech teams, 00:29:11.760 --> 00:29:18.700 they have mobile apps. They're like, they are a tech company in some aspect versus a news organization 00:29:18.700 --> 00:29:22.860 for a town that's got a hundred thousand people like Lawrence, Kansas, where I went to college, 00:29:22.860 --> 00:29:26.320 right? Like they, maybe that's a bad example. That's actually where Django came from, but, 00:29:26.320 --> 00:29:31.740 but in general, like these smaller newspapers don't necessarily have big tech teams. 00:29:31.740 --> 00:29:38.220 Yeah, no, for sure. They, so what a lot of people talk about is that AI and journalism is already a 00:29:38.220 --> 00:29:45.020 significant part, but it's really unevenly distributed. So it's focused on these big news 00:29:45.020 --> 00:29:52.980 organizations that either have their own development team or have a relationship to these big tech 00:29:52.980 --> 00:30:00.520 companies. And I find the topic really interesting because it dives, it gets a little bit into the 00:30:00.520 --> 00:30:05.840 ethics field because what a lot of people, at least according to the survey that I mentioned earlier, 00:30:05.840 --> 00:30:11.160 what a lot of people are afraid of. And I didn't even think about until reading this survey, which is 00:30:11.160 --> 00:30:17.740 unfortunate is that people are afraid of like these big tech companies having more power, 00:30:17.980 --> 00:30:22.720 like not only fueling the power of these big tech companies like Google, for example, 00:30:22.720 --> 00:30:29.920 but also how that would potentially impact the reporting of those big tech companies. You know, 00:30:29.920 --> 00:30:31.560 it's kind of this cycle. 00:30:31.560 --> 00:30:35.780 Journalism is a check on the power of those tech companies, right? And if- 00:30:35.780 --> 00:30:36.080 Exactly. 00:30:36.080 --> 00:30:41.780 Yeah. Do you really want to write a negative article about them? Like what's going to happen to your 00:30:41.780 --> 00:30:45.940 other articles if you become too much of a negative force on them, right? 00:30:46.200 --> 00:30:52.300 Exactly. And I mean, they hold a lot of power in this situation because I mean, a lot of these 00:30:52.300 --> 00:30:57.760 news organizations, specifically the ones that don't have their own development team or have a very small 00:30:57.760 --> 00:31:04.600 development team, really rely on that technology to keep up and kind of keep that pace in this 00:31:04.600 --> 00:31:12.200 online, like very much online, very much 24, 24 hour news cycle. So it's scary when you think about it like 00:31:12.200 --> 00:31:18.600 that it really is a little bit. Talk Python To Me is partially supported by our training courses. 00:31:18.600 --> 00:31:24.640 How does your team keep their Python skills sharp? How do you make sure new hires get started fast and 00:31:24.640 --> 00:31:31.060 learn the Pythonic way? If the answer is a series of boring videos that don't inspire or a subscription 00:31:31.060 --> 00:31:36.740 service you pay way too much for and use way too little, listen up. At Talk Python Training, we have 00:31:36.740 --> 00:31:42.120 enterprise tiers for all of our courses. Get just the one course you need for your team with full reporting, 00:31:42.120 --> 00:31:47.800 and monitoring or ditch that unused subscription for our course bundles, which include all the courses 00:31:47.800 --> 00:31:54.460 and you pay about the same price as a subscription once. For details, visit training.talkpython.fm 00:31:54.460 --> 00:31:58.200 slash business or just email sales at talkpython.fm. 00:32:00.440 --> 00:32:04.500 The first place that comes to mind when I think about like those challenges, it's got to be Facebook. 00:32:04.500 --> 00:32:07.500 But I want to ask you a question, not about Facebook, about something else. 00:32:07.500 --> 00:32:08.500 Google News. 00:32:08.500 --> 00:32:15.720 So you're in Europe right now and Europe has had a mixed, interesting relationship with Google News. 00:32:15.720 --> 00:32:25.460 I feel like, you know, I think Spain had tried to like prohibit or charge Google for like putting the headlines from Spanish newspapers. 00:32:25.460 --> 00:32:26.620 I think it was Spain. 00:32:26.620 --> 00:32:28.120 It was somewhere in Europe. 00:32:28.400 --> 00:32:33.040 And they tried to limit how Google News could sort of use their free like headlines. 00:32:33.040 --> 00:32:34.680 And so they just stopped it. 00:32:34.680 --> 00:32:36.200 And then they're like, wait, wait, wait. 00:32:36.200 --> 00:32:37.080 Where'd all our traffic go? 00:32:37.080 --> 00:32:37.900 Bring back Google News. 00:32:37.900 --> 00:32:38.820 We need Google News again. 00:32:38.820 --> 00:32:39.600 What is going on here? 00:32:39.600 --> 00:32:44.300 You know, I feel like there's something like that happening right now in Australia as well. 00:32:44.300 --> 00:32:47.800 So there always seems to be like this tension of like, oh, we hate them. 00:32:47.800 --> 00:32:48.840 They're like robbing from us. 00:32:48.840 --> 00:32:49.980 Wait, we need them. 00:32:49.980 --> 00:32:50.760 They're our savior. 00:32:50.760 --> 00:32:51.520 Bring them back. 00:32:51.520 --> 00:32:54.700 You know, what's your thought from being more on the inside of that world? 00:32:54.700 --> 00:32:55.460 I'll be honest. 00:32:55.460 --> 00:33:00.560 I haven't been in as many discussions that discuss like Google News specifically. 00:33:00.560 --> 00:33:05.880 But something I do, I have talked about with my friends and I actually saw a Twitter thread about it today. 00:33:05.880 --> 00:33:06.960 I'll send you the link. 00:33:06.960 --> 00:33:07.540 Awesome. 00:33:07.660 --> 00:33:25.060 And what's about how, I mean, this thread didn't mention Google specifically, but come on, has to do with it, is that like how much American media like seeps into European news coverage and what people are aware of. 00:33:25.060 --> 00:33:39.500 And I could imagine also if you're seeking your news online or through something like Google, naturally then a lot of American politics comes up, a lot of American systems come up where you're kind of fed this idea. 00:33:39.500 --> 00:33:42.700 And in a way that is so unique to the U.S. 00:33:42.700 --> 00:33:46.100 Because at least like growing, I grew up in the U.S. 00:33:46.100 --> 00:33:52.860 So I don't remember ever being like, wow, why do we have so much news on any other country? 00:33:52.860 --> 00:33:55.160 I remember if you wanted news on other. 00:33:55.160 --> 00:33:56.740 Why does Brazil always give us their news? 00:33:56.740 --> 00:33:58.120 Like, I just don't need Brazil's news. 00:33:58.120 --> 00:33:58.860 Like, no offense to Brazil. 00:33:58.860 --> 00:34:01.860 I'm just grabbing like a random country that doesn't do that generally. 00:34:01.860 --> 00:34:02.640 Exactly. 00:34:02.640 --> 00:34:03.120 Yeah. 00:34:03.240 --> 00:34:11.780 I remember like if you wanted news, like, for example, my sister studied in Japan and before she left, she wanted, you know, to try to keep up with Japanese news. 00:34:11.780 --> 00:34:17.780 She had to literally go buy the Japanese paper from the actual Japanese store that we had. 00:34:17.780 --> 00:34:29.020 So you really have to seek out that news versus like a lot of American news is just kind of, what's the word, like filtered into the everyday experience of people in Europe. 00:34:29.020 --> 00:34:31.200 And it's kind of like, why? 00:34:31.200 --> 00:34:43.520 And I mean, even, I don't know this personally, but I know there's some even tension between like Western Europe and Eastern Europe as far as like how their news is represented, who, how much do you see of each country? 00:34:43.520 --> 00:34:44.680 How much do you really know? 00:34:44.680 --> 00:34:45.060 Right. 00:34:45.060 --> 00:34:50.200 And you're in Berlin, which is like right on that, that line there in historically speaking. 00:34:50.200 --> 00:34:53.120 But it's definitely still in like the more Western category. 00:34:53.120 --> 00:34:59.600 I would say we hear a lot more about, you know, France or the UK than we do about Bulgaria. 00:34:59.600 --> 00:35:00.480 Yeah. 00:35:00.480 --> 00:35:01.620 Very interesting. 00:35:01.620 --> 00:35:02.580 Just an example. 00:35:02.580 --> 00:35:07.720 One of my favorite songs is Californication from the Red Hot Chili Peppers. 00:35:07.720 --> 00:35:12.240 And that song's all about like how America is exporting their culture and stuff. 00:35:12.240 --> 00:35:16.760 I think generally through music and Hollywood, it's only more so now, right? 00:35:16.760 --> 00:35:19.360 With like the tech companies and online and so on. 00:35:19.760 --> 00:35:20.240 Interesting. 00:35:20.240 --> 00:35:20.820 I know. 00:35:20.820 --> 00:35:25.320 I think there's this tension between both the big tech companies just controlling. 00:35:25.320 --> 00:35:28.060 They're the aggregators of the attention. 00:35:28.060 --> 00:35:31.540 So they're controlling access to what gets attention. 00:35:31.540 --> 00:35:36.040 And then there's this tension between the big newspapers and the small newspapers, right? 00:35:36.040 --> 00:35:39.580 Because the big newspapers have software teams that can just go, yeah, yeah, PyTorch. 00:35:39.580 --> 00:35:40.180 Let's use that. 00:35:40.180 --> 00:35:42.400 And other people are like, what? 00:35:42.400 --> 00:35:42.960 Is there a fire? 00:35:42.960 --> 00:35:43.860 What is this torch about? 00:35:43.860 --> 00:35:44.180 Yeah. 00:35:44.180 --> 00:35:44.620 Yeah. 00:35:44.620 --> 00:35:46.340 So, yeah. 00:35:46.340 --> 00:35:47.020 Very interesting. 00:35:47.020 --> 00:35:48.420 Let's keep going on some of these things. 00:35:48.420 --> 00:36:02.960 The next one that I thought was interesting that you brought up was what ProPublica was doing around analyzing not what people in the U.S. Congress say they're interested in, but what their actions and behaviors and words say they're interested in. 00:36:02.960 --> 00:36:03.460 Yeah. 00:36:03.680 --> 00:36:10.820 So ProPublica took an analysis of thousands of press releases over the course of two years. 00:36:10.820 --> 00:36:17.280 And they trained a computer model to extract which phrases each Congress member uses most frequently. 00:36:17.280 --> 00:36:26.820 And then under the assumption that if these are the phrases they're using most frequently, these are likely the topics that they are pushing for and care about the most. 00:36:27.240 --> 00:36:32.980 Because, or else, why would you be releasing all this press release about it if it's not a topic? 00:36:32.980 --> 00:36:36.420 And some of them, a lot, like some of them were in line with them. 00:36:36.420 --> 00:36:40.060 I don't have the article up right now, but I would suggest checking it out. 00:36:40.060 --> 00:36:45.120 But some of it, the Congress people were really in line with what their beliefs were. 00:36:45.120 --> 00:36:47.660 And a lot of them, it was like, we say this. 00:36:47.660 --> 00:36:47.980 Yes. 00:36:47.980 --> 00:36:49.100 But we don't really agree. 00:36:49.100 --> 00:36:50.280 Yes, exactly. 00:36:50.280 --> 00:36:50.500 Yeah. 00:36:51.020 --> 00:36:53.320 This is what we say we're for and this is what we're actually for. 00:36:53.320 --> 00:36:54.260 How interesting. 00:36:54.260 --> 00:37:02.640 One of the things I was thinking of that's like a cool automate the boring stuff when you're interested in those kinds of things is like speech to text. 00:37:02.640 --> 00:37:11.800 A lot of times you'll be some kind of presentation, like there's a video of the person, but it would be much better if you could just index the keywords of what they said. 00:37:11.800 --> 00:37:18.440 And, you know, the ability to just like take spoken word and video and turn it into written stuff that you can analyze. 00:37:18.440 --> 00:37:20.560 It seems like that'd be pretty interesting in journalism. 00:37:20.920 --> 00:37:21.220 Yes. 00:37:21.220 --> 00:37:25.460 I can't believe I forgot to mention that because I think that's the most tedious part of reporting. 00:37:25.460 --> 00:37:35.980 I remember having to either, you know, have my little hand recorder and record things and then type it out myself later or just take handwritten notes and be like, I hope I got this quote right. 00:37:35.980 --> 00:37:41.600 So I think the idea that you can, I mean, there are apps like right now I use Otter. 00:37:41.600 --> 00:37:42.100 Yeah. 00:37:42.100 --> 00:37:43.020 I like an instant. 00:37:43.020 --> 00:37:43.600 Yeah. 00:37:43.600 --> 00:37:44.020 Yeah. 00:37:44.020 --> 00:37:49.220 I have not had any practical use for Otter, but I've tried to use it for the podcast and live. 00:37:49.340 --> 00:37:56.000 It's like, it'll transcribe multi-person conversations and attribute the spoken word to the different people and so on. 00:37:56.000 --> 00:37:56.180 Right? 00:37:56.180 --> 00:37:57.020 Yes, exactly. 00:37:57.020 --> 00:38:05.380 You still need to definitely read through it because especially if you're, I can imagine, especially in software, we're using a lot of lingo that isn't common speak. 00:38:05.820 --> 00:38:35.800 You have to go through it. 00:38:35.800 --> 00:38:51.200 version of word will now let you like, I heard this just yesterday or the day before you can take an MP3 and upload it to your document and then just grab paragraphs of transcoded text and just drop them into your document, like right out of the MP3 file, which is, that's pretty awesome. 00:38:51.200 --> 00:38:52.100 That'll help a lot of people. 00:38:52.100 --> 00:38:52.980 Yeah, that's great. 00:38:53.020 --> 00:39:04.920 And it also goes along with what we were saying about readability where it's again, an accessibility issue where, you know, a lot of before it was always like, ah, it's so hard to have a video have captions or like, oh, it's really hard. 00:39:04.920 --> 00:39:11.240 If we recorded this interview to have, you know, a written version because it takes so much power or work time. 00:39:11.360 --> 00:39:18.900 And now it's like, there's almost no excuse to make things inaccessible because the resources are there and a lot of them are free and available. 00:39:18.900 --> 00:39:19.620 Yeah. 00:39:19.620 --> 00:39:20.180 Yeah. 00:39:20.180 --> 00:39:21.360 It's super cool. 00:39:21.360 --> 00:39:24.820 So the next one that you talked about was BuzzFeed. 00:39:24.880 --> 00:39:28.780 And to me, BuzzFeed is like listicle type stuff. 00:39:28.780 --> 00:39:34.120 And it's when you think of viral headlines, like they probably got some things that like really recommend headlines. 00:39:34.120 --> 00:39:39.380 That said, this next thing, they do do real news reporting as well in some interesting ways. 00:39:39.380 --> 00:39:42.700 And this next one actually is pretty interesting there, right? 00:39:42.700 --> 00:39:43.060 Yeah. 00:39:43.060 --> 00:39:43.760 BuzzFeed. 00:39:43.760 --> 00:39:50.680 I love a good BuzzFeed quiz, but they also just, they have real BuzzFeed news, really good journalism. 00:39:51.840 --> 00:40:04.480 And this one is, I remember reading about it and I couldn't believe it because it just sounds like sci-fi movie, but they trained a computer model to find and track like secret airplanes. 00:40:04.480 --> 00:40:15.640 So what I mean by that is the computer used a machine algorithm sift for planes with flight patterns that resembled those of the FBI or the Department of Homeland Security. 00:40:15.640 --> 00:40:21.400 Like the plane that goes up and just flies in circles around a city rather than from a city to a city, right? 00:40:21.480 --> 00:40:22.080 Something like that. 00:40:22.080 --> 00:40:22.600 Exactly. 00:40:22.600 --> 00:40:23.200 Yeah. 00:40:23.200 --> 00:40:24.020 I don't know. 00:40:24.020 --> 00:40:24.240 Yeah. 00:40:24.240 --> 00:40:26.980 I could totally imagine it's exactly like that. 00:40:26.980 --> 00:40:30.080 Something a little bit strange in that way. 00:40:30.080 --> 00:40:36.400 But it allowed them to report on a ton of different topics that, again, I just think are wild. 00:40:36.400 --> 00:40:44.220 So like, for example, like how U.S. marshals hunted down drug cartel kingpins in Mexico. 00:40:44.220 --> 00:40:45.520 Like how? 00:40:45.820 --> 00:40:46.640 I don't know. 00:40:46.640 --> 00:40:46.660 I don't know. 00:40:46.660 --> 00:40:53.880 How there was like a military contractor that tracks terrorists in Africa, but I guess they were flying over U.S. cities. 00:40:53.880 --> 00:40:54.480 Right. 00:40:54.480 --> 00:41:01.540 Wait, if your job is to track, you know, military stuff in some foreign country, what are you doing in Dallas? 00:41:01.540 --> 00:41:02.420 Flying around. 00:41:02.420 --> 00:41:03.000 Yeah, exactly. 00:41:03.000 --> 00:41:03.920 That's suspicious. 00:41:03.920 --> 00:41:15.300 And just other topics around like aerial surveillance, which is, again, when I read it, I was like, you know, I think of when I think of aerial surveillance, I think of like deep conspiracy theories. 00:41:15.300 --> 00:41:16.600 Like what are they called? 00:41:16.600 --> 00:41:17.580 Like jet trails? 00:41:17.580 --> 00:41:18.040 Yeah. 00:41:18.040 --> 00:41:18.880 Con trails. 00:41:18.880 --> 00:41:19.440 Yeah. 00:41:19.440 --> 00:41:20.400 Con trails. 00:41:20.400 --> 00:41:23.820 So that's always what I thought of. 00:41:23.820 --> 00:41:28.980 So then when I saw this report, I was like, oh my God, like they did it. 00:41:28.980 --> 00:41:29.480 Yeah. 00:41:29.480 --> 00:41:30.080 Super cool. 00:41:30.080 --> 00:41:32.320 This is really interesting and well done there. 00:41:32.320 --> 00:41:50.480 So I guess the last one I want to talk about is probably the most far out one, which is what if we could have a drone fly a robot in to walk around dangerous places like war zones and investigate human like a humanoid robot, but not a person. 00:41:50.480 --> 00:41:52.120 Yeah, that's from Al Jazeera. 00:41:52.120 --> 00:41:57.320 And they mentioned it at one of their future media leaders summit in 2018. 00:41:57.960 --> 00:42:03.100 It's still from what I can tell, it's still in development and a bit far out. 00:42:03.100 --> 00:42:04.700 It is far out. 00:42:04.700 --> 00:42:06.000 But I think it's really interesting. 00:42:06.000 --> 00:42:16.220 Like the idea that, you know, we're so used to or at least if you look up like drones and war zones, that's a common practice is to have some sort of drone that goes in. 00:42:16.220 --> 00:42:19.480 What it does, you know, depends on who's using it. 00:42:19.480 --> 00:42:23.560 But the idea with this is that it would deploy a robot. 00:42:23.560 --> 00:42:23.880 Yeah. 00:42:23.880 --> 00:42:25.580 That can take video. 00:42:25.580 --> 00:42:26.960 It can surveil what's going on. 00:42:26.960 --> 00:42:29.940 It can, you know, record the sounds that are happening. 00:42:29.940 --> 00:42:36.220 They want it to be able to dodge sniper attacks and assess the situation. 00:42:36.220 --> 00:42:46.400 And what I think is really interesting about that is that, I mean, there is like a lot of human journalists, one, aren't trained for that type of environment. 00:42:46.400 --> 00:42:49.520 And it would be very difficult to train someone in that. 00:42:49.520 --> 00:42:52.940 There are journalists embedded within military. 00:42:52.940 --> 00:42:55.500 So like the U.S. military has journalists. 00:42:55.500 --> 00:43:04.600 But again, it goes into the issue of how much of it is true or how much of it is influenced by their employer. 00:43:05.200 --> 00:43:09.860 You're certainly getting one perspective if you're like with those folks. 00:43:09.860 --> 00:43:18.460 I know folks try to be objective, but they're there for your safety and you can only go and be with them where they are. 00:43:18.460 --> 00:43:19.100 Right. 00:43:19.220 --> 00:43:25.600 So even like no matter whether right or wrong, like you're getting a somewhat influenced perspective from that. 00:43:25.600 --> 00:43:25.840 Right. 00:43:25.840 --> 00:43:26.300 Exactly. 00:43:26.300 --> 00:43:30.340 You can't just walk around like, well, let me go talk to those guys over there and see what they think. 00:43:30.340 --> 00:43:31.140 No, they're shooting me. 00:43:31.140 --> 00:43:31.980 I'm going to not do that. 00:43:33.160 --> 00:43:33.560 Exactly. 00:43:33.560 --> 00:43:33.640 Exactly. 00:43:33.640 --> 00:43:41.740 And I mean, there's also the ethics of sending, you know, civilian human journalists into these kind of hostile spaces and being like, here, report on it. 00:43:41.740 --> 00:43:46.920 I mean, there are crisis reporters and you hear about journalists being captured in places like Yemen. 00:43:46.920 --> 00:43:54.300 But again, it's like an ethics debate of when should that be required and when should it not. 00:43:54.860 --> 00:43:55.820 Yeah, for sure. 00:43:55.820 --> 00:44:05.280 So, yeah, this is a really interesting idea of taking legitimately taking a humanoid drone or robot and having it walk around in war zones. 00:44:05.280 --> 00:44:10.220 So they actually have a YouTube video or a video on YouTube that you can see a little animation of when it works. 00:44:10.220 --> 00:44:12.240 I guess a couple other things we could talk about. 00:44:12.240 --> 00:44:16.160 So this is a few tools that we talked about are open source and people can use. 00:44:16.160 --> 00:44:20.940 But a lot of this is kind of like how news organizations are using this technology on their platforms. 00:44:20.940 --> 00:44:28.180 But there's also some tools that people can use, like Quartz AI Studio and Google News Initiative and stuff like that. 00:44:28.180 --> 00:44:30.840 You want to give us a quick rundown of some things that people can use? 00:44:30.840 --> 00:44:31.520 Yeah, definitely. 00:44:31.520 --> 00:44:33.960 So as you mentioned, there's Quartz AI Studio. 00:44:33.960 --> 00:44:39.640 It's from the Knight Foundation, which is an esteemed organization in journalism. 00:44:39.640 --> 00:44:48.240 And they help journalists, like trained journalists to use machine learning in their reporting and also can provide support and tools. 00:44:48.620 --> 00:44:56.240 And I think that's great because it makes these practices more accessible to these smaller news organizations or even freelance journalists. 00:44:56.240 --> 00:45:00.460 I don't know the exact requirements for what it takes to get their support. 00:45:00.460 --> 00:45:07.280 But again, the fact that they're even offering this access to these smaller organizations, I think, is great. 00:45:08.140 --> 00:45:15.560 And Google, we've mentioned a few times, they do a lot of research in regards to I mean, they're the ones from the survey I keep quoting. 00:45:15.560 --> 00:45:18.100 They do a lot of interesting research on this topic. 00:45:18.100 --> 00:45:23.020 So, for example, they have facets, which is a machine learning data visualization tool. 00:45:23.020 --> 00:45:24.760 And it's open source. 00:45:24.760 --> 00:45:29.780 So you can play with data within it and create visualizations of the information. 00:45:30.320 --> 00:45:38.820 And then finally, there's like Google News Initiative and more specifically journalism AI and Google News Initiative. 00:45:38.820 --> 00:45:40.100 It goes back. 00:45:40.100 --> 00:45:42.640 Everything is basically a circle. 00:45:42.920 --> 00:45:55.580 And it goes back to what we were talking about earlier in regards to these big tech companies and then who provides access to these tools, but also who teaches journalists how to use these tools. 00:45:56.200 --> 00:46:06.160 So the biggest resource that I know is for training journalists for what is machine learning, how to use it in reporting, what are the different tools available? 00:46:06.160 --> 00:46:07.120 Here's an introduction. 00:46:07.120 --> 00:46:09.100 Is Google News Initiative. 00:46:09.100 --> 00:46:12.720 They have about like 40 courses that are available to journalists. 00:46:12.720 --> 00:46:18.760 But again, it's kind of, of course, it leans towards, hey, use our Google products to do these things. 00:46:18.760 --> 00:46:34.840 And it also leads to a, I'm trying to figure out a way to word this, not like a one-way information direction, but like kind of a funnel of like, okay, this is your only information about how to, you know, think about data, how to analyze algorithms. 00:46:34.840 --> 00:46:39.560 But I'm a big fan of like tech is pretty much always biased. 00:46:39.560 --> 00:46:40.480 It's always political. 00:46:40.480 --> 00:46:45.180 So that influence from Google has to be apparent in there somewhere. 00:46:45.180 --> 00:46:45.620 Right. 00:46:45.620 --> 00:46:49.340 And it might not necessarily be overtly intentional, right? 00:46:49.340 --> 00:46:53.820 It could just be the people who built it all have, they generally share one way of you in the world. 00:46:53.820 --> 00:46:56.240 And so that's probably going to show up in there somehow. 00:46:56.240 --> 00:46:57.160 A hundred percent. 00:46:57.160 --> 00:47:08.140 Like algorithm bias, especially, is something that also came up in this survey about something that people are afraid of and something that people are nervous about implementing. 00:47:08.140 --> 00:47:22.960 People being like reporters are nervous about implementing in their work because, you know, if you don't know how to analyze an algorithm and know where it's getting its data and knowing where, how that data is being prioritized, then it's difficult to know. 00:47:22.960 --> 00:47:29.180 Like, am I presenting data that is really reputable and as unbiased as it can be? 00:47:29.180 --> 00:47:31.340 Or if it is biased, what are the biases? 00:47:31.340 --> 00:47:32.000 Right. 00:47:32.000 --> 00:47:37.300 And so they don't want to be publishing like blatantly biased reporting unless they do. 00:47:37.300 --> 00:47:40.300 But I think a lot of people don't ever intend to have that happen. 00:47:40.300 --> 00:47:40.580 Right. 00:47:40.580 --> 00:47:42.040 There's different levels, right? 00:47:42.040 --> 00:47:54.040 Like it could be that you're using an algorithm and it's giving you information and then you're writing something based on that influenced or directed or biased information. 00:47:54.040 --> 00:47:57.200 Or it could be something as simple as like the bot that tells me what's trending. 00:47:57.200 --> 00:48:04.780 It's always interested more in this other part of society than maybe what actually is more important to the most part of society. 00:48:04.780 --> 00:48:09.940 Like it could be it really cares about people in New York and their financial behaviors. 00:48:09.940 --> 00:48:14.320 Or it could be it cares about the plight of middle of the challenges of middle of America. 00:48:14.320 --> 00:48:16.460 Or it could be racially biased. 00:48:16.460 --> 00:48:18.160 There's all sorts of things that it could be. 00:48:18.160 --> 00:48:20.980 And it's not like the algorithm is so incredibly biased. 00:48:20.980 --> 00:48:26.000 It's just like says, hey, you should pay attention to this aspect of life rather than that. 00:48:26.000 --> 00:48:26.220 Right. 00:48:26.220 --> 00:48:28.980 Like that could be really subtle, I think, and challenging. 00:48:28.980 --> 00:48:29.660 Definitely. 00:48:29.660 --> 00:48:36.680 I mean, like for a lot of the information where I was talking about where people get these big data sets and sift through it. 00:48:36.680 --> 00:48:41.320 I mean, that data set that you get could be biased if you don't know how it was collected. 00:48:41.320 --> 00:48:46.320 And for a lot of people, getting a big data set like that is really exciting. 00:48:46.320 --> 00:48:50.080 It's like, oh, OK, like I don't have to go through the work of surveying thousands of people. 00:48:50.080 --> 00:48:54.900 And so it's really appealing, I think, as a reporter to want to act on that. 00:48:54.900 --> 00:48:55.220 Yeah. 00:48:55.220 --> 00:49:00.240 And yeah, there's also just so we don't go down a total rabbit hole. 00:49:00.240 --> 00:49:09.060 There's also Mozilla, although in recent news of Mozilla, I'm not sure how much of this is still in effect. 00:49:09.060 --> 00:49:13.540 But they have a history of partnering with journalists and news organizations. 00:49:13.540 --> 00:49:21.680 So there's a organization called Open News, and it's a network of developers, journalists, designers, editors. 00:49:21.680 --> 00:49:25.820 And they collaborate on open technologies and processes within journalism. 00:49:25.820 --> 00:49:29.380 And that is its own organization now. 00:49:29.380 --> 00:49:31.600 But it was originally incubated within Mozilla. 00:49:31.600 --> 00:49:37.280 And they also have other other ones like the Mozilla Information Trust Initiative, 00:49:37.500 --> 00:49:44.720 which is a collection of comprehensive efforts to keep the Internet credible and healthy and fight misinformation. 00:49:45.320 --> 00:49:51.860 And also, they just announced like a few days ago. 00:49:51.860 --> 00:49:55.180 I'm fact checking myself as we speak. 00:49:55.640 --> 00:50:05.580 The Mozilla Foundation announced that there was going to be a new fund for black artists that examines the relationship between AI and racial justice. 00:50:05.580 --> 00:50:06.060 Okay. 00:50:06.060 --> 00:50:06.860 Yeah, very cool. 00:50:06.860 --> 00:50:15.020 So not maybe that isn't directly related to reporting, but I think a lot of those outcomes can directly influence reporting, 00:50:15.160 --> 00:50:24.200 especially around news coverage, around topics like Black Lives Matter and racial injustice and the justice system in general in the U.S. 00:50:24.200 --> 00:50:25.340 Yeah, for sure. 00:50:25.340 --> 00:50:30.840 Yeah, I think Mozilla is definitely a pretty positive force for these things compared to a lot of the tech companies. 00:50:30.840 --> 00:50:31.240 That's great. 00:50:31.240 --> 00:50:32.000 All right. 00:50:32.000 --> 00:50:35.000 Well, there are so many more things I would like to ask you. 00:50:35.000 --> 00:50:38.540 But I have on my list to talk to you about. 00:50:38.540 --> 00:50:41.360 But at the same time, we're running short on time. 00:50:41.860 --> 00:50:47.900 So let me ask you just one question about kind of what you're up to these days, being a full circle. 00:50:47.900 --> 00:50:53.600 I know that you're looking for maybe your next project, your next thing to be working on and doing. 00:50:53.600 --> 00:50:57.440 Do you want to tell people like what you're interested in, if they've got an opportunity for you out there? 00:50:57.440 --> 00:51:00.660 Yeah, I'm looking for a new role. 00:51:00.660 --> 00:51:04.560 So this Google Season of Docs program runs until December. 00:51:04.560 --> 00:51:08.340 And then after that, I'm hoping to start something new. 00:51:08.600 --> 00:51:12.900 I'm a front-end developer by trade, been a front-end developer for about two years. 00:51:12.900 --> 00:51:16.380 So I've been mostly looking at roles in that. 00:51:16.380 --> 00:51:24.780 But of course, I would love to get back into journalism and tech reporting or even go in from the engineering side. 00:51:24.780 --> 00:51:30.700 So if anyone knows anything, my inbox is fully open for you. 00:51:30.700 --> 00:51:31.400 Awesome. 00:51:31.400 --> 00:51:35.940 And I'll be sure to put your contact information in the show notes so people can get in touch with you. 00:51:35.940 --> 00:51:36.460 Great. 00:51:36.780 --> 00:51:37.520 Yeah, very cool. 00:51:37.520 --> 00:51:37.760 All right. 00:51:37.760 --> 00:51:40.780 Now, before we get out of here, I'm going to ask you the final two questions. 00:51:40.780 --> 00:51:44.340 If you're going to write some code, what code editor do you use? 00:51:44.340 --> 00:51:46.840 I use VS Code because I do a lot of TypeScript. 00:51:46.840 --> 00:51:50.620 So it has the best TypeScript support in my opinion. 00:51:50.620 --> 00:51:51.580 Yeah, cool, cool. 00:51:51.580 --> 00:51:55.380 And, you know, it's written in TypeScript, so it better have good TypeScript support. 00:51:55.380 --> 00:51:56.380 Awesome. 00:51:56.520 --> 00:51:59.660 You basically just follow the squiggly line until you find the error. 00:51:59.660 --> 00:52:00.600 Yeah, perfect. 00:52:00.600 --> 00:52:06.440 And then I always like to bring up like some interesting Python library package for folks out there. 00:52:06.440 --> 00:52:07.900 So what do you got for us this week? 00:52:07.900 --> 00:52:09.040 Got one that's interesting to you? 00:52:09.400 --> 00:52:09.880 Yes. 00:52:09.880 --> 00:52:14.360 There's a package that I was very, very recently introduced to. 00:52:14.360 --> 00:52:16.100 Newspaper 3K. 00:52:16.100 --> 00:52:19.620 And it's like an article scraping and curation package. 00:52:20.560 --> 00:52:29.240 So you can take a URL from an article that is somewhere on the interweb and it will scrape 00:52:29.240 --> 00:52:31.260 it and try to find information from it. 00:52:31.260 --> 00:52:37.080 Like, for example, like the author, the publishing date, some of the text, top images, et cetera. 00:52:37.080 --> 00:52:37.800 Oh, my gosh. 00:52:37.800 --> 00:52:39.420 This is, yeah, this thing is super cool. 00:52:39.420 --> 00:52:43.900 I've heard of this before, but I think it's the perfect fit for what we're talking about 00:52:43.900 --> 00:52:44.240 today. 00:52:44.240 --> 00:52:49.860 Its features include multi-threaded article download framework, news URL identification. 00:52:50.500 --> 00:52:55.220 And I think it'll even do things like you point it at like a landing page, like the homepage 00:52:55.220 --> 00:52:57.940 of a newspaper, and it'll find all the sub articles and stuff. 00:52:57.940 --> 00:52:58.600 Yeah, super cool. 00:52:58.600 --> 00:53:02.660 So if you're into researching news, you want to do web scraping, you might not have to start 00:53:02.660 --> 00:53:04.920 from like low level programming with beautiful soup. 00:53:04.920 --> 00:53:07.400 You could just get more of the direct data here. 00:53:07.400 --> 00:53:08.960 Yeah, great one. 00:53:08.960 --> 00:53:09.720 All right. 00:53:09.720 --> 00:53:10.580 Final call to action. 00:53:10.580 --> 00:53:15.760 You know, speaking to the folks who work somehow with the journalism industry, they want to get 00:53:15.760 --> 00:53:18.060 code and technology more into what they're doing. 00:53:18.060 --> 00:53:18.840 What would you tell them? 00:53:18.840 --> 00:53:22.760 I would tell them that it's really a great opportunity to look into. 00:53:22.760 --> 00:53:29.000 It's something that I really believe is the future of the industry is the future of information 00:53:29.000 --> 00:53:29.840 and reporting. 00:53:30.240 --> 00:53:34.460 But I think I would definitely approach it with caution. 00:53:34.660 --> 00:53:47.660 So make sure that if you're if you are someone who is either going to be building these algorithms or you're going to be using them to make sure that you're asking the right questions about where the data comes from, where is it being prioritized? 00:53:47.660 --> 00:54:11.940 And in general, beyond people who are going to be using them to ask questions, be skeptical, and just be aware that the story that you're reading might be generated by a bot or an algorithm. 00:54:12.300 --> 00:54:13.700 Yeah, good advice. 00:54:13.700 --> 00:54:15.880 Well, Carolyn, thank you so much for being on the show. 00:54:15.880 --> 00:54:20.960 It's a fascinating look inside into the journalism industry and tech intersection. 00:54:20.960 --> 00:54:21.400 Yeah. 00:54:21.400 --> 00:54:22.720 Thank you so much for having me. 00:54:22.720 --> 00:54:24.040 I love this topic. 00:54:24.040 --> 00:54:26.100 So yeah, it's very interesting. 00:54:26.100 --> 00:54:26.540 You bet. 00:54:26.540 --> 00:54:26.840 Bye bye. 00:54:27.680 --> 00:54:30.460 This has been another episode of Talk Python To Me. 00:54:30.460 --> 00:54:36.200 Our guest in this episode was Carolyn Stransky, and it's been brought to you by Brilliant.org and Talk Python Training. 00:54:36.200 --> 00:54:40.720 Brilliant.org encourages you to level up your analytical skills and knowledge. 00:54:40.720 --> 00:54:46.760 Visit talkpython.fm/brilliant and get Brilliant Premium to learn something new every day. 00:54:46.760 --> 00:54:49.100 Want to level up your Python? 00:54:49.100 --> 00:54:53.960 If you're just getting started, try my Python Jumpstart by Building 10 Apps course. 00:54:53.960 --> 00:55:02.120 Or if you're looking for something more advanced, check out our new async course that digs into all the different types of async programming you can do in Python. 00:55:02.120 --> 00:55:06.780 And of course, if you're interested in more than one of these, be sure to check out our Everything Bundle. 00:55:06.780 --> 00:55:08.660 It's like a subscription that never expires. 00:55:08.660 --> 00:55:10.820 Be sure to subscribe to the show. 00:55:10.820 --> 00:55:13.320 Open your favorite podcatcher and search for Python. 00:55:13.320 --> 00:55:14.460 We should be right at the top. 00:55:14.460 --> 00:55:25.520 This is your host, Michael Kennedy. 00:55:25.520 --> 00:55:27.020 Thanks so much for listening. 00:55:27.020 --> 00:55:28.080 I really appreciate it. 00:55:28.080 --> 00:55:29.840 Now get out there 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