 Daily Tech News show is made possible by its listeners. Thanks to all of you, including Scott Hepburn, Bjorn Andre, and Jeff Wilkes. Coming up on DTNS, Apple wants NFL Sunday ticket and more, but the more it might be too much for the NFL. Plus, Andrea Jones Roy explains why scientists don't really know how AI works. It's a black box. This is the Daily Tech News for Tuesday, Tuesday. It's Tuesday. It says Tuesday, Tuesday, November 22nd, 2022 at Los Angeles. Hop job, Eric. Broke Columbus, Ohio. I'm Rob Denwood. And heart skipping a beat is the producer, Roger. Roger at it right. It's all good. Sarah Lane helped produce today's show. She's not with us right now, but her fingers are all over it. We got Andrea Jones Roy coming up. Let's start with the quick hits. Microsoft told the New York Times that on November 11th it offered Sony a 10-year deal to keep the Call of Duty franchise on its PlayStation platform. Since announcing its intention to acquire Activision Blizzard, Microsoft has repeatedly said it plans to continue releasing Call of Duty titles for Sony's consoles. Access to the popular game franchise has frequently been brought up in regulatory reviews of the deal. Automatic CEO Matt Mullenwick said that Social Network Tumblr will soon support Activity Pub, the decentralized social networking protocol used by many so-called Fediverse apps like Mastodon. This would allow users on Mastodon or any other Activity Pub supported app to follow a Tumblr account from their Mastodon instance and vice versa. Mullenwick said support would be added ASAP but offered no specific date. So Mastodon uses Activity Pub to do the Fediverse, so if Automatic puts Tumblr on Activity Pub, would it then be part of the Mastodon Fediverse? Philosophers want to know. The UK's Competition and Markets Authority opened an in-depth Phase 2 investigation that will focus on the supply of mobile browsers in regards to Apple and Google's market dominance, as well as Apple's restrictions on cloud gaming services on mobile. The investigation could take up to 18 months to complete and then forever in the courts, but they're looking at the duopoly. That's the takeaway here. The markup reports that major tax filing services including H&R Block, Tax Act, and Tax Slayer transmit financial information to Meta through the MetaPixel. Data sent varied by company. Tax Act sent gross adjusted income, names, emails, and refund amount. This information was also sent to Google Analytics but omitted names. And H&R Block sent health savings account usage and college tuition information. Tax Slayer used Facebook's advanced matching system to try and link site visitors to Facebook accounts, sending phone numbers, name of the filer, and dependent names. And to its TurboTac used the pixel but only to sign in and send usernames at device sign in time. After being contacted by the markup, Tax Slayer removed the pixel to evaluate its use, into it modified it to no longer send usernames, and Tax Act no longer sent financial details. Oh, that's nice. It's nice that they're not doing that anymore. He got caught. Oops. Reuters sources say the People's Bank of China is readying to fine FinTech giant Ant Group more than a billion dollars. Likely over alleged violations regarding disorderly expansion of capital. That's apparently a crime in China and resulting financial risk to the country. Ant Group has been under intense government scrutiny since it scuttled its 2022 IPO. The scrutiny began and then the IPO got scuttled. The fine may single that the crackdown is near its end. Usually when you write the check then the problem is resolved. We'll see. All right. Oh, that's the end of the quick hits. Let's talk a little more about what's going on with Apple and the NFL, Rob. So the athletics sources say that Apple is still the front-runner to get their rights to NFL Sunday Ticket, but the negotiations are taking longer than expected. NFL Sunday Ticket lets viewers pay to see all the NFL games happening, except those in their home market. Those are exclusive to the local CBS and Fox channels. Direct TV has had the product for decades, but the contractors up after this season and Apple, Amazon, ESPN and YouTube are all reportedly making bids. Now, Apple has been reported to be the front-runner for a while now, but supposedly what the athletic is saying is it wants rights that aren't in the current package that Direct TV has. And that is what's making the negotiations take a while to resolve. And that probably includes things like VR and AR, right? Those wouldn't have been in the Direct TV agreement because there weren't any rights to give there. And it's a pretty good bet that Apple was going to come out with some kind of mixed reality headset early next year. So they might want to write that in. NFL Sunday Ticket also doesn't include international streaming rights right now. So it might be that Apple wants to work that into the deal because Apple likes everything on Apple to be available worldwide. And Apple might want to get the rights to those local games, the so-called blackout games, like it did with its deal with Major League Soccer. Major League Soccer, all the games are going to be on Apple TV, whether they're in your local market or not. So they might be trying to get the NFL to give in on that, which would require Fox and CBS to weigh in, which seems like a difficult negotiation. And another thing complicating matters is that the NFL is selling its stake in NFL media, which operates the NFL website and the cable networks, NFL Network and Red Zone. Red Zone switches between games as teams get close to scoring. Last December, NFL Commissioner Roger Goodell told the Sports Business Journal that the NFL was months away from selling that stake. The NFL may not want Sunday Ticket and the NFL Media stake in the same hands, but Apple does. From an office sports reported in March that Apple wants to buy both Sunday Ticket and that stake in NFL Media. Yeah, that reminds me of what Netflix was saying about wanting to control all the aspects of a relationship with the sport. So we haven't heard Netflix involved in this one, but it does sound like Apple. I don't know, Rob. What's your best guess? Is Apple just trying to get as much as it can and it'll settle for what it can get? Or is this going to be an all or nothing thing? No, there's no question. Apple wants everything it can get. And we will see whether or not they, you know, what they're going to be willing to accept. But the NFL is one of the most powerful brands in America, if not the planet. And I think they're trying to be very careful that we don't want to give up too much of that power to another single entity. I mean, you know, Apple is an even more powerful brand. They don't want to give too much juice to another company that already has a lot of juice. So I think that that's probably a big part of what's going on here. Yeah, I think Apple has got a lot of cash to wave around in front of the NFL. But the NFL is also a brand that has a lot of influence and sway and a lot of cash of its own. So maybe Apple's cash wave and doesn't work as well with the NFL as it does with an AI startup in Silicon Valley, which is probably dragging out these negotiations. That's a really good point. I like what I like the idea. We don't really know what's going on. This is all based on reports, but I like the idea that Apple would like to make it simple. That's a very Apple-y thing to do. And they have done it with Major League Soccer to great effect. You subscribe to Major League Soccer, you get all the games done simple. Don't have to figure out where you are. If you're traveling or whatever, you just you just get the games. If they can pull that off with the NFL, I mean, that would that would be huge, right? It would be huge. The issue that I've seen with this is forever you've been able to watch your local team play with some exceptions. I mean, there definitely are some blackout dumbness that the NFL has. But for the most part, if you live where your team is, you can watch your teams play on Sunday afternoon or, you know, or whatever day they're playing. That would change if now Apple controls this. That means that you now have to get a subscription to see your local games. It wouldn't have to. They've done a deal with Major League Soccer where some games can be broadcast. They could do a deal with the NFL. It says, yeah, Fox and CBS can keep carrying the local games. That's fine. We just want to be able to stream them in that same market. So you have a choice. Yeah. So that's a choice. Fox and CBS are going to say, like, oh, hell no, we don't want anybody going to watch it on Apple TV when it should be us. In which case Apple would then have to come and say, like, well, what if we let Nielsen credit you, the viewers that are on our stream for your local advertisements? And I don't know that Apple's going to want to do that. That's where the headbutting is. It's not that Apple wants you to stop the broadcast. It's that Apple wants to be able to stream it. Because Apple can work its own advertising deals or just promote its own stuff. So, yeah, this is it's taken longer than it should. And there's a reason why you take longer than it should. You're talking about two of the most recognizable brands on earth. And neither one wants to allow the other to have too much. Well, Apple wants it. The NFL doesn't want to give it up. So that's that's what I think a lot of this is, you know, how can we how can we make the money and also keep a bit of the power? Yeah. And it gets confusing, especially if you don't follow it closely, because there's blackouts and blackouts. There's the NFL tradition of a blackout because they didn't sell enough tickets. Right. The Raiders historically never had their home games shown because they didn't sell enough tickets in Oakland back in the day. That's a different situation. And I don't think it happens very much if at all anymore. Right. The blackout we're talking about here is if you have NFL Sunday ticket on direct TV and I live in LA, I can't watch the Chargers or the Rams on NFL Sunday ticket because they want me to watch Fox or CBS for those. But I can watch all the other games and then it's different. If you if you live in Columbus, I assume the Browns are going to be blacked out or maybe the Bengals too. Right. But but but you're going to be able to watch all the other Bengals are Indian. You get a bunch of teams that are close. You know, here in Columbus, you know, just centrally where it sits. So yeah, this is it's interesting. But yeah, those those old we didn't sell enough tickets. The Browns used to get caught up with that when they played in the old Cleveland Municipal Stadium because that thing sat what 90,000 people or something like that. An infinite number of people in there. It's like just I mean, they were giving tickets away to school kids. Like, hey, you people walk over the street. Hey, come in and watch this. This is so our local market. Yeah. You know, you know, connect you watch the game. So I'm glad that that's, you know, pretty much solved smaller stadiums. It does feel like Apple's going to going to get something out of this. I doubt they walk away just because they can't buy. I mean, we didn't even talk about them buying NFL Network. I could see them taking that and just putting it on on Apple TV and say NFL Network now available to everybody. That would be an interesting play. Red zone. Same way. I doubt they'll walk away from this. If they don't get everything they want. I assume it's still worth it even if they just get the same deal as Direct TV. My guess is they'll be able to get more than what Direct TV had, even if it's just the mixed reality stuff. What would be the thing that you that you would like to see as an NFL fan? I don't have a problem with Apple getting it so long as I can easily subscribe to the stuff that I was able to easily subscribe to before and Apple that they may want everything. I don't think they will walk away because I have to remember that there's a big company named Disney that is right behind them in the negotiation of this as well with ESPN. So there are other players so they can't play too hard of hardball just because we'll just go ahead and do that deal with Disney. It was more advantageous to the NFL. So they've got to tether this really carefully to make sure they don't close themselves out by trying to have everything. Apple could write the bigger check. I think that's why they're the front runner. Even the Amazon, they can write the bigger check, certainly over Disney's ESPN or even Google's YouTube these days. So it's a matter of how much they can get. And that's actually a pretty good bargaining position. Would you like to take less from Disney or would you like to give us what we want? All right. There's lots of ways if you got thoughts about this, let us know your favorite team and which streaming service you would like to have get the NFL. How can they folks get in touch with us on the socials? So feeling social, get in touch with the DTNS audience on socials at DTNS on Twitter, at Daily Tech News Show on Tiktok and at DTNS pics on Instagram. Machine learning, deep learning, all these different kinds of algorithms out there that people call AI are used a lot more often these days. They're in your smart devices, they're in your smart homes, they're in your text to image generators. If you're out there doing the Dolly, there's a rising concern though, it's been rising for a while about the fact that we really don't know how these algorithms get their answers. Recently, Chloe Zhang wrote a story called scientists increasingly can't explain how AI works. We asked Andrea Jones Roy, data scientist, comedian, circus performer and host of majoring in everything to join us again to talk about why that is and how big of a problem this might be. Andrea, thank you for doing this. Thank you so much for having me. I always love talking about problems with data science. It's interesting but it's very interesting. Thanks very much. We're happy to benefit from that being your thing. What did you think of this article first of all? It's an excellent article and I'm really glad that it's out there and circulating around. As you said, it's not something that's a new problem or something that is new in terms of data scientist awareness of the black boxness but I think we really cannot say enough out loud and as far and wide as we can that we do not know what's going on in the vast majority of these models. In some cases that doesn't matter but increasingly as you said, if we are going to apply data science, machine learning, AI and we use those words maybe problematically interchangeably as well, if we're going to be using all of that in our research and understanding of what's going on in them and an understanding of the limitations of those models and how those models might actually be incorrect and what that might mean for all of us. I'm so glad that it was written and I'm so glad that you're spotlighting it. Thank you. It is something that I think is becoming more well known that it's a problem. If you can, is there an easy way for people to understand why that is? I think a lot of people say well you built the thing why can't you tell how it works? Right. Well I was thinking about this and one of the easiest ways in I think is to think about machine learning in contrast with statistics and for now I'll focus on machine learning and we can sort of leave aside that AI is a deeper deep learning or more complicated machine learning but even a simple machine learning model one of the things I say to my students is even those we don't exactly know how they work. So if viewers remember their statistics class or even if you don't generally in old fashioned statistics what we're doing is we're saying here's a set of variables that I think matter. Here are a couple of data points that I think might explain the GDP of a country. The population, the this, the natural resources, all that good stuff. And in statistics we the researchers specify that model. We say I think this is going to be positively associated with GDP. I think this is going to be exponentially associated with GDP. We're going to test it and estimate it and evaluate it and we kind of do some diagnostics. Obviously I'm over simplifying but that's what we're doing. With machine learning we're basically saying here's what the ingredients are. This is sort of the simplest version of machine learning. We say I'm going to take those exact same data points. I'm going to take GDP, population, natural resources, all that stuff but I'm going to say hey computer you tell me how they fit together. I'm going to take GDP. In that case even in this very simple model where we've been very clear about what the ingredients are, we may be collected the ingredients. We can look at every data point. We only get output that tells me okay the computer says this is the estimated effect. The computer says this is the predicted value two years from now. Or if we increase natural resources by this amount this is what we would expect the value to be in the future. We don't really know the why. We're very good at point predictions and we can evaluate how good our predictions are by waiting until the future arrives and then saying hey we got it right or we got it wrong. There are other techniques we can use to test within the model but we never walk away with a sense of like this is why it works. You really have to do a lot of gymnastics in machine learning to walk away with something like now I understand why natural resources is a strong predictor of GDP or I have a sense of what the deeper underlying reason is for such and such playing a role. We tend not to get that and in this particular example it may not matter that much because if all I want to do is make some investments in some countries then I just care that I'm right. But if we're doing things like hiring algorithms, assigning scores to students, evaluating whether people are ready for parole, deciding whether or not someone is a viable person to loan make a mortgage out to which are all things that companies use data science and machine learning for and we don't know why they're making those decisions. We can't actually unpack whether or not those predictions or decisions are right or fair or going to lead to longer term problems down the road. We just don't know. Tell me if I have this right. So in the first example, the not AI related example I create a formula a very complex formula where I'm like I will put in these kinds of cows and this kind of grass and it will tell me estimated milk production of the cows and if it's far off I made the math so I can look and go oh I waited that too much or I did this calculation wrong whereas with machine learning, with deep learning we're saying here a bunch of cows here's a bunch of grass we know the math is good at associating things but it's not good particularly at associating cows the way our original example was so it just outputs stuff that looks right but you can't look at the equations it uses because the equations are for associating things not specifically for cows and so you don't know how it got to its answer. That's basically the idea for statistics we also don't necessarily go in and say oh I'm going to wait this 2x or 3x when we estimate a regression line it can tell us that the slope should be 2 or 3 or whatever so there's still an element of guessing there but generally that's the idea and if we think about and you might say well in this simple example I can probably work out backwards what the coefficient on cow and sheep and whatever should be I like this model reminds me of like a settlers of catan model or something like that but you think about the kinds of things that we do for like facial recognition and various you know things in your home you're using maybe thousands of variables and there's just no way as a human to sit down and work out how all those variables are being combined and in which way like we could get the computer to spit out a whole bunch of coefficients but it's actually probably and in deep learning basically what you're doing is you're doing one of those algorithms and then you're using that result to feed into a new algorithm and then using that to feed into another sort of this nested machine learning situation so you simply the amount of time it would take for a researcher to tease out what any one prediction is doing would probably be I don't know maybe a full week of work I'm kind of making this up I don't really know but that would be for every point prediction that you're making like it quickly becomes impossible and intractable which is why we use computers to do it and part of the reason data science it's kind of like that you just need a deep learning algorithm that can tease out why the deep learning algorithm did its thing but then you'll need an algorithm to tease out that one and yeah exactly it's like the I don't know who the philosopher is but it's you know if the brain were simple enough for us to understand we wouldn't be smart enough to understand it like that's kind of what's going on here is that what's cool is that it can do a lot more than humans can do as far as taking all these variables you know we have an intuitive sense of yeah I think you want more cows or more sheep and the computer it can look at all the sheep that have ever existed in the whole wide world and combine it with everything else that we decide to feed to it and we just don't know why we got those outcomes as I said in many cases if what we're trying to do is predict maybe we don't care another limitation on on sort of the machine learning revolution that I talked to my students about in addition to the very profound biases as far as like who gets sentenced to how much time in prison and all of that which shows is very racially biased for example is is we really don't walk away with much of an understanding and so regular statistics call me old fashioned but regular statistics tends to be more useful for saying well like in a more generalizable way what are some of the mechanisms by which increasing cows in our country would give rise to GDP I'm not saying you can't get that from machine learning but you can almost you can in many cases learn more about the world through a simpler model and because it's simpler we can then generalize it and actually think about policies in another way I'm not saying one's better than the other but you know explanation and point prediction are two very different exercises that's great I mean we haven't even gotten into so what do we do we're going to have to have you back to talk about that because that's a whole topic of its own but I would you say if you had to leave the listeners with with one thing it's it's it's that you should just be aware that these tools are useful but they're not perfect or something like that that's pretty close to what I would say I would say the number one thing and as I was reading the excellent article and vice that prompted this conversation I think it's so important they put it extremely well it's just so important that we as humans realize that even though machine learning deep learning AI sounds incredibly sophisticated and it is very complicated it's not the same thing as saying it's a correct or capital T truth interpretation of the world and one of the things that I really get concerned about is when I work with companies and CEOs and really smart people who stare at data all the time and make really big decisions using data and using the output of machine learning variables is we've got to remember that all of these all of these are wrong that doesn't mean they're not useful but every prediction is an estimate we the researchers the humans have chosen I've still told the computer hey pay attention to cows and sheep as opposed to chickens and cars or whatever it is right and I can make that list really broad but I'm deciding what data to look at and I'm letting the computer do what it will with it but that doesn't mean that I've actually gotten to a capital T truth answer because maybe I'm missing a whole bunch of data that I left out maybe the data itself is super biased I over counted cows I under counted chickens because most of them were underground I don't know how chickens work right but I think the humility you know it sounds so technical and it is very technical and it can get very very precise but that's not the same thing as being right and we've got to use our own human judgment and our own ability to say this is prediction makes sense what am I leaving out what am I over counting when I look at the results of my study am I systematically denying more people of a certain type loans from the bank is that a problem can we look into it and so it really you know machine learning outputs are information and they're useful but they're not correct so algorithms are like people basically some of them are good but nobody's perfect I love that. Andrea thank you so much for helping us understand this if folks want to find more of what you do where should they go I am at Jones Roy J-O-N-E-S-R-O-O-Y on all the social medias and at Jonesroy.com fantastic thank you so much thanks for having me real quick I wanted to correct myself earlier I said that the ant group scuttled its IPO in 2022 that would be this year they did it back in 2020 so sorry for the verbal typo Rob you ready to check the mail bag let's do it we got an email from Will who says I'm a 40 year old father and I was lukewarm about 3D programming until I saw Beauty and the Beast in 3D it was like a moving pop-up book and was incredibly entertaining animation is incredible in 3D and for that alone I was super sad it didn't catch on better I'd be thrilled if it came back and would pay a hefty premium for it in Disney plus in particular if they offered that content thanks again for the wonderful work he isn't the only one who emailed us about 3D TV no David sent the letter in and says as someone who bought a 3D TV during the 2010s craze and enthusiastically tried to use it the Sandra Bullock movie Gravity was fantastically immersive in 3D and so were sports like football, soccer to the rest of the world formula one and ten is I can tell you what I think killed 3D Twitter if you recall those were also the hey days of Twitter when we were all talking about the second screen we were all live commenting on sports events as we watched him and you just can't do that with 3D glasses on I remember filling torn technology or community and Twitter one but now that Twitter is dying maybe 3D can make a comeback maybe the rumors of Twitter maybe maybe greatly exaggerated but good one good one David fact that you look down at something else kept the glasses I'll off your face which is which is a good one and then we also got this one regarding the chicken and egg problem with content for 3D televisions there's already a driving force for new 3D content VR virtual reality is the perfect medium for 3D movies shows sports events etc and by targeting VR platforms content providers would at the same time target 3D TVs finally I think the biggest question is what sort of glasses free technology will be used if it's something like a light field display then we're not talking about just 3D televisions but full on holographic displays and those would be awesome that is also a very good point thank you Sergei for sending that one along Rob you weren't here yesterday when we were talking about this what do you think you ready for convenient glasses free affordable 3D television or you do not care well those are all three good descriptions of what it has to be convenient affordable and I'm not wearing a helmet to just watch television just not going to happen so I think this email that was talking about VR that might be the place where 3D really starts to take off because granted those headsets are not going to be inexpensive but they're not going to be as expensive as a 85 inch 3D television that doesn't require you to wear glasses or a headset so I just don't know the technology has caught up to it and the other part is that you have to have all of the content has to be created in 3D the last thing I want is to have this really expensive thing that I watch 3D content and there's no 3D content to watch on it that's another good reason why Sergei may be right either VR or AR or mixed reality right that could do both because then then that addresses David's point where well I can look at Mastodon, Twitter, Facebook whatever in my mixed reality glasses I don't have to take them off to look at my phone I can get both my 3D television and the mixed reality stuff in the same place and then if the content isn't available in 3D I don't feel like I wasted it because I'm using the headset for other things too right absolutely like I said it's these things are not inexpensive so if there's multi-purpose for it I think that you probably win there I could see buying a 3D headset much more quickly than buying a 3D television that does not require a 3D headset to wear and watch with well thanks again to Andrea Jones Roy for talking to us about AI earlier and the black box if you want that Vice article be sure to head to DailyTechNewShow.com and get the link it's a good article and thank you Rob great having you along tell folks what you've got going on these days just a couple of other tech podcasts so you can find me always over on SMRpodcast at SMRpodcast.com and my newest show thetechjohn.com thetechjawn excellent I want to give a big thank you to our brand new boss William just entered the Patreon thank you William for backing us at patreon.com slash DTNS extending the ability of this show to continue to serve you and it's down to William and every other single patron who helps us we give you value you give us a little value back and and we keep going for for almost nine years now so so thank you William welcome to the fold and stick around William patron you get the extended show good day internet you can also catch the show live Monday through Friday 4 p.m. Eastern 2100 UTC find out more at DailyTechNewShow.com slash live we're talking tech layoffs tomorrow with Megan Maroney who just experienced one herself and Scott Johnson will be here too talk to you then this show is part of the frog pants network get more at frogpants.com Diamond Club hope you have enjoyed this program