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[Assistant] I'd rather her be, beat Becky, to be the women's champion. Bel Air won of course, and Doudrop came down afterwards and said, I'll be looking for you at Survivor Series. Won't be hard to find. One of the featured matches.


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[Assistant] Is she? So I'm the team, isn't she? I think she's team captain, right?


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[Assistant] I thought Zelina and Carmella said they were team captains. Maybe that's just a heal thing though. How could they both be team captains?


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[Assistant] Well, who made Bianca Beller the team captain? Who made Seth Rollins the team captain? Well that just makes sense, he's the face of the brand.


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[Assistant] Becky Lynch came down next and thank god, this was actually really good. I thought she'd done an excellent promo, she, the crowd, she was a babyface for three minutes while she was soaking up the crowd, cut a promo on Charlotte, recap Charlotte's promo from Smackdown. Yea, I thought this was a really good treading of the line between


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[Assistant] Kayfabe and leaning into those reports that...


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[Assistant] Think of all the times they did the same match over and over and over again.


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[Assistant] True. I thought the transition in which Lynch went from


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[Assistant] I'm a babyface against Charlotte to I'm a heel against Liv was actually seamlessly done. Unfortunately, it does have the effect on the crowd of


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[Assistant] And you know, it sucks the energy out again. Uh, no fault of Becky or Liv's, I thought they were both very good. Becky brought up this.


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[Assistant] Promo that Liv did on Talking Smack four months ago where she said, when I left, when Becky left, the last word she said to me were, when I get back, you'll be champion. And Becky said, like it cuts back to Becky and Becky goes,


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[Assistant] I wasn't around and look at you, you failed. You can't blame that on me, I wasn't keeping you down, I wasn't here. Some people just don't have it.


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[Assistant] We got the Street Profits vs Alpha Academy. Chad Gable's great.


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[Assistant] I wrote here Otis is so vanilla, like it's depressing to see Otis, like he's joined a Scientology cult or something.


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[Assistant] I thought Ford and Otis had a really good exchange in the midst of all of this.


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[Assistant] Chad Gable did a crossbody roll through and Montez Ford picked up the win. Which is, you know, that was a cool finish, it was a way to protect Chad, but


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[Assistant] He's lost all three weeks of matches on Raw so far, so it's kind of like...


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[Assistant] Yeah, you're, you're dying a slow death. Yeah.


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[Assistant] We got a clip from earlier in the day, it's when Rey wanted the match against Bobby, we've talked about that.


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[Assistant] Now you're right, I can't, I'm sure there is.


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[Assistant] Maybe back in the day, Undertaker return teasers.


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[Assistant] April 2022, WWE has come to terms, unfortunately.


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[Assistant] Post Survivor series, new season. Yeah. Squashes.


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[Assistant] He gets DQ'd against Damien Priest. And then they have another match and it's a countout brawl.


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[Assistant] And then he challenges Damien Priest for a pay-per-view, but then, then he starts to fizzle away.


[User] Please transcribe this text

[Assistant] And then you pair in with a manager. Uh, Nikki, almost a superhero, took on Zelina Vega next. Uh, Zelina won with a little bit of distraction from Carmella. And then Rhea Ripley and Carmella had a match, which I actually got into.


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[Assistant] The Carmella's superkicks were brutal. There was this one where she clocked Riga right in the side of the face. Uh, Ripley won with a riptide in the end and the heels...


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[Assistant] Trash Nikki, because it's like, haha, you're the women's tag team champion and you're not even on the team. You're a loser.


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[Assistant] I imagine post-Survivor series, this will be the women's tag team.


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[Assistant] Big E met with Adam Pearce backstage. Adam Pearce said, don't get involved in...


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[Assistant] Was it the Kevin Owens and Finn Balor match? Yeah. Or I'll fine you.


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[Assistant] They'll never know. They'll get him over. Uh, but yea, Biggie, face, brand leading baby face, was scared of doing the right thing because of money.


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[Assistant] Seth, uh, claimed, said he'll win the WWE title, uh, and then left.


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[Assistant] Then we, then we got a really good match between Kevin Owens and Finn Balor. Of course it was. Uh, KO was like, he was doing Finn Balor's taunts and everything. This was like full on heel KO. Some really good like back and forth stuff. Uh, it was like this.


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[Assistant] KO caught Finn, and swang him round his body and hit a kind of stand-in-air-rate crash. And it just made Kevin Owens feel really strong and powerful, I thought. I don't even know if Biggie's got kids. Is that what you've been thinking about?


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[Assistant] This is how interesting the episode of Roar is. Does big A.


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[Assistant] We're actresses, but not that he's African American.


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[Assistant] You hear that folks? You hear that mum? I've had at least one relationship.


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[Assistant] Surely that's just something they have generically at the bottom of every page.


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[Assistant] Buy my book everyone. This was genuinely one of the better things on the show this match. It was actually really good. Uh, and Kevin Owens won. Uh, pop-up powerbomb kick-out, swanton onto Finn's knees, and then a stunner for Kevin Owens to, to...


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[Assistant] Yes, they're both on Team Raw, right? How will they co-exist? WrestleMania, the pre-bookings opened, the most stupendous two-night WrestleMania in history.


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[Assistant] Women in sports, one of the most powerful women in sports for the third year in a row.


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[Assistant] Wasn't bolded. Stephanie McMahon. Bold.


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[Assistant] Most powerful woman in sport. Bolded. Smart. Got one of.


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[Assistant] It, it really is like they're trying to out meme us sometimes. When we make fun of them and come up with words like this. Like, great balls of fire. He's stuck in, he's stuck in


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[Assistant] Um, and finally, because we've already spoken about the main event, we had AJ Styles and The Moss cutting an interview backstage and then going in to face the Dirty Dogs. This was just a moss. Beating up the Dirty Dogs, didn't take a single bump, tagged in AJ, phenomenal forearm on Ziggler.


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[Assistant] I gave this a 2 out of 5 because there was nothing like, this is the problem with Roar at the moment. It's not actively bad. It's just boring. It's so boring.


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[Assistant] So it's hard to even feel passionate about it. It's just like, it's the slow,


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[Assistant] Chloroform blanket on my face of my love of professional wrestling.


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[Assistant] We've got much better content for you on Patreon though, including next week our review of Survivor Series 2004 in WrestleTalk Extra.


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[Assistant] Shoutouts on this very show with your own wrestling nickname. So thank you, The Overstepper James Knepper. The intellectual saviour of the masses, Damien


[User] Please transcribe this text

[Assistant] I don't want Lee Carvello's putting challenge, I wanted Lone Storm x Lone Storm x.


[User] Please transcribe this text

[Assistant] Oh, that's the, that's the game, right? Yea yea. Yea yea, the arcade game.


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[Assistant] Let's get to the rest of your ultra chat, folks. Remember WrestleTalk.com forward slash support.


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[Assistant] In my opinion, the best part of Survivor Series was always finding out who and how people were going to be on the teams. And now,


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[Assistant] And that was like the one good Survivor series for a decade? There was a couple in the mix there, but overall there's been far more bad meaning in this Survivor series than good ones. I would very much agree.


[User] Please transcribe this text

[Assistant] Get put in charge of WWE Creative, the day after Survivor Series. What are three immediate changes you'd make and the three stars you'd want to push to the main event? Hope you're all doing well.


[User] Please transcribe this text

[Assistant] Ah, Eternal Blue has been a member for six months! Thank you, Eternal Blue. My first main roster pay-per-view was Survivor Series 2014. That might be the last time there was any real effort put into the build of Survivor Series. Keep watching so we don't have to, heart.


[User] Please transcribe this text

[Assistant] Laxmi Narasimhanvi. But I'm never gonna be able to say it properly. All of this seems to be leading to a McMahon Family segment to fire Pearson Deville on an episode titled WWE Raw You're The Authority Twice In A Lifetime.


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[Assistant] Charlesburg. Riddle and Orton are great. Everything else was boring. Oli, you said the podcast is drawing low numbers. Now I'm saying the reason is because I stopped doing the poor review. But I'm also not saying it isn't a contributing factor. Just kidding. Jam that jam. The podc- oh yea, the raw podcast.


[User] Please transcribe this text

[Assistant] Nah, nothing was terrible, but nothing was great. Like all WWE pay-per-views, Survivor Series will have great in-ring action with little to no emotional impact, save Charlotte and Becky. Love you guys. Keep jamming. Also, bad finishes.


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[Assistant] I don't- is that- is that an Irish way of saying?


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[Assistant] Oh I thought, when it was happening I was like, the sponsors are gonna love this. I know. And we're gonna send them the clip and they're gonna be like, oh my god.


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[Assistant] Yeah, uh, good that he's gone to New Japan. And Ishii...


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[Assistant] I guess yea, he won another open weight from, was it Jay White at the weekend? So yes, that's cool as well. I just, yea, I just don't...


[User] Please transcribe this text

[Assistant] I don't know about AEW titles being defended on... New Japan titles being defended on AEW. Yes, that way around.


[User] Please transcribe this text

[Assistant] I guess this can only go one way from here, right? And that's downhill, right?


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[Assistant] So, uh, thank you very much. That's, uhm, that's, uh, you know, uh, a very nice introduction, so I hope I can even get close to living up to that. Uhm, so I would say at the outset, uhm, I try and make matters as interactive as possible.


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[Assistant] So if I do say something that's kind of ambiguous or you don't understand what I've done or said, then please fire away and ask.


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[Assistant] Right, and if it turns out that I've got through two slides after 40 minutes, then I will be very tough with the remaining people who kind of, uh, pop up with a question.


[User] Please transcribe this text

[Assistant] Okay, uhm, just before I start on the talk proper, which is as Michael says, it's about uhm,


[User] Please transcribe this text

[Assistant] The notion that we're trying to outsource a lot of our memorizing onto digital objects. I want to just talk, have a quick plug for two other areas where I'm actively pursuing research. And the first area is online communities, which is, I guess, like a lot of people are interested in that.


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[Assistant] One of the things that we think is missing from this area is there's a lot of empirical study


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[Assistant] But what's not really been tackled is that the majority of online communities fail. So according to our own statistics, something like, by some


[User] Please transcribe this text

[Assistant] You know, moderate criterion, something like 85% of online communities fail. There are two reasons for that. One is technical and the other is, uh, social. Uhm, and what we're interested in doing is trying to build, uhm,


[User] Please transcribe this text

[Assistant] I guess kind of tools, uhm, dashboard type tools which allow designers of online communities to monitor and adjust, uhm, their progress in developing a community.


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[Assistant] And in particular, one of the things we're getting very interested in is


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[Assistant] The whole focus of Web2O has been on end user creation of content, but you know, five minutes on Facebook would tell you,


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[Assistant] That what a lot of people are doing now is they're just sharing links. So it's more about curation.


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[Assistant] And now just on the, on the PIM topic, I just want some breaking news to, a couple of, uh, bits of breaking news here. Uhm, we've got one kind of pretty funky NSA style project going here, which is basically, show me your file system and I'll tell you all about yourself. Right? So uhm,


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[Assistant] What we find, for example, is by looking at your desktop, I can tell on classic Big Five personality dimensions, I can tell whether you're neurotic or conscientious. Just by looking at your desktop. Which is kind of interesting. I mean, I guess the backdrop is spooky these days, but...


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[Assistant] We were just interested, this was something we'd kind of seen empirically.


[User] Please transcribe this text

[Assistant] It was an intuition that people had and I guess now we can kind of basically, uh, we can, uh, prove that relationship.


[User] Please transcribe this text

[Assistant] The other thing, uh, direction that I'm going in which is, uh, again, again pretty interesting to me is, uhm, trying to look at the relationship between the brain actually doing neuroscience and looking at how we, uhm, I guess manage our personal information.


[User] Please transcribe this text

[Assistant] And one of the paradoxes, uh, which Mike will be very familiar with is


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[Assistant] That when we're operating with our personal information, we're very reliant on navigation for it. Right? That's to say we're kind of clicking through an organized file hierarchy to find stuff. And there are all sorts of in principle reasons why desktop search, you know, that thing at the top right hand corner if you're a Mac user,


[User] Please transcribe this text

[Assistant] You know, why that's a more efficient way to do it. Because you don't have to remember, you know, the specific location of something. You can just remember a file attribute. However, you know, time after time, study after study, we find that people prefer to navigate. So it's something like, I guess, people search about 6% of the time. And that's...


[User] Please transcribe this text

[Assistant] That's interesting because that's independent of the quality of your search engine.


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[Assistant] So, one of the things we were interested in was, was there some kind of very deep biological reason why people did this? And what we find is, the answer is yes.


[User] Please transcribe this text

[Assistant] Right? So if, if you give people stuff to find on their personal computer, if they're searching for it, then they use the linguistic part of their brain, which is also known as broker's area,


[User] Please transcribe this text

[Assistant] If you ask them to find something by navigation, they use basically the animal parts of their brain, which is exactly the same part of the brain


[User] Please transcribe this text

[Assistant] That they would use if they were physically searching for an object such as lost keys.


[User] Please transcribe this text

[Assistant] So what our argument is, you know, and, you know.


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[Assistant] Knock me down right now. It's kind of the preference for...


[User] Please transcribe this text

[Assistant] This, uh, this, uh, navigational preference reflects a deep-seated way that we actually want to kind of, in a very highly used way, practiced way,


[User] Please transcribe this text

[Assistant] Of accessing information, which is using physical navigation. Even though of course, if you're looking for stuff in your, that's virtual, you're not moving in a real physical environment.


[User] Please transcribe this text

[Assistant] Anyway, so just that, that's just kind of a placeholder because I think it's an interesting direction that we can maybe take, uhm, HCI research is, I'm very interested in this bridge to neuroscience. Anyway, anybody any questions about either of those two? Uh, the second one, fairly bizarre directions. Okay.


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[Assistant] Some work, I guess I've been doing this style of work for


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[Assistant] It's about ten years now, and uhm, I want to take you through


[User] Please transcribe this text

[Assistant] Three different stages of this research and three different approaches. The idea of, uhm, if you like, remembering or reminiscing using digital materials.


[User] Please transcribe this text

[Assistant] And what I want to do first is I want to take a very classic, uhm,


[User] Please transcribe this text

[Assistant] CS, technology-inspired approach to memory.


[User] Please transcribe this text

[Assistant] Which is the life-logging approach. So how many people here are familiar with life-logging?


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[Assistant] And, you know, in these days of the NSA, you know, we're saying that that's not too weird to be thinking about, right?


[User] Please transcribe this text

[Assistant] And as we move into an era of, uhm, you know, wearable computers, sensors, you know, maybe brain sensing, then a lot, massive amounts of our personal data will and can end up online.


[User] Please transcribe this text

[Assistant] So the idea behind this perspective is just that we'll just be, you know, we'll be recording massive amounts of data about ourselves and that will mean that we basically don't forget anything, right? Because everything is recorded in some way.


[User] Please transcribe this text

[Assistant] So that's the first perspective I'll explore and I'll talk about some studies that we did to actually see, you know, evaluate the truth of, with current technologies, what that kind of perspective looks like.


[User] Please transcribe this text

[Assistant] Then I want to talk about uhm, you know, I guess a reaction against that, which is trying to look at situations where we don't want to remember stuff, right? And I'll explore a study that we did which led to some designs.


[User] Please transcribe this text

[Assistant] Which we're talking about, uh, which we're addressing the problem of, for example, if you're in a relationship, you may not, sorry, if you're in a relationship which breaks up, that might be a situation in which you don't really want to, quote, to remember everything.


[User] Please transcribe this text

[Assistant] So we talk about how people feel about digital possessions in that context, you know, the fact that


[User] Please transcribe this text

[Assistant] Every online song reminds you of that other person. You know, you've got, uh, your Facebook feeds full up of, you know, friends of that person or references to that person. You fall across, you come across digital photos.


[User] Please transcribe this text

[Assistant] Ah, of that person, these are all now part of our extended memory, and that can be kind of destructive in a situation where you're trying to move on.


[User] Please transcribe this text

[Assistant] So we talk about people's experiences, some studies that we've done looking at people's experiences in that context, and I want to present some designs for helping us kind of get through that stage.


[User] Please transcribe this text

[Assistant] And the last thing I want to talk about is some recent work I've been doing with a system called Echo. And what we're trying to do with Echo is we're trying to promote psychological well-being by


[User] Please transcribe this text

[Assistant] Using digital data about our pasts, presenting it back to us, having us kind of rethink


[User] Please transcribe this text

[Assistant] Past events, past experiences, and seeing whether that can actually allow us to be,


[User] Please transcribe this text

[Assistant] If you like more mindful about recurrences of that situation, about situations that upset us, about situations that we like, and maybe take adaptive actions when we have that greater knowledge, right? So this is, you know, I mean, I guess my...


[User] Please transcribe this text

[Assistant] And you'll see I'll present some data about deployments of the system and I think we've got some really striking results which show that basically we can make people a little bit happier by deploying this type of system. And I mean that in a scientific way. I don't mean that like they smile more when they use it. Okay, so uhm,


[User] Please transcribe this text

[Assistant] Michael alluded to this. I want to, I guess, talk about uhm, you know,


[User] Please transcribe this text

[Assistant] Different outputs that I, for this research. Uhm, so I'll talk about new technologies that we devised to evaluate and actually improve the outputs of life logging, which is the kind of remember everything perspective.


[User] Please transcribe this text

[Assistant] Uh, issues that, the issues that people had in moving on from a kind of, uh, a past relationship. One of the things that we ended up doing was scrutinizing Facebook very, very closely. That's looking at, empirically evaluating an existing technology.


[User] Please transcribe this text

[Assistant] And then I want to talk about some, you know, as I weave my way through this, I want to talk about different new technologies that we're trying to invent in this context. And I want to talk about technologies for impulse control.


[User] Please transcribe this text

[Assistant] So one of the problems that people have in the context of moving on from a sort of past relationship is they kind of can't stop themselves re-accessing


[User] Please transcribe this text

[Assistant] Information about that person. So we, I'm gonna present some designs.


[User] Please transcribe this text

[Assistant] For technologies that can kind of help you control yourself better. Right?


[User] Please transcribe this text

[Assistant] So the situation is how do you stop yourself doing that thing?


[User] Please transcribe this text

[Assistant] And then I want to talk about, uh, finally I want to talk about this, um, this, uh, what I call technology mediated reflection, which is these technologies which allow us to scrutinize and better understand our past.


[User] Please transcribe this text

[Assistant] Okay, so I'll be talking about those different types of outputs as I go along.


[User] Please transcribe this text

[Assistant] Okay, so the first thing I want to talk about is life-logging, and I guess I gave you the kind of T-shirt slogan version of it. So basically, the life-logging vision says, what if we could automatically save records of every single bit of information we ever touched?


[User] Please transcribe this text

[Assistant] Every event we experience, our reactions to those events, so you know, you know, like sensors that do uhm,


[User] Please transcribe this text

[Assistant] Potentially all this information, you know, one way of thinking about it is it's like a Facebook timeline on steroids, about ourselves, all this extra information. So what if we had access to all that information? Wouldn't that be a good thing? Because, quote, we would never forget anything. So uhm,


[User] Please transcribe this text

[Assistant] I photoshopped that. I'll just be clear about that. That is not a member of the royal family behaving that way. Okay, so...


[User] Please transcribe this text

[Assistant] The, the idea behind this is that we have a complete and accurate digital record. We'll never miss a thing. And the notion is that in some way our wetware, our brains are, you know, they are substandard. They need to be fixed. So what we're going to do is add all this additional data and then we solve the problem.


[User] Please transcribe this text

[Assistant] Okay, so I just want to talk about uhm, one study and a couple of pieces of technology in this context. And so we asked this basic question, which, which I guess now in the era of Google Glass, this stuff was done about five years ago, is becoming more pertinent. So the question you can ask is,


[User] Please transcribe this text

[Assistant] Excuse me, what happens if we have amazingly rich records of our everyday lives? Can we actually remember more stuff?


[User] Please transcribe this text

[Assistant] And if so, how do we do that kind of thing? I'm not going to talk about ethical questions in this context. I'm happy to talk about those afterwards, but that's not really what I major on.


[User] Please transcribe this text

[Assistant] Right, okay, so it's, it's basically, it's a product that was developed by Microsoft and it's a sensor driven camera.


[User] Please transcribe this text

[Assistant] So you wear it here, around your neck. I'll show you the outputs in a minute. It's got a fisheye lens.


[User] Please transcribe this text

[Assistant] And uhm, it, the, it triggers, it takes an image every time there's a change in light.


[User] Please transcribe this text

[Assistant] There's a temperature change, or there's a motion change. So basically, if I walked out of that door, it would sense my motion, take a bunch of images. Then, if I stepped outside, the light would change, another image would be taken. And what the result of this usually is, if you're wearing one for


[User] Please transcribe this text

[Assistant] Let's say, uhm, you know, normal, I-I-I guess about 14, 13, 14 waking hours, you've got about 7,000 images, right, depending on your activity level. You know, it can be as low as 3, but, you know, around 7,000.


[User] Please transcribe this text

[Assistant] And what we did then was, because this is like prehistoric times, we, uh, separately, this is a Garmin GPS, but we wanted to co-index it to, uhm, location.


[User] Please transcribe this text

[Assistant] So we've now got images that are basically co-indexed locations. So we know where you were when an image was, uh, when a photo was taken.


[User] Please transcribe this text

[Assistant] So uhm, I'm just gonna show you what the outputs are like. So uhm, I'm gonna show you this and then I'll ask you.


[User] Please transcribe this text

[Assistant] Kinda what you think was going on. Right? But this is the output. Remember, it's a fisheye camera. It's worn around the neck.


[User] Please transcribe this text

[Assistant] Okay, so uhm, let's see how much you know. That's a view into somebody else's life, right? Obviously she agreed. Oop, gave one away. I was going to ask you the gender. But you could have all told me that, right? Because...


[User] Please transcribe this text

[Assistant] A drink, right? Because they had two separate, two alcoholic drinks in that time, right? Greek food, you wouldn't have got that. That's reversed. But anyway, so what you can see with this technology is, potentially,


[User] Please transcribe this text

[Assistant] Just with this kind of sensor-driven camera, like something that you could have with Google Glass, you know, you've got these apparently amazingly very, very rich records of what's going on. So anybody any questions about that? Yeah.


[User] Please transcribe this text

[Assistant] So our question, so we basically build, uh, built an interface where you could kind of fast forward and stop through your day, right? And we, our questions, our research questions in deploying that and I'll show you some other interfaces to it are, does this type of life logging help memory? How does it help? So questions we can ask are,


[User] Please transcribe this text

[Assistant] Can people remember more events from their past? Can they remember more details? Then we can ask a question you see


[User] Please transcribe this text

[Assistant] One of the things that I think a lot of people get wrong about, quotes, digital memory is that


[User] Please transcribe this text

[Assistant] These are kind of external indices. They are cues to help us remember these aren't the memories themselves. So said a different way, you might have forgotten that you went out to dinner with that person.


[User] Please transcribe this text

[Assistant] But just seeing that image might actually remind you about it.


[User] Please transcribe this text

[Assistant] Right? So that's a cue to help you remember that the digital stuff is a cue that will actually engender or improve your memory. So I'm sure you've all had the experience of


[User] Please transcribe this text

[Assistant] You know, looking at old photos with a friend, uhm, or a family member and, you know, you're in the photo, you have no memory of it, right?


[User] Please transcribe this text

[Assistant] But you know you must have been there because you can kind of see yourself in the picture, right? So there's this reconstructive element which these types of technologies, uhm, bring.


[User] Please transcribe this text

[Assistant] So the other thing we're interested in is people's emotional reactions.


[User] Please transcribe this text

[Assistant] To, uh, sing these types of pictures. So classically,


[User] Please transcribe this text

[Assistant] You think about social settings, uh, photo sharing is a classically social, uhm, exercise where people, you know, engenders positive emotions and bonds between friends, uh, and family members.


[User] Please transcribe this text

[Assistant] Okay, so we did a study where people, they wear the SenseCam


[User] Please transcribe this text

[Assistant] They have the Garmin. They wore it for 18, uh, they wore it for 2 weeks.


[User] Please transcribe this text

[Assistant] And, uh, five weeks later we tested their Recall and we used the classic, uhm, psychological, oh, oh, sorry, uhm,


[User] Please transcribe this text

[Assistant] We had four different interfaces because we're interested in various different types of ways that you can actually represent the information. So this is me with wetware. So one control condition is I get no technology.


[User] Please transcribe this text

[Assistant] Another condition for retrieval is we're going to ask you a question about what you remember in a day. All you have is GPS information.


[User] Please transcribe this text

[Assistant] Another setting is the top one there called snaps. All you have there is just those images that I showed you with a way to kind of fast forward and stop through your day.


[User] Please transcribe this text

[Assistant] And then this is a combination of those two, which whereby you can basically click on a location, you can see the image. Right? So we've co-indexed those two. Right? And we ask the question, you know, how do those different type of interfaces, how do they promote record?


[User] Please transcribe this text

[Assistant] So the basic question we ask people, we ask them about a particular half day.


[User] Please transcribe this text

[Assistant] And this is a typical question that's used by psychologists to probe how much people can remember about past incidents. So I might say to somebody, what did you do, where did you go, and who did you meet on, you know, whatever, instantiating that, uh, Monday morning, December the 16th, which is a day for which they have data. Uh, sorry, half day for which they have data.


[User] Please transcribe this text

[Assistant] And then when they've told the story, right, so they can use the technology if that's the condition they're in. If they're in the wet wear condition, they just have to try and remember.


[User] Please transcribe this text

[Assistant] And then we ask, uh, once they've done their, uh, recall, we ask them a question which is, you know, do you, do you, did you literally remember it? Right? Did you see it in your mind's eye? Or is it the situation like the one I told you about the


[User] Please transcribe this text

[Assistant] Old photographs, is it one where you see the thing and you kind of know that you must have been there, you can kind of infer that that's an event that you participated in.


[User] Please transcribe this text

[Assistant] But you don't actually literally have a memory of it. So for example, that person might see themselves in that restaurant, they might see their hands there, and they might know that they were there, but they don't have


[User] Please transcribe this text

[Assistant] You know, they don't have a, a, the feeling, they can't see that event in their mind's eye. And the last one is, you know, like, plain guesswork.


[User] Please transcribe this text

[Assistant] And then we give them a simple linear scale where we ask them to rate their happiness, you know, how they felt, you know.


[User] Please transcribe this text

[Assistant] We're, one of the, the thing that we're looking at then is the number of events where an event could be for you guys going to class.


[User] Please transcribe this text

[Assistant] You know, driving the kids to school, going out to lunch, and then, so, and events can also have attributes, so we're scoring.


[User] Please transcribe this text

[Assistant] The raw number of events and also, uhm, you know, these attribute properties of them.


[User] Please transcribe this text

[Assistant] Okay, so in this setting, whatever technology or condition you're in, you know, could be one of those four. Yet as long as you like to look at your life log or other, you know, if it's GPS to look at that log, uhm,


[User] Please transcribe this text

[Assistant] And the question we ask ourselves is, compared with wet wear, how many extra events do you think you'll be able to recall? So this is a group exercise for all of us. You know, so just think, you know, if


[User] Please transcribe this text

[Assistant] If the total recall, if life-logging, if that intuition is correct, you know, you would think it would be kind of about this order, right? You think about your calendar? Yea, Terry.


[User] Please transcribe this text

[Assistant] So we give them these types of examples. So it has to be kind of extended. It can't be at this, this kind of micro level. It has to be something that's extended over minutes. So we give them examples of this, right? And this is what they do in the psychological studies when they're, uh, testing autobiographical memory. But I guess looking at my watch is not


[User] Please transcribe this text

[Assistant] An example of an event, but going to a meeting might be having a coffee, uhm, you know, uhm,


[User] Please transcribe this text

[Assistant] I don't know, going out for a meal. These are all, uh, kind of event types. They're extended over time. They're the kind of things, as I say, that you might calendar. Right?


[User] Please transcribe this text

[Assistant] People didn't, I understand logically and philosophically, have we got any, I mean of course philosophers are going to kill us for talking this way.


[User] Please transcribe this text

[Assistant] In ordinary language, people respond in kind of the right way.


[User] Please transcribe this text

[Assistant] So this to us was, so this, if you remember, it's asked about half day, so we were kind of pretty surprised at this, right? So what we're saying is you get like about 3.5 extra events boost per day through using this type of technology, right? So you can remember three extra things that you did.


[User] Please transcribe this text

[Assistant] And what's kind of very interesting to us down here is that, uh, that's not statistically


[User] Please transcribe this text

[Assistant] Significantly different. So just looking at a bunch of images is no better than, you know, just, you know, trying to remember.


[User] Please transcribe this text

[Assistant] You know, trying to remember it out of your own head. And I should say we're not alone in doing this. Uh, Abby Sellen's group also got very, very similar results when they ran a very similar study. So what's going on?


[User] Please transcribe this text

[Assistant] You know, and they'd say, oh, okay, I guess that was the day when I left work early.


[User] Please transcribe this text

[Assistant] Right? And I decided I was gonna look, I needed to pick up some shoes, or I thought, I'd seen this cute pair of shoes, I thought I'd go and look at them again, you know, and I thought I'd look at those dresses. Right? I'd do some clothes shopping before I met X.


[User] Please transcribe this text

[Assistant] Right? And, and then, so, so then they would, might be flipping in and out, so they might be looking at the map of Cambridge, so they might see themselves kind of traversing.


[User] Please transcribe this text

[Assistant] In a Cambridge, you know, having cycled, you know, so GPS tells you your mode of transport as well implicitly. So, so that's the way they use it.


[User] Please transcribe this text

[Assistant] If this is averaged across, so that's correct. Your interpretation is absolutely correct. So most of the time, when we say to people, you know, if I was to say to you, hey Terry, what did you do, you know, and I don't put you on the spot, but what, you know, what were you doing on, you know, uh,


[User] Please transcribe this text

[Assistant] February the 26th, assuming that's not your birthday or, you know, your wife's birthday, yeaah, in the morning. You say, I don't know.


[User] Please transcribe this text

[Assistant] And it may not be, you know, that may be an undershoot for what your number of events actually is.


[User] Please transcribe this text

[Assistant] Right? And we're, we're just surprised not to see that. Yeah.


[User] Please transcribe this text

[Assistant] Yeah, so I think that's a good question. And I'm saying the cal- we have not done that, but I think the calendar is not a bad proxy, which would basically, you know, so you think, right, my calendar usually, I've got probably about six or seven calendar events, and then I've got family stuff around it. Right? So you know, that's maybe not bad, yeah.


[User] Please transcribe this text

[Assistant] So, we ask people after they've done all that, right, so this is kind of important to understand because it tells us something about memory as well. Okay, so we ask people about that, we're saying...


[User] Please transcribe this text

[Assistant] You know, we didn't say you're absolutely crap, but we said, you know, you seem not to be, you know. So what's, what's going on here? And then the first point is your point, which is like, there's so many images.


[User] Please transcribe this text

[Assistant] I don't know. That's a meeting. I'm meeting, like say I'm meeting Michael. You know, if that's a, if that's a one on one weekly.


[User] Please transcribe this text

[Assistant] I don't know which one it is, right? So, so part of the problem here is, and that's kind of a classic attribute of


[User] Please transcribe this text

[Assistant] Human memory is it moves from being very, very highly specific, so you have these one-off events, which is kind of the way that we tend to think about how memory is. But a lot of it's kind of boring stuff, and the way that human memory works is it kind of evolves a script.


[User] Please transcribe this text

[Assistant] Uh, across, it abstracts out, and you know, if that's a one on one, they all allied into each other. Right? One faculty meeting,


[User] Please transcribe this text

[Assistant] One class is, you know, the first class is exciting, but then they begin to elide. It's an instance of a type, and you don't have a lot of instantiations of different details. And that's the problem that people are running into.


[User] Please transcribe this text

[Assistant] Okay, so, you know, then we looked at other, so this is attributes, so once you're talking about an event, then uhm, you know, one question we can ask is what kind of, which of the interfaces help you get kind of more details, and the snaps, you know, for fairly obvious reasons, they help you get more details about an event.


[User] Please transcribe this text

[Assistant] So people say things like, I found the sensor cam pitch is really useful for those micro events that happen those days. So once you've got the event, you can kind of read out from it, yeaah.


[User] Please transcribe this text

[Assistant] Right, we, we misdesigned the interface, right, because you had to key off, you had to key off location to get to image, and I think that it should, we should just have had those kind of be simultaneous. Right, so that's, I think that's a piece of misdesign, but well spotted, yeaah.


[User] Please transcribe this text

[Assistant] Did you see it in your mind's eye or were you kind of guessing it?


[User] Please transcribe this text

[Assistant] Right? And so what we see, sorry, reconstructing it, so you look at that thing, you say, what must have been happening?


[User] Please transcribe this text

[Assistant] Right? And so what you see is, with these location-based technologies, people are doing more of this kind of inferencing. They're saying, where was I? Oh, I'm gonna kind of work out what I was doing. Right?


[User] Please transcribe this text

[Assistant] Uhm, you know, so this is this interesting cool one, which is about kind of speed of travel.


[User] Please transcribe this text

[Assistant] Snaps made me, uh, made me remember snap tracks and tracks. Those were location-based. Maybe figure out something which must have happened in a particular way. For example, I must have gone home by taxi, right? So this person, they're used to seeing those pins spaced in a particular way because they walk or take a bike, right? Suddenly they see themselves doing these traversals of distance very quickly, so they make an inference, right? And they've got an image of themselves inside a car, so they, they such see that. Okay.


[User] Please transcribe this text

[Assistant] And then, we, we also, and this is completely consistent with the psychological literature, which says that uhm, if you can see something in your mind's eye, not all memories are actually visual, but a lot of autobiographical ones are, there's emotion associated with them. Uhm, organic memory, that's the, you know,


[User] Please transcribe this text

[Assistant] Uh, that's wetware, was useless, I could have no emotional attachment to those memories. But the tools, especially snaps, I guess meaning images across both conditions, were very good at reviewing accurate memories. So people kind of feel more positive about these things.


[User] Please transcribe this text

[Assistant] So what I'm trying to argue here is that a rich record doesn't necessarily guarantee total recall. And part of the problem is that a lot of these images can be confusable.


[User] Please transcribe this text

[Assistant] Uh, and that might relate to kind of the habitual nature of our lives. Uhm, that these memories are often reconstructive. So that's say you're using the digital as a set of cues to reconstruct and these pictures are very evocative, right? Now I don't really want to go into a


[User] Please transcribe this text

[Assistant] Technical digression, because I kind of want to move on to the next conceptual point, but one of the questions which I guess you over there, I don't know your name, I'm sorry, we're talking about how can we process these.


[User] Please transcribe this text

[Assistant] And in particular, one of the big problems is over generation. Right? So we have, I want to talk really quickly about two ideas. One idea is coindexing by attention or user action.


[User] Please transcribe this text

[Assistant] So here's a simple example, uh, and Michael will be totally familiar with this because it's like the one-eyed technical idea I've ever had. Which is like, if somebody takes a note,


[User] Please transcribe this text

[Assistant] Or somebody takes a picture, we can infer actively that they're interested in something.


[User] Please transcribe this text

[Assistant] So what if we could co-index those actions onto


[User] Please transcribe this text

[Assistant] You know, that stream of pictures, then we could infer that those things were kinda more important. Uhm, so that's one way


[User] Please transcribe this text

[Assistant] So that's kind of co-indexing, say, off sensors. And then the other way, which we actually built a couple of systems to do, was if somebody repeatedly accesses the same image, we're going to infer it's important. Right?


[User] Please transcribe this text

[Assistant] Just because they've looked at it multiple times. So uhm, one of the systems we built, uhm,


[User] Please transcribe this text

[Assistant] We use those as queues to import, and you're, Michael, totally familiar with this, right? This kind of crowdsourcing, so it's like individual crowds, so if I multiply, if I access an image multiple times, I'm going to infer that's more important, and in our UI, it grows.


[User] Please transcribe this text

[Assistant] Right, so this is like Manga style, it grows. The more you access it, the bigger it looks and the more salient it's supposed to be. Right? But also we can crowdsource that and we built a kind of lecture deployment, which you're seeing here, which is across a class.


[User] Please transcribe this text

[Assistant] Right? If I see, if multiple people access the same image across a class like this, if we had this type of technology deployed in this type of class, then these


[User] Please transcribe this text

[Assistant] More popular images would be seen as kind of more important in that context. So those are two ways to kind of get ourselves out of this trap of over generation.


[User] Please transcribe this text

[Assistant] It's to try and use people's actions as indicators or proxies for importance.


[User] Please transcribe this text

[Assistant] This idea about kind of fixing memory, right? So the life-logging vision, it basically says, memory's broken, here comes technology to the rescue. Well, what if we try to take a slightly different perspective, right?


[User] Please transcribe this text

[Assistant] So instead of trying to fix organic memory by recording everything, what if we tried to emulate in some ways? Okay, so let's just, I'll give you a quick, tiny tour of how human autobiographical memory works in practice. So,


[User] Please transcribe this text

[Assistant] These are some of the arguments. One argument is that forgetting is adaptive, right? So, simple example. When you go out to the car park looking for your car, you don't want to remember every single instance of when you ever parked that car.


[User] Please transcribe this text

[Assistant] Be kinda nice if you forgot all the previous ones. Right? So that's a simple example.


[User] Please transcribe this text

[Assistant] So that's one argument about adaptivity of forgetting.


[User] Please transcribe this text

[Assistant] Uhm, there's also, uhm, something called positivity bias, which says, and I'll show you the data on this in a second, that one of the things that we as people do is we forget negative or upsetting things.


[User] Please transcribe this text

[Assistant] Okay, so, you know, quick kind of, uh, whiz through, uh, memory and emotion, right? So, there's a lot of, so you can ask people about their past using a variety of different methods, you can ask them to journal, you can ask them to talk about big salient events from their past, whatever you want to do,


[User] Please transcribe this text

[Assistant] There's a repeated result, which is people remember twice as many positive as negative events. Right? And I guess neutral comes in about 30%. So that's one thing that people do. Right?


[User] Please transcribe this text

[Assistant] The second thing, really interesting result is this thing called the fading affect bias, right? And I've tried to depict it here.


[User] Please transcribe this text

[Assistant] So, if something happens to us and we think about it again, generally the affect that we have that's associated with that drops. So here's a highly positive event, right, the ones in yellow, we rethink it, it drops over time. So, you know,


[User] Please transcribe this text

[Assistant] You just won the lottery. You think about it again. You're not gonna be as positive as that first moment when that thing happened. Right?


[User] Please transcribe this text

[Assistant] That's the fading affect bit, now the bias bit. Here are negative events, right? Negative events fade much more quickly


[User] Please transcribe this text

[Assistant] Right? Faster regression to the mean than positive events. So what's, what does this mean? It means that if something really bad happens to you, then you kind of get over it more quickly than if something really good happens to you and that starts slowly attenuating.


[User] Please transcribe this text

[Assistant] Right? So emotional, general emotional strength drops, the negative drops faster. Right? I'm going to talk about that a bit more with our system later.


[User] Please transcribe this text

[Assistant] The other thing, which is kind of cute, is this thing called the Rosie View.


[User] Please transcribe this text

[Assistant] Which is that we tend to kind of edit out the negative, right? So there's these really cool studies where they do things like they ask people about vacations, right? Uh, you know, both at the time and afterwards. So they would have people logging a vacation and they'd have them, you know, on the plane.


[User] Please transcribe this text

[Assistant] Right? And so what people's accounts say on the place, let's say we're going to Disneyland, right?


[User] Please transcribe this text

[Assistant] On the plane and people saying things like, there's this really fat person next to me. God, their elbows are really poking into me.


[User] Please transcribe this text

[Assistant] Right? At the time. So you say, what's your affect like right now? And they say it's, it's about a five on a scale of one to ten. Right? I'm looking forward to being there, but this is a painless flight. Right? You ask them about that part of the trip afterwards, it's a seven.


[User] Please transcribe this text

[Assistant] Right? So we edit out, you know, so you ask Disney, you know, Disney World or whatever, right?


[User] Please transcribe this text

[Assistant] You look at their logs, they're not, you know, at the time they're talking about, oh my god, look at these lines, right, I can't believe that, you know.


[User] Please transcribe this text

[Assistant] You know, a thousand people are waiting for Pirates of the Caribbean, right? And then you ask them about how they felt a month or two later. Fantastic. Right? So that's one thing, that's, so this is


[User] Please transcribe this text

[Assistant] Another way that we're basically self-editing. So we edit out, you know, the negative or annoying details.


[User] Please transcribe this text

[Assistant] Okay, so now what we want to do is we want to take these kind of ideas into a context, into the digital context, because it starts to get a little bit different in the digital context because what we have in the digital context is kind of like, it's veridical.


[User] Please transcribe this text

[Assistant] Right? So if you go onto Facebook and you say, God, X is really pissing me off. Right? You know, and you look at that three weeks later, it's not suddenly got to being, gosh, what a good friend X is. Right? Which is what human memory might say, because it's right there. Right? So you've kind of logged it.


[User] Please transcribe this text

[Assistant] Okay, so this is, so we're interested in exploring this because this is a situation where we may not want perfect record. Okay, so.


[User] Please transcribe this text

[Assistant] Very quickly on break up and memory, so.


[User] Please transcribe this text

[Assistant] The things that we tend to remember are aligned with our self-concept, right? Those are the things we remember best.


[User] Please transcribe this text

[Assistant] One of the problems with breakup is, you know, your relationship is kind of very much part of your identity. What people tend to do in a relationship breakup situation is they kind of redefine themselves and then there's a problem because their new self isn't triggering the right set of memories.


[User] Please transcribe this text

[Assistant] So uhm, as a British person, I love this idea. There's this mechanism which is called selective repression. You know, as a British person, we never want to talk about negative stuff. We never, we just never bring it up, right?


[User] Please transcribe this text

[Assistant] So that's the mechanism that's been argued is helpful in this context. Okay, so we did...


[User] Please transcribe this text

[Assistant] So the idea behind this study is to try and understand this situation where people may, quote, want to forget. And then what we want to do is we want to think about designs that might follow from understanding that situation well. So we have 24 digital natives, you know, so these are people who are kind of living online.


[User] Please transcribe this text

[Assistant] Uhm, and they had to experience a recent breakup following a long-term relationship.


[User] Please transcribe this text

[Assistant] Okay, so here are some of the problems with digital records, which, you know, you guys will be just totally familiar with, right? So there's a bunch of stuff that we have, you know, on our personal systems, which is like super evocative.


[User] Please transcribe this text

[Assistant] Right? So we've all got personal, you know, whether this is on, you know, when I say personal, I mean it could be on the cloud as part of our account, right? But music's very evocative, we've got our personal photos, and obviously stuff we do on the social networking site, it's all kind of, uh, it engenders emotion.


[User] Please transcribe this text

[Assistant] The other characteristic of it is, it's kind of ubiquitous, that's to say we're always kind of encountering it.


[User] Please transcribe this text

[Assistant] Right? So it's, you know, it's part of our stuff. You know, you go into Facebook, you see this, you go into your file system, you go into your email, you're constantly being reminded of significant people in your life. Right?


[User] Please transcribe this text

[Assistant] The other thing is most people's stuff is not particularly well organized, so it's kind of hard to compartmentalize stuff if you actually wanted to do that.


[User] Please transcribe this text

[Assistant] The other thing, final thing is kind of highly available, right? So people we found out have very different strategies on this, right? So you can unfriend,


[User] Please transcribe this text

[Assistant] Right? But if you choose not to unfriend, then it's very, very easy these days to find out exactly what your ex is doing, just with one click.


[User] Please transcribe this text

[Assistant] Right? And we did have people in the study who would unfriend people and then they would go to their friend's house


[User] Please transcribe this text

[Assistant] Log into their friend, you know, their friend, they say, I need to look at your Facebook account because you're still friends with my ex, and then they'd look.


[User] Please transcribe this text

[Assistant] Right, so the problem is this information is just so much more accessible.


[User] Please transcribe this text

[Assistant] Okay, so, you know, life's now online in a serious way. So, in this situation, we're often one click away from highly upsetting information. So, you know, people say, I miss him. His uploads on Facebook make me feel hurt.


[User] Please transcribe this text

[Assistant] What hurts are pictures with his new friends and experiences because I can see him but I can't talk to him, right? And we know there's a bunch of stuff on Facebook and relationships which says it's very, Facebook's very misleading because there's no context for posts.


[User] Please transcribe this text

[Assistant] Right, stuff goes up and you don't know the context. So there's a lot of misinterpretation.


[User] Please transcribe this text

[Assistant] Then this point I already made, that it's very easy, because our personal stuff is a mess, it's very easy to accidentally encounter highly upsetting information.


[User] Please transcribe this text

[Assistant] There are some songs that recall feelings from that period, I listened to them, and this hinders moving on.


[User] Please transcribe this text

[Assistant] Pitch has always made me remember good memories, so I try not to look at them at all, because good memories also link to a bad memory.


[User] Please transcribe this text

[Assistant] Then we started looking at what people did with this stuff, right? And we found, you know, crudely,


[User] Please transcribe this text

[Assistant] So something, you know, and you can maybe think about people's real life strategies to relationships. So we got three types. You know, one, some people like, it's over, they try and trash everything immediately. Then there are some other people who keep everything.


[User] Please transcribe this text

[Assistant] Right? And we'll talk about how they deal with it. And then we've got people who are kind of more, uh, I guess strategic about when and what they delete. So I'm just going to talk through those people. Right?


[User] Please transcribe this text

[Assistant] You know, so some people, they just say, it's over, right? Having photos on my phone and computer did cause me to feel sad, but I immediately removed them all after the breakup in order to move on. Right? Of course, there are problems with this, right?


[User] Please transcribe this text

[Assistant] You know, because, you know, if you immediately delete everything about a relationship, you're kind of, you, at the time you might feel it's, it's a, it's like a negative event, but


[User] Please transcribe this text

[Assistant] If you, when you think back, when you kind of got through it, you can't kind of write off three years of your life. You can't say there was nothing valuable there. So when people feel better about it, they want to salvage something, but then they've trashed everything.


[User] Please transcribe this text

[Assistant] So people say some books and music will continue to remind me of him.


[User] Please transcribe this text

[Assistant] I can keep those because I felt it would not be productive for my attempts to move on. Now I wish I kept them.


[User] Please transcribe this text

[Assistant] Okay, so here's the other type, right? I kept everything, right?


[User] Please transcribe this text

[Assistant] And this can be quite disruptive. Pitch has hindered my moving on.


[User] Please transcribe this text

[Assistant] When I look to them, uh, at them, it would make me remember him.


[User] Please transcribe this text

[Assistant] I just try not to look at them, but at the very beginning of the breakup, I looked at them frequently. Those possessions didn't help me. Right, so this is the kind of immediate accessibility problem.


[User] Please transcribe this text

[Assistant] Okay, and then we've got the, I love this, I just love this, right?


[User] Please transcribe this text

[Assistant] So, so then we've got some people who are kind of thinking about it, right?


[User] Please transcribe this text

[Assistant] So then they do this kind of, you know, technical stuff where they might create a hidden folder, they might kind of, and this is a problem, you know, if I want to find all the stuff about X and put it in one location, that's a tricky thing to do, right?


[User] Please transcribe this text

[Assistant] So I deleted all the messages, I backed them up, I put all the digital material into a file, I set it as hidden.


[User] Please transcribe this text

[Assistant] Right? People might hide stuff on their old phone. And then this is getting to be super interesting, right? So they appoint gatekeepers. So they might give them the memories, they might give them the USB or the old laptop or whatever and say, don't let me have that.


[User] Please transcribe this text

[Assistant] Right? Kind of interesting. So we'll come to the designs in a minute, but that's kind of starting to get really interesting. Okay. So then, uhm,


[User] Please transcribe this text

[Assistant] The, the selective people are doing something in between. I actually had a little clean up there, deleted a bunch of emails I had from her.


[User] Please transcribe this text

[Assistant] Cured all that stuff off my computer and deleted my number from her phone.


[User] Please transcribe this text

[Assistant] Okay, up to photos. Someday I might want to revisit some of the times we share, just not right now. Yeah.


[User] Please transcribe this text

[Assistant] Right, so you sign up, and the idea is, you know, I'm not sure technically how it does it, because I think technically it's a really difficult problem to say, find me all information about x. Right?


[User] Please transcribe this text

[Assistant] And then harvest it in a particular place and trash it, right? So I could see within the scope of particular applications you could do that. But anyway, this is the way this pro- yeaah.


[User] Please transcribe this text

[Assistant] Labeled all your photos in the right kind of way and, you know, been very good about tagging.


[User] Please transcribe this text

[Assistant] Then it would have a chance, but, you know, I just don't see it somehow. Anyway, but that's, so, one idea which, you know, which,


[User] Please transcribe this text

[Assistant] I guess the, the technologies I found more interesting, the designs I found more interesting in this context were along the lines that you were suggesting, which is once you kind of got a lot of that stuff together, which as I say is kind of a difficult technical problem in, in and of itself,


[User] Please transcribe this text

[Assistant] There are things that you might want to do to kind of stop you immediately accessing it, right? Because one of the things that one person talked to us about was like the drunken phone call, right? And this, you know, so it's like you have the contact number.


[User] Please transcribe this text

[Assistant] You know, you're in a poor state emotionally, you can't stop yourself, right?


[User] Please transcribe this text

[Assistant] So what these types of designs might do is if all that material is in a location that's kind of, you have a timeout, right, so you could set some parameter which says, you know, I'm not, you know, I may express a desire to do this, but I can't execute on it for 24 hours.


[User] Please transcribe this text

[Assistant] Right? Till I'm sober, for example. Right? So that might be. Or, you know, you could have the gatekeeper, which is that person owns it and we have to have a chat.


[User] Please transcribe this text

[Assistant] Before, you know, it has to be an exchange of input before they'll kind of give me, you know, the key and I can get in.


[User] Please transcribe this text

[Assistant] The other one was physical distance which is that you could, which some people did, which is they took that laptop and they gave it, put it in a physically different location. So all those things might cause, you know, might help you


[User] Please transcribe this text

[Assistant] Do the impulse control by making it really difficult to kind of follow through on this kind of self destructive urge.


[User] Please transcribe this text

[Assistant] And then there's these other things which we didn't really see, I mean I guess those, uhm,


[User] Please transcribe this text

[Assistant] You know, but they are design ideas that we're really interested in exploring.


[User] Please transcribe this text

[Assistant] Okay, so uhm, anybody any questions about that? By the way, this went viral.


[User] Please transcribe this text

[Assistant] On the internet, I had so many, you know, I, people, I guess, people want to know Facebook's really bad for us, so, yeah.


[User] Please transcribe this text

[Assistant] Yeah, so you could, that sort of, I guess, and there's the reverse, you know, if you, you know, if you don't stay off that, I give $50 to the Republican party, that, you know, you can have them work both ways, right?


[User] Please transcribe this text

[Assistant] Okay, and, and, and then this, this was, this was an idea that some of, so this is in the spirit of kind of actively, physically processing, uh, this stuff.


[User] Please transcribe this text

[Assistant] Okay, and then the last thing, uhm, which I guess Facebook weren't very responsive to was, you know, so at the moment, uhm,


[User] Please transcribe this text

[Assistant] You know, what Facebook is, it's kind of weird. I mean, it's a friend's site, but it's not a good site for managing your relationships through because there's no notion of kind of tying your stuff together to another person's, right? So people talk about a lot of problems where they're


[User] Please transcribe this text

[Assistant] Really private personal stuff was under somebody else's control. So we had ideas that you might actually have, like uhm, you know, there'd be a special area of Facebook which was about, you know, close relationships, which


[User] Please transcribe this text

[Assistant] For relationships which were moving positively could be used for celebration.


[User] Please transcribe this text

[Assistant] Right? And then if things did go wrong, then you could have something that was closer to the kind of kill switch facility, but basically you wouldn't have this problem of kind of managing your relationship through all these different kind of proxy


[User] Please transcribe this text

[Assistant] Okay, so uhm, so, so this is a different approach. Uhm, I talked about the value of adaptive, uh, forgetting.


[User] Please transcribe this text

[Assistant] Uhm, distinct strategies for disposal of, of digital possessions and some design implications for doing digital disposal. Okay.


[User] Please transcribe this text

[Assistant] So the last thing I want to talk about today is some recent work we've been doing on, I guess, reflection.


[User] Please transcribe this text

[Assistant] Which is kind of trying to think deeply about digital records and how this relates to, to well-being, excuse me. So uhm,


[User] Please transcribe this text

[Assistant] We're, we're trying to inst- and so I guess there's this whole quantified self movement which is like


[User] Please transcribe this text

[Assistant] Have data about yourself, understand yourself, right? So we're in this area, but what we want to do is we want to provide ways to use these types of personal records to kind of better understand ourselves. So let me talk about, and we're interested in the emotions realm.


[User] Please transcribe this text

[Assistant] Right? So, one of the problems that we have as people is this problem that we call emotion pattern detection. Right?


[User] Please transcribe this text

[Assistant] So, you know, so let me show you this example. This is from one of our users who used our system for about a year. And this is like, this is her days of the week, right?


[User] Please transcribe this text

[Assistant] So what this shows you is that she's pretty happy on a Monday, she's terrible on a... I guess this mo... No, no, no, that's Sunday.


[User] Please transcribe this text

[Assistant] This is Thursday, right? So she gets sadder during the week, and, but you won't be too surprised about that, but she didn't know it, right? So that's an example of an emotional pattern, which...


[User] Please transcribe this text

[Assistant] These types of systems are actually going to yield up to us and I'll show you a few more of these kind of things later.


[User] Please transcribe this text

[Assistant] So what we can do, so as I say, in our lives, the connection between things that happen to us, things we do, and our emotional reactions are quite complex.


[User] Please transcribe this text

[Assistant] If we can kind of record and kind of externalize and visualize those connections, we can better understand ourselves and we can kind of proceed adaptively. So if we like things, we can seek them out.


[User] Please transcribe this text

[Assistant] If we don't like things, you know, we can seek to avoid them.


[User] Please transcribe this text

[Assistant] Uhm, and I'm gonna talk, what we found really interesting about the deployment of this system is that people are actually using it for kind of behavior change, which is they were using it to monitor things they were doing that made them kind of unhappy and using the system to actually change those behaviors. Okay, so,


[User] Please transcribe this text

[Assistant] What I've shifted to now talking about is not just remembering stuff, but reconstruct. And I want to really quickly talk about some, I think, fascinating, uh, research on emotional writing, which is as


[User] Please transcribe this text

[Assistant] Talking about and reprocessing our past, so I guess you know this, right?


[User] Please transcribe this text

[Assistant] This, isn't this just fascinating? Okay. So,


[User] Please transcribe this text

[Assistant] What Penny Baker does is he has people journal, say you're really upset about something, right? What he has people do is repeatedly write about that event.


[User] Please transcribe this text

[Assistant] Right? And this is the kind of thing that happens, right? So there's now over 200 studies with this kind of reflection on your


[User] Please transcribe this text

[Assistant] Negative past, so what the effect, the effects you get are reduced medical visits, improved immune response, better grades, re-employment, reduced absenteeism, increased subjective well-being, increased working memory, right, and the argument behind this is


[User] Please transcribe this text

[Assistant] What this type of exercise allows people to do is to distance themselves from negative events, but also to see themselves as a kind of hero in their own narrative in which they've kind of


[User] Please transcribe this text

[Assistant] They've accomplished, they've got past some very negative past event. So they say,


[User] Please transcribe this text

[Assistant] You know, some terrible stuff has happened to me, but I feel good about that event now, because that's what happens if you repeatedly write about it.


[User] Please transcribe this text

[Assistant] And so I feel good about it now. Therefore, I'm some person who can cope


[User] Please transcribe this text

[Assistant] With past negative events. Now I told you about ten minutes ago that one of the things our memories do is edit out negative events, so they're not doing anything fantastic, we aren't, but this is how people feel, they take a positive message from this situation, which is, I'm feeling much better about it now, therefore I'm resilient.


[User] Please transcribe this text

[Assistant] Okay, and also you can get the same kind of benefits, but a bit reduced. So if you, if you think about positive things that have happened to you, so this is like the, you know,


[User] Please transcribe this text

[Assistant] Sharing photos, uh, you know, experience, you know, you look at family photos, you share stuff on Facebook, you know, generally if it's a social event, you feel better. And there's a bunch of literature, not as strong events as with the negative past, but still, uhm, good effects.


[User] Please transcribe this text

[Assistant] Okay, so we're on to explore these ideas with a system, because we think, Penny Baker, this type of thing can be done much better with a system, right, because if you've got a mobile app,


[User] Please transcribe this text

[Assistant] Actively present those back to you. So in the Pennebaker style, you have to kind of get your...


[User] Please transcribe this text

[Assistant] Paper log out. You have to make sure you find a time in the day to...


[User] Please transcribe this text

[Assistant] Kind of go back and, you know, write about that past negative event.


[User] Please transcribe this text

[Assistant] What our system does is it presents them back to you, both positive and negative, and asks you about how you're thinking about them now.


[User] Please transcribe this text

[Assistant] So it's pretty easy for you to log things that are happening to you, I guess more veridically, because you log them, you know, I guess now there's no...


[User] Please transcribe this text

[Assistant] There's no particular, you know, people are using their phones all the time, right? So it's not like, what's that person doing? They could be texting, right? The fact they happen to be logging their life is, yeaah.


[User] Please transcribe this text

[Assistant] Sixteen percent of people are quite happy to do this and I would say are kind of active journalists, right?


[User] Please transcribe this text

[Assistant] So we don't have to pay him anything. A lot of the study I'm, we give people like a,


[User] Please transcribe this text

[Assistant] Right, so I think, I think the whole question about uhm, you know, compliance with all these, all health applications is a big one. This one I'd say about 16% of people are doing it naturally. What's really interesting to me is there's a kind of paradox because


[User] Please transcribe this text

[Assistant] I can, at our exit interviews, people are saying, tell me how great this system is. I say, do you want to keep it? They say yes, and then I, you know, I get back in touch four months later.


[User] Please transcribe this text

[Assistant] You know, I've seen, I can make them happier objectively, they think they're happier, they stop using.


[User] Please transcribe this text

[Assistant] Right, but that's human, you know, we all know there's a bunch of things we can do to, you know, be better people and healthier people, but we don't do them. Right.


[User] Please transcribe this text

[Assistant] Free downloadable Android iPhone, right? I'll finish with those links. I'd love, if you want to use them, fantastic. Okay.


[User] Please transcribe this text

[Assistant] Uhm, you journal any daily event, you emotionally rate it, we just play it back to you, you know, like, uhm, so this is uhm,


[User] Please transcribe this text

[Assistant] You know, one of our long-term users, one event from three, oh you take a photo, evocative two, an event from three years ago. So she can now reflect on that one, two years ago, one year ago, one month ago.


[User] Please transcribe this text

[Assistant] Right, so you're just logging as many, most people, most of our long-term users, they're doing like two or three a day. And then, you know, however long, we've got some not very clever algorithm which presents the fact to you which is normally like a month later, a year later, because there is something to kind of calendar cyclicity.


[User] Please transcribe this text

[Assistant] Okay, so let me just, uh, so this, you create an entry this way, take a photo. I want to get to the data, and I'm a bit concerned about time here. So let me just whiz through.


[User] Please transcribe this text

[Assistant] So uhm, what we did was a kind of a classic intervention study.


[User] Please transcribe this text

[Assistant] We measure people's well-being, you know, using standard scales. Then we have, uhm, three different conditions.


[User] Please transcribe this text

[Assistant] Uh, record and reflect is our ecosystem. Record is we just have them record events, that's, you don't see them back.


[User] Please transcribe this text

[Assistant] They get, they see the events multiple times. Everybody understand that difference? Okay. Uhm.


[User] Please transcribe this text

[Assistant] So, these are the wellbeing scores, and uhm, all of these groups, surprisingly to us, both recorders and reflectors,


[User] Please transcribe this text

[Assistant] And then we did, uhm, we did textual analysis of the logs, which they shared with us. So everybody gets a chance to edit their logs and share or not share, because this is private stuff. And then we did textual analysis, because we wanted to see


[User] Please transcribe this text

[Assistant] What were the kind of ways that people are talking about their emotions that would, that would kind of correlated with improvements of well-being?


[User] Please transcribe this text

[Assistant] And what we saw was that people are just recording, they're using the system very differently from the people who are reflecting, uhm,


[User] Please transcribe this text

[Assistant] You know, so people are recording, they're talking about relationships, reflectors are talking about actions, and that, uh, they're often future-directed. So I'll give you a couple of examples of that.


[User] Please transcribe this text

[Assistant] You know, so recorders analyze their life, especially their relationships.


[User] Please transcribe this text

[Assistant] The reflectors report on their activities, but later do lessons when they had perspective, both from positive and negative events.


[User] Please transcribe this text

[Assistant] So uhm, I'm not gonna dwell on that. This is, yeaah, this is one person just reflecting on her attitudes to men and, you know, I guess these are eternal gender issues.


[User] Please transcribe this text

[Assistant] Okay, so uhm, so this is how you can kind of use the, uh, the study to kind of reflect on, uh, emotional habits. I'm kind of getting worried about my assignment because there's some questions, items on there that I don't know how to do or answer. I'm afraid I won't get full credit. So she's got a six. She's not amazingly happy about it, right?


[User] Please transcribe this text

[Assistant] You know, I'm starting to realize that so long as we do a decent job on the assignments, we get full credit, so I don't feel like it's stressed so much. So she's thinking about that past event, although her score hasn't increased, she's getting happier about it. Or, I mean, in her text, she's getting happier. Here's another one.


[User] Please transcribe this text

[Assistant] This person's really unhappy. I should say that one is the unhappiest you could be. My boyfriend's not texting me often enough. Frowny. I really wish he would text me more often. Right?


[User] Please transcribe this text

[Assistant] And then, I'm sorry you can't read this probably, I guess I'm not that angry, I'm sure I can at times be that crazy girlfriend who texts too much and is overbearing, so I'm sure he can get annoyed too, right?


[User] Please transcribe this text

[Assistant] So uhm, I guess I elided this, but she's gone up to about a five here.


[User] Please transcribe this text

[Assistant] So, she's thought about her beha- past behavior and kind of got happier about it. So this is the way that they're using the system a lot of the time.


[User] Please transcribe this text

[Assistant] Okay, so I'm not gonna have time, I just want to make sure, so this is...


[User] Please transcribe this text

[Assistant] Learning from positive. So, one question we had


[User] Please transcribe this text

[Assistant] You know, was, we're actually messing with memory here.


[User] Please transcribe this text

[Assistant] So the point I made at the outset was, you know, what people are now seeing is actually how they felt, warts and all, about past events, but,


[User] Please transcribe this text

[Assistant] You know, so what the technology is showing us is what we actually thought at the time, but nevertheless, we're still getting these positive effects. So even though you can see literally how you felt at the time, right, so this is not edited memory like we usually get, but you still get these well-being effects.


[User] Please transcribe this text

[Assistant] So I just wanted to really quickly finish up with this long term stuff which to me is really interesting. So we show people visualizations. So we show people like days of the week.


[User] Please transcribe this text

[Assistant] And then we asked them, you know, did you know that you had this cycle throughout the days of the week?


[User] Please transcribe this text

[Assistant] Right, so this person's kinda getting happier throughout the study. She got a new job here.


[User] Please transcribe this text

[Assistant] Right? So we say to people, you know, well, what can you tell from that kind of visualization? So she told that, you know, people are interested in maxima and minima in these situations, right? So what led to that increase? They want to think about these types of events and obviously having these types of records gives them insight into things that were happening in their lives.


[User] Please transcribe this text

[Assistant] This one is not the greatest visualization. I'm not a visualization person, as you probably knew. Right, but this shows the redemption narrative. So what we do here is we take all the negative events


[User] Please transcribe this text

[Assistant] And we show their gradient, right, and so what a


[User] Please transcribe this text

[Assistant] Uh, you know, I guess a positive gradient shows is that you started sad about something and you got happier. And what you'll see is there's only one exception.


[User] Please transcribe this text

[Assistant] In this person's data, where something started happier and got sadder.


[User] Please transcribe this text

[Assistant] Right? So you can show them that kind of thing and, and they, you know, you explain it to them and then they start drawing these inferences about their own resilience. Right?


[User] Please transcribe this text

[Assistant] Which is bad things happen, I tend to feel happier about them subsequently.


[User] Please transcribe this text

[Assistant] Okay, so just want to finish it, you know, so what people say about the system at the exit interviews, it gives you a window onto what you're feeling, it gives insight in order to change.


[User] Please transcribe this text

[Assistant] Both from a positive perspective. If something makes me happy, I'll do it again. Negative. Echo gave me a snap.


[User] Please transcribe this text

[Assistant] Uh, snapshot of meltdown moments. I could look at those and see that those things weren't normal. It forced me to confront my reactions to myself. I learnt from it.


[User] Please transcribe this text

[Assistant] When I saw one of those memories, I saw that I needed therapy. And this is true.


[User] Please transcribe this text

[Assistant] This person actually went and sought professional help as a result of using our system.


[User] Please transcribe this text

[Assistant] Recording it, putting it down, thinking that that's not the way I want to be. Okay, so I'm just going to skip through, we've got a lot more. I just want to finish up, just to leave a bit of time. So.


[User] Please transcribe this text

[Assistant] Lifeblogging doesn't totally fix fallible memory. That's what I tried to show you at the outset.


[User] Please transcribe this text

[Assistant] There are arguments that we can learn from these ideas that memory is adaptive, get some interesting designs, looking at contexts where people want to forget.


[User] Please transcribe this text

[Assistant] And then what I want to argue is that I think there's some real opportunities with well-being and mental health.


[User] Please transcribe this text

[Assistant] You know, there are contexts in which where we have own and appropriate this data, we can use it to help ourselves. Okay, thank you very much.


[User] Please transcribe this text

[Assistant] Okay, so, so those are, I just want to thank those people who have collaborated on various of these projects. And then, as I say,


[User] Please transcribe this text

[Assistant] The Echo stuff now, you can download it free, and if you like it or you hate it, let us know.


[User] Please transcribe this text

[Assistant] In fact, I'd go so far as to mention the name of Blunch Ingram and the word marriage.


[User] Please transcribe this text

[Assistant] Although I told Miss Ingram something about the Rochester property, which made her look quite depressed. If a wealthier gentleman comes along, Mr. Rochester might lose his beautiful bride.


[User] Please transcribe this text

[Assistant] But I came to hear about my future, not Mr. Rochester's.


[User] Please transcribe this text

[Assistant] It depends on whether you're going to stretch out your hand for happiness. Let me look at your face.


[User] Please transcribe this text

[Assistant] Everyone smiles a lot here. Oh, is that right? They don't smile? I mean, it's so nice. They're, they're more, they're very, you know, cool. That's a French thing, right?


[User] Please transcribe this text

[Assistant] Uhm, it's, you get, I didn't realize that I got used to it until I got here and I was so surprised when like, you get in an elevator and someone started a conversation and be like, they're so nice. Or like, someone would smile at my child and I'd be like, what a good person.


[User] Please transcribe this text

[Assistant] Well, there's a lot of like, I feel like they're very inter- there's a lot of rules of like politeness and like codes of behavior.


[User] Please transcribe this text

[Assistant] But there's like, like, a friend of mine taught me that when you go in some place you have to say bonjour before you say anything else. Hello.


[User] Please transcribe this text

[Assistant] And then you have to wait like two seconds before you say something else. So...


[User] Please transcribe this text

[Assistant] If you go into a store, you can't be like, do you have this in another size? Or they'll think you're super rude, and then they'll be rude to you.


[User] Please transcribe this text

[Assistant] But if you're like, hi, do you have this in another size? They'll be like, sure, I'll help you.


[User] Please transcribe this text

[Assistant] And that gave me an opportunity for the filibuster.


[User] Please transcribe this text

[Assistant] Members, I'm rising on the floor. That meant I had about 13 hours of talking to make sure that this bill did not pass.


[User] Please transcribe this text

[Assistant] So I had a pair of running shoes on my feet and I had a catheter bag. Uhm, a doctor had come over at about 6 o'clock that morning and inserted a catheter so that I'd be able to make it through the day.


[User] Please transcribe this text

[Assistant] Procedural rules of order were broken in order to bring my filibuster to an end.


[User] Please transcribe this text

[Assistant] And the people who were there understood that rules were being broken in order to silence opposition to a bill.


[User] Please transcribe this text

[Assistant] And as they began to scream, everyone up and down the hallways began to scream and at every level of the rotunda and outside on the Capitol lawn.


[User] Please transcribe this text

[Assistant] It ate up enough of the clock that the final vote wasn't taken until 1203.


[User] Please transcribe this text

[Assistant] Hello everyone, sorry I was late a little. I was thinking about this thing previously and the concept of AI Winter.


[User] Please transcribe this text

[Assistant] Which is also considered quite powerful and there is specific, uhm,


[User] Please transcribe this text

[Assistant] Due to the fact that not all, uhm, about this knowledge, what genes are responsible for specific functions, it can cause certain issues. And I think here it's also, we may not know the social consequences of this technology further in the long run.


[User] Please transcribe this text

[Assistant] So I think the proposition to slow it down, uh, is, I, I find it, uhm, in a certain extent beneficial. So of course it requires a lot of evaluation how to actually do this.


[User] Please transcribe this text

[Assistant] But it would help to put actually regulations in place to balance, uh, this rapid.


[User] Please transcribe this text

[Assistant] Uh, quite rapid, uh, progress with the benefits and actually drawbacks that also exist.


[User] Please transcribe this text

[Assistant] So, uh, it is quite clear that it was done by private companies.


[User] Please transcribe this text

[Assistant] And, uh, this is why the slowing the process, so not absolutely stopping, which also, uh, there's a question about this, so.


[User] Please transcribe this text

[Assistant] And the slowing down may benefit in production of the regulations to make it actually safe and sustainable in the long run, because uhm, there are still many questions that it raises, especially such powerful updates that are currently going on.


[User] Please transcribe this text

[Assistant] And uhm, I think it would help, but uhm, there are more discussion how actually it can help and more analysis I think is required.


[User] Please transcribe this text

[Assistant] Yeah, I absolutely agree. So maybe the term research wasn't exactly the one I was thinking about. So the implementation then, because research indeed provides this, uh, also analysis of the situation. So it would maybe also depend the direction of research. So.


[User] Please transcribe this text

[Assistant] How would, uh, whether it goes further into practice and how it goes into practice. So because making the AI winter.


[User] Please transcribe this text

[Assistant] Actually can have a negative consequences for the maybe system functions also and the possible


[User] Please transcribe this text

[Assistant] To have it, so can we actually maybe lose control over the further progress in this area?


[User] Please transcribe this text

[Assistant] So it also requires more evaluation because I haven't seen too many papers, uh, on the AI winter and how it will have a social consequence or what actually the like this evaluation.


[User] Please transcribe this text

[Assistant] Uh, maybe it would be also beneficial to


[User] Please transcribe this text

[Assistant] Have this kind of, uh, more control, uhm, in the sense, uh, that we regulate, uh, the technology by, uh, regions.


[User] Please transcribe this text

[Assistant] And, uh, it is also quite complicated in a sense.


[User] Please transcribe this text

[Assistant] In by private companies and how it also draws on data from Europe and, uh,


[User] Please transcribe this text

[Assistant] There was an idea to have this kind of established offices or certain teams that cooperate.


[User] Please transcribe this text

[Assistant] And actually help to bring the changes like ethical regulations into place. So actually implementing this work on the implementation that would help to develop more sustainable.


[User] Please transcribe this text

[Assistant] AI and technology that is based on AI.


[User] Please transcribe this text

[Assistant] But yea, about AGI, it's like there's still be question whether it is at all possible, since currently we're quite far from that level.


[User] Please transcribe this text

[Assistant] In my opinion, at least. Well, there are different opinions, different opinions in this.


[User] Please transcribe this text

[Assistant] Like I read about 20 years, but I think it's still early.


[User] Please transcribe this text

[Assistant] I wanted to add maybe to the idea of AGI and generally, uh,


[User] Please transcribe this text

[Assistant] So I think the issues that I have read about and actually also saw, uhm, the certain actions that were taken is that


[User] Please transcribe this text

[Assistant] Some focus on the AGI so much that they actually miss the current, uhm,


[User] Please transcribe this text

[Assistant] Issues and challenges like both ethical, moral and others that we face today.


[User] Please transcribe this text

[Assistant] So for example, the discrimination, which is, uh, one of the biggest issues and the data.


[User] Please transcribe this text

[Assistant] So that also can be biased, uh, in different ways.


[User] Please transcribe this text

[Assistant] So which already exists today because as we know, like this biases get automated and spread on a mass scale, which are definitely is a complex issue.


[User] Please transcribe this text

[Assistant] Uh, and uhm, so another thing is that


[User] Please transcribe this text

[Assistant] Maybe, I understand why you may be hanging around around them, because it's interesting to know like what actually the idea, so.


[User] Please transcribe this text

[Assistant] What the concept there might be and how they see to do this. Uh, I think it's also uh, currently the focus


[User] Please transcribe this text

[Assistant] Uh, ethics of artificial intelligence and ethics of data, but more of AI. So algorithmic ethics as well.


[User] Please transcribe this text

[Assistant] I'm writing a thesis on this topic, and uhm, yeaah, so about policy-making and social impact.


[User] Please transcribe this text

[Assistant] Thank you. I will look into this. So uhm, I'm just really think that the regulations are certain steps or code of ethical conduct.


[User] Please transcribe this text

[Assistant] The progress that, uh, taken place in the practice. So to, uh, like slow down the experiments.


[User] Please transcribe this text

[Assistant] So uhm, maybe it would be necessary because some are also worried, for example, in AI powered worker that they will fall back.


[User] Please transcribe this text

[Assistant] But I think that building most sustainable system in the long run is more important than having short-term benefits.


[User] Please transcribe this text

[Assistant] Uh, I think it shouldn't, like some worried that the regulations as well was such a pause.


[User] Please transcribe this text

[Assistant] Uh, can actually uh, hinder the progress.


[User] Please transcribe this text

[Assistant] That's a human element, so that we trust it. So Trustworth.ai or Explainable.ai, like Trustworth.ai is a concept more in the European Commission that they use, they focus on. But uhm, Explainable.ai is more maybe popular.


[User] Please transcribe this text

[Assistant] So that we actually can understand the decision and how technology works. Open up the black box.


[User] Please transcribe this text

[Assistant] There is also, I understand that it's complicated and currently there were like tries to do like lime and sharp systems, or more like models for explanations, so local and federal model, agnostic explanations and sharp latest explanations.


[User] Please transcribe this text

[Assistant] Activities because it is heavily regulated and also in a sense that it, for example, uh, has such thing as, uh, their black box or a different one. So the one that record actually actions of a plane to actually look into how certain events unfold.


[User] Please transcribe this text

[Assistant] So maybe in the sense of it would be good to have explainable AI by design.


[User] Please transcribe this text

[Assistant] It actually takes a lot of effort, so rebuilding AI into explainable way, it's maybe even more complicated, but the main goal is that human can understand and explain the decisions, so maybe we do not have to see


[User] Please transcribe this text

[Assistant] All the details, all the minor elements that took place, but we need to get a good idea of what happened, why it happened.


[User] Please transcribe this text

[Assistant] Explain, uh, to the people, for example, since AI is also used in the social world, for example, for credit scoring, right? To get a loan.


[User] Please transcribe this text

[Assistant] It's already implemented, uh, hiring mechanisms for job placements as well as court systems.


[User] Please transcribe this text

[Assistant] Take some time, but step by step, I think it's possible so that, um, especially if you do explainable AI by design.


[User] Please transcribe this text

[Assistant] So I have studied from a more social tech, uh, social technical perspective, but, uh, I also looked into the details and actually found really nice presentation on that topic.


[User] Please transcribe this text

[Assistant] Uh, it was a group of scientists, uh, it was a conference, also accompanied by the paper, uh,


[User] Please transcribe this text

[Assistant] But a small presentation of their topic.


[User] Please transcribe this text

[Assistant] I got the impression that actually there is uhm, already going quite uhm, on good, I would say.


[User] Please transcribe this text

[Assistant] Uhm, there are possibilities to actually, uh, make it now, but it is still, uh, early and complicated. This is here once again, by pause may come beneficial to actually, uh, more work on the, this side of explainability.


[User] Please transcribe this text

[Assistant] I also mentioned your comment that was just about to address it.


[User] Please transcribe this text

[Assistant] I think the decision tree, uh, would be nice, but, uh, it doesn't come from uncertainty.