Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Hey everyone, and welcome to Forward Thinking, the
Speaker 1: podcast that looks at the future and says, I'll say
Speaker 1: it again in the land of the Free, use your
Speaker 1: freedom of choice. On Jonathan Strickland, I'm Lauren Volt and
Speaker 1: I'm Joe McCormack. Today we're gonna talk a little bit
Speaker 1: more about stress and data overload and maybe choice overload,
Speaker 1: and how information and stress levels are maybe in a
Speaker 1: related and uh and kind of a sort of a
Speaker 1: technological approach to fixing that. I mean, as it turns out,
Speaker 1: we have this astoundingly amazing tool at our disposal. I'm
Speaker 1: talking about the Internet. I mean, if you think about it,
Speaker 1: it's like the largest machine that's ever been built. If
Speaker 1: you think of it as as one cohesive machine, it's
Speaker 1: not bigger than Unicron. Are you sure you don't mean Galvatron.
Speaker 1: We re recorded this bit and I'm not letting Joe
Speaker 1: go back anyway. Yes, so un chron would technically be bigger,
Speaker 1: but that's a fictional machine. I hate to burst your bubble. No, okay,
Speaker 1: uh no, the Internet it's super cool. It is super cool.
Speaker 1: It gives us gives us access to more information than
Speaker 1: we've ever had before. So your average person who can
Speaker 1: connect to the Internet can get huge amounts of information.
Speaker 1: And that's right. It's just it's just unequivocally good, right,
Speaker 1: and not unequivocally I wouldn't say say that, I'd say
Speaker 1: that getting lots and lots of information can create some stress.
Speaker 1: I mean, for instance, all three of us here and
Speaker 1: and well, heck, i'll say all four of us, NOL,
Speaker 1: I'm including you in this too. You're welcome, NOL. So uh,
Speaker 1: all four of us are used to using the Internet
Speaker 1: to research, to find information, and I'm sure that we
Speaker 1: have all encountered that experience of during our research getting
Speaker 1: results back that just seemed like a mountain of data,
Speaker 1: and some of it seemed like it might be relevant
Speaker 1: to what we're looking for. Some of it maybe not
Speaker 1: as relevant, but it wasn't necessarily easy to tell upon
Speaker 1: first glance. So that means it's a lot of work.
Speaker 1: And then you're just thinking, before I didn't have the
Speaker 1: information I needed. Now not only am I not sure
Speaker 1: if I have the information I need or not, but
Speaker 1: I've got lots of stuff that I have to go
Speaker 1: through to find out. Yeah, it's a sinking feeling you
Speaker 1: get when you realize that there's potentially a lot that
Speaker 1: you should know you don't know. This will come into
Speaker 1: you don't know if you should know it or not.
Speaker 1: This will come into it to light. When we talk
Speaker 1: about the overwhelming choice issue, which is a great discussion,
Speaker 1: I can't wait to have it. But that's definitely part
Speaker 1: of it. And you you hit right on there, you
Speaker 1: know it's it's the not knowing part that can be
Speaker 1: really irritating, like just irritating, but can cause real anxiety. Yes, yes, exactly.
Speaker 1: And when you start thinking about how much data we
Speaker 1: reduce every day, that we're not making this problem any easier.
Speaker 1: We're making it more difficult every single day. Now, if
Speaker 1: you've listened to our podcast where we talked about big data,
Speaker 1: one of those two podcasts we talked about the sheer
Speaker 1: amount of information that we generate every day. If you
Speaker 1: haven't listened to it, go ahead and do that. We'll wait. Okay,
Speaker 1: welcome back if you If you have decided that you
Speaker 1: really didn't want to go back and listen, here's some
Speaker 1: quick sound bites of information about how much data we
Speaker 1: produce every day. So Twitter you know, you can produce
Speaker 1: a a message of up to characters in a single tweet.
Speaker 1: You wouldn't think that that much information would be generated
Speaker 1: by Twitter. As it turns out, it's about twelve terabytes
Speaker 1: of tweets. It's interesting. You ever tried to sort out
Speaker 1: an argument or public debate on Twitter, and you all,
Speaker 1: if you're like me, you always get the feeling that
Speaker 1: there's something you're missing. It helps, It helps if you
Speaker 1: find uh, I mean, the still can be an issue.
Speaker 1: It helps if you use certain Twitter clients that that
Speaker 1: string together right, so that if something is a reply
Speaker 1: to someone else, then you get the full thread and
Speaker 1: you can read through it. However, if someone has not
Speaker 1: replied to someone, if they haven't replied to an actual
Speaker 1: Twitter message but instead have written a message and then
Speaker 1: just put the at sign of whomever in there, that
Speaker 1: won't be included in that in that conversation necessarily, so
Speaker 1: then you end up missing key parts. This is a
Speaker 1: really interesting debate. I think I can't quite tell well,
Speaker 1: you think that you understand that there are definitely parts
Speaker 1: you're not getting in this. You can't really contribute. It
Speaker 1: can also be sort of like listening to one half
Speaker 1: of a telephone conversation where your brain starts to really
Speaker 1: work and overdrive trying to figure out what the heck
Speaker 1: the other half of that conversation could be. If it
Speaker 1: weren't for my horse, I never would have made that
Speaker 1: year in college. So in two thousand twelve, all of
Speaker 1: Facebook was collecting more than five hundred terror bytes of
Speaker 1: data per day. Of course, we have the the YouTube
Speaker 1: measurement one hours of video footage uploaded every single minute.
Speaker 1: So the point being that we are generating lots of information.
Speaker 1: There's already a lot of it all available on the Internet,
Speaker 1: and we're making more every single day, so it's just
Speaker 1: making this problem of sorting through data even more challenging.
Speaker 1: According to IBM, the big numbers that we create two
Speaker 1: point five quintillion bytes of data every day. That's a
Speaker 1: lot of cat videos and that's yeah, and it's it's
Speaker 1: so much of what we've of the data that is
Speaker 1: floating around our world right now, has been created in
Speaker 1: the past two years alone. So that means up until sayven,
Speaker 1: we only had generated ten of all the information we
Speaker 1: now have at our disposal. The other we managed to
Speaker 1: do in the last two years. That's and how much
Speaker 1: of that information is really useful. Well, okay, that's fair.
Speaker 1: And see that's part of the problem, right, is that
Speaker 1: when we're trying to access this information, we have to
Speaker 1: try and sort through what is useful and what is
Speaker 1: not useful. This is, by the way, different from what
Speaker 1: is important versus what is not important, But that's a
Speaker 1: relevant question also, it can be yes, it all depends
Speaker 1: on the context of what you are looking for. Right.
Speaker 1: If you're just looking for something uh, you know, frivolous
Speaker 1: in the grand scheme of things, then important is not
Speaker 1: really necessarily an issue. But but you still want it useful.
Speaker 1: You wanted to be relevant to whatever it is you
Speaker 1: are searching for. So if you wanted to search for
Speaker 1: super cute cat video, you want a super cute cat video,
Speaker 1: you know, you don't want something else that's not you know,
Speaker 1: here's a psychological breakdown on why we like super cute
Speaker 1: cat videos. That might be interesting, but it's not relevant
Speaker 1: to what you were actually looking for. Fortunately for the
Speaker 1: people who really love super cute cat videos. Uh well,
Speaker 1: let's say for the people who are exclusively searching for
Speaker 1: super cute cat videos because who doesn't love them? Yeah?
Speaker 1: I mean, first, see if you don't love them if
Speaker 1: I love them. Uh, well, I mean you're listening to
Speaker 1: you can listen to our show anyway. But we just
Speaker 1: you know, we realize that, but we don't recognize your existence.
Speaker 1: But but if that's all you're looking for, this this
Speaker 1: is less of a problem for you. It's it's much
Speaker 1: more of a problem when you're looking for information that
Speaker 1: matters in one way or another. Well, yeah, and I
Speaker 1: mean it's an irritating experience no matter what you are
Speaker 1: searching for, right, It's just sometimes it can be irritating
Speaker 1: and also uh, really critical. So for example, if you
Speaker 1: are looking up information for let's say that you're in
Speaker 1: a part of the world where you don't necessarily have
Speaker 1: access to a really good medical facility, and you're needing
Speaker 1: to do some research on a medical issue that's really important.
Speaker 1: I mean you may you may come to the conclusion
Speaker 1: that you have got to find a way to go
Speaker 1: to some other place in order to have something addressed.
Speaker 1: I mean, that's that's critically important. But if you do
Speaker 1: a search and you get back a bunch of results
Speaker 1: that are related to, but not critical to whatever it
Speaker 1: was as you were looking for, that's that's a problem. Yeah, yeah, yeah, right,
Speaker 1: if you've got an emergency going on and you get
Speaker 1: results that are that are partially blog entries and partially
Speaker 1: advertisements and and partially you know, maybe information from a
Speaker 1: source that isn't a really good one. You know, it's
Speaker 1: it's hard to talk people through. You know, we all
Speaker 1: do this every day, probably, but but trying to explain
Speaker 1: to someone that everything that pops up on Google's front
Speaker 1: page isn't necessarily what you want to click on, it's
Speaker 1: hard to explain. So has research showed that these effects,
Speaker 1: that this plethora of information has any effect on us?
Speaker 1: There has been some research on this, and in fact,
Speaker 1: this is where we need to create a distinction, because
Speaker 1: there's been some research on the overload of choice, which
Speaker 1: is where you are given a whole bunch of different
Speaker 1: options and then you have to choose between all those
Speaker 1: options to figure out which one or which few are
Speaker 1: the best for whatever your goal happens to be. And
Speaker 1: then there's just data overload. And the two are connected
Speaker 1: and not necessarily you can't necessarily say one is more
Speaker 1: important than the other. It's more of the combination of
Speaker 1: the two that create that Maybe what are creating a
Speaker 1: real problem as far as stressors are concerned. I say maybe,
Speaker 1: because when you come to psychological research, it's tough. You've
Speaker 1: got a lot of variables you have to account for,
Speaker 1: and and it's still fairly early on in this kind
Speaker 1: of research, so we don't have a lot of definitive
Speaker 1: studies and it's really hard to isolate variables exactly. So
Speaker 1: so let's talk a little bit about some of this
Speaker 1: work that has been done. So there's a psychologist named
Speaker 1: Barry Schwartz who wrote a book called The Paradox of Choice,
Speaker 1: Why More is Less? And uh? And Dr Schwartz was
Speaker 1: looking into he was he's a psychologist. He was looking into, um,
Speaker 1: how do people react when they have a lot of choices?
Speaker 1: And and do more choices mean more freedom and more
Speaker 1: happiness or does it actually, somewhat paradoxically make us feel
Speaker 1: less happy? What is it better to have a lot
Speaker 1: of choice or is it better have just a few options? Now,
Speaker 1: in his thesis he laid out a set of steps
Speaker 1: that consumers go through. This is really looking at a
Speaker 1: customer who is, you know, shopping for something and is
Speaker 1: coming up with a whole bunch of different examples of
Speaker 1: whatever that thing happens to be. So, he said that
Speaker 1: they go through several steps. The first step is they
Speaker 1: determine what their goals are, so what is it that
Speaker 1: they're trying to accomplish. Then prioritizing those goals so if
Speaker 1: there are more than one. If there's more than one goal,
Speaker 1: they have to figure out wharri which goals are the
Speaker 1: most important, which are the ones that have to be
Speaker 1: met first, then examining the options that are actually available,
Speaker 1: so what things are out there that are made specifically
Speaker 1: to address this kind of goal. Then to measure those
Speaker 1: options against the goals to determine which of those options
Speaker 1: are most likely to fulfill those goals. Then the next
Speaker 1: step is to pick the best option out of all
Speaker 1: of them to meet the goals. The final step is
Speaker 1: modifying goals if necessary. So this would mean if there
Speaker 1: are more goals that need to be met, deciding which
Speaker 1: ones you might be able to let go of, or
Speaker 1: maybe even modifying your goals after you've made your choice,
Speaker 1: so that psychologically you feel better about the choice you
Speaker 1: have made. Right, So it's like, well, I really wanted
Speaker 1: something that was sweet and crunchy, but this is just crunchy.
Speaker 1: But hey, I really just wanted crunchy, So I'm taking
Speaker 1: this that would be a very simplified example. Now, Schwartz
Speaker 1: suggests that stress was created by having too many choices
Speaker 1: and that contributed to unhappiness overall and decreased satisfaction. But
Speaker 1: his studies have also received some criticism from other psychologists
Speaker 1: who have suggested that perhaps this is too simple of
Speaker 1: a view and have said that, you know, replicating these
Speaker 1: these studies that Schwartz has done has been somewhat problematic.
Speaker 1: They haven't really shown replicable results, and there might not
Speaker 1: be a direct clear connection between the number of choices
Speaker 1: you have and your general level of happiness and sense
Speaker 1: of freedom. Now that being said, there was a study conducted.
Speaker 1: This is one that everyone tends to cite. It was
Speaker 1: done back in by a professor of business named Shena Angar.
Speaker 1: And what Sheina did was set up a display in
Speaker 1: a grocery store that was a jam display. So you've
Speaker 1: heard about this one, right, Joe YEA. So it was
Speaker 1: two different displays. There was one that had six jams
Speaker 1: and there was one that had twenty four jams. And
Speaker 1: so twenty four jams, twenty four different jams, twenty four
Speaker 1: different jams, six different types of jam, and twenty four
Speaker 1: different types of jam. Thank you yeah, not just hey,
Speaker 1: that's my jam. You. So every few hours they would
Speaker 1: swap out what which display they were using, and they
Speaker 1: discovered something interesting. With the larger display, they attracted more customers,
Speaker 1: but fewer of those customers would actually make a purchase.
Speaker 1: Out of of all the customers who would stop at
Speaker 1: the larger one, only around three percent of them would
Speaker 1: go on to buy one of those jams. They In
Speaker 1: both cases, the the customers sampled an average of two
Speaker 1: of the different types of jam, whether there were six
Speaker 1: types there or twenty four. The people who saw twenty
Speaker 1: four three percent of them bought jam. The people who
Speaker 1: saw the six selections of them bought jam. So the
Speaker 1: conclusion was that by offering fewer choices, you actually increase
Speaker 1: the likelihood of selling a product. Now, extrapolating that to
Speaker 1: mean things that you know, too much choice stresses you
Speaker 1: out or or contributes to unhappiness is beyond the scope
Speaker 1: of that survey, but that's what some of the future
Speaker 1: studies have done. Now this being said that you know,
Speaker 1: there there are people like uh Benjamin Shibehne know I
Speaker 1: butchered his name. He's a Swiss psychologist who said that
Speaker 1: perhaps it's more the relationship between choice and the information
Speaker 1: we have a out those choices. So it's not just
Speaker 1: the fact that we have a lot of different options
Speaker 1: to choose from. It's that we don't know enough about
Speaker 1: each option to feel confident in our choice, so that
Speaker 1: the more choices you have, the less likely you're going
Speaker 1: to feel that you have enough information about all of
Speaker 1: them that you have made the right decision. So that's
Speaker 1: where you get that buyer's remorse, where you have, you know,
Speaker 1: forty different examples of whatever it was you went shopping for,
Speaker 1: and ultimately you you settled on one, but there's a
Speaker 1: nagging suspicion in your head that you didn't do enough research. Yeah,
Speaker 1: perhaps one of those other ones. In fact, the likelihood
Speaker 1: is that one of those other ones is better for
Speaker 1: you than whatever it was you bought, Like you know,
Speaker 1: you just started going through Like statistically speaking, there's a
Speaker 1: very good chance that I have something that's not the
Speaker 1: best version of whatever it was I was going after,
Speaker 1: and therefore that contributes to unhappiness. So it's not just
Speaker 1: how many choices you have, but how much information you
Speaker 1: have in general about both your goals and the options
Speaker 1: that are available to you. So it's a more complex,
Speaker 1: uh system than just how many choices are there. I
Speaker 1: was gonna I was gonna mention Amazon. UM. You know
Speaker 1: the part of part of what has made that site
Speaker 1: so successful is that UM it allows you to It
Speaker 1: allows users to leave reviews and other users to read
Speaker 1: them and UM and you know that's that's an example
Speaker 1: of having way more information about a product than you
Speaker 1: generally would if you walked up to a store shelf.
Speaker 1: The shelf isn't going to tell you anything about it,
Speaker 1: you know about when other users have have enjoyed that jam.
Speaker 1: But although we've talked about augmented reality UH applications that
Speaker 1: could eventually do that where that's really cool, Yeah, where
Speaker 1: you'd be able to look at products and actually see
Speaker 1: in real time what people people you trust have said
Speaker 1: about it. So that's kind of interesting. But so uh,
Speaker 1: if you want to translate this to what we were
Speaker 1: talking about before, I mean, if twenty four jams upsets
Speaker 1: people and makes it harder for them to choose, it
Speaker 1: sounds like a party to me. What about twenty four
Speaker 1: million Google results when you're trying to find a piece
Speaker 1: of information. I did a search just as an example,
Speaker 1: just a random example, I guess, not truly random, but
Speaker 1: as an example to see how many results I might
Speaker 1: get from a search that I might typically do in
Speaker 1: the course of my work. So I searched for dark
Speaker 1: matter and I got two hundred and eight approximately two
Speaker 1: eight million results on dark matter. That's a lot of information.
Speaker 1: Now there are things I can do to narrow that
Speaker 1: search down, where I can use search term parameters that
Speaker 1: will tell Google this is specifically what I'm looking for
Speaker 1: when it comes to dark matter, whether it's the most
Speaker 1: recent research or it's the you know, any sort of
Speaker 1: controversy that's related to dark matter, or any sort of
Speaker 1: early proof of dark matter, anything like that I could
Speaker 1: I could give Google more information and then could receive
Speaker 1: from the exactly it would narrow down that net by
Speaker 1: by by clicking on a tab for for for news items,
Speaker 1: if you're looking for or scholar or scholarly work, if
Speaker 1: I want to look at papers. But when you do
Speaker 1: something like that, it's interesting to point out that a
Speaker 1: lot of the heavy lifting being done there is being
Speaker 1: done by you and by Google, and not by the
Speaker 1: pages themselves. Well, yeah, because what Google is doing is
Speaker 1: it's trying to find the best results for whatever search
Speaker 1: query you have typed in. So you have to be
Speaker 1: somewhat savvy to know how to do this. It helps. Yeah,
Speaker 1: I mean, Google definitely has been getting better at at
Speaker 1: tweaking its algorithm to weed out any any sort of
Speaker 1: results they're trying to game the system. In the early days,
Speaker 1: do you guys remember web search in the early days.
Speaker 1: So in the early days, the way search engines would
Speaker 1: work is they would build dogpile. They would they would
Speaker 1: rely on things like information that would be in the
Speaker 1: header and footer of a website, so or web page.
Speaker 1: So let's say you create a web page and your
Speaker 1: web page is really all about I don't know, maybe
Speaker 1: it's it's all about hamsters. You've written a web page
Speaker 1: about hamps. But you really you also happen to have
Speaker 1: in those early web days, you know one of those
Speaker 1: you have, no, you have one of those visitor counters.
Speaker 1: You remember those the counters that would click up every
Speaker 1: time someone click on It actually became kind of a
Speaker 1: status symbol among certain people who had web pages, and
Speaker 1: you really really cool people. Yeah, Okay, let's not make judgments.
Speaker 1: I was one of those really cool people. Um so,
Speaker 1: so you have this page about hamsters, and you're genuinely
Speaker 1: interested in hamsters. You you've raised hamsters, you love hamsters.
Speaker 1: You want to share this passion of hamsters with the world.
Speaker 1: But you know you're getting maybe three visitors every month,
Speaker 1: and you think, wow, I was really kind of hoping
Speaker 1: that I'd be able to share the love of hamsters
Speaker 1: with a wider audience. So you think, how can I
Speaker 1: get more people to come to my page. One if
Speaker 1: I just put a whole bunch of random terms in
Speaker 1: the header and foot her of my web page, things
Speaker 1: that I know people are searching for, and then that way,
Speaker 1: my page is gonna pop right up at the top
Speaker 1: of search results for anyone who searches for that stuff.
Speaker 1: And even though they're going to arrive at my page
Speaker 1: and realize that I don't have a website that's all
Speaker 1: about making a cake, it's all about hamsters, maybe they
Speaker 1: will discover the joy of hamsters. And if they don't,
Speaker 1: at least my counter will start popping up. So people
Speaker 1: were gaming the system, and web search engines, including Google,
Speaker 1: spent a lot of time tweaking their search algorithms to
Speaker 1: try and negate anyone who was trying to to learn
Speaker 1: the way that the search engine was finding information and
Speaker 1: returning it as a search result, so that only in
Speaker 1: theory the relevant pages would remain and that the most
Speaker 1: relevant pages would be pushed to the top. So that
Speaker 1: I mean, because you know, I I have on occasion
Speaker 1: gone maybe ten pages back into Google Search. But that's
Speaker 1: about my and that's not that's not even I mean average,
Speaker 1: it's nowhere near them. In fact, I read one result
Speaker 1: that said that on average, around thirty six percent of
Speaker 1: all Google users choose the top result, and then from
Speaker 1: two through five equals another around thirty four. There was
Speaker 1: a study done at Cornell in two thousand seven that
Speaker 1: UM used eye tracking to UM. It gave students a
Speaker 1: Google Search result page of UM of abstracts from research
Speaker 1: papers and UM and with this eye tracking software, they
Speaker 1: found out that the students trusted Google's Google's ranking system
Speaker 1: more than they did reading the abstracts to see what
Speaker 1: was more important to them. Well, and I mean, you know, again,
Speaker 1: we get to this idea of you have all these options,
Speaker 1: and if you're searching for something, chances are the reason
Speaker 1: you're searching for it is because you want to know
Speaker 1: more about it, which means you already do not know
Speaker 1: which options are going to be the best for you.
Speaker 1: So you know, that might just be your brain saying, look,
Speaker 1: let's not let's not complicate this, let's not do too
Speaker 1: much work. Let's just pick that first one. It's got
Speaker 1: to be the best. But so, what we want to
Speaker 1: talk about here is the fact that Google is doing
Speaker 1: the hard work. Their algorithm has to look at these
Speaker 1: pages and figure out what makes them important or useful
Speaker 1: or relevant to you, and they're using them two hundred
Speaker 1: factors or more dators and and so what if the
Speaker 1: pages actually did some of the work here, What if
Speaker 1: it went beyond the pages doing the work and you
Speaker 1: didn't have to worry about pages at all. Because that's
Speaker 1: the other issue, is that let's say that I do
Speaker 1: a search and I get a really good scientific paper
Speaker 1: on something that I'm researching, And it may be that
Speaker 1: it's a perfectly good scientific paper and it's still read
Speaker 1: the paper. I have to read the whole paper to
Speaker 1: find out if in fact it addresses the specific question
Speaker 1: I had, and if it doesn't, then I have to
Speaker 1: go and find more information. Or maybe that it has
Speaker 1: part of what I needed to know, but not everything
Speaker 1: I needed to know. So that means that I have
Speaker 1: to research maybe twenty papers in order to get a
Speaker 1: full picture of whatever it is I'm really looking into.
Speaker 1: And even then you have that anxiety like there's something
Speaker 1: else that there could have been yet another piece, a
Speaker 1: twenty first piece out there that I just didn't even
Speaker 1: know about. And that's because it's human to human, right,
Speaker 1: It's because you're the one dealing with it, and you're
Speaker 1: dealing with documents that were made by humans for humans
Speaker 1: to read, right, So you're talking about you know it,
Speaker 1: even if it's not a static web page like those
Speaker 1: are the I remember the old days where you would
Speaker 1: make a web page and that's pretty much how it
Speaker 1: would stay unless you had a couple of hours at
Speaker 1: your disposal to go and put in some more content.
Speaker 1: Nowadays we have dynamic web pages, but even then, you know,
Speaker 1: the general web page doesn't change that much unless it's
Speaker 1: something that's got a lot of user generated content in it.
Speaker 1: So in that sense, how do you get all this
Speaker 1: information in one place? And one approach that may address
Speaker 1: this is a dramatic reimagining of what the web can do.
Speaker 1: It's called the semantic web, the semantic web a k A.
Speaker 1: Web three point oh. You may well, you know, some
Speaker 1: people call it that, some people call it some people
Speaker 1: already calling it Web four point oh because they say
Speaker 1: that Web three point oh is the social web. So
Speaker 1: what web one point oh is before the dot com crash?
Speaker 1: Web two point oh is when we started including things
Speaker 1: like user reviews and some integration. Web three point oh
Speaker 1: is social media. Web four point oh is semantic web.
Speaker 1: All of these are largely meaningless. But this concept goes
Speaker 1: back to before the dot com crash. Oh yeah, yeah,
Speaker 1: well this this goes back in fact that one of
Speaker 1: the people who talked about was a guy who knows
Speaker 1: a thing or two about the web, seeing as he's
Speaker 1: the guy who invented it. To tim Berners Lee, Sir
Speaker 1: tim Berners, Lee old, Sir tim Berners, he uh so.
Speaker 1: One of the things I read when I was looking
Speaker 1: into this was goes way back. It was actually a
Speaker 1: Scientific American article from two thousand one. Do you think
Speaker 1: about how primitive the web was in two thousand one
Speaker 1: compared to now, Dinosaurs roam the web, been fire had
Speaker 1: yet to be discovered. Here there will be dragons in
Speaker 1: geo cities. I think I think we were still trying
Speaker 1: to phase out the under construction Gift and the looping
Speaker 1: Midi at that point. Yeah, oh, the Midias. Do you
Speaker 1: remember the Midias? Do you remember my Space which would
Speaker 1: automatically play whatever horrible music your friends were listening to? Yeah,
Speaker 1: the dancing Oh yeah, good times. Well, anyway, sir Tim
Speaker 1: berners Lee, he offered this idea that that was really interesting. Um,
Speaker 1: it was sort of what we've been talking about. What
Speaker 1: if the web, instead of being a collection of interlinked
Speaker 1: documents for people to read, was a collection of interlinked
Speaker 1: data sets. All right, and the listeners, if you're hearing that,
Speaker 1: we got as there was a thunderstorm. We're gonna keep
Speaker 1: on going because that's how dedicated to the future we are. Yeah,
Speaker 1: so we're risking our lives stormers. Yeah, so I did
Speaker 1: the semantic web. The semantic web. Yeah, so we're talking
Speaker 1: about a system of instead of sort of partially unstructured
Speaker 1: documents for people to read with links connecting them, uh,
Speaker 1: sets of data where each piece of data is connected
Speaker 1: by what's known as an ontology. Yes, so and on
Speaker 1: and and this is crucial toes are critical to the
Speaker 1: way this works. So to think of an ontology, and
Speaker 1: ontology means the nature of being sort of like what
Speaker 1: is it? And by creating the semantic web, what you'd
Speaker 1: be doing is sort of creating libraries that identified pieces
Speaker 1: of information by what type of information they were. And
Speaker 1: this wouldn't just be at the page level, you know,
Speaker 1: where you have meta tags that might say this page
Speaker 1: is you know, encyclopedia or something like that. But for
Speaker 1: every individual piece of information or fact on the page,
Speaker 1: there would be a data point saying, um, okay, so
Speaker 1: this number is a time right, and this, uh, this
Speaker 1: string of text is the address. And you can actually
Speaker 1: even go deeper than this and say this is a
Speaker 1: billing address, and this is a shipping address, and what
Speaker 1: we and and beyond that you can even go into
Speaker 1: Let's say that you you want to uh, explain all
Speaker 1: the tags that are related to a single person. So Joe,
Speaker 1: for this this example, I'm going to suggest that you
Speaker 1: are my cousin, so you could have you know, Joe
Speaker 1: Jonathan's cousin would be one bit of information how stuff works.
Speaker 1: Employee could be another bit of information. Podcaster for forward
Speaker 1: thinking could be another bit of information. Now, all of
Speaker 1: these bits of information pertained to him. Uh, some of
Speaker 1: which are are maybe very specific and unique to Joe,
Speaker 1: some of which would be things that other people could
Speaker 1: belong to. And so you would have to have build
Speaker 1: a system that could understand the relationship between Joe and
Speaker 1: various organizations, people, places, times, and also how other people
Speaker 1: could be a part of that. Like some things, you're
Speaker 1: gonna say, this is going to be a category that
Speaker 1: Joe is the only person who belongs to this category.
Speaker 1: It is unique to Joe. There are other categories that
Speaker 1: Joe will be in that other people like Lauren would
Speaker 1: be in. If I said how stuff works employee, then
Speaker 1: obviously both of you would be in there. If I
Speaker 1: said host of forward thinking, both of you would be
Speaker 1: in there. If I said my cousin, only Joe would
Speaker 1: be in there because Lauren disowned me years ago. So
Speaker 1: these are examples of just relationships. And this is just
Speaker 1: one bit of data that the semantic web would have
Speaker 1: to understand for it to work. Because what needs to
Speaker 1: know is it needs to know what is the significance
Speaker 1: of every single point of data and its relationship to
Speaker 1: other points of data. Right, So we're talking about a
Speaker 1: web that wouldn't just serve you up a page of
Speaker 1: data that you'd have to understand. It would be a
Speaker 1: web where your client or your age understands the information.
Speaker 1: It understands what each little bit of info on the
Speaker 1: page means and what you're looking for, and how each
Speaker 1: of these pieces of information are relevant and related to
Speaker 1: each other, right, and then it would serve it up
Speaker 1: to you in a way that was digestible. So, for example,
Speaker 1: if I wanted to search for, uh something where I'm
Speaker 1: looking for a very particular set of parameters for I
Speaker 1: don't know what, what's a what's a fun thing? Not?
Speaker 1: I was gonna say dark matter to go back to Google.
Speaker 1: But let's say that's something goofy uh potato gun potato gun?
Speaker 1: All right? Yeah, I like penguin gun penguin gun. Oh no, no,
Speaker 1: that's terrible. No, Okay. So I'm gonna say. I'm gonna
Speaker 1: say things like maybe maybe I'm just doing a search
Speaker 1: on a year, all right, and I do a search
Speaker 1: on it, yeah, because I'm because I don't want the
Speaker 1: I don't want to be shooting penguins, So I'm gonna
Speaker 1: pick up early year to you would like shoot them
Speaker 1: with bullets. I'm saying I'm gonna When I said shooting penguin,
Speaker 1: did I say shooting at penguin's No. I think the
Speaker 1: ambiguity was there for a reason. So so yeah, I
Speaker 1: put in a year, and then maybe in a semantic
Speaker 1: web search, it would give me all this relevant information
Speaker 1: to that that term. So it wouldn't just be like
Speaker 1: things that happened that year. It might also be relative
Speaker 1: to my personal experience if it's a year that was
Speaker 1: in my lifetime. We can talk about semantic web being
Speaker 1: so personalized as to give two different people separate kinds
Speaker 1: of answers depending upon their own personal context. The idea
Speaker 1: being the semantic web would not only learn how the web,
Speaker 1: all the information on the web works, but learned that
Speaker 1: what your own personal preferences and needs are, so it
Speaker 1: can anticipate how what information to send to you. So
Speaker 1: Lawrence search and my search might give very different results.
Speaker 1: An example that Berner's Lee gave back in two thousand
Speaker 1: one in this article is something about like healthcare providers say, okay,
Speaker 1: you're you're looking for a specialist in in something your
Speaker 1: doctor recommends a specialist, Well, you can search for a specialist.
Speaker 1: And based on all these different tags, on this information, uh,
Speaker 1: your your semantic web agent can help you find a
Speaker 1: specialist that's not just the kind of specialist you need,
Speaker 1: but one that's located near your house, is covered by
Speaker 1: your insurance provider, is rated within a certain like price
Speaker 1: range or within a certain user review category, you know,
Speaker 1: say only rated excellent, essentially narrowing down all that data
Speaker 1: we had talked about before that and that you know again,
Speaker 1: so that the fact that you don't you know, if
Speaker 1: you're presented with all those different options at at once,
Speaker 1: just even if even if they're being completely transparent about
Speaker 1: all the services they provide and whether or not they're
Speaker 1: part of your plan or whatever, you still have to
Speaker 1: go through all of that to make sure that you
Speaker 1: narrow down your choices to the viable options and then
Speaker 1: pick the best viable option. The semantic web does all
Speaker 1: of that work for you, and it does it by
Speaker 1: analyzing all this data because again it understands it. And
Speaker 1: how does it understand it? We build it in there.
Speaker 1: Although part of this exists today, I mean, you know,
Speaker 1: for for example, Google gives you if you're logged in
Speaker 1: or if it has any history of your behavior, UM
Speaker 1: will give you slightly personalized results. You know, it will
Speaker 1: move to the top. For example, if I search about
Speaker 1: a topic and Jonathan has written an article about it
Speaker 1: that he has UM tagged himself as the author of,
Speaker 1: it will pull that towards the top of the list
Speaker 1: for me because it knows that we're friends on the internet.
Speaker 1: Because Google's creepy like that. Yeah, well it also if
Speaker 1: it knows where your location is, then when you do
Speaker 1: certain searches, it's going to bring up a lot of
Speaker 1: results that happened to be local to you. So if
Speaker 1: I did a search as a as just a whim
Speaker 1: after after I did my search on dark Matter, I
Speaker 1: did another search which was just Italian food, I just
Speaker 1: searched Italian food, and then the very top of the
Speaker 1: search results were all about Italian restaurants there in the
Speaker 1: Atlanta area, and then below that were more general links
Speaker 1: to Italian food topics. So again, it's one of those
Speaker 1: things where Google has built this into the algorithm to
Speaker 1: start looking for things that are tagged in a certain way.
Speaker 1: Now that's a very primitive approach to what we're talking about,
Speaker 1: but we've seen some other examples of it, right like
Speaker 1: if you search for something specific, like maybe you search
Speaker 1: for weather in San Diego, then not only do you
Speaker 1: get search results that might take you to something like
Speaker 1: weather dot com or some other website, you might get
Speaker 1: a little thing at the top. And this this applies
Speaker 1: both to Google and to other search engines like being
Speaker 1: You can get a little thing at the top which
Speaker 1: will tell you what the current weather is. It just
Speaker 1: gives you an answer, yeah, a little widget right there.
Speaker 1: And uh, you know, then there are other well they
Speaker 1: don't like to call it a search engine, they call
Speaker 1: it a knowledge engine. But well, from Alpha is it
Speaker 1: does a very similar sort of thing so that when
Speaker 1: you type in a particular kind of query for usually
Speaker 1: it's something that's scientific or mathematical in nature, it will
Speaker 1: pull back the relevant information, uh and serve it up
Speaker 1: to you right there, so that you know you're not
Speaker 1: going to a web page that contains the information that
Speaker 1: you still have to read through and find. It gives
Speaker 1: you just the pertinent information. Yeah, but a lot of
Speaker 1: that's just computation and hard coding, and that that's what
Speaker 1: makes it different from this idea of the semantic web,
Speaker 1: which the semantic web, the search engine there doesn't anticipate
Speaker 1: your searches. It actually can make use of the semantic
Speaker 1: web to answer any kind of problem. So if you
Speaker 1: want to say, you want to ask a question that
Speaker 1: has a specific answer, but there's nowhere out on the
Speaker 1: web where this answer is phrased, like you ask it
Speaker 1: right now? Sure? Sure, because everything is so keyword driven
Speaker 1: and um and and furthermore, you know, the even just
Speaker 1: the order of the keywords as you type them can
Speaker 1: have a strong effect on what results. And machines are
Speaker 1: not capable of reading human language. So or if you know,
Speaker 1: if if something really important about dark matter came up
Speaker 1: in a paper that wasn't about dark matter at all,
Speaker 1: or it was so long ago that they weren't calling
Speaker 1: it dark matter yet they were using a different form
Speaker 1: of terminology, you know, and this is the kind of
Speaker 1: thing that the semantic web would would learn. Hey, people
Speaker 1: have previously found this bit really useful exactly that that
Speaker 1: would be a really important autology like previously known as
Speaker 1: could be one tag that could for like a person's name.
Speaker 1: If somebody changed their name but you're searching for their
Speaker 1: new name, it could also serve up that person was
Speaker 1: previously known as this person and you know right now,
Speaker 1: and they're using to to You know, again, this isn't
Speaker 1: something that's just magically getting generated. It's not like computers
Speaker 1: are generating the foundation for the semantic Web. This is
Speaker 1: stuff that goes on a hard work. Yeah, it's a
Speaker 1: lot of hard work by people, and it's using really
Speaker 1: two primary tools. Extensible Markup Language or x m L
Speaker 1: and the Resource Description Framework or r DF. So m
Speaker 1: L lets you label data in a way that machines
Speaker 1: can associate with particular taxonomys or ontologies. So it's it's
Speaker 1: a tagging mechanism. So XML is not like some sort
Speaker 1: of coding or anything. You're just tagging data so that
Speaker 1: so that machines can understand how to sort that data,
Speaker 1: how where does that data belong in the universe of information.
Speaker 1: And then r DF is that's when it's the you know,
Speaker 1: the Resource description Framework. That's what's expressing the actual meaning
Speaker 1: of the XML tags. Because you can tag stuff with
Speaker 1: XML like crazy, but if you don't have a framework
Speaker 1: somewhere that explains what the tag means, then it's useless.
Speaker 1: It doesn't mean anything because you haven't given the meaning
Speaker 1: to it. So you have to use this combination of
Speaker 1: frameworks to build out what is going to be useful
Speaker 1: to the semantic web. That's a lot of work. I mean,
Speaker 1: and like we said, with all the data we're generating,
Speaker 1: we're just making more work every single day. So it's
Speaker 1: it's something that is is not a it's not a
Speaker 1: tiny challenge. It's a big challenge. But if we were
Speaker 1: able to create a semantic web that could make it
Speaker 1: very useful to pull up information in a lot of
Speaker 1: different applications. And and there are commercial motivators, I mean,
Speaker 1: and and this is a little bit where people start
Speaker 1: to get peeved about the entire process. But um, but
Speaker 1: for for advertising purposes and marketing purposes, certainly people want
Speaker 1: to be able to serve you the most relevant response
Speaker 1: that hopefully will also be something that you're going to purchase. Um.
Speaker 1: But you know, and and and that gets this is
Speaker 1: actually not not just the most relevant response, but serve
Speaker 1: it to you in the most relevant way. Like they
Speaker 1: might be serving different products in different ways based on
Speaker 1: semantic ontologies, the same way that you know, the same
Speaker 1: movie can be to feel good comedy of the year
Speaker 1: and also the action drama of the year in different trailers.
Speaker 1: But yeah, if it knows what you like. You know,
Speaker 1: you've you've dealt with the tags before. You know, I'd
Speaker 1: rather see something that's a good deal than something that's
Speaker 1: the best on the market, you know, either way it
Speaker 1: can be marketed. Yeah, the in fact, the semantic web
Speaker 1: could be incredibly disruptive because think of the way we
Speaker 1: we get pages served up to us right now, Yeah,
Speaker 1: they have to go to our page, right, there's a
Speaker 1: there's a revenue model there, right. I mean that's the
Speaker 1: downside for the the user, is that going to a
Speaker 1: link means, you know, you're actually sending your browser to
Speaker 1: pull back a page from some server somewhere, a document
Speaker 1: that a document that may or may not have the
Speaker 1: information you need on it. But that document often, in fact, frequently,
Speaker 1: depending upon the what kind of document is, has ads
Speaker 1: on it. Those ads help support the creation and maintenance
Speaker 1: of that document. The fact that the document is out
Speaker 1: there is because there was someone somewhere who is willing
Speaker 1: to put forth the energy and money to generate it
Speaker 1: and to continue to support it. Right the web server
Speaker 1: is still turned on, it's still it is still pulling
Speaker 1: out electricity so they can continue to serve up these
Speaker 1: web pages. Still has a job, etcetera, etcetera. Hopefully the
Speaker 1: writer still has a job in this economy. We know
Speaker 1: how tough that is. So anyway, the fact is there
Speaker 1: is money that goes into the generation and maintenance of
Speaker 1: these pages. So the way that some of that is
Speaker 1: captured is through advertising on these on these web pages.
Speaker 1: Web advertising. That that's a you know, that's been a
Speaker 1: big revenue model in the past. So if you go
Speaker 1: out to these pages, that counts as an impression and
Speaker 1: maybe you click on the ad and that might be uh,
Speaker 1: that might give the person whoever or the entity that
Speaker 1: has created a click through v exactly. So, uh So
Speaker 1: it's an important part of the financial support of this network. Now,
Speaker 1: if you have a semantic web that's pulling information from
Speaker 1: all sorts of sources across the entire inner net, and
Speaker 1: it is serving it up to you, uh and in
Speaker 1: a way that doesn't necessarily indicate that you know this.
Speaker 1: You need to go to this web page now, you
Speaker 1: need to go visit this web page, because if you don't,
Speaker 1: then there's no impression on that ad. The ad was
Speaker 1: never seen. You don't uh, you know, the there's no
Speaker 1: reason to advertise on the web anymore. The entire revenue
Speaker 1: model goes away. So then you're left with the question
Speaker 1: of how do you keep supporting this incredible tool we've
Speaker 1: talked about if there's not a way of generating revenue
Speaker 1: to offset the cost. And I mean it could be
Speaker 1: a relatively I mean I know that. I'm like, this
Speaker 1: would be totally simple to coach. Somebody get on this
Speaker 1: right now. But you know it's you could you could
Speaker 1: have some kind of fee for every time your your
Speaker 1: engine returns to a paragraph from whatever page or you know,
Speaker 1: you you you can find ways of monetizing it. I
Speaker 1: believe in people's powers of monetizing data. Yeah. The the
Speaker 1: key I think would be that the old web could
Speaker 1: not go away. If the old web were to go away,
Speaker 1: if everyone were to do just switch over to the
Speaker 1: semantic web, then think about it, why would you block Well,
Speaker 1: I mean what they're talking about, the semantic web isn't
Speaker 1: a replacement of the web. It's sort of an extension
Speaker 1: the web. Right. But but if people, if you were
Speaker 1: to think, well, instead of using a web grade, instead
Speaker 1: of choosing a traditional web browser, I'm just gonna use
Speaker 1: my semantic browser. So if I'm just using my semantic browser,
Speaker 1: then I'm not gonna necessary You know, a lot of
Speaker 1: people would say this removes artistry from generating content for
Speaker 1: the web, Like why would I create any sort of
Speaker 1: short story that I would publish the web, or a
Speaker 1: poem or anything like that unless you had a way
Speaker 1: of actually navigating to that information directly, as opposed to
Speaker 1: having an agent who just brings you stuff that you like. Yeah,
Speaker 1: I think that's not going to go away, because as
Speaker 1: you've just identified with say a story or something like that,
Speaker 1: there's always going to be stuff on the web that
Speaker 1: you want to read all of your not just looking
Speaker 1: for one piece of information for right and and furthermore,
Speaker 1: you know you're I don't foresee social networking going away
Speaker 1: anytime soon, and so I would suspect that perhaps if
Speaker 1: if a lot of the rest of the web turned semantic,
Speaker 1: that U that more of that creative content would go
Speaker 1: on on social sites where you know, you're still being
Speaker 1: I mean on Facebook every day. It is. It is
Speaker 1: using metrics to figure out what it thinks you want
Speaker 1: to read and showing you that rather than started Oh sure, sure,
Speaker 1: you know, it's Facebook's idea of what I find important
Speaker 1: in my idea of what I find important are two
Speaker 1: very different things. Absolutely, I'm not saying that it doesn't
Speaker 1: work Facebook. You better than you know your stuff. Well,
Speaker 1: if it does, then it's not working because I'm not
Speaker 1: clicking on any of those links. I don't care how
Speaker 1: muscular that that guy is on that you get those
Speaker 1: two Yeah, you know what I'm talking about. Yeah, Okay,
Speaker 1: I don't. I don't get those ads. We'll show you
Speaker 1: sometimes excellent, yeah, Facebook, but even even even adds aside um,
Speaker 1: it's it's picking what content from um, what users you
Speaker 1: follow to to show you in your main feed m.
Speaker 1: And you like that, you like this one guy, he's
Speaker 1: a total freak, So here's something freaky for you to
Speaker 1: look at. Oh no, I know he's a total freak.
Speaker 1: I like him anyway, not because of it, well, but
Speaker 1: you know it's it's fascinating. And they use the rate
Speaker 1: at which you scroll through your feed page to help
Speaker 1: it decide what posts to show you. And that would
Speaker 1: be so much more relevant to me if I weren't
Speaker 1: constantly interrupted by people while I'm trying to read my
Speaker 1: Facebook feed because I stop and I turned around to
Speaker 1: talk to someone, which means that Facebook always stop on
Speaker 1: the muscle dude. He just he just shows up all
Speaker 1: the time, man, Like he's like some sort of stealth
Speaker 1: muscle ninja. I mean, just what is it? They always
Speaker 1: say something like, you know, learn this muscle trick before
Speaker 1: it's illegal, right right, It's the cool It's the medical
Speaker 1: approach that no doctor wants you to know, because doctors,
Speaker 1: of course don't want you to be healthy. Good times.
Speaker 1: I'm okay, yeah, sorry, I'm full of horrible predatory ads
Speaker 1: aimed at you. Oh no, no it is, but it's
Speaker 1: just giving me um engagement rings and um yeah, pink
Speaker 1: sparkly things. Yeah, okay, that's fair Facebook for me. It's
Speaker 1: like it's like muscles. It's like, uh, he joined the military.
Speaker 1: You know, I'm not kidding. I get lots of those.
Speaker 1: Oh Joe, the muscular military dude. I want to see that. Yeah,
Speaker 1: see where I would be happy with the pink sparkly stuff,
Speaker 1: but I just keep getting this muscle dude. Whatever. Anyway,
Speaker 1: the semantic Web cool idea. So the point being, this
Speaker 1: whole discussion has been leading up to this idea of
Speaker 1: developing more sophisticated tools for us to navigate this amazing
Speaker 1: Internet so that we are able to get information that's
Speaker 1: relevant to us that doesn't overload us with stress because
Speaker 1: we suddenly don't have any idea of how to choose
Speaker 1: the right option. So the semantic web almost goes to
Speaker 1: the furthest extreme, and if you want to look at
Speaker 1: it in one way, it goes to the extreme of
Speaker 1: don't worry about options. That's not your problem. I'll take
Speaker 1: care of the options, and here's the information you want.
Speaker 1: In a lot of cases useful, I think. Yeah. So
Speaker 1: uh interesting approach. And whether or not we see the
Speaker 1: semantic web rolled out in a wide format in the
Speaker 1: foreseeable future remains to be seen. It does require an
Speaker 1: awful lot of work, but it's work that a lot
Speaker 1: of people are really they really believe in, and they're
Speaker 1: really trying to make it become a reality. So it'll
Speaker 1: be interesting to see how that that unfolds. So, guys,
Speaker 1: if you have any episode ideas, or you have any
Speaker 1: comments that you want to make about this episode, I
Speaker 1: highly suggest you write us because we can't hear you
Speaker 1: from here. Our email address is FW thinking at Discovery
Speaker 1: dot com or go to FW thinking dot com and
Speaker 1: there you can see all the videos. You can read
Speaker 1: the blog posts you can listen to more podcasts like
Speaker 1: the this one, and we had a lot more information
Speaker 1: out there that is really really cool that we think
Speaker 1: you guys would be really excited to see and explore.
Speaker 1: So thanks for listening and we will talk to you
Speaker 1: again really soon. For more on this topic in the
Speaker 1: future of technology, visit forward thinking dot Com, brought to
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