Speaker 1: Brought to you by the reinvented two thousand twelve Camray.
Speaker 1: It's ready. Are you get in touch with technology with
Speaker 1: tex Stuff from how stuff works dot com. Hello everyone,
Speaker 1: and welcome to tech stuff. My name is Chris Poulette
Speaker 1: and I am an editor at how stuff works dot com.
Speaker 1: Sitting across from me is a man who needs no introduction.
Speaker 1: But I'm paid to do it, so I'll do it anyway.
Speaker 1: Senior writer Jonathan Strickland, Hey there, right. You know I
Speaker 1: should have put that in context. Yeah. Yeah, it turns
Speaker 1: out that without context, it can be a really confusing
Speaker 1: thing for me, also for computers. Because you know, today
Speaker 1: we wanted to talk a little bit about the future
Speaker 1: of search on the Internet. And the reason why we're
Speaker 1: bringing this up is the week that we're recording this podcast.
Speaker 1: The today's date as we record is May eighteen. Now,
Speaker 1: this past week, there was some news that came out
Speaker 1: from Microsoft and from Google about some additions to their
Speaker 1: their search products. So Bing and Google obviously those would
Speaker 1: be the two UM and uh. And in both cases
Speaker 1: there's some information about kind of a new approach to search,
Speaker 1: and not not even that new, it's just that there's
Speaker 1: sort of a new way of presenting the information. But
Speaker 1: it's sort of a kind of a trend that we're
Speaker 1: seeing building towards something we call semantic search, which is
Speaker 1: sort of an intelligent search that's pulling information it's relevant
Speaker 1: to your search query from multiple sources and putting it
Speaker 1: together in a way that's understandable for you so that
Speaker 1: you don't have to actually do all that work yourself.
Speaker 1: Where you you know, when you typically do a search,
Speaker 1: you get all the different results, and then you have
Speaker 1: to go and visit the different sites find out if
Speaker 1: the information you need is there. Maybe it has of
Speaker 1: the info you need, but not everything, So you have
Speaker 1: to go to another link and see if maybe that
Speaker 1: has twenty of the fort that was missing. And so
Speaker 1: you're piecing it all together bit by bit, and you're
Speaker 1: having to make incredible notes and if this is really data,
Speaker 1: and and so you might even have an enormous spreadsheet
Speaker 1: right and it just it's a lot of work. Well,
Speaker 1: the idea behind semantic search is a lot of that
Speaker 1: work gets done for you, and you look for something
Speaker 1: very specific and you'll get very specific results back, even
Speaker 1: if those results are pulled from multiple sources. So in
Speaker 1: other words, you don't have to do all that work.
Speaker 1: There's still a lot of work that has to be done, though,
Speaker 1: So we're gonna cover all of that. And I think
Speaker 1: the best way to start is when we talk about
Speaker 1: how typically a a search is done in the traditional
Speaker 1: it's kind of weird to say traditional when you're talking
Speaker 1: about the Internet, but traditional Internet search style. Yeah, yeah,
Speaker 1: well you think about it. There there have been search
Speaker 1: engines out since probably around um, the early early nineties really,
Speaker 1: and that that was when we have been talking about
Speaker 1: the public using the Internet as tool for information, a
Speaker 1: tool for communications and entertainment really um. And these the
Speaker 1: earliest search engine relied on some some pretty brute force technology,
Speaker 1: one of which was was either a a a script
Speaker 1: called a spider that would go crawling the web, you
Speaker 1: get it. It would it would going here, yeah, it
Speaker 1: would go link to link. It would actually follow links
Speaker 1: and index pages. It would it would record the content
Speaker 1: on pages. Now it did not understand that content, but
Speaker 1: just merely said all right, these are the words that
Speaker 1: appear on this particular web page, and so that gets indexed.
Speaker 1: And then it would go to the next link and
Speaker 1: it would index that one. And you know, there's what
Speaker 1: I'm saying, one spider. Clearly, that are countless spiders crawling
Speaker 1: the web, and there still are. Yeah, absolutely absolutely, And
Speaker 1: a lot of the search engines, the ones that that
Speaker 1: we use today, still use that technology, but they're not
Speaker 1: necessarily as reliant on that technology alone as they used
Speaker 1: to be. There's more sophistication on the back end. Yeah.
Speaker 1: And then you have Yahoo, which used a very uh
Speaker 1: It's genesis used a very um, I don't know, would
Speaker 1: you say, a intelligent way of going about indexing web content.
Speaker 1: Out of all the web indexes in the world, Who's
Speaker 1: was the most Yes, exactly. They had people. It's a
Speaker 1: little star trek to reference, going going through and recording
Speaker 1: what was on web pages. Um, which is again you
Speaker 1: think about it, that's an excellent way to do it.
Speaker 1: It's also very time consuming and very expensive pay people
Speaker 1: to do this, right, it turns out people will not
Speaker 1: do this for free, all the kindness of their heart.
Speaker 1: And with quadrillions of web pages out there these days. Yeah,
Speaker 1: back in the early days of the web, even then,
Speaker 1: indexing everything was a was a huge effort, all right,
Speaker 1: it was. I mean it's not like it's not like
Speaker 1: they were man, I remember back when there were only
Speaker 1: three web pages, so easy. I was like, how do
Speaker 1: I find one? Oh? Right, I need to go to
Speaker 1: this one of the three. Now it was always a
Speaker 1: little more complex than that. And so by the time
Speaker 1: that you know the the we get to Yahoo coming around. Uh,
Speaker 1: it's it's a big job going through and checking all
Speaker 1: these pages. And of course it gets even more complex
Speaker 1: today when we have dynamic web pages. Because back in
Speaker 1: the old days, when you would create a web page,
Speaker 1: it was a very static document. Yeah, it was written
Speaker 1: in in hypertext markup language STML, and for the most
Speaker 1: part there you know, there weren't scripts in there. There
Speaker 1: weren't server generated pages like you'd find with something like PHP,
Speaker 1: for example, and people just didn't make that many changes
Speaker 1: like you might you might make a reference page for something,
Speaker 1: so it's almost like an article in a magazine or
Speaker 1: a page out of a book. It did not change
Speaker 1: very much once you put it up there. Now, of
Speaker 1: course you would always have the the the required under
Speaker 1: a construction gift in the bottom to let people know that, hey,
Speaker 1: there's still more stuff I'm going to add to this,
Speaker 1: so you should come back again, yes, usually accompanied by
Speaker 1: a MIDI file. Also, there was very I can't believe
Speaker 1: we've never been this. When we talk about the stereotypical
Speaker 1: stuff you would find on the old web pages, there'd
Speaker 1: be a visitor counter. Oh I forgot about the little
Speaker 1: It would look like an old analog digit counter, and
Speaker 1: it would go up each type someone would visit it,
Speaker 1: and there were people who were really proud of their
Speaker 1: visitor numbers and other people who are very upset that
Speaker 1: they would be like hovering around seven and you know
Speaker 1: six of those are your mom? So, um, yeah, what
Speaker 1: not the it's the universal mom. Sorry sorry but but yeah. So.
Speaker 1: So the typical way is that these spiders crawl out
Speaker 1: across the web. They index the pages. That index is
Speaker 1: built into the search engine, so that when you type
Speaker 1: in a query, the search engine compares your query against
Speaker 1: the database and it starts to try and pull the
Speaker 1: pages that appear to correspond with whatever your query is. Now,
Speaker 1: in the early days, this was very primitive, where essentially
Speaker 1: you were just getting pages ranked, sometimes just by the
Speaker 1: number of times a certain term might have here on
Speaker 1: the page. Oh yeah, they used to uh the early
Speaker 1: days of search engine optimization UM, which is basically if
Speaker 1: if you're unfamiliar with the term a way to get
Speaker 1: more people to visit your website, so they would mention
Speaker 1: certain words over and over again. So it's a law firm,
Speaker 1: where where are lawyers and attorneys are? Blah blah blah.
Speaker 1: And if you have a legal case that needs lawyers
Speaker 1: and attorneys at this law firm, this law firm can
Speaker 1: provide you lawyers and attorneys for your lawsuit, you know, etcetera, etcetera.
Speaker 1: And then you would even find on some pages there
Speaker 1: would be like at the very bottom of the page
Speaker 1: all this tiny, tiny, tiny tiny tech sometimes in the
Speaker 1: same color as the background of the page, so you'd
Speaker 1: have to highlight to see it, and all that would
Speaker 1: be would be a list of unrelated terms. And it
Speaker 1: was all about trying to game the system so that
Speaker 1: the web search engine would point you at that page,
Speaker 1: because the web search engine wasn't smart. It didn't know
Speaker 1: that that page wasn't really about kayaking, Right, you type
Speaker 1: in kayaking in this page pops up and you go
Speaker 1: there and instead of it being a page about kayaking,
Speaker 1: it's something else, like a car dealership, And you're like,
Speaker 1: what the heck is this? Why is this on kayaking
Speaker 1: right now. I guess I could fit a kayak on
Speaker 1: some of these cars, but other than that, I don't
Speaker 1: really get it. And it turns out they were trying
Speaker 1: to just drive more traffic, which most of us. You know,
Speaker 1: you sit there and you think about for two seconds,
Speaker 1: you think that's a stupid way to drive traffic, because
Speaker 1: if if you go to a page that does not
Speaker 1: have the stuff you're looking for, you are not going
Speaker 1: to stand that page for very long. But in the
Speaker 1: early days of the web, people would do practically anything
Speaker 1: to try and get you to their website and rename
Speaker 1: photos so that they would work on search terms. The
Speaker 1: kayak page, the kayak photo one. Also my favorite, which
Speaker 1: is you go to bookmark a page and you realize
Speaker 1: that the page was titled something about a paragraph long
Speaker 1: with just search terms. Yeah, yeah, which just drove me crazy.
Speaker 1: So at any rate, search engines have become much more
Speaker 1: sophisticated since then, where they are able to weed out
Speaker 1: a lot of that stuff. Google, for instance, it's very famous.
Speaker 1: They have their algorithm and the Google algorithm page rank. Yeah,
Speaker 1: the page rank is a lot different than just trying
Speaker 1: to find a page that has a certain term A
Speaker 1: certain number of times. In that case, page rank also
Speaker 1: takes into account how many other sites link into that page.
Speaker 1: So if a lot of sites link into that page,
Speaker 1: the Google algorithm draws the conclusion that that page must
Speaker 1: be good because a lot of people are linking to it,
Speaker 1: and if they, if there are a lot of links
Speaker 1: that point to that page, then by Google's definition, that
Speaker 1: one must be more relevant than one that has fewer
Speaker 1: links into it. Now we all know that doesn't necessarily
Speaker 1: correspond with reality, but in general it's a fairly safe approach.
Speaker 1: And another thing it does is uh it recognized as
Speaker 1: attempts to try to game the system, like we were
Speaker 1: just talking about. If you do the lawyers and attorneys
Speaker 1: law firm thing that Jonathan was just talking about, it
Speaker 1: will go wait a minute, you're obviously over linking. You know,
Speaker 1: you're over quoting these terms, and I'm going to rank
Speaker 1: you lower because you are obviously spamming essentially page ten
Speaker 1: or eleven of the search results. And most people don't
Speaker 1: go much further beyond the first page. Some people do
Speaker 1: the first two or three. I because I do research
Speaker 1: for a living, I will often dig fairly deep down
Speaker 1: to try and find especially if I find something that
Speaker 1: looks really uh like a remarkable fact, I kind of
Speaker 1: want to make sure I get some confirmation that this,
Speaker 1: in fact is true, and I want to see if
Speaker 1: I can find anything that does not reference the same
Speaker 1: source material as whatever I found, because you know, you
Speaker 1: want to you want confirmation, but you don't want to
Speaker 1: just like look at eight articles that are all based
Speaker 1: upon the same news press release, right, because then you're
Speaker 1: just like, well, all this is regurgitation of the same facts.
Speaker 1: I need something. I need an independent confirmation that this
Speaker 1: is true. So yeah, there's you know, but for most people,
Speaker 1: they don't tend to go beyond the first couple of pages. Um,
Speaker 1: but this is all still ultimately kind of a dumb search. Yeah,
Speaker 1: go ahead, I'm sorry. Well, I was just gonna say
Speaker 1: that Larry Page and Sarah G. Brand when they launched
Speaker 1: Google they revolutionized search because it made it so much
Speaker 1: more effective than it had been in the past, and
Speaker 1: it really weeded out a lot of the people who
Speaker 1: couldn't do something as sophisticated, uh search the web as
Speaker 1: sophisticated as you know, in such a sophisticated way as
Speaker 1: Google was doing it. Now. Of course, Microsoft came out
Speaker 1: with Bing, which uh, if you pull out your fanboy
Speaker 1: is um, you know, and put it aside for a minute.
Speaker 1: From whichever of the major tech companies you prefer. I
Speaker 1: think Bing and Google both do an excellent job of
Speaker 1: searching the web. They do. You'll come up with different
Speaker 1: results when you go between the two, but they'll both Um,
Speaker 1: they'll both come up with excellent results, which is why
Speaker 1: I like some search engines like Dogpile, which aggregate search
Speaker 1: results from these different search engines. Um. But but yeah,
Speaker 1: I mean they these guys are obviously the heavy hitters
Speaker 1: because they're very good at coming up with results that
Speaker 1: people find effective. And that's why they're the big, the
Speaker 1: big guys in the market, if you will. Now, but
Speaker 1: they're not stopping and resting on their laurels either. Right, Well,
Speaker 1: we've pretty much covered what the present day search uh
Speaker 1: landscape is. Now let's talk a little bit about the future.
Speaker 1: And some of this is rolling out presently. In fact,
Speaker 1: by the time this podcast goes live, many of you
Speaker 1: may have access to the tools we're gonna be talking
Speaker 1: about because Google, for example, is rolling out what they
Speaker 1: call the knowledge graph and being has this nose as
Speaker 1: in k in Ows approach, where it has three different
Speaker 1: columns called search knows, being knows, and friends know and
Speaker 1: um uh. In both cases, they're trying to incorporate a
Speaker 1: little bit of what we think of as a semantic search. Now.
Speaker 1: Semantic search is where the search engine has at some
Speaker 1: level a comprehension of what it is you're trying to find.
Speaker 1: And that's really important because, as it turns out, people
Speaker 1: like to name stuff similar things, even if those two
Speaker 1: things are themselves not similar. I'll give you a very
Speaker 1: easy example. Let's say you're doing a web search on
Speaker 1: son s u N. Well, that could mean the Sun,
Speaker 1: as in the star that's at the center of our
Speaker 1: solar system, or it could mean something like Sun Microsystems.
Speaker 1: And without any other context the web, the search engine
Speaker 1: doesn't know what you want. So it's going to start
Speaker 1: pulling up stuff that ranks really high based upon whatever
Speaker 1: algorithm they use, whether it's Googles or Microsoft's or whatever
Speaker 1: search engine, and it's going to present those to you
Speaker 1: on a page and and maybe mixed up it. Maybe
Speaker 1: that you've got, you know, three or four astronomical links
Speaker 1: and three or four technology links. And that's fine, but
Speaker 1: let's say that you know you have a semantic search
Speaker 1: where it can tell what it is that you want
Speaker 1: and every single link on that page is relevant, Well,
Speaker 1: that would be preferable and there it's tricky to do that,
Speaker 1: especially if all you're doing is a one word search term,
Speaker 1: because you know there's not there's no context there. If
Speaker 1: you added more context in your search, then that would
Speaker 1: narrow down the results. So the more terms you put,
Speaker 1: in general, the more terms you put within a search bar,
Speaker 1: the narrower the results will be when they're presented to you.
Speaker 1: But you have to start trying to think the way.
Speaker 1: You know, whoever is generating the information that you need,
Speaker 1: you have to kind of think in their way in
Speaker 1: order to get those search results narrowed properly, because if
Speaker 1: you put something that's outside of that realm and it
Speaker 1: makes sense to you, but it didn't necessarily make sense
Speaker 1: to them, you might not get a result at all. Um.
Speaker 1: I think it's important to note too that we're not
Speaker 1: what we're talking about now is not necessarily the semantic web. Um.
Speaker 1: The semantic web is uh, and I just wanted we're
Speaker 1: not gonna get into that because that could be a
Speaker 1: series of podcasts. UM but basically the idea is similar.
Speaker 1: What what we're talking about is context, um and uh.
Speaker 1: In the semantic web, you're trying to create context between
Speaker 1: two entities, this person doing the web browsing and the
Speaker 1: stuff on the other end. Um. And that requires use
Speaker 1: of a markup language called r d F Resource Description Framework.
Speaker 1: And uh. There's actually a very excellent article on how
Speaker 1: stuff works dot com written by pop stuff host co
Speaker 1: host Tracy Wilson, um and our lovely site director. Um
Speaker 1: and uh. And if you want to learn more about that,
Speaker 1: then I would recommend that you go read that article.
Speaker 1: But um, you know, and at least until we talk
Speaker 1: about it. But um, it's not and Google is careful
Speaker 1: to make this note to They're not this isn't actually
Speaker 1: the semantic web, but they are trying to create context
Speaker 1: by referencing what they know about you and the stuff
Speaker 1: that is out there on the web. So it is
Speaker 1: it is similar in that sense. Yeah, it's kind of
Speaker 1: a it's kind of a step towards the entic web.
Speaker 1: I think of it as semantic search simply because ultimately
Speaker 1: it is looking for bits of information about whatever the
Speaker 1: subject is that you are interested in and presenting it
Speaker 1: to you in a unified way where you so that
Speaker 1: you do not have to do all the searching yourself.
Speaker 1: It will actually get into why that might be a
Speaker 1: problem too, because there's there's some very legitimate concerns not
Speaker 1: from a technology standpoint, but a business standpoint about this approach.
Speaker 1: But let's say that, Uh, a computer, for example, doesn't
Speaker 1: know the difference between any two categories of anything without
Speaker 1: you telling it what that is. So so let's say
Speaker 1: that you know you've got a picture. Let's say that
Speaker 1: you've got a computer that can scan things, and it knows,
Speaker 1: like you can type in what that thing is. All right,
Speaker 1: so you scan a picture of a puppy dog, and
Speaker 1: you scan a picture of a locomotive, and you say,
Speaker 1: this is a puppy dog, that's a locomotive. Well, the
Speaker 1: computer doesn't know that a locomotive and a puppy dog
Speaker 1: don't belong to the same category like other than stuff.
Speaker 1: It doesn't know that a puppy dog is an animal.
Speaker 1: Doesn't know a locomotive is a technology, it's a it's
Speaker 1: a vehicle. Uh. It cannot make that determination until you
Speaker 1: tell it. And then to get more complicated, let's say
Speaker 1: William Shakespeare. You say, William Shakespeare is a playwright or
Speaker 1: was a playwright, it's not currently working. William Shakespeare was
Speaker 1: a playwright. William Shakespeare was a man. Well, you also
Speaker 1: have to let the computer know that not all all
Speaker 1: people who fit into the man category also fit into
Speaker 1: the playwright category, right, Nor are all the playwrights men
Speaker 1: like the computer does not know that automatically. Sorry, I
Speaker 1: was something like Bacon. The point being that that very
Speaker 1: small Shakespeare. I'm a mar Lovian myself. Actually it's not true.
Speaker 1: I'm a stratford Ian. But the the entire, the entire
Speaker 1: relationship of all these different categories are things that a
Speaker 1: computer does not natively understand. So you have to actually
Speaker 1: build this. We call these things, you know, we call
Speaker 1: them taxonomy's, we call them ontologies. It's all about creating
Speaker 1: these structures so that the computer can relate bits of
Speaker 1: information to one another in a way that makes sense,
Speaker 1: because otherwise, when you would do a search term, even
Speaker 1: using one of these new products, you would just get
Speaker 1: a a big batch of data that is not organized
Speaker 1: in any meaningful way, you know. But this way, when
Speaker 1: you actually categorize stuff and you build out these these structures,
Speaker 1: within the computer system, so that computers can understand, all right,
Speaker 1: we need to put this bit of information here and
Speaker 1: this bit of information here, and that this is how
Speaker 1: it's all going to be organized. Then it means something
Speaker 1: to you when it comes back. So you might have
Speaker 1: born on, died on, you know, famous works, that kind
Speaker 1: of stuff. And in fact, both Bing and Google are
Speaker 1: working on actually they're rolling out products that incorporate this
Speaker 1: sort of result in searches. So with Google it's called
Speaker 1: the Google Knowledge Graph, and it's a an idea where
Speaker 1: if you were to do a search on any particular topic, UH,
Speaker 1: you would have the main search results underneath the search bar,
Speaker 1: just as as per normal, but on the right there
Speaker 1: would be a little uh window that could open up
Speaker 1: that would have information about whatever the subject of your
Speaker 1: search was. And it's possible that the information you want
Speaker 1: is contained within that window. So if you're doing a
Speaker 1: search on someone famous Leonardo da Vinci, you could end
Speaker 1: up which is actually an example in one of the
Speaker 1: videos they show if you do a search for for
Speaker 1: the Google Knowledge Graph, there's a video and on the
Speaker 1: Google blog. Let's say you do that that, well, you
Speaker 1: might get um, famous works of art that Leonardo da
Speaker 1: Vinci painted as well as some of his contemporaries. Uh.
Speaker 1: During the Renaissance, he painted his contemporaries. Uh, if they
Speaker 1: stood still long enough. He was a very busy man. Anyway,
Speaker 1: you would get like little details about his life. You
Speaker 1: would see that kind of stuff, and and maybe the
Speaker 1: answer to your question could be right there. So you
Speaker 1: don't have to go into a search result. Uh. Now,
Speaker 1: if you wanted to know about the inventions Leonardo da
Speaker 1: Vinci made, and all you're getting back is art, then
Speaker 1: you would require you to do another search or to
Speaker 1: actually go to one of the links that pops up
Speaker 1: under the normal search results. Um. But the Google says
Speaker 1: that they have already more than five million objects indexed
Speaker 1: for this, with three point five billion facts about those
Speaker 1: objects and about the relationship between objects, so that when
Speaker 1: you do these searches, you're going to get the most
Speaker 1: relevant information. And they say that the way that they're
Speaker 1: determining what is relevant versus not relevant is they're actually
Speaker 1: looking at what people are searching for. So if if
Speaker 1: nine percent of the people who are searching for Leonardo
Speaker 1: da Vinci are searching for information about his works of art,
Speaker 1: that's the information that's gonna pop up, and you do that,
Speaker 1: just do the search without any more modifiers, which makes sense.
Speaker 1: You know, if the majority of the people are interested
Speaker 1: in just that one aspect of him, then clearly that's
Speaker 1: what should be presented to you. And then anyone who
Speaker 1: needs more information just goes into the link, just like
Speaker 1: you would on any other day. Well being is doing
Speaker 1: a similar thing. Rhyme, rhyme um. They have instead of
Speaker 1: instead of the two column approaches, because you know, Google's
Speaker 1: got the search results under the main column, and then
Speaker 1: to the right it's got the little window that pops up.
Speaker 1: Being does three columns. The first column are the search results,
Speaker 1: so just like a normal search, and then the middle
Speaker 1: column is the contextual results where it will pull information
Speaker 1: about whatever it is you're searching for. So let's say
Speaker 1: you're searching for a city and you you do, um, oh,
Speaker 1: let's see what would be a good one. Let's or no,
Speaker 1: not a city. I'm not gonna go say, I'm gonna
Speaker 1: go to a place that I visited last year that
Speaker 1: I loved. Because one of my one of my family
Speaker 1: members ruled it for a while, Malta, really Malta was
Speaker 1: ruled by Strickland. Did you not know that? Yes, I
Speaker 1: took my I got a picture taken in front of
Speaker 1: my distant relative, and in fact he is part of
Speaker 1: my branch of the family. So um, they did not
Speaker 1: give me really awesome seats at restaurants or anything. I
Speaker 1: was very disappointed in that they should have at least
Speaker 1: given you a free Malta milk. I figured I should
Speaker 1: have at least had a parade, at the very least
Speaker 1: a parade anyway, but I enjoyed my time on Malta.
Speaker 1: Let's say, type in Malta and you want to know
Speaker 1: you know all the different little facts about like where
Speaker 1: is it located, you know what what kind of climate
Speaker 1: is it? And the contextual result within being will give
Speaker 1: you basic information about Malta, and it might even tell
Speaker 1: you what the weather's like on that day. That kind
Speaker 1: of stuff. It's the and this is the stuff that
Speaker 1: we've seen in search for a while. It's just that
Speaker 1: now it's organized in such a way that it is
Speaker 1: pulled out from the regular search results and put it
Speaker 1: in its own column, so that way it's not interspersed
Speaker 1: with the links to the different web pages. So it
Speaker 1: makes it easier to see and then on the third
Speaker 1: column it's your friends no column and that what that
Speaker 1: does is it crawls social networking sites like Facebook, Twitter.
Speaker 1: Eventually supposedly it will do Google Plus as well, and
Speaker 1: you know you have to sign into being for this
Speaker 1: to work. But when you sign in and you've got
Speaker 1: linked to your accounts, it's going to search through all
Speaker 1: the people that you're connected to through those accounts and
Speaker 1: look for anyone who has posted anything about the subject
Speaker 1: you've searched for. So, for example, with Malta, if you're
Speaker 1: a friend of mine and you search for Malta, you
Speaker 1: might see that it pops up that I've got some
Speaker 1: pictures of the time when I was in Malta and
Speaker 1: some some posts about what I did while I was there,
Speaker 1: and maybe about a restaurant I went to. And that
Speaker 1: would allow you to do things like touch base with
Speaker 1: me and say, hey, you know you've been to Malta.
Speaker 1: I was thinking about going. I wanted to talk to
Speaker 1: you a little bit about your experience and find out,
Speaker 1: you know, what should I do? And uh So it's
Speaker 1: an idea of making the search more relevant. Now this
Speaker 1: is really interesting to me because this shows a divergent
Speaker 1: future of search. Right, You've got the what I call
Speaker 1: the semantic approach, where's the artificially intelligent approach, where the
Speaker 1: computer is putting together information based upon algorithms and connections
Speaker 1: between objects. And then you have the social approach, where
Speaker 1: it's pulling information from your friends and and other people
Speaker 1: you know and giving you sort of the personal approach
Speaker 1: to search. Two different ways of doing this. I'm curious
Speaker 1: to see which one's going to win out, and maybe
Speaker 1: maybe win out is the wrong phrase. I'm curious to
Speaker 1: see which one people will rely upon more frequently, and
Speaker 1: it maybe that it all depends on what kind of
Speaker 1: person you are. I also am sort of dreading this
Speaker 1: in a way. The reason I'm dreading it is I'm
Speaker 1: worried about people doing searches for It's lots of stuff
Speaker 1: that I post about and then they get results about me,
Speaker 1: and the next time I log into Facebook, I've got
Speaker 1: seven and fifty three messages asking me about the restaurants
Speaker 1: I eat at, the theaters I go to, UH support
Speaker 1: questions about the multiple things that I've written about. For
Speaker 1: how stuff works, I mean, you know, you could you
Speaker 1: could see that for certain people who post a lot
Speaker 1: to social networks. It could mean you get a flood
Speaker 1: of folks contacting you about stuff that you either haven't
Speaker 1: thought about in a long time or you know, yeah,
Speaker 1: you just you're like, well, I don't mind sharing that information.
Speaker 1: It just feels like I'm getting uh buried under requests
Speaker 1: and UM. I talked about this with some some tech
Speaker 1: friends of mine, and they all thought I was being
Speaker 1: a bit of a grouch. But I said, no, seriously,
Speaker 1: think about it. Think about all the things you do
Speaker 1: and post about and so well, isn't that why you
Speaker 1: post about these things like you post about it in
Speaker 1: order to share the information with people? I said, sure,
Speaker 1: But in that case, it's me shooting information out to
Speaker 1: the world. It's not the world coming to me for information.
Speaker 1: That's a very different thing. It's it's you know, taking
Speaker 1: the bullhorn and saying something through that as opposed to
Speaker 1: having to accept call. After all, after call, people don't
Speaker 1: call me, do not call me. Well, there's also another
Speaker 1: thing to be concerned about for a lot of people,
Speaker 1: what um. In order to build a context between you
Speaker 1: and the other web pages out there, it has to
Speaker 1: know things about you, and some people are shall we say,
Speaker 1: skittish about uh providing information about themselves. They might be
Speaker 1: looking for more privacy understandable, and this is one of
Speaker 1: those trade offs that that you have to make. Uh.
Speaker 1: This is incredibly convenient in a lot of ways if
Speaker 1: you think about it, but in order to do this,
Speaker 1: you have to give up information about yourself. Otherwise the
Speaker 1: search engines can't provide that context and the relevancy that
Speaker 1: you would get with these enhanced search features. So you
Speaker 1: have to make a decision do you want to share
Speaker 1: this information with being Do you want to sign in
Speaker 1: and let them peruse your accounts so that they can
Speaker 1: find out that Jonathan knows something about Malta? Or do
Speaker 1: you want to say, you know what, I don't want
Speaker 1: to share that information with you, and I will take
Speaker 1: my chances with the search results. And that's, you know,
Speaker 1: something that that is an individual, person by person thing
Speaker 1: to consider, but I think it is worthy of mention
Speaker 1: because privacy, of course, is one of those things that
Speaker 1: that is a greater concern for many people. UM and UH.
Speaker 1: Another thing to consider is UH the filter bubble, which
Speaker 1: is a term that Eli Harriser came up with UM
Speaker 1: or at least he wrote a book about it, and
Speaker 1: the idea being that the more you search, the more
Speaker 1: your search engine knows about you, the more stuff you're
Speaker 1: going to see that is uh, you know, tailored towards
Speaker 1: your interests, which is okay in way good, But in
Speaker 1: another way, it's not because you're not seeing uh, divergent opinions.
Speaker 1: You're not seeing opinions from people who are not like you. Um,
Speaker 1: if you are, say a Republican, you're gonna see things
Speaker 1: that are more conservative and fewer things that are more liberal.
Speaker 1: Just becomes an echo chamber. Yeah, and and so therefore
Speaker 1: you are not proposed to opinions from people who are
Speaker 1: not necessarily you know, don't necessarily hold the same beliefs.
Speaker 1: And there are a lot of people. I used politics
Speaker 1: as an instance because a lot of the the people
Speaker 1: that I've sort of paid attention to in the political
Speaker 1: world want to know what's going on on both sides
Speaker 1: of the aisle. They want to know what people who
Speaker 1: are like them think, and they want to know what
Speaker 1: the other people think too, um, so that they can
Speaker 1: make an informed decision, which I think is you know,
Speaker 1: something to be something to be mindful of. Two. Um,
Speaker 1: not that not that these these search tools are bad
Speaker 1: in any way, But it's something to consider when you're
Speaker 1: using them, is that you're trading off privacy to gain this,
Speaker 1: and you're also UM insulating yourself to just things that
Speaker 1: you like, which is frankly a very comfortable position. Uh.
Speaker 1: You know, it's we're staying inside our comfort zone and
Speaker 1: not and not treading very far outside it. UM. So
Speaker 1: it's it's you know, those are just a couple of
Speaker 1: the things mentioned. Also, I've heard critics say that they
Speaker 1: don't like UM the idea of of what Google is
Speaker 1: doing because they said, well, you know, you're you're putting
Speaker 1: a lot of the information up on from these pages
Speaker 1: on Google. What incentive do people have to click through
Speaker 1: to the people you're linking to, Which is where I
Speaker 1: was saying. You know, it's not a technological problem, it's
Speaker 1: a business problem because if you're if your business is
Speaker 1: web based and you depend very heavily upon ad revenue,
Speaker 1: then that means you depend upon people visiting your site
Speaker 1: to get information or to have an experienced whatever your
Speaker 1: site provides you. You depend upon people coming to that
Speaker 1: site in order for that impression to be made, and
Speaker 1: that impression goes toward UM generating money through advertising. So
Speaker 1: that because we we assume that if you're on the
Speaker 1: page that you're seeing the ad or the ad is
Speaker 1: somehow affecting you, and that that means the advertiser is
Speaker 1: getting value for the money they're spending to reach you. Well,
Speaker 1: if you never go to the site, then and that
Speaker 1: impression never happens, which means that the the proprietor of
Speaker 1: that site is out of advertising money, and in fact,
Speaker 1: the advertisers aren't happy with it either, because it means
Speaker 1: that their ads aren't being seen. So you know, you've
Speaker 1: got the advertisers who are unhappy because their ads that
Speaker 1: they're creating aren't being seen by anyone. If in fact,
Speaker 1: you can get all the information you need just through
Speaker 1: typing in some search terms into Google. Uh, and the
Speaker 1: the proprietors the sites that actually are providing that information
Speaker 1: that their owners aren't happy either because no one's going
Speaker 1: to visit them. So then you think, well, would anyone
Speaker 1: even bother to build a website? Um, once you got
Speaker 1: to a search that was sophisticated enough that all the
Speaker 1: information comes back upon the search, why would you bother
Speaker 1: building a site at all? You're really all you're doing
Speaker 1: at that point is just building a database for Google.
Speaker 1: That's all it is. It's not you're no longer building
Speaker 1: something for yourself. You're really just adding to Google's data storage.
Speaker 1: So that's a legitimate question. I mean, uh, now, if
Speaker 1: if the information the way Google is presenting this, it
Speaker 1: tends to be not a huge amount of information. It's
Speaker 1: it's stuff that's relevant to your search, and it might
Speaker 1: answer very basic questions you have about that. But if
Speaker 1: you want something more thorough than just like a birth
Speaker 1: date to death date and some major achievements in a
Speaker 1: in a particular historical person's life, then you would need
Speaker 1: to go further in. It's not like you're going to
Speaker 1: get a full biography with lots of detailed information within
Speaker 1: that search. Right. What I've what I've seen Google say
Speaker 1: is that they believe that it will wet people's appetites. Uh,
Speaker 1: they'll get you started, but if you really want to
Speaker 1: know more, you need to continue follow the link into
Speaker 1: the what the other sites and see what else there is?
Speaker 1: Um which you know for some of us, um you know,
Speaker 1: not speaking necessarily for Jonathan, although I suspect it might
Speaker 1: be the case, uh, you know, for people who are
Speaker 1: intellectually curious you do start wanting more. It's like, okay,
Speaker 1: well this is great, uh and it answers my question,
Speaker 1: but now I want to know more. You kind of
Speaker 1: fall down the rabbit hole where you just start chasing
Speaker 1: more information and uh and offend. That will lead to
Speaker 1: you looking at related things that had nothing to do
Speaker 1: with your initial search but are interesting, so you want
Speaker 1: to pursue them and find out more about them, which
Speaker 1: is great. Uh, It's just that I know a lot
Speaker 1: of I don't know. I suspect a lot of advertisers
Speaker 1: and website owners are a little leery of this. I'm sure.
Speaker 1: Uh yeah, that's that's one of the one of the issues.
Speaker 1: And here's another problem. This one is more technological. So
Speaker 1: building out these these results, these connections between objects. Like
Speaker 1: I said, a computer does not natively understand relationships between
Speaker 1: any two things, so you have to not yet they're
Speaker 1: working on it, but that means you have to build
Speaker 1: stuff out. And I wanted to bring something else into
Speaker 1: the discussion to kind of give an example. There's another
Speaker 1: project called the Silk Project, which is a I think
Speaker 1: it was eleven developers, eleven Dutch developers who put this together,
Speaker 1: and the idea is that it's a it's sort of
Speaker 1: a database management system in a way not necessarily meant
Speaker 1: for the Web, meant more for like an internal system.
Speaker 1: Like let's say that Chris and I, you know, we
Speaker 1: work for how stuff works. Let's say that we build
Speaker 1: out a um like We're going to build out a
Speaker 1: wiki that contains all the information about our company and
Speaker 1: processes within our company, the procedures you're you're supposed to follow,
Speaker 1: basic information within the company. The Silk Project would allow
Speaker 1: you to classify the information in different ways. You tag it,
Speaker 1: you create meta data. Now, meta data is information about information,
Speaker 1: and this allows you to to classify the information in
Speaker 1: ways that the computer can on a very surface level
Speaker 1: understand understands really the wrong word, but it can work
Speaker 1: with that classification system. So, for example, if I were
Speaker 1: to have a whole site about dogs, then you know,
Speaker 1: dog might be one category. I might even go further
Speaker 1: up and say it's a mammal and that means that's
Speaker 1: an animal, etcetera. But then I would have breeds underneath
Speaker 1: the dog, and each of those breeds would go back
Speaker 1: up to ultimately two dog. Right, Well, that would help
Speaker 1: me build out these relationships between whatever it was that
Speaker 1: was in my database. So Silk, what you do is
Speaker 1: you get this tool you classifile the information within your system.
Speaker 1: So let's say it's lots of different numbers, lot's of
Speaker 1: different like sales figures and advertising figures, that kind of stuff.
Speaker 1: You would classify all that information and then whenever you
Speaker 1: needed to pull data from your database, you would type
Speaker 1: in a query into the soap Projects search tool and
Speaker 1: it would automatically aggregate all the information for you and
Speaker 1: put together in a way that makes sense and make
Speaker 1: it easy for you to generate graphs and charts, sometimes automatically,
Speaker 1: so that you could have a visual way of presenting
Speaker 1: that information to someone, so you can make a larger impact. Uh.
Speaker 1: And this makes a lot of sense because otherwise, again,
Speaker 1: what you have to do is you have to do
Speaker 1: searches into all of these individual documents, pull the relevant information,
Speaker 1: put it into a spreadsheet, manipulate it to the way
Speaker 1: that you need it, and then create whatever graphics that
Speaker 1: you want from that. This does it all automatically. However,
Speaker 1: the first thing you have to do is you have
Speaker 1: to tag all that information. If you're building out from scratch,
Speaker 1: it may not be a big deal. You know, you
Speaker 1: just it's just part of the building process. You know,
Speaker 1: you create information and you tag it, and you create
Speaker 1: a way of tagging it that's consistent across the company.
Speaker 1: Because here's another thing. The way Chris thinks and the
Speaker 1: way I think are not perfectly aligned, and so sometimes
Speaker 1: he might tag a certain kind of information one way
Speaker 1: and I might tag it another way, and that might
Speaker 1: be problematic depending upon how the computer handles tags and classifications.
Speaker 1: So that if I were to do a query and
Speaker 1: Chris were to do a query, and we're doing the
Speaker 1: same query, but we've both set up our systems in
Speaker 1: different ways, even though we have the same basic information
Speaker 1: in both of our systems, so we're pulling from the
Speaker 1: same pool of data. We've just built out our systems
Speaker 1: slightly differently, my query might pull up a different result
Speaker 1: than his because we use two different tagging systems. I
Speaker 1: think the Silk Project actually tries to take that into account,
Speaker 1: but even so, it's a little tricky. So anyway, that's
Speaker 1: a lot of work for a person to do, because
Speaker 1: again the computer cannot determine automatically yet what categories certain
Speaker 1: tags fall into, or certain certain terms fall into not tags.
Speaker 1: I'm sorry, So without knowing that that means someone else
Speaker 1: has to go in and tag it themselves. If your
Speaker 1: database is enormous, then that can be a really long,
Speaker 1: work intensive process. And you know, you keep in mind
Speaker 1: that most companies, most organizations are continually adding to that.
Speaker 1: That means that you are not just trying to get
Speaker 1: all the information you already have, you have to do
Speaker 1: all the incoming information as well. So it can be
Speaker 1: quite uh an extensive problem. And what you're really doing
Speaker 1: is your frontloading all that work that you would be
Speaker 1: doing if you were to do it the old fashioned way.
Speaker 1: So by frontloading it, you've made the actual handling of
Speaker 1: the data further down the line much easier. But it
Speaker 1: means that those early days are going to be kind
Speaker 1: of painful, a lot of a lot of data entry stuff. Yeah, well,
Speaker 1: I think this is it's good to talk about these things.
Speaker 1: I mean, for most of us, using the search engines
Speaker 1: as they have been has been pretty effective. But you know,
Speaker 1: I do think it is good that Google and Microsoft
Speaker 1: and and the other companies who are doing search are
Speaker 1: are not sitting around and and saying, well, you know,
Speaker 1: we're making billions of dollars as we as we are
Speaker 1: making search you know, doing this and selling advertising along
Speaker 1: our our search results. They're actually trying to find ways
Speaker 1: to make search more efficient and more relevant to those
Speaker 1: of us. So I think, um, despite you know, the
Speaker 1: shortcomings or potential shortcomings depending on on your take on
Speaker 1: the issues, UM you know, those things, I think have
Speaker 1: a very real trade off. I don't think that what
Speaker 1: you're giving up is for nothing. I think, you know,
Speaker 1: having the ability to have personalized search results can present
Speaker 1: an amazing positive experience. UM So, so please don't take
Speaker 1: my my mention of those criticisms as you know, necessarily
Speaker 1: my opinion on it, because I do have sort of
Speaker 1: mixed opinions about about these things. But I do most
Speaker 1: of all, I think I appreciate that they are trying
Speaker 1: to find ways to improve um and I think that's
Speaker 1: always a good thing, especially in technological circles, because these
Speaker 1: are our tools that can be made um you know,
Speaker 1: available to thousands and millions more people, um and can
Speaker 1: make our lives even easier tomorrow than they do today.
Speaker 1: It's just something that I think we should always take
Speaker 1: with a grain of salt on what are we giving
Speaker 1: up by accepting this? Ultimately, we might reach a point
Speaker 1: where and navigating the web is like having a personal
Speaker 1: assistant that is constantly filtering everything for you and pulling
Speaker 1: stuff for you, so that UM that you have a
Speaker 1: a really customized um experience, so that when I get
Speaker 1: on the web and when Chris gets on the web,
Speaker 1: it will be completely different, even if we're looking for
Speaker 1: the same information, because it's been tailored to our particular style.
Speaker 1: And we'll we'll probably see this rollout to not just
Speaker 1: you know, things like web searches, but into other products
Speaker 1: as well. I mean, they're already talking about making sure
Speaker 1: that this sort of sort of search information is also
Speaker 1: available mobile platforms. But then you look at things also
Speaker 1: like Google's Project Glass, the glasses. I can totally see
Speaker 1: that being part of it too, that the glasses, you know,
Speaker 1: linked back to your Google account. Uh, start to take
Speaker 1: into account how you're using the glasses, and it can
Speaker 1: help start to customize your experience based upon the way
Speaker 1: you use a device like that in the real world
Speaker 1: all the time. And if it's like, Wow, you know,
Speaker 1: he's really interested in food a lot. So if there's
Speaker 1: a term he searches for that could possibly be food,
Speaker 1: remember that because it maybe they's looking for the food
Speaker 1: and not the place name or character name or whatever.
Speaker 1: The answer is food. Food. The answer for me will
Speaker 1: almost always be food. And I think, uh, I think
Speaker 1: that pretty much wraps up our discussion. You anything else
Speaker 1: to add before I wrap it up? No, not really,
Speaker 1: all right, it was it was kind of fun getting
Speaker 1: into this because on the surface it seems like a
Speaker 1: small thing, but when you get into it, there it's
Speaker 1: it's fascinating. Layers within layers. It's like a great, big
Speaker 1: old internet onion. I was gonna say it's like an
Speaker 1: ogre also that I am going to wrap this up now.
Speaker 1: I'm gonna go and grab some shwarma, which is it's
Speaker 1: all all the rage right now thanks to uh, thanks
Speaker 1: to some some superpowered people. Um, but I was a
Speaker 1: fan of it before. Hey, you know what, I like
Speaker 1: shwarma before it was cool. I'm gonna shwarm my hipster.
Speaker 1: But yeah, I'm gonna go get some food and I'm
Speaker 1: gonna wrap this up. Guys, if you have any suggestions
Speaker 1: for future topics that we should talk about in episodes
Speaker 1: of this podcast, tech Stuff, let us know. Send us
Speaker 1: an email our addresses tech Stuff at Discovery dot com
Speaker 1: or contact us on Facebook or Twitter, or handle it.
Speaker 1: Both of those is text stuff. Hs W and Chris
Speaker 1: and I will talk to you again really soon for
Speaker 1: more on this and thousands of other topics. Is it
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