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Speaker 1: Brought to you by the reinvented two thousand twelve camera.

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Speaker 1: It's ready. Are you get in touch with technology? With

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Speaker 1: tech Stuff from how stuff works dot com. Hello there, everybody,

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Speaker 1: and welcome to tech Stuff Live from Studio fifty seven.

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Speaker 1: My name is Chris Peltim, an editor here at how

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Speaker 1: stuff works dot com, and sitting next to me as

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Speaker 1: usual smirking at my goofy intro as senior writer Jonathan Strickland. Huzzah.

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Speaker 1: So today we're going to talk about a subject that

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Speaker 1: I know a lot more about than I normally do

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Speaker 1: on these things. Well from Alpha right, which is not

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Speaker 1: a surge engine, not in the least. No, it is

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Speaker 1: a computational engine, Yes it is, indeed. So um, this

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Speaker 1: was one of those things that's interesting. You know, we

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Speaker 1: always hear about the killer uh apps, or the killer websites,

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Speaker 1: or the killer gadgets that are designed to knock off

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Speaker 1: whatever the most popular version of you know, that thing,

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Speaker 1: whatever that is. You know, when when a new one

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Speaker 1: comes out, it's the killer version. So like the HTCG

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Speaker 1: one was the iPhone killer, and then the Palm Prex

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Speaker 1: was the iPhone killer. iPhone, by the way, still doing

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Speaker 1: really well. Um, I hadn't. I hadn't heard anything about that. Right. Well,

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Speaker 1: Wolf from Alpha was supposed to be the Google Killer

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Speaker 1: according to some, just like Cool was supposed to be

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Speaker 1: the Google Killer according to something. But the thing is,

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Speaker 1: that's not what Wolf from Alpha is about. And if

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Speaker 1: you get away from that, if you can clear your

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Speaker 1: mind of the fact that this is uh you know

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Speaker 1: that you put in a query in a little box

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Speaker 1: and you hit a button. As that's the only similarity

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Speaker 1: to the search engine really, I mean, it's it's not

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Speaker 1: designed to pull up links to um two various other sites.

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Speaker 1: Well it does search, it does, but it's searching data.

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Speaker 1: It's not searching. It's not giving you links to other

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Speaker 1: websites exactly. UM. The reason I the reason I do

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Speaker 1: know more about this than normal is instead of doing

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Speaker 1: an hour or two of research for the podcast, I

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Speaker 1: actually wrote an article on that for the website. UM.

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Speaker 1: And one of the cool things about it is, other

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Speaker 1: than the you know five and thirty three articles that

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Speaker 1: I saw that had Google Killer in it somewhere, UM,

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Speaker 1: I think of it as sort of a difference between

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Speaker 1: uh Wikipedia and Britannica because the because Wikipedia allows you know,

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Speaker 1: anyone to write or edit an article. H well, you know,

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Speaker 1: in general anyway, and Britannica, at least up until recently,

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Speaker 1: was a closed system. Everything in it was prepared by

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Speaker 1: the people at Britannica exactly. So the difference here is

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Speaker 1: that while Google is allowing you to search the web

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Speaker 1: and anything and everything on the web that it can

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Speaker 1: get its little spiders on anyway, um well from Alpha,

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Speaker 1: all the information provide it on that engine is actually

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Speaker 1: provided by Wolf from Research itself. So it is being

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Speaker 1: vetted by experts who are putting it together. It's not

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Speaker 1: just not just you know, it's not just looking for

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Speaker 1: the top result and pulling the numbers from whatever that

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Speaker 1: result is and assuming that because of the top result

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Speaker 1: it has to be true. It's actually being looked at

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Speaker 1: by a human being and and uh and validated yep.

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Speaker 1: And that that's causing a lot of frustration. I wrote

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Speaker 1: a couple of blog posts about it, and I got

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Speaker 1: some comments in which people were saying, you know, I

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Speaker 1: plugged some stuff into to Wolf from Alpha and I

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Speaker 1: I'm just not getting anything useful. Well, the thing is,

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Speaker 1: if you're looking for you know, Britney Spears pictures or

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Speaker 1: the latest flight to Newark or whatever. You're not going

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Speaker 1: to find it. That's not what it's for. Kind of

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Speaker 1: why I'm not using Wolf from Alpha that much because

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Speaker 1: you know, hit me baby one more time. Please don't

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Speaker 1: tempt me. Um nice, Uh No, it's it's it's there

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Speaker 1: for it's it's more of a research site, right, Um,

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Speaker 1: you know, talking about science or mathematics at least that's

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Speaker 1: what it's being used for now. Even even music. You

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Speaker 1: can find a lot of information on music there, but

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Speaker 1: not you know, popular music. We're talking like music theory. Sure,

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Speaker 1: let's let's talk a little bit about the kind of

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Speaker 1: queries you can put into Wolf from Alpha. That's sort

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Speaker 1: of clear this up a little bit. So for example,

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Speaker 1: let's say that you are interested in, um, what was

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Speaker 1: the average temperature in a certain city in a certain year.

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Speaker 1: So you could say, all right, well back in what

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Speaker 1: was the average temperature of I don't know, San Diego, California.

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Speaker 1: That might be a piece of data that Wolf from

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Speaker 1: Alpha could actually pull back and give you. Or you

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Speaker 1: could say, give me a range of averages over a

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Speaker 1: range of years, or you could perhaps even compare two

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Speaker 1: different cities together. Um, that's the kind of data that

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Speaker 1: Wolf from Alpha might be able to pull up that's

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Speaker 1: just one tiny example. Uh. Whereas if you were to

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Speaker 1: search it for Google, you might get a whether website.

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Speaker 1: As a as a recommendation, you might get one that's

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Speaker 1: maybe the San Diego Chamber of Commerce page, maybe an

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Speaker 1: almanac page. Uh. You wouldn't necessarily get the data itself.

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Speaker 1: You would be getting links that could point you to

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Speaker 1: the data. Yeah, you go to Wolf from Alpha if

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Speaker 1: you're looking for the answer. If you were looking for

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Speaker 1: a page where you might find the answer, you would

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Speaker 1: look at a search engine like Google or Yahoo, Are Asked,

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Speaker 1: or Microsoft, Kumo or whatever it end up being called.

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Speaker 1: Since we're recording this before it actually launched, I had

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Speaker 1: to throw that joke in there anyway. UM so one

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Speaker 1: of my um one of my favorite queries to plug

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Speaker 1: in there, just because they've done a lot of playing

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Speaker 1: around with it. UM. I plugged in red Panda and

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Speaker 1: Giant Panda, and what Wolf from Alpha will return if

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Speaker 1: you pull plug that in? It actually compares, It builds

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Speaker 1: a table and shows you the differences in this easy

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Speaker 1: so you might see about how tall each of them gets,

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Speaker 1: and about how long each of them gets, and about

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Speaker 1: how heavy they are um and it actually will show

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Speaker 1: you a breakdown of their classification, which is which is

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Speaker 1: a pretty cool application. Sure, And just for fun, I

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Speaker 1: started throwing in other kinds of animals just to make

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Speaker 1: a really goofy look and graph. So I started throwing

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Speaker 1: in stuff like giant squid and you know, all sorts

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Speaker 1: of things that are way on the other side yet

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Speaker 1: exactly exactly. Um. So if you're if you're into visualization

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Speaker 1: of data, um, this was will really help you with

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Speaker 1: that because you can you can actually get a visual

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Speaker 1: picture of it that will actually put together for you

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Speaker 1: on the fly, which is very cool. Yeah, And you

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Speaker 1: can even do things like let's say you are considering

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Speaker 1: a new diet and you want to know the nutritional

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Speaker 1: information of various foods. You can even plug that stuff

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Speaker 1: into Wolf Malfa and find out more about it, right,

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Speaker 1: that's true. You can put in something like chocolate and

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Speaker 1: it will instantly bring back some information that. The funny

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Speaker 1: thing is it's going to make an assumption if you

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Speaker 1: say chocolate and plug it in there, and it's going

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Speaker 1: to say, we're assuming you mean milk chocolate. I'm guessing

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Speaker 1: I haven't actually tried that one, but it will bring

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Speaker 1: you back the average fat um, the calories, and all

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Speaker 1: the other nutritional information about that. Now you say, oh,

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Speaker 1: well I meant actually dark chocolate, you can go in

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Speaker 1: and clarify that search to bring you back more accurate information.

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Speaker 1: So it actually does. It makes an assumption based on

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Speaker 1: how popular you know it thinks it's going to be,

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Speaker 1: and if it's if you're looking for something more specific,

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Speaker 1: you can make adjustments. Right. And there are some queries

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Speaker 1: that may come back with essentially an error saying that

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Speaker 1: there's no information on that. And in some cases it's

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Speaker 1: really just that the search the search algorithm doesn't recognize

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Speaker 1: the way you've worded your query, and if you reword it,

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Speaker 1: you might be able to get that information. But in

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Speaker 1: other cases it's just simply a fact that they haven't

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Speaker 1: managed to get a round to entering that data in yet.

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Speaker 1: So um, you know, you can try a few different

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Speaker 1: ways and if nothing's working, that might be a sign

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Speaker 1: that they just that the database doesn't have that info

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Speaker 1: at the moment. Yeah, that's um, that's true. It does

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Speaker 1: require a little bit of uh, I would say practice.

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Speaker 1: You might need to to get used to the syntax.

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Speaker 1: Um they've actually built in some natural language processing based

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Speaker 1: on what they think you're going to ask it. For example,

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Speaker 1: you know it has a lot in there about mathematics.

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Speaker 1: If you you can actually use some mathematical shorthand to

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Speaker 1: generate a query and will from alpha and it will

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Speaker 1: bring back some results. Now, um, you know, if you said,

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Speaker 1: say a thousand pound feet to newton meters, it may

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Speaker 1: not get that. You might need to say, convert a

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Speaker 1: thousand pound feet to newt meters and it will go essentially. Oh,

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Speaker 1: now I see what you mean, kay, I can I

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Speaker 1: can convert that for you. Here's what it is one about.

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Speaker 1: If um, let's say that you gave it the dimensions

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Speaker 1: of a building, would it be able to give you

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Speaker 1: the volume? Um? You know what, I'm betting that it

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Speaker 1: probably could have. It's got quite a few algorithms in it.

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Speaker 1: But that's sort of the idea is that eventually you

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Speaker 1: would get to the point where if you had just

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Speaker 1: an interesting question, thinking like well, this building is so

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Speaker 1: tall and it's so wide and and so deep, I

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Speaker 1: guess you could say how how how much volume is that?

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Speaker 1: And it would be able to calculate it. Uh. I

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Speaker 1: can see this being a big help to people when

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Speaker 1: they're doing their math homework. Yeah. Actually, uh, it might

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Speaker 1: be a little too much in some regards, UM, because

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Speaker 1: you can plug in, um, say a calculus problem into

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Speaker 1: Wolf from Alpha, and it will give you a result.

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Speaker 1: And it also has a little link off to the

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Speaker 1: side that says show steps, and it will break it

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Speaker 1: down into each individual step that you need to go

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Speaker 1: through in the process of this complicated problem, and you

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Speaker 1: know how you get the result. Now, theoretically you could

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Speaker 1: go in and copy this all down, UM, but if

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Speaker 1: you do that and cheat on your homework, you're not

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Speaker 1: actually learning anything and you're going to fail a testing class.

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Speaker 1: Although I think that would have been very handy back

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Speaker 1: when I took Calculus, because um, I had I had

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Speaker 1: difficulties grasping certain concepts, and if I had had this

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Speaker 1: to show me each step, perhaps I would have been

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Speaker 1: able to follow the logic of each step a little better. UM.

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Speaker 1: It felt to me back when I was taking Calculus,

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Speaker 1: and granted this was as many moons ago, but when

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Speaker 1: I when I was taking it, it just felt like

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Speaker 1: there was no rhyme or reason to the rules that

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Speaker 1: I was applying, and so perhaps using something like this

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Speaker 1: would have kind of made me understand and therefore do

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Speaker 1: better on a test than I would have before. Now, granted,

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Speaker 1: that's only if I were actually using it to see

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Speaker 1: how how it got from one step to the next,

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Speaker 1: as opposed to just copying it down so that I

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Speaker 1: could do my homework and record time and I can

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Speaker 1: go play Halo for another hour or so. Well, you know,

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Speaker 1: if you actually learned, earned what you were supposed to

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Speaker 1: be learning, and could actually go ahead and play Halo,

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Speaker 1: then you'd be killing two birds with ones. Yes, yes, yes, yes. Um.

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Speaker 1: I don't wish to cast expersions upon my calculus teacher either,

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Speaker 1: but I don't think she listens, so it's all right.

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Speaker 1: I had similar problems grasping calculus, and um I probably

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Speaker 1: also would have welcomed the additional you know, taking you

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Speaker 1: step by step through it, because she really lost patience

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Speaker 1: with me. Now, the cool thing about Wolf from Alpha

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Speaker 1: is that's really going to make research a lot easier

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Speaker 1: for for certain, um certain applications, and I'm looking forward

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Speaker 1: to using it more in the future when the database

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Speaker 1: is a bit more robust, because there are certain things

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Speaker 1: that you know, facts and figures that I would love

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Speaker 1: to have access to and I can't always be certain

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Speaker 1: that I have the latest information. Sometimes I find information

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Speaker 1: that's a year or two old, and I don't really

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Speaker 1: want to depend on that because it could have changed dramatically.

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Speaker 1: For example, just throwing a random thing out there, and

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Speaker 1: I'm not saying that Wolf from alf actually has this.

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Speaker 1: In fact, I know for I know that my query

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Speaker 1: didn't work most recently. But let's say I wanted to

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Speaker 1: find out how many users MySpace has. UM. That would

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Speaker 1: be a useful thing, you know, just or being able

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Speaker 1: to compare various social networking sites across uh an array

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Speaker 1: of different criteria. It would be great to be able

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Speaker 1: to do that and just take a quick look and say, oh,

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Speaker 1: look at this. It's kind of surprising that such and

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Speaker 1: such social networking site has gained a lot of ground

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Speaker 1: over the big names over the last twelve months. UM.

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Speaker 1: So I can see this being a very useful tool

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Speaker 1: for me in the future and for lots of other

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Speaker 1: people as well. Students, scholars, you know, more, other journalists,

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Speaker 1: and just if you just have one of those things

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Speaker 1: where you know, you just need to know the answer

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Speaker 1: to something like what's the population of Detroit compared to

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Speaker 1: the way it was ten years ago. I mean, that's

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Speaker 1: kind of it's kind of cool to be able to

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Speaker 1: get all that at your fingertips instantly. Yep. And it

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Speaker 1: it does calculate a lot of that stuff UM on

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Speaker 1: the fly, things like UM currency conversions. You can find

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Speaker 1: out what temperature it is at any place in the world.

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Speaker 1: You know. It actually gets information from local weather stations. UM.

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Speaker 1: So you can look up, say Detroit, and find out

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Speaker 1: what temperature it is. You can find out when the

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Speaker 1: sun's gonna set. UH. You'll even you can even find

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Speaker 1: out when the next lunar eclipse is going to be UM.

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Speaker 1: And it tells you that in real time. You can

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Speaker 1: get stock quotes just by by putting that in. UH.

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Speaker 1: If you know stock symbol or a company name, it

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Speaker 1: will give you company information. If you put any date in,

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Speaker 1: it will give you anything significant about that date, including

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Speaker 1: any holidays that follow on that date, uh, any historical

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Speaker 1: events that happened on that date. UM. So you can

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Speaker 1: put your birth date in and it will tell you

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Speaker 1: what day of the week it was. I'll tell you

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Speaker 1: what the what what, what phase the moon was in

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Speaker 1: on that day. UM. It's kind of an interesting thing

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Speaker 1: that you can and you can even see like how

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Speaker 1: how far into the year that date was, how far

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Speaker 1: into the month that date was, in case you weren't

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Speaker 1: able to work it out yourself. But uh, it's you know, right,

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Speaker 1: I guess like, oh gosh, you're right June twenty six,

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Speaker 1: that is twenty six days into June. Um, no, no,

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Speaker 1: it's but it's really a neat, neat tool that you

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Speaker 1: can you know, it's one of those things where you

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Speaker 1: plug any date and so of course everyone just immediately

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Speaker 1: because puts their birthday in to see what happened. By

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Speaker 1: the way, nothing significant other than my birth happened on

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Speaker 1: my birthday, according to Wolf from Alpha. Well you know

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Speaker 1: we had to reserve it, right, just thank you. I

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Speaker 1: would hate to be overshadowed by something so um. Yeah,

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Speaker 1: actually this uh, this was all sort of sprung on us,

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Speaker 1: which is very very surprising considering all the hype for

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Speaker 1: all the other tech things that go on, you know,

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Speaker 1: the lot of stuff we talk about too. Um but

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Speaker 1: it actually sort of goes back to a March fifth,

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Speaker 1: nine posts by Stephen Wolfram in the Wolf from blog.

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Speaker 1: And uh, you know he said Wolf from Alpha is

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Speaker 1: cuming and narrowly, you know, we might not be so interested.

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Speaker 1: But you know, Stephen Wolfram is a former child prodigy.

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Speaker 1: He actually got his doctorate and when he was twenty

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Speaker 1: years old from cal Tech, which means no one from M. I. T.

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Speaker 1: Will be using Wolf from Alpha um it now. He

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Speaker 1: he wrote his first scientific paper at fifteen and got

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Speaker 1: his PhD in theoretical physics when he was twenty UM

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Speaker 1: and then Wolf from Research, which is the company that

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Speaker 1: actually you know, he started and produces. Wolf from Alpha

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Speaker 1: created a piece of software that I'm sure many of

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Speaker 1: you have heard of, but like me, sort of only

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Speaker 1: vaguely are aware of, called Mathematica UM, which basically crunches

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Speaker 1: numbers in serious, serious ways. It's not the kind of

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Speaker 1: thing that you would buy for fun. It's, you know,

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Speaker 1: three piece of software for the home version. I can't

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Speaker 1: even imagine what the business version costs UM. But that's

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Speaker 1: actually what Wolf from Alpha is based on. It uses

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Speaker 1: the computational algorithms and mathematics to bring down, you know,

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Speaker 1: the data that you're looking for and crunch it together

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Speaker 1: to make results page. It has a lot to do

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Speaker 1: with the the way it looks too, because um um

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Speaker 1: mathematic it helps you visualize mathematical information. So, UM, you

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Speaker 1: know this, this works a lot different from the Google

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Speaker 1: algorithms that Google uses to search the web. Right. Google

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Speaker 1: algorithms are more about finding the links that most people

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Speaker 1: are linking to, really and judging that as a a

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Speaker 1: symbol of quality. So if a lot of people are

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Speaker 1: linking to a particular page, that page and Google's eyes,

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Speaker 1: is more important than other pages that contain the same

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Speaker 1: keywords and therefore goes higher up on the list. Um.

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Speaker 1: That's a very that's a huge oversimplification of the Google

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Speaker 1: algorithm because there's a lot more to it than that.

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Speaker 1: But that's the basic idea. Uh. But yeah, like we said,

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Speaker 1: since the Wolf from Alpha is not a search engine,

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Speaker 1: it doesn't really apply and it's not the kind of

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Speaker 1: place that you will find advertising yet. Um, Google has

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Speaker 1: um pulled down one billion dollars in revenue, that is

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Speaker 1: advertising revenue. UM. As of right now, Wolf from Alpha

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Speaker 1: does not have any advertising on it, although there is

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Speaker 1: some code UM that's commented out apparently that has room

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Speaker 1: for possible advertising. UM. So there that may be a

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Speaker 1: clue as to what they're going to do with it

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Speaker 1: eventually once it gets up and running and more robust. UM.

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Speaker 1: You want to have some technical stuff about Wolf hit

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Speaker 1: me because it's Wow, you're just so generous with the

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Speaker 1: offers to hit you today. Yeah, well, I'm I'm feeling

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Speaker 1: like a target anyway, so just go for it. UM.

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Speaker 1: Wolf from Alpha runs on a system uses the world

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Speaker 1: sixty six fastest supercomputer, a system of custom Dell hardware

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Speaker 1: UM built by our systems UM. It's called are Small

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Speaker 1: UM and it it can do thirty nine point six

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Speaker 1: trillion mathematical operations per second. It's got a four thousand,

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Speaker 1: six hundred eight processor corps UM with five seventy six

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Speaker 1: squad core harpertown Zon machines. It's got sixty five thousand,

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Speaker 1: five thirty six gigabytes of memory, which is just slightly

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Speaker 1: faster than my laptop computer at my desk here how

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Speaker 1: stuff works dot com they actually have five co location facilities,

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Speaker 1: so it's technically two supercomputers. And uh, you know that's

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Speaker 1: that's a whole lot of processing power. But they're probably

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Speaker 1: gonna need it if they're going to keep adding information

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Speaker 1: to this uh to this computational um. Yes, computational engines.

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Speaker 1: I mean, uh, well, and and before we before we

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Speaker 1: go too far, I also wanted to mention that, um,

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Speaker 1: the builders, the developers of Wolf from Alpha have a

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Speaker 1: sense of humor. Ah, yes, so you mean not. You

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Speaker 1: can't just plug in a mathematical con version or you know,

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Speaker 1: some kind of um musical note series and find out

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Speaker 1: the distances between the notes. Can do that, but you

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Speaker 1: can do more than that. There are there are certain

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Speaker 1: easter eggs that are in Wolf from Alpha, and some

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Speaker 1: of them have been publicized, and I'm sure there are

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Speaker 1: many others that we haven't even thought of yet that

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Speaker 1: someone's eventually gonna uncover When they just say, I'm going

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Speaker 1: to be a smart ale, I can put this in

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Speaker 1: and then they're cann't find out there's actually an answer.

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Speaker 1: For example, you can open up Wolf from the Alpha

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Speaker 1: and type in why did the chicken cross the road?

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Speaker 1: The answer you will get is to get to the

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Speaker 1: other side. So it's not that it doesn't tell you

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Speaker 1: that there's no answer. It gives you the answer to

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Speaker 1: the joke. And it's of course, just like everything else

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Speaker 1: in Wolf from Alpha. It's just presented there in black

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Speaker 1: and white, you know, matter of fact. So that kind

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Speaker 1: of makes it even more funny to me, because there's

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Speaker 1: no indication that this is a joke, but there are

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Speaker 1: a lot of others that you can put in as well. Yep,

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Speaker 1: I know that in one preview blog I read when

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Speaker 1: I was trying to collect information from my article. Um,

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Speaker 1: you know, they're all kinds of well this is really

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Speaker 1: cool or I don't know if it'll be able to

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Speaker 1: knock off Google random comments like that. But somebody said,

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Speaker 1: I want I want to know what the air speed

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Speaker 1: is of an unladen swallow, and if it can tell

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Speaker 1: me that, I'm quitting Google forever and paraphrasing this. As

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Speaker 1: it turns out, you can plug that question and get

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Speaker 1: an answer. Yes, the answer. The answer is not do

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Speaker 1: you mean an African or a European swallow? It's more

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Speaker 1: complicated than that, but it is in fact a reference

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Speaker 1: to the fantastic film Money, Python and the Holy Grail.

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Speaker 1: And it does make an assumption. It says, assuming you

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Speaker 1: mean right, exactly which is right? And then there's um,

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Speaker 1: there are other ones as well. I'm sure. Well, like

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Speaker 1: I said, we'll find more. Um, we'll see what exactly

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Speaker 1: what is what is the answer to life, the universe

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Speaker 1: and everything? Forty two? So they obviously some Douglas Adams

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Speaker 1: fans over in the Wolf from ALFA team as well.

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Speaker 1: So yeah, I mean it's it's great to know that

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Speaker 1: not only are are there these dedicated people trying to

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Speaker 1: put in this this very important information that will soon

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Speaker 1: be at our disposal at a moment's notice, they are

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Speaker 1: also putting in goofy answers for those of us who

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Speaker 1: are complete and utter dorks. Well, there are a lot

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Speaker 1: of people who, uh, who are taking shots at at

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Speaker 1: Wolf from Alpha because it isn't Google and also because

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Speaker 1: it doesn't have every answer to every problem ever invented. Um,

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Speaker 1: it's gonna take them a while. They're about two fifty

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Speaker 1: people working on the project right now, and you know,

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Speaker 1: there's a lot of info out there. They have to

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Speaker 1: break things down into little snippets. Um. Apparently there are

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Speaker 1: more than ten trillion pieces of tagged information in the

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Speaker 1: system already. Yeah, and when you're actually going in and

402
00:21:48,280 --> 00:21:50,280
Speaker 1: making sure that the answers are correct before you put

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Speaker 1: them in, that that's sort of time and labor intensive.

404
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Speaker 1: So um, you know, I think we're gonna need to

405
00:21:55,600 --> 00:21:58,040
Speaker 1: be somewhat patient. But it's I think it's a really cool,

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Speaker 1: uh really cool and very useful tool. And you're not

407
00:22:00,600 --> 00:22:03,560
Speaker 1: the only one. There are companies like Google that also

408
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Speaker 1: think it's a very cool and very useful tool and

409
00:22:05,480 --> 00:22:09,080
Speaker 1: are incorporating bits of the functionality of Wolf from Alpha

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00:22:09,200 --> 00:22:12,320
Speaker 1: into their own coding. Yeah. I actually, uh, I had

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Speaker 1: heard that Stephen Wolf froman Seragey Brin actually talked before

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Speaker 1: the release, and you know, he seemed very impressed. Mr

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Speaker 1: Brenn seemed very impressed with what And for a while

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Speaker 1: Google has been doing some similar things to this. For instance,

415
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Speaker 1: if you put in a city name, it might actually,

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Speaker 1: you know, at the top tell you what the temperature

417
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Speaker 1: and other little bits of data are about that city

418
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Speaker 1: before you go and click on a link, but not

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Speaker 1: to the extent that Wolf from Alpha does. But it

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Speaker 1: was clear that Google was already interested in this kind

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Speaker 1: of thing from the get go, and you could also

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Speaker 1: do things like you know, conversions and stuff um through

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Speaker 1: Google as well, UM to some extent. And I think

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Speaker 1: that we're going to see a lot more search engines

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Speaker 1: try and incorporate this sort of approach and presented in

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Speaker 1: such a way where it doesn't dwarf out the search results,

427
00:23:06,480 --> 00:23:11,840
Speaker 1: but it's sort of in addition to them, so sometimes

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Speaker 1: you just need the answer. Yeah, and uh, yeah, sometimes

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Speaker 1: you don't want to click on three or four links

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00:23:16,920 --> 00:23:19,159
Speaker 1: trying to get to that one piece of information that

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00:23:19,240 --> 00:23:21,679
Speaker 1: you want. That's true, That's true. That's the way I

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00:23:21,680 --> 00:23:24,240
Speaker 1: feel about it. Anywhere pretty much everybody, I think, I mean,

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00:23:24,240 --> 00:23:25,639
Speaker 1: there are may be a few people who kind of

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00:23:25,720 --> 00:23:28,720
Speaker 1: enjoy the whole discovery route where you find something that

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00:23:28,720 --> 00:23:31,760
Speaker 1: you weren't expecting, But if you really need that one

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00:23:31,760 --> 00:23:34,439
Speaker 1: piece of information, you pretty much put blinders up and

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00:23:34,440 --> 00:23:37,320
Speaker 1: you just scan for that info anyway. So yeah, I

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00:23:37,359 --> 00:23:40,359
Speaker 1: think there's room in uh, in the average person's toolbox

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00:23:40,480 --> 00:23:43,760
Speaker 1: or something like that. So, um, you know, good luck

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00:23:43,800 --> 00:23:46,800
Speaker 1: to the Wolf from Gang and hopefully they'll they'll find

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00:23:46,800 --> 00:23:49,680
Speaker 1: a place in everyone's heart, certainly hope. So I mean

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00:23:49,800 --> 00:23:53,199
Speaker 1: it definitely helps uh everyone, really, I mean, it just

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00:23:53,440 --> 00:23:57,520
Speaker 1: it just adds more options whenever you have to get

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00:23:57,560 --> 00:24:01,040
Speaker 1: that a So good luck guys. Now, did you have

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00:24:01,040 --> 00:24:03,280
Speaker 1: anything else to add for Wolf from Alpha? No? No,

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00:24:03,440 --> 00:24:05,719
Speaker 1: that's that's it for right now. I guess that just

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00:24:06,000 --> 00:24:16,680
Speaker 1: leaves listener made all right, then, all right here we go.

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00:24:17,680 --> 00:24:20,440
Speaker 1: My name's Josh, and I was listening to your podcast

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00:24:20,480 --> 00:24:23,960
Speaker 1: and found these three mistakes that on their video game

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00:24:24,119 --> 00:24:29,119
Speaker 1: related Number one in your Emulation or Virtual Machine podcast,

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00:24:29,400 --> 00:24:32,000
Speaker 1: you referred to the old games on the we shop

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00:24:32,080 --> 00:24:36,120
Speaker 1: channel as we wear we wear is games. I guess

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00:24:36,200 --> 00:24:39,880
Speaker 1: we were our games made especially for the wee example

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00:24:39,960 --> 00:24:42,879
Speaker 1: World of Goo or Tetris Party. I believe that you

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00:24:42,960 --> 00:24:47,520
Speaker 1: meant virtual console titles, which are games for older systems,

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00:24:47,560 --> 00:24:49,480
Speaker 1: and yeah, that's kind of what we were talking about.

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00:24:49,480 --> 00:24:52,280
Speaker 1: Were the old games on the Nintendo. But number two

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00:24:52,960 --> 00:24:56,320
Speaker 1: in the same podcast, you said that those games were emulated,

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00:24:56,320 --> 00:24:59,480
Speaker 1: which is not true either. The games have been reformatted

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00:24:59,480 --> 00:25:02,120
Speaker 1: in a way that the we can read them, sometimes

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00:25:02,200 --> 00:25:05,479
Speaker 1: with added features such as Pokemon Snap, which lets you

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00:25:05,520 --> 00:25:08,439
Speaker 1: save pictures to the Wei message board. I heard the

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00:25:08,520 --> 00:25:12,680
Speaker 1: Nintendo wouldn't let developers just emulate the games. Also true,

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00:25:12,760 --> 00:25:16,560
Speaker 1: it's reformatted as opposed to emulate it. If you're emulating

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00:25:16,560 --> 00:25:20,760
Speaker 1: a game, then you're trying to recreate the uh the

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00:25:20,800 --> 00:25:23,639
Speaker 1: way that the original hardware, right, the environment that the

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00:25:23,640 --> 00:25:26,840
Speaker 1: original hardware and software had in order to play the game,

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00:25:27,320 --> 00:25:30,520
Speaker 1: whereas this was reformatting so they would work with different hardware.

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Speaker 1: Number three. In another podcast one after the April Fools podcast,

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00:25:35,880 --> 00:25:38,280
Speaker 1: someone told you that you forgot the legend of Zelda

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00:25:38,320 --> 00:25:41,399
Speaker 1: movie trailer joke by YouTube. YouTube did not in fact

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00:25:41,440 --> 00:25:44,120
Speaker 1: make that joke. I g n S video team made

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00:25:44,160 --> 00:25:46,719
Speaker 1: the video, and that's why it was so believable. Someone

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00:25:46,800 --> 00:25:50,199
Speaker 1: on YouTube copied the video, so that was an our mistake.

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00:25:50,320 --> 00:25:53,520
Speaker 1: That was a listener mistake. That makes me feel better. Yeah,

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00:25:53,760 --> 00:25:55,560
Speaker 1: just so two out of three were ours and the

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00:25:55,600 --> 00:25:58,080
Speaker 1: other one with someone else's. Um. Actually, I knew that

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00:25:58,119 --> 00:26:00,320
Speaker 1: YouTube hattent made that video as well. I knew that

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00:26:00,359 --> 00:26:04,399
Speaker 1: it had been posted to YouTube, but yeah, because that

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00:26:04,440 --> 00:26:06,320
Speaker 1: was a listener mail, I had not actually looked up

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00:26:06,359 --> 00:26:12,359
Speaker 1: the original creators, though I had seen it the video

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00:26:12,359 --> 00:26:15,560
Speaker 1: in question at at some point. So thank you very much,

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00:26:15,640 --> 00:26:18,000
Speaker 1: Josh greatly appreciate it. If any of you want to

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00:26:18,000 --> 00:26:21,440
Speaker 1: write to us, you can text stuff at how stuff

485
00:26:21,480 --> 00:26:24,800
Speaker 1: works dot com and we will talk to you again

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00:26:25,680 --> 00:26:31,080
Speaker 1: really soon. For more on this and thousands of other topics.

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00:26:31,320 --> 00:26:33,600
Speaker 1: Is it how stuff works dot com and be sure

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00:26:33,600 --> 00:26:35,720
Speaker 1: to check out the new tech stuff blog now on

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00:26:35,760 --> 00:26:42,639
Speaker 1: the house stuff Works homepage. Brought to you by the

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00:26:42,640 --> 00:26:46,040
Speaker 1: reinvented two thousand twelve camera. It's ready, are you