This is dedicated to all the hackers and the crackers, to the hackers and the crackers.
I see in binary, I speak source code, step on my toes, I'll post a million jpegs with you in a fag pose.
In your digital stance, sitting firewalls hoes, cause I'm riding the net in my six foes.
I was a dick with my 56, now with my cable I'm able to get that stable.
On the out, my name is Ace and I'm a Leo. On the digital highway, my name is Neo and I'm a Nero.
In a flash, I'll screw you on burning dreamtacks. You need some ISO? Let me do my hard drive rifle.
Our exchange, you could never stifle. In a digital hood, you just caught the love bug.
I bootlegged your CD, I caused the fight between Un and Jay-Z.
Your CG, it's all gonna be free, whether we take it by force or we take it night free.
You feel that rattle in your bones? When I tell you we just hacked out Jones.
And NASDAQ leaves you flat on your back, cause I am he who loves to hack and crack.
Cause I am he who loves to hack and crack.
This is dedicated to the hackers and the crew.
Serial pose, swords, toes, ISO, rad, fifth, fifth.
This is dedicated to the hackers and the crew.
Welcome to Binary Revolution Radio. This is episode 161.
And we're gonna rename the show to Ben Verbradio, I think.
Because Mr. Verbal, again, has come through and is the man about the show.
And I'm Droops and Verbal's over somewhere.
Hello, everyone.
And we're recording on Asterisk.
So, maybe the sound will be a little bit better than the last time Verbal was on.
Um, admittedly, I have not listened to episode 159 yet.
It's really hard to hear.
I hear the sound quality is lacking.
But that's okay.
The content is there.
And that's all that matters.
Exactly.
Well, I don't have any viewer mail other than Mr. Vaughn wrote in and said that Verbal had a sexy voice.
Mr. Vaughn, give me a call.
We're stepping out.
I think he's over from the West Coast also.
You might need to hook up.
Oh, sweet.
That's what I'm talking about.
That's why I'm doing this in actuality.
If you meet other men.
Yes.
All right.
Actually, viewer mail.
I did not get any viewer mail.
I am pissed off.
If you remember, in episode 159, I had talked about that mentoring program that I started.
I mean, or I want to start.
And I said, if you want to be mentored, or if you want to donate your time as a mentor,
please email me at verbal at gmail dot com.
And no one has effing emailed me.
And, which is fine, but I see people asking questions on IRC, and people are still posting
in the forums.
So I know some of you out there want help, and you're not emailing me, emailing me.
So after the show, you better fucking get on your computer and email me.
Or email me right now, okay?
You should be in front of your computer anyway.
Anyway, so if in the next show I do, I get no more email, I'm going to come to your house
and beat you.
That's right.
Even though he won't know who you are, because you didn't email him.
Details.
I will work that out.
Well, you had quite a few topics for us to go through on this show.
Do you want to do the personal hacking, or do you not like that section?
What is the personal hacking?
When you tell what you've been up to lately.
No, I hate that.
Awesome.
I hate that segment.
If there's something worth talking about, I will talk about it.
Good deal, because I'm going to talk about what I've been working on.
Yeah, no, go for it.
Go for it.
No, no, I'm going to include it into the show.
Okay.
You won't even know.
Oh, I see how it is.
You're going to whip it in there.
Okay.
I'm just going to whip it out, and you're not even going to notice.
All right.
Shut me up for the first segment, then.
Well, why don't you start us off there, buddy?
Yes.
Oh, you want me to start?
Oh, yeah.
Oh, okay.
I'm sorry.
Okay, today we have actually two main topics, but I'm going to split them up.
So, first we're going to talk about the AOL data that was released.
There was a giant thread about it on the forums.
And then we're going to have something a little different.
We're going to have an actual live female call, and she's going to talk to us a little bit about kind of women in the hacking scene and all that stuff.
So, more on that a little bit later.
And then, finally, I'm going to go over something that some of you may not have heard about, but it is becoming a very important part of your hacking toolkit.
And it's an area of computer science called machine learning and data mining.
So, that should be fun.
That will be the technical part.
Excellent.
I thought you said you were only talking about two things.
No, but it's split up.
Ah.
So, I'm going to do the AOL data thing and do the non-technical part first.
And then I'm going to split it up and do the technical stuff at the end.
Good deal, dude.
Do you even know what the show is about?
I'm not even here.
This is actually just the machine talking about me.
Get out of here.
Okay.
It's an AGI script.
I have an asterisk.
I'm just wearing the numbers.
Thank you.
And come again.
Alrighty.
Ow.
I just watched that family guy last night when Brian goes back to college.
I love that one.
There's that Stephen Hawking guy.
And the sex scene is hilarious.
The ow, ow, ow.
Not so hard.
You are hurting me.
All right.
Anyway, let's start.
All right.
We're going to have fucking.
Okay.
Woo.
So, I don't really post on the phone that much.
Or do I read it for whatever reason.
But lately, I just had to go on because Stephen's giving me a hard time about it.
So, I look and there's a thread that says that AOL has released research.
I mean, has released search data that they want people to use for research.
Troops, have you seen that thread?
I have seen that thread.
Okay.
So, I don't know how much you went into it.
But basically, they released about two gigs of data.
And it is web queries from AOL users.
And it doesn't give anyone's name.
But each user is assigned a unique ID.
So, you know, like who, or not who, but, you know, you know that this person executed all these queries.
So, they're grouped per person.
And now, you get to go and look through them and do whatever you want.
And people have been having a field day with this because of all the weird, random shit that people are actually looking for.
And, you know, there are a lot of examples of that on the forum.
But the thing I want to talk about is the privacy issues surrounding this, right?
Because, you know, this has really been, like, a hot topic lately, especially with Google and how, you know, they have, like, all of your data, right?
So, it's like they know what you're searching for.
You know, you do your email to them.
And now there's Google Calendar.
And, like, if you use Google Desktop, then you can, like, somehow speed up indexing or actually put your hard drive index onto their server so it's integrated when you search on the web and all this stuff.
So, this privacy issue is kind of, you know, really important right now.
And there has been a debate, actually, about this because people – so, AOL, obviously, is of the position that they are not violating any privacy.
Laws or, you know, or whatever that they promise their users because, you know, they're not saying, okay, this guy executed these queries.
But they have a unique ID.
And, you know, if you're smart enough and clever enough and you go into the data set and you look at it, you know, you could figure out what some of these people – who some of these people are, at least, you know, where they are or something about them.
You know, like someone from the forums that old people really love family genealogy.
So, you know, if you see, like, one person who's, like, constantly searching for some last name and, like, variations of the first name with that last name, then, you know, chances are pretty good that, you know, that's their last name.
And you're dealing with an old person.
Right.
Yeah, exactly.
So, Troops, what do you think are the privacy implications of this?
Like, is it wrong of AOL to do this and if they're violating any agreements?
Well, I don't know.
It's kind of a toss-up.
I don't know the AOL agreements they have with their customers.
I haven't been an AOL customer for years and years and years back when that was my only option.
But, like, if I go to, like, Yahoo.com, I'm not paying them any money and I use their service.
I would expect them to retain that data of me doing my searches and try to make their money off of it.
Like, if I call a 1-800 number, I expect them to keep my phone number, you know, my caller ID, and use it for whatever purposes they want because they're paying for it.
You know, I'm getting a free service off of it and nothing's free.
But with AOL, now, this is their actual users.
Like, you have to have the AOL application, I believe, whatever it's called.
Yeah, yeah, yeah.
And do those searches.
It's a gateway to the Internet.
Yes.
It's not like people that are just going to AOL.com and using that for search, right?
Right.
Okay.
Just making sure.
I wasn't 100% on that.
I wasn't any percent on that.
I was just making it up.
But, like, with AOL, you're paying them money for a service.
And you would kind of expect, even though you're not paying Yahoo or not paying Google when you are paying AOL, that if they're going to try to make their money off of that, they're not going to release pertinent information about you.
You know, like...
Well, hold on.
Let me hear what I'm saying.
I don't know.
Well, I mean, the thing is, it really depends on the user agreement.
Like, even if you're paying for it, if the user agreement says, you know, all the data we collect are ours and we can sell it, then they can do that, even if you're paying for it.
But, I think, in most cases, I mean, like, I'm no lawyer and I don't read the U.S.
Well, you, of course, expect to have that privacy.
Right.
Right, right, right.
It's a common person.
Right, right.
So, I think what happens is they say, okay, you're paying for this, you know, we retain the rights to, like, so-and-so, but we're not going to, you know, invade your privacy.
We're not going to sell your, you know, fucking credit cards to the bank.
I mean, that's the bank.
Yeah.
Yeah, we're not going to sell your credit cards to, like, Russian mobs and we're not going to do this and we're not going to do that and you're going to be safe.
Now, I think the issue is that they're not technically breaking any laws by releasing this data, but because, you know, of the way the searches are and what people do, you know, you can figure it out.
And it's kind of, you know, it's basically a PR nightmare.
Yeah.
So, you know, it's like, should companies do that even if they're not breaking any rules?
Well, I personally feel it's a corporation or a business's response.
Like, they're responsible to be ethical.
You know, like, they shouldn't just be going around shitting on people, even though maybe they're allowed to.
Right.
And in this modern age, of course, you know, that idea is out the window.
Right.
Do whatever the fuck you want.
And, I don't know.
I'm kind of at a toss-up with just because AOL, I haven't seen their agreement.
Right, right, right.
But you aren't paying them for a service.
You wouldn't expect them to do that.
Now, we built, like, a search engine little part of the application we're building for work.
And we keep all those searches, obviously, in the database so that I can go in there later and look and see what screw it up or who's mistyping what.
But, you know, like, if they keep mistyping my last name, searching for me, I can, you know, point it to me a lot easier and see how that is.
Right, but, I mean, that's, right, but that's, right.
But I'm not exactly calling you.
Right, right.
I mean, right, right, right.
Because, I mean, obviously, everyone at Google knows what I'm searching for.
But what I care about is they don't tell, like, the rest of the world.
Yeah.
I'm not publishing that data.
Right, right.
So, I mean, like, you know, like, Google tries a lot.
Like, I have a ton of friends who work at Google, and they tell me that they try very, very hard to not be evil and to not be creepy.
I mean, sometimes it's very hard.
Like, for example, if you work there, then, like, they do so much more stuff for you because you work for them.
So, they will do all the creepy stuff and the weird stuff that they won't do for public users.
Like, they'll track exactly where you are and, like, where you go if you use any of their services that do that, you know, that gather that information, and they'll track what you're searching for.
You know, they'll track your email and, like, because everyone at Google uses Gmail.
So, they'll track all of that, and they'll, like, actually look at it and, like, try to find out stuff to improve on, you know, their current products.
But they won't do that for public people.
Yeah.
But, you know, for AOL to release this, I mean, personally, I don't know what the motivation is because it's like, okay, we're releasing this because we're good people
and we want to, like, you know, give a data set to whoever, you know, researching these kind of things and, like, to help further academic studies or whatever.
That's fine.
And, see, that's definitely a good thing about all this is that now we do have a data set that we can go into, you know, and do data mining on it or whatever we need.
Right.
But then you get, like, these people who, like, figure out, you know, who is user, like, you know, FUBAR, and they call them and, like, go to the house and say stuff like, oh, hey, what's up?
I found all your info and what you're searching for from the data set that AOL released.
And the AOL user's like, what?
Huh?
Yeah, you know, like, wait, like, so on the forums, like, some people have done this, like, Rikos does.
And, you know, he's not, like, I know him, and he's a great guy.
So I know he's not going to, like, stalk someone or, like, you know, find people who are into, like, pyro necrobeciality or something, you know,
and then, like, you know, find where they live and, like, have flaming cat sex with them, flaming dead cat sex with them.
But, like, other people could do this.
And, you know, AOL has to be aware of that because, you know, it's like they don't just randomly pick two gigs of data and release it.
Or maybe they did, and if they did, that's really stupid of them.
But they should know what the data set contains, and they should know that it is possible to find certain people from this data set.
So I don't know, like, what they're thinking.
I mean, that's just weird to me.
Well, so it's a bad thing for the AOL users, but it's a good thing for people that need a good viable data set that's not just randomly made up crap.
Right, like, there are tons of data sets out there for training your spam filters.
You know, like, people just collect spam, and they have them bundled, and you can just download it, and, you know, you can use it for, like, if you're writing a spam folder.
And that's fine.
I mean, you know, that's not really hurting anyone because it's just spam.
But, like, other more sensitive data, like, you know, I think...
Well, it's hurting the spam guys.
Yeah, that's true.
True.
I mean, I guess...
I mean, criminals have rights, too, I guess.
Yeah, they do.
But, all right, let's move on, then.
So, I was looking...
Well, the lady, the chief information officer at AOL, I forgot her name, but she resigned.
Oh, oh, really?
Oh, really?
Because of this?
It was right after this, like, a couple days.
Right, but, like, she resigned because of this incident?
Well, it didn't say why she resigned.
Oh, right, okay.
But that's what one would assume.
That's ridiculous, because you would think that she would, like, make someone lower rank than her take the fall.
Well, see, I'm a firm believer in the...
If you make a mistake and you learn from it, then you're better off.
Right.
So, like, I would rather have an employee that has made a thousand mistakes, and we've already, you know, gone through it and resolved the issues,
and have that employee that have to fire him and hire a new guy that's going to make the same thousand mistakes.
Oh, I mean, of course.
But, I mean, the whole reason they do that is because for PR reasons, you know.
It's like, they can't keep her around because she's a person who did X and it was bad.
Yeah.
You know, like, it's really not.
You know, like, because people are stupid.
Basically, it comes down to people are stupid, and that's the end of that.
So, okay.
So, let's change this topic a little bit.
So, I looked at the forums, and basically, I want to see what people are doing with this data, right?
I mean, it's there, and, you know, hopefully, you can extract some cool information from it and learn something about the data set
or, you know, find interesting, like, clumps of information from these things.
And it's like, you know, I'm looking, and people seem to be grepping for weird shit.
And that is pretty much all that's going on.
Like, I don't know if you play with it, but what kind of things would you want to figure out?
Like, you know, like, what would you do to the data set?
Like, you have this data, right?
Like, how are people going to use it?
Are you asking me?
Oh, I'm putting you on the spot.
Oh, I'll tell you what I would do is I would first search for porno passwords to see if anybody had found any good sites with good porno passwords.
You're glad Mrs. Joops is not on the show tonight.
Yeah, she went to bed early.
And that's actually a really dumb joke.
Well, I kind of am interested in how sick fucking people are.
And I would probably search for just sick things.
And then when I found a user ID that this guy was into something weird, I would just grab for his user ID and see what else he was into.
So to see if he's, like, into, like, stock car racing and then, like, other weird shit.
People have definitely done that.
And, you know, there are a lot of – I mean, like, the thing that surprised me most is that people search for, like, questions.
Like, do you remember the website I think called Dogpile?
Yeah.
People – yeah, like, you're supposed to ask it questions, you know, so it's more user-friendly.
But, like, you know, people still do that with AOL, and there's these, like, long, long strings of questions and other weird crap.
I like how people will search for Google.
Yeah.
Or they'll search for, like, you know, like, something.com instead of just typing it into the address bar.
Yeah.
I mean, I mean, there are definitely some really weird shit in there.
Like, someone actually made a search – a web front end for the search, and, like, search what people has queried for,
if you put in, say, like, a text string or the user ID.
And, like, there's some really, really weird stuff here.
Let me give – okay.
This is a user that was actually posted to the forum, and just to give you an idea of this weird stuff – okay.
This is a search for how to destroy demons that live in apartment above.
Okay.
Is hip-hop and rap music a form of Satanism?
Are niggers Satan or demons or gremlins?
Like, like, questions.
Gremlins.
Oh, that wasn't a question.
I'm sorry.
You know, do niggers have x-ray vision?
I mean, there is just fucking weird shit.
Like, I don't even – I mean – but I digressed.
And then there's something normal, like, jobs going bad.
You know, I don't understand.
And my favorite, this guy searched for this about 20 times, old, rushed nuns for sex.
Sweet.
I don't know what to say about that.
See if anyone searched for, like, A-plus exam.
Yeah.
So, later in the show, actually, we're going to change gears again a little bit.
Later in the show, I'll get back to talking about machine learning and data mining and basically how these techniques can help us maybe extract useful information from data theft.
Because the point is, you're going to end up with a sea of crap, just, you know, like, gigs and gigs and gigs of text that's data from anything.
It doesn't matter.
And you're not going to be, you know – you can only grep for cool shit for so long.
But if you want to find useful, like, information in it and, like, how things relate to each other, then you're going to need to do something a little smarter than that.
So, later in the show, I'm going to talk about some of the ways you can do that and software that's out there, you know, so you can easily implement this kind of stuff.
But right now, we're going to do a segment, which is unnamed, and we're going to invite a guest, actually.
A friend of mine, whose name is Nolani, is going to come on, and I'm just going to ask her some questions, and we'll see what she says, okay?
Hi, Nolani.
Thanks for calling into the show.
So, we have Nolani, who's a friend of mine, and she has been married to a hacker for how long, for four years or so?
Yeah, four years, but we've been together for about seven.
Okay.
Okay, so that's great.
So, she knows what it's like to have a relationship with a hacker and just a geek in general.
So, I understand that you were at DEF CON recently, right?
I was.
And it was your first time?
Yes, it was.
Um, so, okay, what I was saying, actually, in episode 159, when we're doing the DEF CON wrap-up,
I was very surprised at the amount of female attendance at the CON, because I was there, like, you know, three years ago,
and, you know, there was, like, no women there at all, you know,
and this year we have women presenting with people in contests and everything, and just generally attending.
And so, I was, you know, so I want to ask your perspective of what you thought, you know,
about kind of, like, the role of women in hacking and, you know, attending the CON and getting involved in the community.
Well, my husband, the first year that he went was, I think, DEF CON 7, and he said there weren't many females there.
And he said the turnout, just like you, has gone up greatly.
And I think, just like any profession, whether it's, you know, a physician or a hacker,
you're always going to start out with a low number of females, because, you know, the man's always keeping us down.
And then we slowly...
I don't know, right.
What exactly do you mean by that?
Can you elaborate on the man putting us down or keeping us down?
Well, I think that, you know, a common misconception is that women can't do a lot of things as well as men can,
when, in reality, we can do it sometimes just as good or better.
All right, right on.
Okay.
So, I think, you know, the representation, of course, is going to go up year by year.
My husband and I are growing our two hackers.
We have a lockpicker in the family.
So, hopefully, we'll still grow up to be good hackers, too.
Okay, yeah.
Yeah, I mean, that's something I think we're going to see.
But, so, you were mentioning that, you know, the female attendants in, you know, the tech field,
not just the hacker field, but, you know, people being programmers or being electrical engineers or whatever,
just generally in the tech field are lower for, you know, for females just because of, you know,
not necessarily of any actual, like, ability, you know, differentiation or whatever, but, you know,
something like a social thing, right?
Yeah, it is.
Yeah.
So, what do you suggest, you know, maybe, like, someone who is interested in getting into the field
but kind of has, you know, feel that invisible, like, social pressure that, like, okay,
maybe girls shouldn't be doing math and, like, they shouldn't be, like, good at science, et cetera, et cetera.
And, you know, like, if someone really went against that field, like, kind of, not can't, but it's hard for them to,
like, what do you recommend they do?
I think one of the things that they can do is just really attach on to a group of guys that are in the field.
And, you know, a lot of the guys that I know really are willing to take someone in,
regardless of how you look as a female, to really teach them the tricks of the trade and teach things.
One of the misconceptions, I think, of hackers is that they're not really friendly.
And all the people that I met at DEF CON were excessively friendly to me and, you know, and my older daughter.
And we didn't have any problems with people being rude or anything.
So, I think just stepping into the arena would be good and going from there.
Okay.
But I think you actually mentioned something that's, like, a stickler for me.
You know, girls in general, there's this whole thing that happens in middle school
where math and science doesn't become important to them because their hormone levels start to go up.
Right, right.
And it's been taught that, you know, in order to get a good guy, that you've got to be a student.
Right.
So, I mean, like, one of the things I'm trying to get at is, you know,
if you're in that position, and you're kind of in middle school or early high school,
like, how do you fight against that, you know?
Like, you know, like, now, I'm okay.
So, since we're all older, we know, like, okay, it doesn't really matter, you know,
if, like, people don't get you or, like, they shun you for, like, being in stuff you're not supposed to.
But from the point of view of someone in that position, you know,
having friends and being popular is actually, you know, really important.
So, you know, like, you know, to them, how would you tell them to kind of, you know, do both?
Yeah, I think, well, I mean, and this is from my parents' point of view.
It really starts with, you know, building the good foundation when they're smaller.
But, you know, just because you're smart doesn't mean that it's a bad thing
and you're not going to be able to hang out with people or get a boyfriend or a girlfriend or whatever.
And, you know, a lot of the schools, at least in our area, are focusing on that
and really getting teenage girls involved in these programs where they are exposed to, you know,
geeky women in their field being successful and having a happy life.
And I think that schools are starting to recognize this whole thing.
And letting people know, you know, at least with our kids, we say,
it doesn't really matter what happens now.
Because all those people who make fun of you for being smart are going to be fat and ugly and unsuccessful.
I know, right?
Totally.
Well, we see the unsuccessful part.
Yes.
Yeah, I mean, yeah, but, you know, it's so hard to get that point across to, you know, people in that situation.
Because I was in that situation not too long ago.
And, you know, it's really hard to see that because, you know, you're living at that point in time
and not, like, in five years in the future.
But, I mean, it's something to definitely have to learn.
Yes.
Okay.
So another thing I wanted to ask you, actually, is what is it like being married to one of the biggest geeks I know?
I would say that the thing that definitely attracted me to him first was his passion and something,
just his pure passion into something.
And, you know, it's definitely something that I've had to get used to.
I know that he goes through periods of time where he just needs to focus and be very productive.
And I need to step back and work on my own things.
And I'm totally okay with that.
So it's just a whole adjustment period and learning, just like any relationship.
Right, right.
About a person and whatnot.
But I think geeks are definitely a special type of person where they're just,
they get so focused on one thing or another and you have to learn not to take it personally.
But it's not that they love you any less or think you're less attractive.
It's that, you know, this is it.
Right.
Now, has he had to make any adjustments for you?
I mean, you know, so you're coming from, you know, what you're doing for him and all this stuff.
But, like, have you guys kind of made, you know, a conscious effort to kind of make a compromise somewhere?
You know, let's say, like, maybe he'll, like, set aside specific chunks of time that he's not staring at his computer for 16 hours a day.
You know, he really has his priorities set.
And we go through periods of time where, you know, he is staring at his computer for 16 hours a day.
And I'm okay with that because I'm actually accomplishing my own things, like working on my photography or working on movies or whatnot.
And it's times that I can really focus on things that I'm working on.
And, you know, I'm just not one to sit around waiting for him to finish what he needs to.
So, I mean, do you think it takes, like, a certain type of women to date geeks and hackers who are, you know, prone to prolonged periods of isolation and, you know, being immersed in what they're doing?
You know, like, you have your own thing going on.
So, you guys are both working on something.
So, it's cool that he's off doing whatever.
Yeah, I think it definitely, I don't think it takes a special type of person, per se, but I think it takes somebody who's definitely patient and willing to work in a relationship.
You know, just like any relationship, it doesn't come naturally.
So, the first time he sat down to work at his computer for 16 hours, I was like, you know, hey, what's going on?
Come on, I want to do it.
Exactly.
True, what about you?
I'm just enjoying listening to the show.
You're hosting the show.
Stop listening to it.
My wife and I have similar problems that I, I don't know how you do it, but when I'm working on my computer, or if I'm playing on the computer, which we tend to do that sometimes also,
it's always just playing on the computer.
Like, I think she's getting it now, but in the beginning, she would look at me working on the computer as me just playing.
I see, yeah.
And that would aggravate the ever-living hell out of her.
Yeah, I've never thought that, because I think for me, it was something that we talked about when we first got together.
And, you know, the first time I was like, what the heck?
Why are you on your computer?
He explained to me in detail what he's done.
And I actually see the things as tangible, and he's sharing, even though I don't understand, like, 50, probably 70% of what he's saying to me when he's talking about his coding or this really cool thing you made at work,
I can see the passion in his eyes and understand that there's something tangible that I'm seeing that he's done.
That's hot, right?
Well, Ms. Troops is very supportive when I do something that's cool to me.
And granted, she doesn't get a flip about it.
But, you know, I'll get all excited, and I'll show it to her like I'm a little kid.
And, you know, she pays attention to it, and she's like, oh, that's really cool.
You know, doesn't understand what I'm doing, but she's not a nerd, so she doesn't have to.
All right, so that sounds good.
So there's hope for me, yes.
There is hope.
And she's also into photography.
Were you into photography before the nerd?
I was into photography before, but I got more into it because my nerd is also a hot skater boy.
Awesome.
So I get to take pictures of a hot skater boy and other hot skater boys at the skate park.
So I edit and do all kinds of fun things with those.
It wasn't just the fact that he was on his computer for 16 hours and you had to entertain yourself?
No, it wasn't.
Okay.
I mean, because he was on for 16 hours, I learned how to do like CSS and make my own web page
and I started blogging and all that kind of stuff, but.
Good deal.
It wasn't.
Okay.
So now this is the part where you have to help me.
Okay.
So if I'm looking for someone to go out with and I, bear with me.
So, okay, so from what I understand, normal people will go to like bars and clubs to meet people to like do or something.
But if I'm looking for someone to go out with that is, let's say, of a certain disposition, where would I go to find those people?
I mean, you know, it's like, I don't know where they are.
I would first move to New Zealand because the women to men ratio is like, what is it, like three to one.
Do you say New Zealand?
Yes.
You would have a good chance there.
I mean, you can go to like certain computer stores.
See, that's kind of creepy because like if I go to Borders or like Fry's and I'm just standing around, you know,
it's like analogous to picking up women at the supermarket.
Who does that?
I know, but like if you're going, you know, say to the Apple store to buy like a MacBook Pro or something.
Or a MacBook.
There's hot chicks.
No, MacBook Pro.
There's hot chicks there standing around.
And you could like, you know, go over to the software section where they are and be like, oh, I was looking for that SimCity video game too.
Okay, okay, okay.
So now let's say I found her at, you know, your local friendly Apple store.
Now, what do I do to like, but I don't want to say pick up mine because I think personally that's really stupid.
But how do I start a conversation and like engage her without scaring her away?
Because I'm not going to say like.
Ask her how she likes her tech product.
Be like, how do you like your MacBook Pro?
Because then they'll be like, oh, I love you, I love you, this, this, blah, blah, blah, blah.
And then just pretend like you're interested.
Okay.
Take me through the conversation from when I see her to when I end up with a date.
And go.
So, all right, so you're standing there and she's looking at Final Cut.
Okay.
She's looking at Final Cut.
And you could say, oh, you know, I was really interested in buying that software, but, you know, I'm not sure how user friendly it is.
And then you can go on with the conversation and she can tell you about it and you guys can discuss the different things that you do in Final Cut.
And then you could always say at the end, you know, you're a really good source of information.
Do you mind if I get like your email address?
Because I'm going to buy this and I really need a good source of information to, you know, ask questions about.
Okay, so, so, so the first thing is trick her into giving her your contact information.
Okay.
Okay, I got it.
Well, no, no, no.
The first thing is ask them a question and see how they talk.
And see if they annoy the shit out of you, like the first minute they're talking.
You just want to make an excuse and walk away.
Exactly.
Okay, okay, okay.
See, because in movies, what happens is you see him go up and say, hi, how are you doing?
And the next thing is there's sex.
I don't know what goes on in the middle.
I mean, that whole conversation period is undocumented.
Well, you know, even though it's not documented, stuff that happens in the movies does not really happen.
It's just not true.
Are you sure?
Okay, my husband did not come riding up to me on a white horse with roses to ask me to marry him.
So I'm pretty sure that the stuff that happens in movies doesn't happen.
All right, so you would say the, so basically get a conversation started and then express interest by, like, you know,
asking her for her e-mail or her number to, like, talk about some mutual interest that you guys have.
Yeah, something like that.
Is that a good first step?
Yeah, something like that definitely is, you know, non-threatening.
And, you know, it's not like, oh, Final Cut Pro.
Hey, that's really cool.
How does that work?
And then, can I have your e-mail address?
Okay.
So you have to have some conversation skills.
Yes.
Hmm, maybe I'll just stick with MySpace.
Just keep asking them questions.
That's all you have to do.
Like, you just have to listen for keywords and ask them a question.
If she's like, well, the filters on Final Cut are really cool, you can do this.
What kind of filters are there?
Okay.
Okay, so I'll write a script to do that, and then we'll see how it works.
Okay.
Gotcha.
Okay.
I would help with this market research, but I can't.
You can't.
Tell Mrs. Droops that this is the educational purpose.
It's for science.
She's going to be like, it's for your stupid computer friends, isn't it?
All right, well, I think that's all the time we have for this segment right now.
No, Alani, thank you for coming.
I'm sure we'll be talking to you again.
All right.
Thanks for having me.
Thank you very much.
No problem.
You guys enjoy the rest of your show.
We will.
Now we can talk, go into our technical topics for the evening.
And as I said before, we're going to talk about machine learning and data marketing.
So, Droops, have you heard about this machine learning, data mining thing?
Only from what I've heard from you today.
Okay.
That works.
Now, machine learning is pretty much artificial intelligence.
Now, when you think of AI, what do you think of?
That Will Smith movie.
Okay.
Now, artificial intelligence is actually much less awesome than that.
Machine learning is a little option of AI.
And it's pretty much all of the algorithms and all the theorems and all the stuff that people can actually use.
So, if you think machine learning, think practical AI.
Now, for machine learning, there are a lot of ways to do it.
You know, there are what they call supervised learning methods and unsupervised learning methods.
But all of that stuff basically is trying to do one thing.
You have the assumption that all types of data, like all types of things, whether it is, you know, where Droops walks in his house throughout the day to, you know, who searches what on the Internet.
All of those things can be modeled by a mathematical function.
Now, that function can be really complex and, you know, some crazy shit that no one can solve.
But it exists.
So, what that means is, you know, you can predict what's going to happen if you know how something works.
I mean, that's a really general idea, but, you know, I mean, it's always been there, like, with physics and all that stuff.
You know, it's basically what science is about.
So, let me get into some detail.
Now, there are two main categories of machine learning algorithms, supervised and unsupervised.
Now, you always start with a data set, like the old data set they gave you for research.
Now, for supervised, what that means is while you're running the algorithm and getting, you know, your software to learn what that function is that you're trying to have your machine learn,
you have outputs that you're, you know, trying to match against.
So, you know what it should be.
So, you know, it's like, think about it as a function with inputs and outputs.
You have a data set that's representative of the correct one.
Now, you take those and you're trying to predict future occurrences of that, of items from that data set, right?
When supervised, you know what the output is.
So, let's say, you know, you have a mystery function and you don't know what it is.
So, you plug in one and three and then you get out 206, right?
Having the answer 206 means it's a supervised, I mean, the data set can be run with a supervised learning algorithm.
Now, if we're unsupervised, that's just random data.
That's just random crap that's out there.
You know, so something like that would be the web search.
Like, there's really no output.
Like, you know, you have to figure it out yourself or it just isn't there.
Or, you know, it's not that kind of data.
So, an example of supervised that's actually pretty pertinent to this is spam, right?
So, you have all these attributes for a spam email, like the link and, you know, like what's in the header and what kind of words are in the message, who it's from, et cetera, et cetera.
And your output is whether it's a spam email or not.
So, you know, there's some classification that you already know, but not everything is like that.
Now, I'm going to go over four prominent algorithms, and those are agentry, key nearest neighbors, artificial neural nets, and clustering.
Now, the first three are supervised algorithms, and clustering is the only unsupervised algorithm I'm going to go over.
Now, at the end of the show, well, actually, in the show notes, I'm going to put in a link to a framework.
Well, it's really a software package called WECA, W-E-K-A, and that has all this stuff implemented.
So, what you can do is go and just run these algorithms on whatever data set you want, and then it'll spit back results, and all you have to know is how to interpret it.
So, stuff I'm talking about now won't be a complete waste, and you don't have to implement it yourself.
Now, okay, so let's start with decision trees.
Now, decision trees is one of the simplest, simplest things you can do.
Okay, actually, let's talk about the data set a little bit.
So, you have – so, if you looked at the AOL data set, for example, they say, okay, each line, it's either a, you know, a query that someone did.
So, you'll have the UID, and then I'll have the query string.
And then another one is UID, query string, and some other variables.
Now, those variables mean that they actually clicked on a result that was in, you know, what they queried for.
So, there are additional attributes for that.
Now, each attribute is one dimension of the data set.
Now, one of the most important things is to figure out which attributes you want and which attributes you're going to use.
Now, you may get a data set, and you don't want to use every single attribute because some of them maybe, you know, don't care,
or they're not pertinent to what you're doing, or you may not have all the values for it.
Now, there are techniques to deal with that, but you don't have to worry about that right now.
So, once you have your data set, and since we're doing a supervised learning algorithm, you need to have an output.
So, I don't, you know, so the AOL data set probably wouldn't work too well unless you categorize them yourself first.
And what we're trying to do here is get, build like a software model.
I mean, you know, you can even say some sort of, like, brain, right?
What that can do is you're going to train it.
So, you're going to take the data set and feed it into this algorithm,
and it's going to give you a configuration for some kind of, you know, brain.
I have no better word for it, but it'll probably come later to me.
But you have some configuration, then what you want to do is predict future values.
So, in the context of spam, you train your whatever learning algorithm you have with all the spam you have, right?
And then, hopefully, the new spam will be close enough in certain characteristics with the old spam.
It can recognize it as a spam message, and a real email is not a spam message.
So, decision tree.
What happens is this.
You have a bunch of inputs.
So, let's say you have an input that has a bunch of attributes.
So, each attribute is going to end up being a split in the tree.
So, you create this tree where you say, like, where you end up following it down.
So, you say, okay, let's say I have, you know, a data set where each input has ten attributes,
and the attributes are, you know, just some simple greater than or less than thing, right?
So, let's say I put it in the input, and the first decision I make is, okay, is attribute A less than or greater than ten?
And if it is less than ten, then I go one way, and if it's greater than ten, then I go the other way in the decision tree.
Now, you keep doing that, and then you end up using all your ten attributes.
And at the very end of the attribute tree, you know, so you follow some path, right?
And at the very end, you come up with a classification.
You say, oh, okay, I'm at the end, and from what I, you know, from the decision tree, I classify this as, you know, something or something else,
and you defined that before.
So, what you do is you end up training your decision tree on all of these example data,
and then you kind of get some sort of notion of how well you do, right, because you're not always right.
So, since you, I mean, you know, you would be always right if you had all the data possible from that function that you're trying to deduce,
but you don't, so you're not going to be 100% correct.
So, at some point, you're going to be wrong, so that's going to give you, like, a percentage of how well you're doing.
So, that is what a decision tree does.
Now, the interesting thing is how you make one.
Because when you start, right, you don't know what your decision tree is.
Like, when you take attribute A and say, you know, is A greater than 10?
Like, you don't know how to do that, because A is just some value that all the input has, and, you know, that's it.
So, you have to figure out what to make the decision tree.
And the most important thing to do that is a concept called information gain.
Now, so picture it this way.
You have all your data, and now you have, let's say, let's take attribute A, for example.
If you put in a decision that says, okay, let's split all the data, the input data,
on if attribute A is less than 10 or greater than 10.
Now, you do that, and then you figure out how that splits the data.
Now, if it only splits out one input out of, you know, like, the thousands you have,
then that's not very good at, you know, sorting out the info you have, right?
So, what you want to do is find attributes and, you know, those kind of decision splitting points
that's going to give you the most split.
Now, for example, one of the best things to do, you know, at least in the beginning,
is find one that is going to give you an even split.
So, that's going to kind of narrow your chances the most.
Now, so you do this in a greedy fashion, and so what that basically means is, okay,
you used up attribute A, you split the data set in half.
Now, the next decision you make is you do it with, you know, attribute B, right, the next one,
and then you keep doing this until you end up with a whole tree that uses all your attributes.
And since you have the results from the input, at the very end, you know, you categorize all your data,
and you're going to get clumps of them, right, because you have, like, thousands and thousands of input,
and you only have, say, like, a fraction of attributes.
So, when you get down to the end of the tree, you're going to end up having, like, 200 things that are categorized by,
that have the same attributes, or, you know, are categorized by the same attributes.
So, when you get to the very end, you're going to look at the results or, you know, the output is for all those sample data.
And, you know, they're not all going to agree, but you take the ones that's the majority.
And then you say, okay, if I see an input in the future that takes me down this tree, down to that point,
then I'm going to say the output, I'm going to predict the output is going to be, you know,
this thing where it's the majority of the output that I've seen in the past.
And then once you have that tree, then you can start, you know, making decisions and predicting output for future input.
The next supervised algorithm that I'm going to talk about is something called K-nearest neighbor.
Now, all this is, is you plot your, okay, so forget about the tree, you know, forget about, you know,
all that crap right now, take your data set.
Now, it has, let's say, the same 10 attributes, right?
Then what you want to do now is to make a 10-dimensional graph.
Just, just visual, not, you know, not visually, but plot, you know,
pretend like you're plotting all the inputs on this 10-dimensional graph.
So, you have this 10-dimensional space and all your data points are in there, right?
And they're just floating around.
So, so it's just this big blob of shit.
Now, what you want to do is find things that are alike, you know?
Like, that is kind of the, the, the overall strategy of most of these things.
You want to find things that are alike so you know what, you know, some future input is going to be.
So, so it's called K-nearest neighbors because you kind of group things and you make these little groups of K dots, right?
Now, let's say you have 2,000 input points, right?
Now, you get to choose what K is.
So, what that means is, let's say I make K 10.
So, from, you know, 1,000 of these points, I will make 10 groups.
Now, you know, you can do K to whatever you want, but the point, the key is to find something interesting,
like an interesting K, so it actually, you know, puts a circle around the dots that, that makes sense.
Because ultimately, you want to say, okay, this group right here that I drew a circle around means, you know,
it's people searching for grandmothers who want to have sex.
And, you know, this other group are people who want to learn how to swallow swords, you know, or whatever.
See, like, let's say you make too big.
Let's say you make K 2,000.
So, every single dot is its own group.
That doesn't really tell you that.
Now, we can make K something ridiculous, like 2,000.
Then, then each, each input would be its own group.
Now, you can say something meaningful about each one of those groups,
but that's not going to really help you classify future data into, you know,
into people like, who like to swallow swords or people who want to have sex with grandmothers.
Now, so, you know, because not every single person is going to end up being the same.
So, the key is to find a K that works.
Now, what you're going to do is not worry about it,
because the algorithm will pick K for you.
See, because you end up kind of defining a distance that, you know, some distance where, okay,
if two points are sufficiently far apart, then we don't want to put them in the same cluster.
So, we'll, you know, put K to be like K plus one or something.
So, there are ways to find K, but I'm not going to go into them here.
Now, to illustrate how this works, there are several methods for doing K nearest neighbor.
But, for example, this is one method to do it.
You start with K being, let's say, 2,000.
So, everyone's in their own group.
Now, what you do is take two of the points that are the closest, then you group them together,
and then make K one less.
So, you know, if two points are really, really close, then you say, okay, those people are essentially the same.
So, you know, at some point, we can extract some meaningful data out of that group.
Then you keep doing that until, you know, so you can keep doing that until there's only one group.
But what you end up with is like this tree again.
Now, it's not like the decision tree, but it's a tree of kind of the order you group people in.
So, you know, so this is kind of like a Deitcher's algorithm thing where you take two points that are the closest,
and then you say, okay, these are one group, and then the next closest.
Now, they might be two separate points completely, but you don't really care because eventually they're going to hook up with something else.
So, when you make this tree of, you know, so you start grouping things together,
you have this tree, and you can look yourself at like, okay, let me look at when K is equal to 7, you know.
Like, what does that graph look like, you know, when you put the dots all in, you know, your 10-dimensional graph?
You know, like, how does that look?
Like, what if I go to K equals 6?
Then am I going to put two really large groups together?
Because if I am, then maybe those two groups have something interesting that, you know,
that there's, you know, have some individuality that I can extract from it.
So, maybe I don't want to put them together.
Now, so you see what I mean when I say that K is kind of dynamic, depending on what you're trying to find out?
So, you do that, and then when you get to the K you want, you end up with, okay,
so let's say what makes sense for me is K equals 7.
So, I have seven groups.
So, I've grouped my data into seven things.
Now, then I have to go and say, okay, what do these things have in common?
But the hard part is done for you.
You know they have something in common.
I mean, you know, so basically it's trying to tell you something,
and you need to figure out what it is.
That becomes your job after you run the algorithm, you know.
You look at the data, and you're like, oh, okay, these people look like they're into, you know,
interspecies erotica, so maybe that's what this is telling me, you know.
Like, all these people are into that, and that's cool,
but they're different from this other group of people who might be into whatever else.
So, that is a supervised thing, because what happens is, you know, so this is supervised for them.
So, you end up having kind of the, not the answer, but you have the output.
So, you know what, you know, these clusters of things are.
Well, actually, cluster is not the right word, but you know what this group of dots mean.
Because, you know, you, once again, you can do what you do in the decision tree,
and you can say, like, okay, all, you know, out of these 367 groups, you know, of input,
you know, like 300 of them have, you know, this for an output.
So, we can safely say that, you know, if at some other point we get a dot that falls into that area,
the output will most likely be whatever the output is for those 300 people or inputs that we have.
So, you know, that's what, you know, that's kind of the same way you would use it.
So, you know, let's run this, and you figure out what each of those groups mean.
You can save that, and if you later get more data points from that same source,
then, you know, then you can know where they belong, and you don't, you know, like,
if, like, let's say, once again, you're predicting spam, right?
Then, if you get an email in, and that dot falls into that group for whatever attribute,
then you know that it's probably spam or not spam based on the, you know, 500 dots surrounding it.
So, that's how you would use something like that.
Now, the next supervised and the last supervised thing I'm going to talk about is artificial neural nets.
Now, you might be fairly, you know, if you haven't heard of anything I've talked about yet,
you probably may have heard about this.
Now, neural nets is, you know, was invented in, what, like, the freaking 70s, probably?
And, you know, it's been around forever.
And the premise is you were trying to model what the brain does.
So, you have, you know, so back, you know, when they were doing this, the biologists had figured out, like,
okay, you know, you have these neurons in your brain, and you have inputs and outputs.
Now, what happens is you get electrical signals, and they, you know, they go in the inputs,
and then depending on how much of a voltage you get, like, how strong this signal is from your various inputs,
then your output will fire, or it will not.
So, you have one of these things, and then you hook them up, and your entire brain is made of this.
And then, you know, so you have inputs, connect, or outputs, so everything is networked together,
and that somehow, you know, leads to an eventual output that says, you know, like, do this, or, you know, don't do that.
So, the AI, you know, the computer scientists and the AI people wanted to kind of utilize that,
because we want to, you know, the goal, I guess, is to make, you know, an actual intelligence that can make decisions for you.
But, as it turns out, 20, 30 years later, the brain is much more complicated than that.
But, we still manage to find ways to use it.
Now, the way a neural net works is you have this concept of a perceptron.
A perceptron is exactly the same thing, or logically the same thing, as what they thought a neuron was.
So, you end up having these inputs in your perceptron.
Let's say you have three inputs.
Now, the input can either be, well, it can be anything between zero and one.
So, any real number between zero and one.
Now, you have an output.
The output can only be zero or one.
So, a perceptron either fires or it doesn't.
Now, inside the perceptron, not inside, but the perceptron has this notion of a threshold.
So, let's say my threshold is, I don't know, two and a half.
So, if every input I have, you add up the input values, they're from zero to one.
And, if that value is greater than your threshold, which is two and a half, then your perceptron will fire, and it'll send out a one.
So, that's the basic building block.
Now, you hook all these things up into some sort of network, and you have this, you know, this is where neural nets come from, like the terminology.
So, you have this thing, and now, we go back to the inputs that you have.
You have 10 attributes, okay.
So, we have 10 perceptron, and you hook them up in some fashion, and then you get one output.
So, going back to our example with spam, you get, you know, so you get all these attributes from the email,
and it'll have one output, and either say it's spam or not.
So, I'm going to skip, kind of, you know, how you determine the middle right now, and I'll go back to that.
But, before I do, so let's say you, you know, so, think back to the decision tree.
Let's say you have an attribute A, and, you know, you say, okay, if attribute A is less than 10, then put in a zero.
Or, if it's greater than 10, put in a one.
Now, that's good and all, but you also get a range of values as the input.
So, what you can do is normalize your attribute into, from zero to one.
So, let's say if I have a range for my attribute that's like, I don't know, 20.
Then, you know, then I can normalize it to zero and one, and based on every single value of attribute A,
I can put into the first perceptron some value between zero and one.
So, you know, the closer you're at 20, the higher, the closer you're going to be at one for the input.
So, you do that for all the attributes, and you end up with, you know, some perceptrons firing and some not firing.
So, the natural question, then, is, say, okay, how do you set thresholds?
Because you don't know what to put them.
So, there's an algorithm for neural nets that lets you train a neural net that kind of starts at a default factory setting state.
So, it starts at some, you know, stupid thing where, like, all the thresholds are, like, one or something,
and then you keep putting data in and you train it.
And in your neural net, you can, most times, you can have a feedback loop.
So, what happens is, since in your output, you know, in your input set, you know what the output is for that training data, right?
So, what you can do is put one in, see what happens.
You know, if, say, the end result fires, then, you know, if the end result is a one,
then you can look at what the output actually is for your training data.
And then, if it's right, then you say, okay, guys, good job, you know, moving on.
But if it's wrong, then you say, okay, why the hell is this wrong?
So, you have a feedback loop that goes back to the beginning and says, okay, you guys are wrong.
So, someone screwed up, and someone's threshold is not correct, so you need to change it.
And we'll see, you know, how well you do.
So, I'm not going to go into details on how that works,
but there's a process whereby you put in your input one after one,
and then you keep adjusting the thresholds based on what you see.
So, this is kind of, you know, like a steady, I guess what they call a gradient descent
towards the approximation function that you want.
So, you little by little get to the place where, you know, it would categorize each thing correctly.
So, this works totally, completely differently from the decision tree and the k-nearest neighbors algorithm.
Now, this is, like, because the decision tree and the k-nearest neighbor kind of make sense
and kind of relate to each other in that you're, like, grouping how close things are,
and it's kind of like a discrete value sort of thing.
But for artificial neural nets, it's a totally different thing.
So, you're training your neural net, and at the end of that, you end up with this network
that has different weights in all of the nodes or the perceptron.
Then you can use that, like, you use the other two methods to try to classify new things that come in.
Now, before, I didn't talk about how you kind of construct that network, right?
Because you know you need to have, say, ten inputs, because that's, ten inputs into your neural net,
because that's the number of attributes you have.
But, and you know you need one output for the actual classification of whatever you put in the neural net.
But all that stuff in the middle, it's kind of vague.
The reason that it's vague is because no one really knows what's the best thing to put.
So, you can do anything you want.
You can have, you know, you can have 70 layers of neural nets,
and you can just keep hooking them up to each other,
and, you know, just have a full mesh of layers and layers of perceptrons.
And each time you go through the training data, you know, those weights will change.
Now, or you can have just one row where you have a perceptron for each input,
and all of them are just going to hook up to one perceptron for the output.
You can do that.
Now, depending on what you do in the network, your outcome is going to be different.
Like, your neural net is going to be, you know, it's going to perform differently.
So, what you have to do is you have to tell it how many layers to have
and also how many, how wide each layer is,
like how much input aggregation you do as you add each new layer,
because you eventually have to get from the number of attributes down to one.
Now, what I have found works the best is to have two layers.
Now, sometimes, not always, so you have to try this out.
If, you know, sometimes for something, having one layer works really well.
And if you add a little layer to it, either it doesn't do anything or it fucks out completely.
And at other times, it's vice versa.
But in my experience, one or two layers is enough to capture the complexity of a function.
Because that's, I mean, that's what adding layers means.
You know, you add a level of indirection so you have room to tweak any intermediate values that you want.
So it's not like, oh, if this is on, then that's on, right?
So you let, you allow for the possibility of other, you know, of things affecting each other
because they're going to be in a full mesh, right?
So, you know, so what I found out is try one or try two layers with the software.
But if neither of them works, then neural nets are probably not the best thing to classify that data set.
Pause.
What's up, Droofs?
You're typing.
No, that was low-tech coming through the door.
Sorry.
Hello?
Sorry?
Hey.
Hey, what's up?
That was low-tech coming through the door.
Oh, cool, cool, cool.
Okay, so I just finished talking on neural nets.
I'm going to type with, like, the last thing and then close it.
Okay.
Okay?
Sounds good.
Okay.
So still no questions?
No, I haven't come up with any questions yet.
I'm sorry.
That's all right.
I'll just keep talking.
All right, start.
You know, I've mentioned three supervised algorithms.
Now, those tend to be a little more tame when you're doing machine learning because you know, you know, you know what the output is.
So you have a set to train on.
You know, that's the most important thing because all of these things have, like, configuration parameters,
whether it is deciding which decisions to have in the branch or, you know, how to group your input data or, you know, what to make your thresholds for your neural net.
You know, all of that requires some sort of training.
And ultimately, you know, you're never – sometimes you'll get a data set that has no clear, you know, has no clear output.
So it's just going to be, like, data of people doing stuff.
And you're not going to know, you know, what it means.
So, you know, let's say I take, like, traffic patterns, you know, or like how people, you know, call each other and all that stuff.
Like, you're not – you don't know, you know, what the end goal is.
You just know that you have this massive collection of data and you know that, you know,
there's probably some sort of interesting information in there and you don't want to just rep it and find weird shit.
So you need a way to extract that information.
And there are a bunch of unsupervised learning algorithms, machine learning algorithms.
But this one, I think, is going to be the easiest to kind of grasp.
And I think it works the best, actually.
So this is called clustering.
Now, clustering is going to sound very similar to King Nearest Neighbor,
but it's slightly different in the mathematical sense.
But you don't care about that.
What's going to happen is – I mean, this is the easiest thing to use.
You take the software.
Now, you feed it the data, and then you run a clustering algorithm on it.
Now, there's a bunch of them.
But what it basically does is what King Nearest Neighbor does.
It looks at your data set, and it tells you what circles to draw around which dots.
So it's going to cluster all the data, and then it's going to say, like, hey, I found, you know,
like one or two clusters of interesting things.
Like, these things over here are the same somehow.
And those things over there are the same somehow.
And we don't really know why, but that's what you need to figure out.
Now, when you have something like this, the hardest part is to, you know, figure out – to interpret the results.
But you can do that by looking at the data and, you know, doing whatever to it.
So you don't have – okay, so when I was mentioning a function, like you have an actual function that you're trying to get, you know,
trying to model, right?
There's no notion of that in an unsupervised learning environment.
But you end up having, like, a generator.
Like, you have the concept of a generator that generates the actual model, right?
And then you have sample data that's not linked.
So you don't know really what you're learning, and you don't really know what your goal is.
But the good thing is, clustering is going to tell you what you need to know.
Now, there are a lot of ways to cluster.
And I guess for every single way that you cluster, you can apply it to the K-Nurse and everything.
So you can do, like, the mean distance between clusters, and you can, you know, calculate things like the minimum distance between clusters,
clusters, and the mean internal distancing cluster, and all this stuff.
And what you end up with is just groups of things.
So you can tell a lot by grouping things, to my surprise when I first learned about this.
So what you're going to end up having to do is, you know, okay, so once you have this data,
you can find things like, you know, where noise is and where an interesting segment of data is.
So you don't have to waste your time looking, you know, combing through gigs and gigs of stuff.
And you can just concentrate on the meaningful centers of, you know, the nuggets of interesting things.
And you can find out how they relate to each other.
Now, a lot of this stuff is, you know, so I'm not talking, I'm not really talking about how the clustering works.
But once you get it, get the clusters, you know, you need to find a lot of visual way to represent the data that, you know, that you have isolated.
And you have to, you know, kind of like, from then on, it's kind of like create a process of trying to figure out, you know,
what is important from, you know, whatever the algorithm tells you.
So those are the predominant machine learning algorithms.
And I really suggest you guys go.
Just fucking download Wicca.
Download It's in Java.
Download it.
Run it.
Just feed AOL data into it, you know.
Let your machine, you know, do something with the idle cycle and just feed the data into it and see what you get, you know.
So it could be nothing, but you may find something interesting, you know.
And it's all in how you interpret it.
Well, that's all the time that we have here for Binary Revolution.
And as we say every week.
We'll see you again next week.
Same hack time.
Same hack channel.
It's 3 a.m.
And I want to go to bed.
I got a lady running through my head.
Ran out of money looking for the night shift.
It's 3 a.m.
And I want to go to bed.
I know a lady way down in my country.
She was so pretty that my eyes threw the sizes at me.
Now we will sit and we'll wonder about our future.
But now I'm thinking that today is down front of me.
Well, I've been working five days full time.
Ain't got no money, but everything is going fine.
Well, I've been tired in my head.
It's 3 a.m.
And I want to go to bed.
I got a lady running through my head.
Run out of money looking for the night shift.
It's 3 a.m.
And I want to go to bed.
I know a lady whose eyes fly right to me.
And she will sit and stare directly at me.
And that laugh will take me to my future.
Throughout my past, there is nothing left of me.
But I've been working five days full time.
Ain't got no money, but everything is going fine.
Well, I've been tired in my head.
Said, I've been tired in my head.
It's 3 a.m.
And I want to go to bed.
I got a lady running through my head.
Run out of money looking for the night shift.
It's 3 a.m.
And I want to go to bed.
I feel my lady late night.
She comes to me and baids my mind.
Reminds me of where we should be.
So we just sit and we dream about our future.
Throughout my past, there is nothing left for me.
But I've been thinking five days.
Full time.
Ain't got no money, but everything is going fine.
But I've been tired in my head.
Well, I'm tired in my head.
It's 3 a.m.
And I want to go to bed.
I got a lady running through my head.
Run out of money looking for the night shift.
It's 3 a.m.
And I want to go to bed.
My lady won't leave my head.
I just want to rest my head through bed.
She won't let me go.
I don't want to go.
Good night.
I don't want to go.
Give it to her yes.
I don't want to go.
You know what it is.
I don't want to go.
You