Episode Transcript
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the know. Listen to Make Me Smart wherever
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you get your podcasts. Mike.
2:01
Lauren. How long have you been
2:03
covering the Internet for now as
2:05
a journalist? Oh over 20 years. How
2:08
long have you been a wire? Over 20
2:10
years. Basically since the earliest
2:12
days of the consumer Internet. Yes
2:14
I've been online since I was a
2:16
pre-teen sort of like that
2:19
character in Almost Famous following around the band while
2:21
I was a youngster. Don't
2:23
make friends with the rock stars. Yes or the nerds. How
2:26
often would you say you still get tripped
2:28
up on internet terms and protocols and acronyms?
2:30
Oh a lot. I mean it's never-ending especially
2:33
now with AI which I don't really follow
2:35
as a journalist and even if I did
2:37
I don't think I could keep track of
2:39
all the acronyms. I agree and
2:42
I really think we need to demystify all
2:45
this for people and what better way to
2:47
do it than to make ourselves look like idiots? Oh
2:49
yeah okay sure. I think I can think of better
2:51
ways. Let's do it. Hi
3:01
everyone. Welcome to Gadget Lab. I'm Lauren Goode.
3:03
I'm a senior writer at Wired. And
3:05
I'm Michael Kalori. I'm Wired's director of
3:07
consumer tech and culture. We're also joined
3:10
this week by senior writer Will Knight
3:12
who covers AI for us
3:14
at Wired. Hi Will. Hello
3:16
there. It's great to have you back on. So
3:19
we're doing something a little bit different
3:21
today. We are turning the Gadget Lab
3:23
into a quiz show but before you
3:25
all turn to another podcast seriously
3:28
stay with us because we're going
3:30
to attempt to define and explain
3:33
all of the acronyms of the earliest
3:35
and the most current consumer internet. Stuff
3:37
that you're hearing about or maybe you
3:39
even say all the time like DARPA
3:42
and PCPIP and
3:44
SMS and LLM.
3:48
How many times? Do a shot every
3:50
time you hear LLM on a Wired
3:52
podcast these days. Now this was partly
3:54
inspired by an early 2000s book that
3:57
I happened to be reading recently. It's
3:59
called dot-con by the New Yorker
4:01
writer John Cassidy. And despite the fact that
4:03
I know a little bit about the early
4:05
internet, I mean, like Mike, I've been online
4:07
since the mid-90s, I
4:10
was actually floored by how many terms I
4:12
didn't know. And it just
4:14
got me thinking, we should do a podcast on
4:16
this. Like let's break this down into three parts,
4:19
the early internet, the mobile era,
4:21
and now the era of AI.
4:24
And we've brought in Will because he is our
4:26
expert AI reporter. So first,
4:28
Will, you and I are going to quiz
4:30
Mike because he's the guy who's
4:32
already established that he's been around like forever on
4:35
the dinosaur age of the internet. Thank you. You're
4:37
welcome. And we're gonna quiz
4:39
you on all the, you have not seen these
4:41
in advance. Nope. Will and I have a shared
4:43
dock with each other. We've
4:46
crafted some ideas that we think, like
4:48
some you're going to get easily. And some
4:51
I think might stump you. Okay, I look
4:53
forward to this. I'm not allowed to look them up while you're
4:55
asking, right? No, you were not allowed to look them
4:57
up. None of us were. And
4:59
the way we're gonna break it down is after this,
5:01
you and Will are going to quiz me on the
5:03
era of kind of the mobile internet because that's when
5:05
I started covering tech. And then finally, we're gonna end
5:07
with Will on AI. Will,
5:09
would you like to go first in quizzing Mike
5:12
on the wonky acronyms of the early internet? Sure,
5:14
I would be delighted. I
5:17
wanna just
5:20
to preface it by saying, I
5:23
am terrible at remembering acronyms in the best
5:25
of times and there are a billion out
5:27
there in AI. So I'm gonna do terribly,
5:30
but the first one- Look at this copy
5:32
adding already. The
5:34
first one on our list is,
5:37
it's not DARPA, it's ARPA, but
5:39
they're related. ARPA, oh
5:42
boy, kicking it off with a
5:44
bang here. It
5:46
predates the consumer internet. See DARPA, the
5:49
D is defense because
5:51
the internet started as a
5:53
defense project, US government defense
5:55
project, right? I
6:00
can't remember what comes after Defense in DARPA,
6:02
so I can't remember what DARPA is. Research
6:06
project something, something? Yeah, no, you're getting it. Is
6:08
that right? Yeah, yeah. I
6:11
mean, if you think of the
6:13
letter A and what the internet was at the
6:15
time, it was pretty like... Autonomous?
6:17
No. I don't know. I
6:20
don't know. It has something to do with... It was
6:23
the Defense Amazing research
6:26
project. American? No.
6:28
No, it actually didn't start in America. Okay. Did
6:31
it? I don't... It was
6:33
like the earliest internet did not. I give up. What does
6:35
ARPA stand for? Will, would you like
6:37
to tell them? It's Advanced Research Projects Agency.
6:41
Advanced Research Projects Agency, okay. So
6:44
was DARPA the Defense Advanced
6:46
Research Projects Agency? Yeah. Yes.
6:48
That's right. Mm-hmm. This
6:51
one I think you're going to get. Okay. BBS.
6:53
BBS, Bulletin Board System. Yes, BBSs.
6:56
Good job. So a
6:58
bulletin board system was like a
7:00
computer in a basement
7:02
somewhere or under someone's
7:04
bed or in the closet in their mom's
7:07
bedroom where they
7:09
hosted a message board. Mm-hmm.
7:13
And you would call on your modem to
7:15
the message board and leave messages
7:17
and then hang up and go about your
7:19
day and then hours later go back and
7:22
read people's replies. It was a community forum.
7:24
Mm-hmm. Do you know where it
7:26
started? This is a bonus question. Where
7:28
BBSs started? Mm-hmm. I
7:30
would just assume they started in suburban North America. Close.
7:33
Well, it was Chicago in the 1970s
7:35
during a blizzard. Oh, nice. Yeah. Two
7:38
guys kind of patched it together. Nice. Uh-huh.
7:41
Okay. Wow. I was
7:43
an avid bulletin board boarder, I guess. Yeah. When
7:45
I was quite young. Yeah. Yeah.
7:47
I was not. Under it.
7:50
It's all there was. Are they still around? Yes.
7:53
I'm sure they are. I mean, it's basically Reddit, right?
7:55
Mm-hmm. It's all that was around. Like, you... There was
7:57
not a whole lot you could do on the internet.
7:59
rather than go to BBSs and chat rooms. Right,
8:02
and Google, I think, acquired
8:04
it. Am I remembering that correctly? What was
8:06
the- They acquired the
8:08
big users net. Oh, Usenet,
8:11
correct. They acquired Usenet. Which
8:13
is- Usenet was started, yes. BBS was started
8:15
by two guys in Chicago in the 70s. Usenet
8:18
was actually started at Duke University, I
8:20
believe. Yes. Yeah. Okay,
8:23
fun one. Similar times. Similar
8:26
times, indeed. Mike,
8:28
what about ABR? Mm-hmm.
8:31
ABR. I didn't know this one. Will put this on
8:34
there. I don't know what that means. Did I?
8:36
I think you did well. I can't, I
8:38
don't know what that means. Did I dream
8:40
it up? I
8:43
don't know what ABR is. Available bit rate. Oh,
8:46
available bit rate, okay. Okay,
8:48
so that's like when you call a server, the
8:50
maximum BOD, you
8:53
can, the maximum BBS, BOD, you
8:56
can connect that. So like 1200, 2400, 9600. You're
9:03
just staring at me. I don't know.
9:05
How about you go on. Okay,
9:08
these are good. We
9:10
don't use BOD enough. I think of- We
9:13
don't. It's a great term. I don't
9:15
know what it means, but it's a great term.
9:17
BAUD, it's BPS, basically. It's the same bits per
9:19
second. Right, yeah. Okay. Makes me think
9:21
of one of those modems going, making
9:23
that noise. Yes, that's exactly
9:25
what BOD is. Next
9:29
one, TCP, stroke IP. TCP
9:32
stroke IP, otherwise known to Americans
9:34
as TCP slash IP. Oh,
9:37
sorry, yeah. Okay. I'd
9:39
never heard that before. That's how they say slash
9:42
in Britishese. Okay,
9:46
all right then. Transfer,
9:50
transfer content protocol, internet
9:53
protocol. Mm,
9:55
close. Did I miss the C? Pretty close.
9:58
No, you had control of that. I think just had to all jump. out
10:00
there. Transmission control
10:02
protocol, internet protocol. Transmission
10:04
control protocol, internet protocol. TCPIP
10:07
is what, it's packets on
10:10
the internet. We still use TCPIP today,
10:12
right? Yes. Correct.
10:15
Internet traffic is TCPIP traffic. I just realized
10:17
I'm not counting how many you're getting, but
10:19
I think... I think I've gotten two. Two
10:21
out of four. Okay. Okay.
10:24
That's stakes and catalacts in the big leagues. I admittedly
10:26
didn't know that this was an acronym.
10:29
Basic. Basic. Mm-hmm.
10:32
Of the programming language. Oh, I don't know what it stands for. Beginners
10:36
all-purpose symbolic instruction code.
10:40
That sounds utilitarian and correct.
10:46
All right, two out of five. Okay. How
10:48
many are we doing? Okay. I
10:50
don't know how many we're doing actually. This is all very organized. GUI.
10:55
GUI? GUI. graphical
10:57
user interface. GUI. Correct.
11:01
Yeah. Correct. So,
11:03
like, that was a new thing
11:05
when we moved away from text-based
11:07
interfaces and we got a mouse
11:09
and a pointer and icons and
11:11
a desktop. That's a GUI. Yeah.
11:15
Yeah. I remember the birth of GUIs.
11:17
That's correct. Sweet. This
11:19
one comes directly from the book that I was reading. It's
11:22
a little unusual. Okay. I
11:24
would say it's not an internet protocol. Okay.
11:27
It's very related to the early 2000s internet. Okay.
11:32
PCLN. PCLN. Not
11:35
an acronym, but it stands for something. Just
11:38
breaking all the rules here.
11:46
I don't know. It's a company. It's a
11:48
company. PCLN. Okay.
11:51
What does this company make personal computers?
11:54
This company was one of
11:56
the prime examples of boom
11:58
and bust. boom and bust.
12:01
Is it Petco? No. Oh, is
12:03
it a stock ticker? Mm-hmm. Ah,
12:06
okay. It's a stock ticker for PCLN.
12:10
Um, uh, is it, it's
12:12
not, it's not Compaq. It's
12:14
not Gateway. It starts with P. Sorts
12:16
of P. I
12:20
don't know. Priceline. Priceline. Mm-hmm. Never
12:22
would have gotten that. I know. That's
12:24
kind of a tough one. Yeah, that is a tough one. Will,
12:26
would you have gotten that? No,
12:29
not at all. What is Priceline?
12:32
Priceline was a travel website.
12:34
Yeah. Oh, is that the thing with
12:36
William Shatner? With William Shatner? I should have
12:38
given you that. He built it, I believe. Yes.
12:40
Yeah. Definitely was not just
12:43
paid loads of money to promote
12:45
it. He built it. Okay, give
12:47
me one more because I'm deeply uncomfortable and I
12:49
want to end this on a pretty good value.
12:51
Oh, Will, do you want to do this next
12:53
one? It's going to make you deeply uncomfortable. Okay. Okay.
12:57
A slash S slash L.
13:00
Okay. A stroke S
13:02
stroke L. Is that what
13:04
you're saying? Yeah, no problem.
13:06
Okay. That's A, that's age,
13:08
sex, location. Is that
13:11
right? That is correct. Yeah. All right. So
13:13
like if you were chatting with somebody and
13:16
you would usually say A slash
13:18
S slash L, question mark, because you
13:20
wanted to know their age, their sex,
13:22
and their location. Sex,
13:24
of course, meaning gender. And
13:27
that was like an AOL kind
13:29
of chat thing that I
13:31
did not really participate in as much.
13:35
But I'm aware
13:37
of that because I was
13:39
editing Wired Stories where people
13:41
were referencing that acronym. That's
13:43
all I've been doing. That's
13:45
pretty great. That did make me deeply uncomfortable.
13:47
Thank you. Brought back memories, huh?
13:49
Yeah. It was just for finding friends.
13:54
Sure. Yeah. Just like the rest of the internet.
13:56
Just like the rest of the internet. Okay. So
13:58
is this where I get to start asking? asking
14:00
you questions and Will and I. Yeah,
14:02
how did you score? I really wasn't keeping track.
14:04
I think you got four out of? I
14:06
think I got all of them right. Let's go
14:09
with that. Three, four, five, six,
14:11
seven, eight, nine. We gave you eight and I
14:13
think you got about half. Okay. I
14:15
don't know what the prize is, but congratulations. The
14:17
prize is bragging, right? So as always. Okay.
14:20
All right. Okay, Will, we're gonna
14:22
quiz Lauren now. So we're moving on to
14:24
the mobile era, which I guess is like
14:27
roughly the turn of the century to about
14:29
two years ago or like 2005, six. I
14:34
would classify it as 2007 when the iPhone was launched
14:37
and the app store the following year, 2008 onward.
14:41
Okay. Yeah. Okay, we'll
14:43
do that. Aren't we still in the mobile era? No,
14:46
we're in the metaverse era. Yes, we're, it
14:48
was gonna full meta. All
14:51
right, well, okay. Okay, so here we
14:54
go for the mobile era. Okay, I want to, I
14:57
don't know if this, how big this
14:59
was in America, but WAP, W-A-P,
15:02
do you know this? Not
15:05
the song, Lauren. Not
15:08
the song. All
15:11
right, okay. No, I'm
15:14
gonna make this a wireless access
15:16
protocol. That's
15:19
like close enough. Very, very close. Okay,
15:21
what is it? What is application protocol?
15:23
Oh. This is where they were
15:25
like, we're gonna reinvent the
15:28
web for the mobile era and
15:30
it'll be really terrible and
15:32
clung in and
15:34
just little pixel-explanated websites. It
15:37
was kind of a thing on Nokia phones in Europe for
15:40
about a year. We had that
15:42
here too. Okay. Yeah, we did.
15:44
Yeah, the first mobile browsers were WAP browsers.
15:47
I don't think I realized they were called that. Yeah, Safari, I
15:49
think, was the first mobile browser that was an
15:51
actual browser. Huh. Yeah.
15:54
One note about this era, I think that
15:56
if I'm gonna get something confused, quite a bit, it's service and
15:58
system. It's a lot of these. Actors can
16:01
refer to either mobile quite.
16:03
A lot of s is that me neither
16:05
of the isn't This List member re okay
16:07
okay second one. M V
16:09
N O. O
16:11
o o on. Mobile
16:15
Video network operator. Close
16:17
Mobile. Mobile.
16:22
It's not video, Is it?
16:25
It's mobile. I'm
16:31
just gonna make up. And then mobile
16:34
sector know. Where people
16:36
use. Virtual Virtual little boy.
16:38
Virtual? Yes, No. or you remember
16:40
that. So what is it? Can
16:43
you define it? No, I don't
16:45
remember what it is. It's like
16:47
when a company leases spectrum. From.
16:50
Somebody who own the High A network.
16:52
Of yes it's all coming back in his
16:54
early mobile. yeah this is an i was
16:57
thinking up air and and okay well at
16:59
this is fun. The Okay:
17:01
next on sim. Or. As
17:03
I am. Oh. My. God. It's
17:05
the desert so yard hi I'm.
17:07
Lisa think I'm going to.
17:10
Quit. Lama hadn't just quit
17:12
Now a fan Will He
17:14
isn't as electronics them but
17:16
Sim is. In
17:23
a hockey thinking of right now know all this is
17:25
dead or bond. Life.
17:30
On this is that so Sim is is
17:33
a really hard one because but I'd never
17:35
would have guessed that at Stanford. what is
17:37
everyone. Okay so it is the
17:39
thing that gives you connectivity on
17:41
your mobile device. So I'm gonna
17:44
go with satellite. Is
17:46
that correct? Okay, I'm. System.
17:49
Know. Okay simulated
17:51
know I'm will tell
17:53
you it's very as
17:55
a direct. And and
17:58
pace. I
18:01
don't know. I don't know if
18:03
the subscriber identity module. Oh
18:05
come on, yeah you. Know
18:08
if we're done is very weird. Oh
18:10
wow oh the poor I mean it puts it probably
18:12
is a simulated I don't see more. do some where.
18:16
He loves thinking about one. Meal
18:20
is okay. Ah well. I'm I
18:22
think I'm over three or four.
18:25
Will. Get some good ones. Okay, okay,
18:27
I'm. But. It or of
18:29
a softball sms. Sort.
18:32
Messaging service. Yeah, such
18:34
the simulated miss. The.
18:37
Skies A. Bookcase
18:39
related question Mms. The.
18:42
Know Afp, Rcs, Rcs
18:44
is. This. Is one of the ones
18:46
where I'm going to get system in service confused.
18:48
It's rich and communications. Service.
18:51
Yes, okay phrase. yeah so that
18:53
is is. so there's Sms which
18:55
is sort messaging services tax base
18:57
and happens over the wireless networks.
18:59
Them and Mms is when Sms
19:01
basically got upgraded to multi media
19:04
a service and then am Now
19:06
Rcs is the wireless network that
19:08
and Google backed sort of new
19:10
era I'm at of Mms. It's
19:12
bringing richer communications yeah to what
19:14
would typically be. Texas messaging? Yes,
19:17
yes. Tax stickers? Yeah, that fun
19:19
stuff. Holloway seeking any element of
19:21
entire relationship now through tobacco. Thanks.
19:25
For that easy one guy is so great! Okay,
19:28
we'll pick another one. Okay
19:30
Bbm. Blackberry,
19:32
Messenger. which predates
19:35
I message as one
19:37
of be ah the
19:39
first at. Peer. To Peer
19:41
direct messaging services on your phone that
19:44
was actually owned and operated by the
19:46
Blackberry network. Hey now that was really
19:48
fun! Sign This Bbm. it's my
19:50
favorite bbm thing is when
19:52
they started buying product placements
19:54
in television shows so characters
19:56
would say sodium me or
19:58
b b m And
20:01
then they pull out their blackberry and the camera would
20:03
show them on their blackberry doing that. Yeah.
20:06
Terrible. Yeah. Okay.
20:09
Here's another one for you. Okay. So see, sometimes
20:12
I'm on a chip. Yes. Yeah. That
20:14
refers to when it's one piece of silicon, but it's a
20:16
put together to create a system. And so you might have
20:19
one core or basically
20:22
one chip in the system that's dedicated to
20:24
ML, and
20:26
then you might have, which is machine learning, or you
20:29
might have another one that's basically dedicated to like
20:31
the core processing and yeah, but you put
20:33
them all together as a
20:35
system. Qualcomm is a very well known maker of
20:37
system on a chip. Apple makes its
20:39
own new. Lots
20:42
of people make them. Okay. Will,
20:45
throw her another one. Okay. How about
20:47
CDMA? This one is really
20:49
tough. Yeah. Yeah.
20:55
Was it like a precursor to 3G
20:57
and it didn't? No, 3G, it was
20:59
a type of network. So
21:03
early on mobile phones, I don't, were
21:05
pretty much divided between GSM and
21:08
CDMA. GSM was more popular in
21:10
Europe. So, Will, you might've been on GSM.
21:14
Here it was, yeah, you basically,
21:16
when you bought a mobile device, you had
21:18
to specify based on which wireless carrier
21:20
you were on, what kind of device you
21:22
were using. Oh right, that's right. Because you
21:24
had different ones. Yeah. Verizon was our CDMA
21:26
network. That's right. That's
21:29
right. And 3G was more in the category of like LTE,
21:32
which stands for long term evolution, the
21:34
type of service you would get. But
21:37
okay, I'm going to- You should have waited for us to ask you that if
21:39
we could get another point on the board. Oh
21:42
shoot. Okay. CDMA, it's consolidated? Nope.
21:45
Concentrated? Communication? Uh,
21:55
can you give me the first word? Code. Code?
21:59
DMA. I would like to talk to the manager. It's
22:01
just so obvious, so logical. What are you talking about? This is
22:03
really fun though. Bring it on. What's another one? I
22:05
don't know if this is really mobile era, but it's
22:07
three emojis. An
22:13
iemojis, an iemojis, an iemojis, an iemojis. What?
22:21
An iemojis? Yep, an eyeball. And
22:23
then the mouse. And then the mouse.
22:25
And then an eyeball. This is actually
22:27
a way more recent thing, I
22:29
think. Okay. I can't believe
22:31
it. I think it's okay. I'm a little
22:38
bit too old, actually, because I think I'm going to
22:40
have to get my eye on it. This
22:47
is actually a way more recent thing, I think. Okay.
22:50
So it's I. Here. No,
22:52
that's an ear. I talk. I
22:55
have no, no one's ever sent this to me. Am
22:58
I being left out? What is this? It stands
23:00
for it is what it is. Really?
23:04
Yeah, it's kind of a TikTok thing, I think. Oh,
23:09
wow. That's amazing. I
23:14
have to say, I
23:17
was picturing a totally different era here. Well,
23:20
this was the very beginning of the
23:22
pandemic, this ramped up. Oh, I
23:25
mean, no, not what I mean
23:27
is you went really early mobile and
23:29
then you fast forward it into the
23:31
future. I was waiting for like ARPU
23:34
and ATT and GPS and stuff like
23:36
that. Oh, GPS would have been a
23:38
good one. Yeah. I think
23:40
we should have had it. App tracking transparency. I
23:44
was like clearly in kind of
23:46
like app mode. All right, though,
23:48
I didn't. You know what? I had
23:51
a lot to learn. I think you did. We
23:54
clearly need some Gen Z person
23:56
to come on and do TikTok.
24:00
All right,
24:05
so Mike, I think that you're in the lead, technically, whatever
24:07
this competition is. I
24:09
wasn't keeping score, but okay, thank you for keeping score.
24:12
Yeah, okay. This is really fun. Thank you for schooling
24:14
me, guys. We are going to take a quick break.
24:17
And when we come back, we're going to spend the
24:19
entire next segment talking to Will, quizzing
24:21
Will about AI, because it's what
24:23
everyone's talking about. So
24:26
stay tuned. Support
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for today's show comes from Deloitte. If
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25:56
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25:58
Decoder is moving to Monday's. and
26:00
Thursdays. We're adding a second episode of the
26:02
show. On Mondays we'll have
26:04
our classic interviews with CEOs and other
26:07
troublemakers. I think we're gonna have to
26:09
start having conversations about how do we
26:11
pay those jobs that can't be done
26:14
by AI. And on Thursdays we'll be explaining
26:17
big topics in the news with Verge reporters,
26:19
experts, and other friends of the show. There's
26:21
a new generation of people on the Internet. Google
26:23
search has always sucked for them so you know
26:25
there's no reason for them to be loyal so
26:27
they can just go to TikTok. This
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26:31
all this. So go subscribe wherever you get your
26:34
podcasts now. Okay
26:37
so now we've brushed up on basically 25 years
26:40
of tech acronyms. Congratulations. You're
26:44
now everyone's favorite dinner party guest. It's
26:48
time for AI. This is the part I think
26:50
everyone is going to be most interested in right
26:52
now because you're hearing these phrases literally all the
26:54
time and some of them are probably hard to
26:58
grok. See what I did there?
27:01
That's Elon Musk. No
27:03
it's not an acronym. We can't get through an entire show
27:05
without mentioning Elon Musk. Mike, do
27:07
you want to go first and grilling Will? Sure.
27:10
Okay. Will, I'm gonna kick off with the hardest
27:12
one on the list. Okay. LLM. Oh,
27:17
okay. That's a
27:19
large language model which
27:22
is what everything's built on
27:24
these days. Chatbots
27:26
and half of the internet. Built
27:29
on language, large language model. Yeah.
27:33
What to say about LLMs? They are, yes
27:35
it is an acronym that you hear it
27:37
hearing all the time. So if someone
27:39
is at a dinner party and someone starts
27:41
talking about LLMs in the context of AI,
27:44
how would you describe like here
27:46
was this era of AI but now everyone's
27:49
talking about LLMs because they do X. Okay.
27:53
Yeah. So language
27:55
models are this example of
27:57
generative AI. Generative AI
27:59
has been... around for decades and decades in
28:01
that you generate stuff with
28:03
an algorithm. But the
28:06
large language model era is this
28:08
era where they figured out feeding
28:10
it enough information, feeding it basically the
28:12
whole of the internet and as many books, a
28:15
lot of copyrighted material as well, it turns out, into
28:18
these particular types of algorithms,
28:22
enables them to conjure up very
28:24
convincing seeming text. But the large
28:26
is the key word there because
28:29
they're absolutely enormous algorithms and also
28:32
fetch huge amounts of information or data.
28:36
Nice. Nice. Thanks, Will. And
28:38
that's our show. That's
28:41
all you needed to know. No,
28:43
there's another one that we're hearing a lot these days, GPT.
28:46
GPT, oh, shh. OK. Generative,
28:51
pre-trained transformer, right? Yes.
28:54
So it's a generative. So that's the word.
28:56
Talking about the generative models is
29:00
the type of AI algorithm that doesn't
29:03
discriminate, doesn't recognize things in
29:05
text or images, but generates
29:08
stuff when given a prompt. And
29:10
pre-trained means that they are pre-trained
29:12
actually on specific types of data
29:15
before being trained on the
29:17
entire internet. And transform is a certain
29:20
type of algorithm that
29:22
is a neural network that
29:24
can focus on lots of different stuff at once, which
29:26
turns out is very useful for language because you kind
29:29
of need to know the end of a sentence in
29:31
the beginning to make sense of what's in the middle
29:33
of it. Stephen
29:36
wrote a great story about the
29:39
paper, attention is all you
29:41
need, which came out of Google, which sort
29:43
of transformed AI
29:45
by revealing
29:48
you could do kind of amazing things
29:50
with language using these models the
29:52
first time, or yeah, pointed that way. That's
29:55
our colleague, Stephen Levy, who wrote that. We'll put
29:57
a link in the show notes too. Yeah, you
29:59
can. read it now, it's really good. And
30:02
a lot of people probably know
30:04
this, but for those who don't, when you refer
30:06
to something like ChatGPT, which was released
30:09
by the company OpenAI, the
30:11
P in ChatGPT comes from that Google
30:13
paper, that group of Google researchers.
30:16
So it's kind of a derivative of Google's
30:18
talk. Yeah. Okay.
30:21
Yeah. Pretty much, I mean, this is
30:23
in Stephen's great story, pretty much all of the
30:25
people on that paper, the Transformer
30:27
paper, ended up creating
30:30
their own, well, a lot of them went off and
30:32
created their own startups and got billions
30:34
of dollars of funding. That's
30:36
why we all do it though, right? To make billions of
30:38
dollars. Humanity
30:42
be damned. Speak to yourself. Yeah.
30:44
Okay. Here's another one for you. The third
30:46
one. You're two for two, by the way.
30:48
Congratulations. You're already whooping at us. He's going
30:50
to sweep. NLP.
30:54
Okay. That's
30:56
natural language processing. Yes.
31:00
Pardon me. Well, it's processing. Processing,
31:02
sorry. Yeah.
31:07
As opposed to NLU, natural
31:09
language understanding. So that means doing
31:12
stuff with language, not just understanding,
31:14
not comprehending the language, but doing
31:17
also synthesizing sort of stuff. Okay. So
31:19
natural language understanding is so that a
31:21
computer can understand what you're saying when
31:23
you say something to it, but natural
31:26
language processing so that it can answer
31:28
you in a way that is like
31:30
human-ish. Is that right?
31:33
Yeah. I think it refers to
31:35
the bigger field of doing stuff
31:37
with language, I think. So it
31:39
could be, yeah, I think
31:41
it can encompass voice recognition
31:43
and processing, that sort of stuff. I think. I
31:45
could be wrong. We're going to go
31:47
with your right.
31:49
Yeah. Here's the next
31:51
one, which I think you'll get. RL. Oh,
31:55
reinforcement learning. So
31:58
this is... He's crushing it. Now these are
32:00
all more recent, and I think you're being kind
32:02
to me because there are a
32:05
billion abstract acronyms out there.
32:08
RL is the type
32:11
of algorithm that AlphaGo was, so
32:13
it's quite different to things like
32:17
chat GPT in that it's the
32:19
idea of having a computer learn through
32:21
experimentation how to solve a particular task.
32:24
So they deep-mined, didn't
32:26
invent reinforcement learning, that was this
32:28
guy who was rich something or
32:31
he was a pioneer of it. And then, but
32:34
they figured out you could with more
32:36
powerful computers get machines to
32:38
do quite impressive tasks that it's impossible to
32:40
program a computer to do. So
32:42
they started off with having it play Atari games better
32:44
than a person and then they
32:46
famously demonstrated it on Go,
32:50
the board game which is very difficult to
32:52
learn and play and it's very sort of
32:55
difficult to describe what makes a good move, so
32:57
it's hard to break up. The
33:00
idea is through sort of it has reinforcement
33:02
in the form of positive or negative feedback
33:04
for a good or a bad move moving
33:07
towards a goal. Interestingly,
33:11
the big thing that people are trying to do now to
33:14
move beyond chat GPT is combine some
33:17
of these two things. A
33:19
lot of places are trying to do that because so
33:22
chat GPT sort of goes off the rails and says mad
33:24
things and it doesn't understand what numbers are,
33:26
which is kind of a problem
33:30
in an intelligence. But you can use
33:33
reinforcement learning to have it possibly figure
33:35
out how to perform specific tasks maybe
33:37
including things like math, but it's
33:40
not demonstrated that it will
33:43
work but this is sort of an idea to
33:45
take these two big things in AI and
33:47
combine them. Okay, here's another one for you.
33:52
LSTM. Okay, this is, I have
33:54
to mention the name of the
33:58
board game. of the guy
34:01
who invented this because he's famously
34:03
gets upset when people don't it's
34:05
um Jurgen Schmidhuber invented the LSTM.
34:08
Yeah he's
34:11
quite a character um long
34:13
short shoot
34:16
something memories long short term memory.
34:19
Yes correct oh boy yeah I
34:21
know we should have made this
34:23
harder. So that's a type
34:25
of neural network that can
34:29
tap into memory essentially which is something they don't
34:31
have and it's there there are a lot of
34:33
these different architectures um this
34:36
is one that's quite was quite old it sort
34:38
of predates a lot of deep learning stuff but
34:41
it was very important and influential hence why Jurgen
34:44
likes to be credited with stuff.
34:46
He's actually I spoke to him recently and he was
34:48
doing some very interesting things trying to build new
34:51
these these models that kind of argue with
34:53
each other in order to figure out a
34:55
task like a kind of
34:57
Gestalt thing that if one
35:00
isn't good at it if another one
35:02
can figure it out you have these specialized networks.
35:04
Wow is
35:07
he a big Stanislaw Lem fan? He's
35:10
a huge science fiction fan actually yeah he's
35:12
he's about why do you why is oh
35:14
because of the computers arguing with each
35:16
other and like competing to try
35:19
to figure out problems. Maybe
35:21
that's where I didn't think of that yeah
35:23
he's a huge science sci-fi nerd um it
35:26
must be probably his inspiration for us. So
35:30
this is what you're describing is the new
35:32
era of the virtual assistant remembering
35:34
what you talked about before because there
35:37
was a version of this like on Google Home Assistant
35:40
several years back at this point I would say at
35:42
least five years back where Google would
35:44
say like you would say to the Google Home
35:46
or your assistant on your phone hey Google ask
35:49
it a question like how
35:52
tall is LeBron James and
35:54
then without having to prompt it again saying
35:57
okay and what team does he play for and having
35:59
these this volley back and forth
36:02
where it had a limited amount of memory to
36:04
remember what you asked initially. But now,
36:06
right, well, it's this movement towards you
36:09
could ask Chachi BT for an itinerary
36:11
for Barcelona and then come back days
36:13
later and pick up the thread and
36:15
just something like, what else should I
36:17
add to my trip? And
36:19
it's going to remember what it was you talked
36:21
about. And people are talking about this too, not
36:24
only in the form of
36:26
like customer service agents and stuff like
36:28
that. But even in
36:30
like EQ AIs, like ones that
36:32
are meant to do more emotional tasks,
36:34
if you're using one for therapy, imagine
36:37
coming back a week later and having
36:39
it remember what you talked about before. Yeah.
36:41
Yeah. That's it. Yeah. The idea
36:43
of having Chachi BT doesn't remember anything but
36:46
beyond a long streak, these
36:48
previous prompts. Typically, I think they've added some
36:50
more memory, but that idea of having a
36:52
bigger memory. Yeah. Okay. It's a big thing.
36:54
But the other thing that he's looking
36:56
at is the idea that because you can
36:59
have a small model that's better than GPT-4
37:01
if it's very much trained
37:03
on a specific task. So this is the
37:05
idea that you have a bunch of these ones that
37:08
interact with each other and then they
37:11
can work as well as a really big one. But
37:13
also, I think they're back and forth. The idea is
37:15
that it kind of shakes out more something
37:18
cleverer. I don't
37:20
know if it works. Okay. The
37:22
next one, Palm,
37:24
which I'm pronouncing like a word, but
37:27
it's P lowercase a and then capital
37:29
L M. Oh,
37:31
God. I'm not going
37:33
to get this one.
37:35
Yeah, we stumped out.
37:37
I'm going to make
37:39
something up. I wouldn't
37:42
get it. It's a language model at the end,
37:44
probably. Oh, yeah. Well,
37:51
just so you know, before this episode, we
37:53
joked about calling this WILM, which would be
37:56
W-I-L-L-M, the Will
37:59
Learning. large language model.
38:02
One day my language model would be able
38:04
to come in my place and
38:07
get it all right because it could look it up. I
38:10
don't know. Free-trained,
38:16
amazing language model. Nope.
38:19
Good guess. It's Pathways
38:21
language model. Oh. Okay.
38:25
Yeah. Yes. We got them. So who
38:27
does this? This is the one, this
38:29
is the precursor to chatgbt.
38:32
They, right, they built
38:34
some pretty amazing chatbots on top
38:36
of it but never released them I think.
38:38
Okay. That's right. Well here's
38:40
another one with a lowercase in it.
38:42
This one is llm.
38:48
I don't
38:51
know this one either. I
38:53
think you do only because I'm
38:55
gonna give you a hint because I think
38:57
you know the company behind it and that
39:01
letter factors into this. Well,
39:06
so meta's behind llm.
39:14
Two Ls. Language,
39:18
how does meta, I don't know. I'm
39:24
stuck. No idea. It's
39:27
large language model
39:29
meta AI llama.
39:33
Makes perfect sense. Yeah, right. So
39:35
there's like extra, so there's large
39:37
language, lowercase a, model, meta AI,
39:40
but there's not two M's, it's
39:42
just llama. Should
39:44
have even got large language and made
39:46
the rest up. They really just weren't
39:48
meta in there. They did, yeah. Because
39:52
it was an alpaca I think. I think they wanted
39:54
to get something, I think.
39:56
I might be hallucinating that. Anyway,
40:01
the next one, which will be the last one, is it's
40:04
a bit of a trick because
40:06
it's not an acronym, but maybe you can describe where
40:08
it comes from. DALI. D-A-L-L
40:11
dash E. Oh, I do know this. It's
40:13
a, I might get this word wrong. Is it
40:15
a portmanteau? Is that what you say? Yes,
40:17
portmanteau. It's a portmanteau, sorry, of Salvador Dali
40:19
and Walle. Good
40:24
job. Excellent. Wow. I
40:27
just remember that because it was, every
40:30
story had to explain that
40:32
it was a portmanteau or whatever
40:34
the word is. And so I was like, shit, I
40:36
better learn that word, which I haven't done. This
40:39
is an image generator. Right, right.
40:42
This is the open AI
40:44
image generator that is
40:47
trained on lots of
40:49
nice artwork. Yes,
40:52
trained on the whole internet, which is also artwork, I
40:54
guess. All belonging to open
40:56
AI. Yes. Only
40:58
within copyright. No issues there. Yeah.
41:02
I think Will won. So I... Handily.
41:05
Did I? I think so. Yeah,
41:07
you got six out of eight. Okay. I
41:10
felt like I fell off a cliff. Well,
41:13
yeah, but you win all your games in the first
41:15
half of the season, it's okay to lose
41:17
some in the second half of the season. Is that how
41:19
it works? Don't you get closer to the playoffs in the
41:21
second half? Yeah, but your stands are
41:24
full all year round if you win all your
41:26
games in the first half of the season. Spoken
41:28
like a true capitalist. I'm an A's fan. He's
41:30
bringing A's hat right now. But
41:35
it might be slightly different if
41:37
you guys were writing about CDMA
41:40
and the like every week. That's true. Yeah,
41:43
that's true. This is true. I
41:45
mean, I referenced those things a lot.
41:49
When I first started writing about tech, I
41:52
was a video journalist at the journal and then I
41:54
started writing and I remember my first story was about
41:56
MDTV, mobile DTV. But
41:59
then after that, I just... I just sort of cruised right into like,
42:03
modern smartphones and App Store. The
42:06
early stuff is really, that was really fun.
42:08
Foundational. Super fun to learn about. You did
42:10
well. I don't know. Not
42:13
so much. I can admit defeat. What
42:15
does Will get? What's his prize? He gets to
42:17
go first on recommendations. Ooh, good
42:19
one. Let's take a break and come back with
42:21
those. This podcast is supported
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43:45
Well, as the winner of our
43:47
acronym competition, all
43:50
we can give you is the option
43:52
to go first in recommendations. So
43:55
have at it. I wish
43:57
I'd known, because I would have deliberately failed
43:59
to buy my... have more time. I'm trying
44:01
to come up with this on the fly,
44:03
but I did think of it before. So I'm
44:05
going to recommend a book
44:09
I read recently, The
44:11
Rise, I'm holding it up for the listeners
44:14
on the Rise and the Fall of the
44:16
East by
44:18
Yashan Huang. It's a, the
44:21
East in the title stands for exams,
44:23
autocracy, stability and technology. It's a kind
44:26
of incredibly in-depth
44:29
historical analysis
44:31
of Chinese bureaucracy, which sounds like
44:33
a real page turner, but
44:35
it's actually incredibly well done
44:37
and does a lot to actually
44:39
explain how China got
44:42
to where it is and why sort
44:45
of this paradoxical place
44:47
where it seems very
44:49
innovative, but in a way, in some ways, it's
44:52
not quite, it's not as innovative or it's sort
44:54
of held back. And I find
44:56
it very convincing as to why
45:00
also China is where it is right now with the
45:02
current leadership. And
45:05
it's really fascinating if you're interested
45:07
in China and its technology. Hmm.
45:10
And how did you hear about this book?
45:12
I, so I know Yashan Huang
45:15
from MIT and
45:17
I know his work. He's quite a famous scholar on
45:19
China and it's tech industry
45:21
and they asked me
45:23
to interview him on
45:25
stage there where I lived down the road from IT.
45:27
So I had to furiously read it in two weeks,
45:29
which was kind of intense, but I sort
45:31
of knew, I knew he had one in the works because
45:36
I'd been talking to him. This is how
45:38
modest Will is. I think a lot of us would
45:40
lead with him. You know, so I spoke to this
45:42
really smart thinker and author recently and our talk was
45:44
so scintillating and also he has a book and
45:47
Will kind of led with, you know, I'm
45:49
reading this book and then how do you
45:51
know him? And oh, I happen to enter
45:53
MIT. I happen to interview him. I'm just
45:55
very incompetent at presenting things. Not
45:59
at all. Will we will not only link to
46:01
the book in the show notes, but we'll link to your talk as
46:03
well. Thank you Mike What's
46:05
your recommendation? My recommendation
46:07
is on topic for this week's show It's
46:10
called the jargon file and it's this open
46:12
source Text document that lives out on the
46:15
internet that you can print out or you
46:17
can just look at it can be copied
46:19
and Paste it in other places, but it's
46:21
basically a big Glossary
46:23
of like hacker slang and acronyms
46:25
and fun terms. There's a lot
46:27
of humor in it and
46:30
it's a fun way to like look at
46:32
computer history through like the language and the
46:35
rhetoric that people have Like
46:37
associated with computers and how hackers talk to
46:39
each other So yeah, the jargon file is
46:41
just it's basically just like an A to
46:43
Z dictionary and you look up words For
46:46
example some words that are in there. I'm
46:48
just gonna flip through bit rot
46:51
Is a good one Cyber space
46:53
and cyberpunk are both defined in here dancing
46:56
frog Kill
46:58
file The
47:01
pumpkin The
47:03
patch pumpkin the pumpkin holder Spoiler
47:07
space vaporware is defined
47:09
in here Windows
47:11
with a Z is defined in here. So
47:14
it's it's really fun like way of Looking
47:16
into into hacker culture and computer
47:19
science over the years. Cool. Yeah,
47:21
who created it? Uh, it's a it's
47:23
an open source project So a bunch of
47:25
people have edited it and contributed to it
47:27
over the years someone must have started it
47:29
I'm sure somebody did start it. Yes, I
47:31
don't know who that person is But
47:34
it's very easy to find because it's
47:36
hosted multiple places and you just need
47:38
to search for jargon file You
47:41
search for those two words together and you'll find it. It's
47:43
just a big HTML document
47:46
Super cool. Yeah, super nerdy. Yeah, that's what we're
47:48
here for. It's a lot of fun I thought
47:50
we should rename our podcast super nerdy super nerdy
47:54
Then we actually have to live up to that descriptor
47:56
and I don't know that's a couple of english majors
47:59
sometimes I Yeah, sometimes my
48:01
recommendations are very basic. Okay, I
48:03
want to hear your, is this all CAPS basic?
48:05
B-A-S-I-C basic? No, but I like how
48:07
you're bringing it back. Okay. My recommendation,
48:10
it's not at all academic or
48:12
nerdy. It's an app called
48:14
Forest. Some of you might be
48:16
familiar with the Pomodoro method of working, which
48:20
means you set a timer and you work
48:22
for 25 minutes straight,
48:24
no distractions. There are
48:26
a lot of different apps that
48:30
sort of take advantage of the Pomodoro
48:32
method and then create different user interfaces
48:34
for it and different mechanisms. There
48:36
are also web versions for people who get
48:39
really distracted on their desktop
48:41
with stuff flying in their browsers and 18 different
48:44
browser tabs open. This one
48:46
is a mobile app. I think it cost me $2.99
48:48
to download. And
48:50
I just clicked the button and showed my face and
48:52
I had it, but I'm pretty sure it was $2.99.
48:55
Is that how we buy things on the internet?
48:57
No. And
48:59
it simulates planting trees. So every time
49:02
you work 25 minutes uninterrupted, set the
49:04
timer, you've planted a tree at the
49:06
end of it. So it's the Pomodoro
49:08
method, a little bit gamified, and it's
49:11
just a good way to cut down
49:13
on distractions if you feel
49:15
like you're a little bit ADHD as
49:17
you're working. Okay. Yeah. So check out
49:19
the forest app. Then you can, if
49:22
you want to work for an extended clip, you can
49:24
just set 25 minute increments with a
49:26
five minute break in between. I'm not using it
49:29
quite like that yet. I'm just picking a 25
49:31
minute block and going for it. That's
49:33
nice. Helpful for writers. Yes. Or
49:36
anything task-based. Correct. Yeah.
49:38
All of a sudden thinking about doing administrative
49:40
work or filing expenses or something like that
49:42
becomes a lot more tolerable if you just
49:45
think, I can do
49:47
this for 25 minutes. Go make a tree.
49:50
Go plant a tree, a little virtual tree.
49:52
That's lovely. In a simulated world like
49:54
Sim. I had to bring
49:56
it back too. Before we say goodbye, I just
49:58
want to give a shout out to... So folks who
50:00
have been leading us reviews on the Apple Podcasts
50:02
app, we love the reviews. We genuinely
50:05
read them. We
50:07
appreciate the feedback and thank
50:09
you for everyone who's listened to
50:11
this point. Go leave us a review. We could
50:13
say thank you to the people who've left
50:15
reviews on the Google Podcasts app, but that's
50:17
gone. So there is a one-day. RIP Google
50:19
Podcasts. I know where else do people
50:21
leave podcast reviews? YouTube probably.
50:24
Oh, all right. We'll see.
50:26
We're not on there. Sure we are. We
50:28
are? YouTube publishes there, I think. But
50:31
you don't see our faces. You just see
50:33
the robot blowing the bubble. Yeah. Are
50:36
you guys on TikTok? Do you have
50:38
a TikTok? Sometimes. Our podcast
50:40
is on TikTok. I go on. Mike's on
50:43
TikTok a lot. He needs the Pomodoro
50:45
app to focus because TikTok
50:47
is ruining his life. He loves the
50:49
dancing teen videos. It's really weird. He's
50:52
shaking his head right now. Because I don't.
50:56
I don't. You knew what the emoji, the
50:59
TikTok emoji were. Well, yeah, because I
51:01
had the sheet that Will and I shared. Oh,
51:03
right. Okay. Speaking of, Will,
51:05
thank you so much for joining us. Thank
51:08
you for having me and for letting me win. I appreciate
51:11
it very much. You're very welcome. It's been an absolute
51:13
pleasure. And Mike, thanks for being a great
51:15
co-host. Of course, anytime. And thanks to
51:17
all of you for listening. If you have feedback, you can
51:19
find us all on the social networks. Just check the show
51:21
notes. Our producer is the
51:24
excellent BA, otherwise known as
51:26
Boon Ashworth. Goodbye for now. We'll be back
51:28
next week. Hackers
51:36
and cyber criminals have always held
51:38
this kind of special fascination. Obviously,
51:41
I can't tell you too much about what
51:43
I do. It's a game. Who's
51:45
the best hacker? And I was
51:47
like, well, this is child's play. I'm
51:49
Dina Temple-Reston. And on the
51:51
Click Here podcast, you'll meet them and the people
51:54
trying to stop them. We're not afraid of the
51:56
attack. We're afraid of the creativity
51:58
and the intelligence of the human beings. behind
52:00
it. Click here. Stories about
52:02
the people making and breaking our digital
52:04
world. AI machines,
52:06
satellites, engine ignition. Click here.
52:09
And with that, click
52:11
here every Tuesday and Friday wherever
52:13
you get your podcasts.
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