In today's episode, Frank and Andy sit down with special guest
Max Sklar to delve into the world of artificial intelligence, data
science, and data engineering. Max, a
trailblazer in location data and machine learning, shares
insights from his extensive experience at Foursquare, including his
work on local search and bias correction. Get ready
for a thought provoking discussion about groundbreaking projects, tech
drama, and the ever evolving landscape of technology.
So sit back, relax, and prepare to be amazed.
Hello, and welcome to Data Driven, the Podcast, we explore the
emergent fields of artificial intelligence, data science, data engineering,
and all that good stuff. With me on this ever present,
journey down the information superhighway is Andy Leonard. How's it going, Andy?
Good, Frank. How are you doing? I'm doing alright. It's been a wild 24
hours in, Maison Levin, or or
Maison, Lavinia, depending on your, how you wanna pronounce it.
At one point, we will share those crazy
details, but it's been good most of the part. I I am,
recovering from a, a bout of COVID,
that hit the entire house. I think we all picked it up on the cruise.
Again, there are worse places to pick up COVID, and there's worse
things that could happen. I've I've sneezed quite a
bit, I've coughed quite a bit, but The thing that's bugging me the most is
this headache I had now for 48 straight hours.
But it's okay, like I'm kind of living, learning to live with it, and I've
actually given it a name, I call it Charlie. So, you
know, Charlie is is is gonna be on the show today as
well. I see. Yeah. You do sound a little different, but not much.
Nah. That's why I need to clone my voice, and, maybe how they
do the avatar stays. As I was saying in the virtual dream room, this is
the 1st time I've I've felt fit for camera In about
a week. Perhaps you can use some of that good
AI voice modulation. There you go. We are
pleased to present, with us, today is Max Sklar.
Max is a, not only a fellow, data
guy, but also a Fellow podcaster.
Welcome to the show, Max. Thank you so much, Frank and Andy,
for, for having me on. I'm looking forward to it. And,
yeah. Thank you. Cool. So you you've been at you were at a
company for a number of years. You were talking about this in the virtual dream
room. You were at Foursquare for, oh, quite some time. Right.
That's unusual, actually. 10 years at a at a company as as, you
know, building software is pretty unusual. But I was really there For, I
think, like, 3 different phases, so I kind of break it up into 3
jobs. Interesting. So, you know,
Foursquare is one of those companies that
On when they broke up into 2 different parts, it kind of I didn't
understand it. Like, this is just user. I'm not I'm not I know 1 I
know that sounds terrible, but despite being my headache, Charlie talking, but, like,
what, you know, obviously, that was a decision
that was made in the marketing department, and I don't think people probably thought that
through. But I love that location. Like, I I think, you know, like, I was
the mayor of, like, 5 6 different places. It was such a cool
creative, concept that if you go there long
enough, you're the mayor, and some places would offer the mayor, like, a free coffee
and a donut or something like that. Like, it was really clever.
I know. I used to I mean, that was one of the things that got
me really excited about it back in the day. And this is already 10 years
ago. I assume you're talking about the, The app split back in
2014, which I guess is That sounds about right. I I guess
almost 10 years. I guess it's It it's actually been 10 years since we started
working on that, which was also how it worked. But,
no. I I mean, I actually remember a lot of what was going on
at the time, and, we could talk about that a little more in a
second. I I I I I'm happy to talk about that.
I I think the the whole gamification thing was really
exciting. And I really loved the idea that I can go into
a place and be like, hey. I was here for 5 times. Oh, we're gonna
give you some free, you know, free, you know, dessert or
free chicken nuggets or whatever. Maybe these days, you know, kind of trying to watch
what I eat a little bit more, it might not be as exciting. I I
it was really sad that that didn't scale as a business. I think what
would happen was, and and this I both experienced
this personally. And also from talking to the leadership there, it was clear
that they felt it it, You know, it it it wasn't a
sustainable thing because you'd often go into a place and say, hey, you know,
I checked in here all these times. I got all these rewards. You know,
I'm supposed to get, like, a dollar off, or I'm supposed to get some
some free dessert or something, which is It's always exciting
to get a little reward, even if it's not, you know, even if it's just
a dollar or whatever. It's always but I think what would end up happening is
like the the man, the The management who worked there or the or the
bartender or whatever would know about it. And, you know,
once a few times that, you know, you got that thing. Oh, oh, let me
go in the back and check. And then they come back in, like, 20 minutes,
and it's like a whole big deal. They're like, maybe I don't wanna do this
again. That's interesting, because, like, from my point of view,
it was always very It was very fun, like, so I was mayor of,
there used to be a ferry service between, in the
suburbs of DC, between Poolesville, Maryland and
Leesburg, Virginia. And, I would take that ferry a lot so
much. So I was actually the mayor of the ferry. It didn't get
me anything, like, in that case, but it was this kind of cool, like I
I love to I I always imagined, like, there would be, You know, like
the ferry could have some kind of a a little like TV screen that could
like show who the mayor was. And then you can, you know, and and and
then then then we can like kind of scale up those fights, But alas,
society didn't go in that direction. Interestingly,
Foursquare, what, like, I have been interested in that this like local
local search space since since well before forest grabbing even my, you
know, my my senior project as an undergrad, back,
though, starting in 2005, was this, like, you know, this this website
called sticky map, where people would post little, Icons all
over Google Map. It's kind of inspired by Wikipedia. You can add messages.
And I just thought it was pretty cool. People started marking up the, the campus,
and then people started, you know, marking up. It was like, well, it's based on
Google Maps API, so you could just mark up anything you want. And
I think in that first project, I noticed All the problems
that still exist with that kind of data today. Okay. What happens when you
add duplicates? It's the first thing that happened. As, you know, As an undergrad, I
was like, oh, I'm so excited. Let me show something like this. They're like, okay,
let me create a marker. And I'm like, don't create that one. It's already been
created. And then it's like, okay, now we have duplicates Right off the bat. And
that is still something that, you know, Foursquare
deals with and I'm sure Google deals with and Apple Maps deals with and then
they they all deal with it. So,
Yeah. It it it it I I think when I
discovered Foursquare, you know, several years later,
it was the it was innovative in several ways. It was, first of
all, it was based on on mobile apps actually being at the place when you're
commenting on it, which is exciting. It was the gamification of it. And the
fact And and for those listening Yeah. For those listening, that was new. Like,
that was brand new. So sorry. I didn't mean to cut you off, but, like,
the context is important because All I had was was something on a on
a website. You know, I I wasn't thinking of the I the iPhone didn't
exist, for actually, pre Foursquare,
there there was something called dodgeball, which was kind of the the
predecessor to Foursquare, Which I wasn't involved with, but it but, it
it was based on, like, you know, SMS kind of text messages where people were
messaging on. If you remember, You know, those, you know,
the the the what was it? The t nine texting where people were Oh,
God. Yeah. Yeah. Yeah. But people would use that and and Foursquare and,
Google bought that. And then the the the founders,
Dennis Nautz went it went in and started Foursquare after that.
So, very, there there's a very
interesting history of kind of, like, Local search, city guide,
and basically sort of social kinda local applications. We're
we're very big at the time. Nowadays, I think we need to find a new
take on it, but, that when I joined Forescord
in 2011, it was very exciting. I'm glad you mentioned that because there was
a a real so I started my career in New York City. Right?
So I worked at Barnes and Noble com. So I was there in the fairly
early days of Silicon Alley, and that was a huge thing. It was
Microsoft. I think it was Microsoft had something called Sidewalk.
And then there was there was maybe it was maybe it wasn't Microsoft. Maybe
somebody else had something called sidewalk AOL had something, going and the fact
you can't remember it, I think says it all. Right? Like, it was like in
in, you know, anybody that could register a .com could spell and could
spell HTML To get funding back in those days, a
bit like the way the AI startup ecosystem is kinda
today. But, no, there was I mean,
people there was a time, young children
out there, That when, you know, people saw the online
world is slightly different than the real world, and they saw this as an opportunity.
Right? But no, I'd like That takes me back. As soon as
you said to, like, the local kind of connection guides, I was like, wow, it
takes me back. Yeah. Yeah. Coming back to
the app split, and I wasn't expecting to talk about this today. I'm sorry if
it brings up If for what it's worth, I'm a former Windows For what it's
worth, I'm a former Windows phone developer, and I wrote a book on silver light.
So I understand the pain of working on
It'll fade and I worked at barnes and noble.com. Right? So there's my trifecta of
ill fated technology projects. Yeah. I I think it's
A lot of technology companies, in order to become successful, actually have
to go through big changes where people yell at them.
And so it's like, how do you know whether you're breaking things or whether you're
actually doing what you're supposed to do? And so that's kind of a Right. That's
that's kind of a tough decision. I I think For Foursquare
at the time, there was always, like, kind of a design,
and product, like, tension between the people who
wanted to be there As essentially like a Yelp replacement, kind of
like a a local search city guide. And then there and then versus the people
who were there for the the life logging, the check ins, the game. And I
think, I think the separation could have been
done. I mean, my personal thing is I think there could have been a
Separation, I could think, could have been executed a little bit better. I
think technically we did a good job. I think the apps that
we ended up with were well designed, but I think, I think
we needed to do is take into account how the how people were using the
apps, at the time and not just, like, Kick all the
people who are checking in, which was which was Foursquare's kind of bread and butter
and just, like, kick them to the side with this other app. And then it's
like, well, what is this? I'm calling this something different. Sorta. That was that was,
I think, too much. But, again,
there there were people saying it at the time. But
The problem is, I I guess, you know, there whenever you make a
change, there's always a great many people saying a great many things. So
We could wax this we could wax nostalgic about Foursquare because I used to
like, when I I was travelling a lot at the time when I worked I
worked at Microsoft about 10 years ago. But I remember Sometimes I would actually
choose different connecting airports, so I could get, like, the the
jet that was it, the the jet set tag, like level up in my achievement
there. Right. Which is kinda sad, but, we could
we could wax nostalgic about that all day, and I would love to. But I
think what What was the role of AI and ML in that
space? Right? Because you're obviously collecting a lot of data. No. Like, I'm just curious,
like, because how how was that being used? How was that,
leverage. In in in a lot of ways,
and, you know, many of which I I worked on over all those years.
You know, one of them was, I mean, just, you know, search
ranking in general, which, you know, Foursquare had a lot of ex Google engineers.
So I learned directly from them so they they knew what to do.
But search ranking, search ranking was a was a was a big project.
This is kind of more of a statistical Problem where you were kinda trying
to weight different attributes, like, is this related to the search the
person put in? Is this related to how much do we wanna, You know,
score things that are, you know, maybe someone's
friends went to. So something like that. I think that
The biggest well, I'll talk about the one that I think is the biggest deal
and then the one that that I worked on the most. The one I think
was the biggest deal for for Foursquare, which I did work on a little
bit, is basically trying to figure out where
someone is given. So we know where someone is given there that long
from their phone. But it's like, what are they actually in a particular
store? Like, are they in the Starbucks? Are they in the, you know, are they
in the office, over there? Are they Are they just walking down
the street? And so using the fact that people were were
giving us training data, essentially, which was a big theme there, which is, you know,
I think, something that,
data scientists and data entrepreneurs need to need to look in closely, which is
like, How can you get people to give you training data? Because it is really
useful. So if you have people giving you where they are and
then you could see the information from their phone, not just a lot long, but
like what, You know, things like what Wi Fi's can you see? What, you know,
other sensors from your phone, can you figure out where they are? And then there's
the whole stop detection, problem. And so, Yep.
Foursquare essentially can kinda figure out, you know, where you went day
to day, and it's actually pretty good. Like, you know, if I Don't tell Foursquare
where I went. Even today, I still look at it and, you know, it tells
me what, what actual stores I was in. Now maybe there's a question of,
you know, whether whether our apps are knowing too much about us, but
that's that's a whole another question. But that was a very important,
a resource for the business. And the one that I worked on the most, that
was the most exciting though for me was the natural language
processing Pipeline. And, of course, you know, text text
data today is is is having such a a resurgence
with, You know, I don't need to tell your audience with AI and all that.
But, you know, it it it back then it was like, well, people were giving
us, you know, several sentences called tips on Foursquare Venues,
which would often be like, here's, you know, here's what you should do here. Here's
what you should try. Here's a little review, something like that. People were
leaving text with their check-in. So there's a bunch of texts, there's
menus, things like that. So there's a bunch of texts in the system. And so
it's like, what do we do with all of that? And, one of the things
that we did was we pulled out key terms, you know, noun phrase detection.
This is all kind of standard natural language
processing. You know, not
you know, you know, people often ask, oh, you know, I I think
Nowadays, I'm often thinking everyone's thinking, oh, you were probably using, like,
generative AI or something. No. It was just kind of standard NLP that had
been developed over the last, you know, several decades. But,
we did sentiment analysis and we used that to come up with the ratings for
the venues, which which are used today. So you could tell
how good something is. And,
you know, I did some things that were a little bit
more interesting that, you know, maybe get overlooked, but they're
they're kind of unique to To to what we did there, which was sort of
like timeliness and seasonality, which is so, like, if you check into
a diner in the morning versus in the afternoon, It'll statistically
give you different suggestions based on how timely it thinks each
each suggestion is. Because with every Check-in where someone is doing
something in real time. We have the timestamp. We know what time of day. We
know what time of week. We know what time of year. And so it's kind
of cool to to put that all together. And some of
the some of the, some of the
models, got pretty,
you know, it was it was pretty neat how it all turned out. I think
that one I you know, I still talk about that one is one of my
That's my favorite one after being in the industry so long, even though it was
like 10 years ago, because it was like, okay, we had training
data again from on these tips where, You know, we
could tell if the person liked the venue or disliked the venue and because
they they told us, and they also left the tip. There were a lot of
people who did that. So that just gave us training data for sentiment analysis.
And at the time, I'm sure the tools now are much more sophisticated at the
time when we use pretrained sentiment analysis tools, Didn't really
work well on our data because it's just it was just a different kind of
text. People wrote on Foursquare differently than they did on Twitter, for
example. So, so that gave us training data. Give
us training data for every language. And so that was nice. We got kind of,
like, you know, 90 languages for free just by just by
using that Strategy of Oh, wow. Using the data that people gave us.
Probably not probably didn't work very well in all 90, but certainly worked
well. Well, the beauty of it is It ends up working
well so long as we have good language detection, it ends up working well
in, any language that has any Particular,
you know, any particular popularity in Foursquare.
So for example, if, the Turkish was very popular. Okay. Well, that
means we have a lot of Turkish training data. That means that the
the model, which trains monthly, is Is going to use all that training data. That
means it's going to work very well. And so, and
so that the fact that the models were always regenerating
And they were always regenerating based on the latest data
was was really cool because oftentimes you think these think about
ML teams kind of building a model, and then they kind of throw
it over a wall. They they productionize it. And then you have to
work on the next one, but you have to you have to do some work.
It's not automated, you know. So it's like, well, this is this is gonna start
going downhill If we don't, if we don't interact. And the fact that we were
able to set it up where it was just constantly getting smarter was,
was pretty neat. So MLOps and pipelines before they were called
MLOps. Well, they might have been called pipelines, but yeah. Interesting. Yeah.
Pipeline was a big Big big key phrase. So what
what did the data what did the back it because like one of the one
of the jokes that we have, and in fact, it's a domain name that I
registered. 1st, you get the data, is a phrase that
a lot of data scientists will often use, much to the chagrin of a lot
of data engineers, because a lot of data,
you have to get the data in a certain way to to format it and
and and to get it trained. And if you go to first you get
the data.com, it should redirect you to our website,
hopefully. God only knows if it works. 1st, I think
yeah. I'm gonna I'm gonna try to get the data.com.
I'm shattered to think that okay. Good. It does work. Okay. DNS
and me have a long history. Yes. It's going back.
Good. I I it's always good to start off a week with a win with
DNS. What did it what did the
because I'm curious, like, Foursquare was one of those early, kind
of mobile first, kind of success stories. I'm
always curious, what did the back end data platform look like? Right?
Because, and again, going back 10 years, I
mean, I mean, did you use what was the name of the,
gosh. Can't think of the name of the platform, but what sorts of technologies did
you did you guys use? Yeah. I'm I mean, I'm sure
it works similar today in in at at Foursquare. Mhmm.
Well, we were using data pipe I assume. But, yeah,
if I remember correctly, we had, you know, our transactional database, our Mongo
database that was sort of like, Every once in a
while. And so that was kinda like the baseline. And then there'd be a series
of jobs that, like, you know, built it up, that that
that kind of Calculated things off of that, and that
would, in the at the end of that pipeline, you know, release
a, a dataset that would then be kind of,
Automatically, deployed and then read by,
read by the server in real time. So, if I can think of, like, the
technologies, I think the, the pipeline technology, the
pipeline, what was it? It was
like Luigi. It was written in pipe. Python. I don't know if that's too interesting.
There's a lot of different ones you could use these days.
It's an interesting question of, like, you know, which one do you use? I,
it's It's probably,
you know, from from my point of view, it's always like, well, the company kinda
chooses it. You don't really have much of a say.
And then then it's like, well, well, how do I know how to compare? But
let's see. Like, you know, we were using MapReduce jobs. We're using Hadoop At the
time, I think scalding, was was one that's that's maybe kind of out of fashion
now. That was a, a scala based framework for for
some of these jobs which were, which which
was based on abstract algebra. So it's actually pretty cool. I wish it
was. It was kinda hard to to reason about sometimes if you
It kind of went too far, to the side of, okay,
you know, I love abstract algebra, but I don't want everybody
who I don't I don't want that to be a barrier to entry for people
who are we're working on this. But, I'm
just trying to remember, like, some of the, You know, some of the some of
the tech bud buzzwords. But if you have any specific questions, maybe they'll jog jog
my memory. I don't know. Like, one of the things that was popular about that
time was, HBase. Oh,
yes. I, were we using interest? I think we were using,
Yeah, I remember that Term, but I I know. I know. I was as you
were talking, I'm like using it or if we wanted to use it. It was
one of those 2. Now from that if memory serves, I think Facebook is the
one who pioneered h base because it was really it was a right once read
many thing, and basically, the last one in when,
last last I can't talk, sorry. Last one wins.
Let Andy help might help me out if with the
technical term for that last one. Last one wins. What's the
Oh, yeah. So right. I remember they were called h files, so it must have
been yes. It must have been that. Yeah. Yeah. Yeah. That was one
of those sorry, Andy. Go ahead. That's okay. You were I was thinking,
you know, ChipLogic last in first out. Yeah.
Yeah. Something like that. Yeah. Know if that's what you were after or not. Last
one wins. Last one That was their concurrency strategy.
That's, I know there's a better term for that, but again,
it's a Monday and, I have
a headache. But no, it's it's it's it's
fascinating to kind of Almost like technology
archaeology. Like what worth the big projects
that were popular at the time. Right? You know, and it's
just, And it's scary to think that, you know, we're talking 10 years
ago. I mean, I mean, you Not even though. A lot of this stuff was
I mean, a lot of this stuff is probably still in place at Foursquare today.
Yeah. I mean, what's interesting to me is you you mentioned a lot of
the NLP, techniques that, You know, for lack
of better term, people would consider legacy now, right? Because they're pre
transformers, right? They're pre GPT, right? Sentiment
analysis, a lot of, you know, I I speak with a lot of people with
varying degrees of technology skills, and they
assume that this field of research didn't exist prior to
last year. And, very
much not the case. It's just that radically changed about a year ago.
Right. I mean and and this is something that I'm trying to figure out how
to do, which, I I might not be alone. It's like, okay. I did
all these things. How do I reinvent myself now in this new world?
And, you know, once you realize it could be exciting thing, then
it's maybe not so much of a drag, you know, because there's there's so many
opportunities out there. But it's like, but I can't be
the only one out there who's struggling with this being like, okay,
wow, I've got a, You know, I I've gotta,
you know, work or at least do projects for companies that
are at the cutting edge here in order to, In order to be,
you know, in order to be at the forefront. Yeah. It's funny, like, you miss,
like, I was, You know, offline for I tried to be offline, but for the
better part of a week and for vacation.
And like During that week, AMD announces that they are
producing their own, GPU LLM
type hardware. Gemini comes out and all
these other innovations that come out, and I'm like, I feel, like, hopelessly behind
now. I'm being offline for a week. Yeah.
Yeah. It's I mean, I I guess the
only, consolation there is everyone's dealing with that.
Right. You know? Yeah. Right. And Kinda like impostor
syndrome. Right? Yeah. Yeah. I think I think the
question is, especially in this new world of generative
AI. And and the question I'm asking I don't necessarily have answers. But it's
like, how do you so You wanna jump in the stream
and get all the latest stuff, but you also want to leverage your experience
and understanding. Cannot be leveraged. And
so what's the best way to to, you know, what
what's the best way to balance that? I think that's something that I would like
to see more people asking. And I would like guidance on this. I know I'm
the guest. I'm supposed to say what I know, but No. But try now. You
know, Mads, the, the thing is a lot of the stuff that's
That's new. I'm doing the air quotes here for people who are listening.
A lot of things that are new are really coming out of tech That was
developed. The math was developed, for instance, in the late sixties, seventies,
eighties, nineties. So a lot of that is just being reapplied
Back when the math was developed and the theorems and and such,
we didn't have machines fast enough to do it or at least do it
usefully. So I wouldn't feel bad at all about,
you know, having a bunch of, a bunch of experience that seems dated
right now because A couple of weeks to a couple of months. That might
be the new shiny. Right. That's true. When I went back to
a a university computer science program, You know, they're still studying
data structures and algorithms. It's still very relevant.
And, you know, I think a lot of outsiders think, oh, everything's
gonna Turnover in, in a year and a lot of things
do. But there are also a lot of kind of like universal,
kind of, there's a lot of universal Theory that's, good to know
about. Sure. The fundamentals don't change that often.
Nope. And it's a lot of reapplications. I see a lot of people
reapplying stuff 2 or 3 times. I mean, I've been I've been
around computing since 1975. So I've seen kinda like these meta
patterns flow, you know, through several generations,
and they kinda keep just resurfacing. One of one of the
interesting ones is, like, the, well,
both the chatbot and the text based interface versus the,
Graphical interface seems like we keep going back and forth. You know,
I I remember chatbots back in the, you know, AOL days.
AIM days probably way before that too.
And then, you know, and then There was kind
of a a a chatbot resurgence in, you
know, 2016, 2015, whenever when every company wanted
a chatbot and we're excited about that. Yeah. It didn't quite work. It
seemed to fizzle out. Then, you know, the
the then nowadays, we have So many chat interfaces,
chat GPT and and generative AI seems to be resurgent again.
So there are these weird sine waves, these weird
cycles, and I almost think of it as a coil where, you know, you're starting
at the bottom and you're cycling, but you're also moving up at the same time.
And so How do you how do you surf the wave? That's, that's,
something that's once you kind of, understand the
fact that that's what you're doing, then then then you can be excited about it.
I I think it's fair. Well, we're at that point in the show
where we transition to our, questions. And, we
dropped them into the chat here for you. Our very first one is how did
you find your way into data? Did data find you or did you find
data, Max? Interesting. Well, I
guess I was always interested in math and computer
science. You know, going back to undergrad, you
know, it was like there was a lot of different areas I could choose. I
had a hard time going into a field that, you know, where I
wasn't, using all different parts of my brain and
computer science department, it was it was not just the
mathematics. It was, you know, there was, you know,
there was a bunch of creativity in it as well. There was human computer interface.
There was it. So, So I was kind of, I gravitated to that field
as an undergrad. When I graduated, I I joined a company
called wireless generation, which, today is called Amplify.
And that's it was an education tech company. And I was
doing, you know, some simple kind of software engineering work. Actually, back
then, It was, which sounds really dated now, but, you know, they
were probably doing this up to, like, 2010, which was, you know,
writing c plus plus for the palm pilot. You know, we yeah.
Because it was they were assessing students and then it would sync to to
the web and all that. And Sure. It was a lot of, like, taking
stuff, Taking that information out of databases and putting it into a a
dashboard. And it was it was you know, I I felt like there
could be something more interesting I was doing even though I love kind of the
mission of that company there. So I ended up in grad school. I ended up
at NYU and I went there from I guess
2009 to 2011 really discovered,
you know, data mining, was the 1st related
class. Then I took, You know, machine learning, natural language processing. Actually,
the the machine learning class was with, Jan Lacun, who is,
a very well known machine learning researcher. He's Like The Lani.
The the the the the the the the the the the the the the the
the the the the the the the the the the the the the the the
the the the the the the the the the the the You know, all the
stuff that exists today. Like, even this was 2010. He would show us a camera
where he would point to different objects. He'd be like key, wallet,
chair, and it would like, the the the text would appear
on the the screen based on what he pointed at. So they knew how to
do all this stuff, that that you think of as as kind of
it it's it almost seems crazy that that was not, like,
and and turned into a product that anyone could use back then that it almost
seems crazy that it took you know so long to do it but they and
actually it it may have been Used by someone.
It's, sure. Maybe we just don't know about it.
Sorry. My paranoia. No. No. You're right. I'm sure it was used quite
a bit, but it it it's just like what it was that kind
of Sitting on his laptop was so much more sophisticated than anything that
that I I saw a year later. But,
Yeah. So it was That was kind of inspiring. And so it was
like, you know, it was
to me, it seemed like a much more interesting problem. Well, how do you How
does the machine learn? You know? How do you, you know, I don't I don't
wanna sit around writing code that's just dead. I want it to To
be alive, I wanted to to learn from experience. And so when you dive into
that question, well, then you get into machine learning, which is actually Pretty well
named. And then and, you figure, okay, well, you need
data to learn from, and then that that ends up being a statistical model
and so on and so forth. So, you know, when I
so Foursquare, was a company that that essentially came
out of NYU And, you know, it kind of intersected. So
and and they wanted to, to learn from from
their data. They wanted to kind of, sort of a
to build a data science team. And so I had already been
working on that sticky map project, And I was into local search. I
loved the the product aspect. I didn't have my new
interest in machine learning and LP in there. So it all kinda came together. And
so that's why I think that was such a good fit for me and probably,
probably would be very difficult to find such a fit again.
Our next question is, what's your favorite part of your current
gig? And that was, in The virtual green room, you said you
kinda had a good story about that.
Right. So I don't. Well, I don't exactly have a a
current gig right now. I have a bunch of different projects that I'm working on.
It was you know, I think It it was on one hand, it was
nice in Foursquare to be able to focus on one thing, and I'm gonna come
back to that. But I feel like you need these periods, almost like the same
as the grad school period That I had, back in 2010 where it was
like, well, you're working on a few different side projects, but let's see.
Hopefully, like, eventually it'll coalesce into something,
you know, something a little bit more long term and permanent. So I'm working on
several projects. One is with with the Foursquare founder, Dennis
Crowley. And we are Working on a new product, a new
kind of city guide where you walk around the city with your headphones in,
with your AirPods in or whatever. And We kinda know what you're passing,
by. We sort of are are using some of the Foursquare
tools that are publicly available that we know about, but also, You know, we're
kind of rigging up our our own thing because we've just done it so many
times. You know how to do it. We're okay. We know what
stores and stuff you're walking past. So what kind of sounds can we play? Right
now, it's a bunch of text to speech. Essentially, the way I've rigged it up,
the the old version 0, the alpha version is, you know,
we asked chat g p t or OpenAI API what to say. So it's
basically like you're you're walking down the street hearing,
content From OpenAI. Interestingly, OpenAI
seems to the the GPT seems to know,
stuff about Every place along the way, like, you don't
have to go into, like, location based database.
It seems to seems to know quite a bit. There is a question of the
all the content is there's some interesting content in there, but it all ends up
being kind of mediocre. So it's like, okay, well, how do we turn this into
something really cool? I think, you know, in the end, having,
you know, you know, maybe music and and and speeches and an art
project somehow in there, based on where you walk is an interesting
idea. So if I could That'd be cool. Yeah. I could be like a
platform that people can use, like a cultural version of
Foursquare. Yeah. Yeah. And or maybe it's just
like an enhancement of the the sounds of the city. Or maybe
it's, You know, I mean, a lot of people think, okay, maybe maybe a tour
guide. I I don't know. But, you know, it it's it feels like,
It feels like there needs to be, a variety
of use cases tried because there's there's a lot you could do with it. And
and Maybe, you know, if if you put this in the hands of more
creative or of of additional creative people, they would,
ultimately find something interesting. I'm also working
yeah. Oh, I could answer questions about that. But then my other project is my
other 2 projects are are kind of interesting as well. Well, I have the
podcast, The Local Maximum. So still doing that every week and, you know,
interviewing people. Talking about,
talking about data, talking about AI, you know, few episodes on the
whole. You know, all the drama around OpenAI recently.
I I never wanted to become kind of the the the,
the the the tech drama, you know, what's it called?
TMZ of technology? Yeah. Yeah. But but that's something that happened because
I remember, like, last year, a couple years ago, there was all this craziness coming
out of Google with, You know, there was 1 guy at Google who said,
you know, he thought that the LLM has come to life. And Oh, yeah. And
then and then there was a there was A whole
bunch of stuff with, like, the the AI safety, you
know, seemingly staffed
by, people who are a little nutty. And
so, it was a And they fired a bunch of
people From that team too. So, like, there's
definitely, it was something weird some weird mojo
was going around. That's for sure. Yeah. And when when I cover that, I
mean, it's hard to, you know, it's hard to hide the fact where it's like,
wow, everyone in this story seems kinda nutty. But I also try to, you know,
I try to take a step back and say, okay, this is what we know.
These are a few things that could be happening internally, but we don't know everything.
I'm not gonna jump to conclusions. But,
I I I I try when I'm covering a story in a local maximum to
give, like, a a balanced, a balanced version of
of whatever story I come across. You know, maybe it's my show as I try
to give my opinion. But, yeah, I I
I my attempts, which, you know, some people have have,
said I I I've captured that. But my my attempt is to sort
of, try to try to
approach each Story with a little bit of humility and try
to help people understand what's going on without the
raw emotion that you get often on on Twitter. Gotcha. That's a good
point. Yeah. So we have, go ahead. I'm
sorry. Oh, no. No. It's okay. Go ahead. Okay. So we got,
3 complete dishonest. And, the first is when
I'm not working, I enjoy blank. Right.
So now that I've moved to Connecticut, I feel like I
am such a a Connecticut stereotype where I kinda, like, Drive
around, going for walks in the woods and into
various malls and stuff. So it's like it's like it's When the
weather's good, you go into the woods. When the weather's bad, you go into the
mall. Yeah. So I I actually like enjoy doing
that. I enjoy listening to podcasts.
I, honestly, enjoy hanging out with friends. You know,
after, I used to live in New York City. I enjoyed
it a lot, and I sorta had this, situation where
I had this be careful what you wish for because, at the end of 2019,
I was like, oh my god. I'm going to, like, events every single day. It's
just it's just too much. How can we, like, how can we, you know,
cut back on that. And then COVID came. And then to me, it was just
it was the worst thing because it was like, okay, you stay in your apartment
in New York City all day and you don't go and and talk to anyone.
And it was just like it it it was just awful. It just
felt like a a prison. So I I
moved to New Hampshire for a couple years, then I came back. But, you
know, nowadays, when I get a chance to hang out with with friends and and
family, I just I try to do it, whenever I can because I'm
not like, you know, it's not like when I was living in New York in
the 2010s and got kind of overload on that.
Right. Right. So, yeah. That's my answer there. And we have another complete this
sentence. I think the coolest thing in technology is blank.
The the way I've been putting it recently Is this,
where, you know. It.
You know, back maybe 10 years ago, the story we
were getting that the hopeful story we were getting was that, okay, if you're an
engineer, you could Build anything you want at
a very low cost or if you're not an engineer for anyone because we
have access to social media. You know, you can,
you can put anything out there into the world that you want
and and have people read it if if if they want to, or have people
look at it if they want to. And so that was kind of the new
exciting world. I think today, The new exciting
world goes well beyond that, which is going to be like,
you you can create worlds. Any Any world that you wanna
build, any scenario that you can imagine, you
can just have a machine fill in all the gaps for you and, You
know, write the write the story, make the
image and maybe, like, you know, make the make the video, make the whole
world. So I think, I I I think the
idea with generative AI that I want people thinking about more that that I
I also wanna think about more is, like, Okay. If you could create any world
you want, to explore, to live in, just
to, you know, maybe it's something to to teach us about something. Maybe it maybe
it's just an artistic adventure
venture, you know, what kind of world do you want to create because that's
that's going to become very cheap very quickly. Yeah. I could
see that. So I'm gonna skip to,
share something different about yourself. But we remind our guests
to remember it's a family podcast.
Okay. And I'm, you know, I'm I'm trying to,
I'm I'm trying to think of an answer here, and it's not because,
it's it's not because of the, of the of the caveat there,
but it No. No. I get it. Well, you've already covered a lot
that's It's different. I did. It is good stuff. Yeah. I mean, I
think, I think one thing that, It's,
I I enjoy doing that that that I forgot to mention, because
I'm actually doing it again for the 1st time in in in 6 years was,
I was a member of the Yale Alumni Service Corps. It's not
a member. It's like you can do a a it was essentially we were doing
trips to underserved Communities around the world and,
you know, doing little, like, kinda, kind of service
trips where you'd ever Either build a structure or work with small business
owners or go, you know, teach in a school. And so I've been
to, Nicaragua and Ghana And I actually
got to lead one of their trips in 2017 and that was to the Fort
Mohave Indian reservation. Very different kind of a trip because
it was Within the United States here. And
so it was honestly a lot easier. Because that's very cool
flying to Vegas. But yeah. But we're actually going back there, in in a few
months after 6 years. And so I was so even though it was less
convenient this time around, I'm I'm very excited Do that. And so
I I don't know. I really like learning about different cultures,
different philosophies, different religions. I think A lot of people might
assume given the you know given the tenor of my
podcast that I'm very like you know rationalist and I talk about Bayesian
inference a lot. But, I I
sort of venture out of that a lot. I don't think that,
That raw math can, can explain everything in life. And I also love like the
diversity of, of cultures and stuff. So No. It's cool.
That so that's maybe a positive thing, so I I don't know. It's very positive.
Something different. No. It definitely is. It definitely is. So where can
folks find out more about you and what Sure. So you mentioned you have a
podcast, which I love the name, The Local Maximum.
Right. Yeah. Local maximum's triple entendre, because it's got my name, Max.
It's a local maximum is, of course, you know, in machine learning,
when you're when you're trying to find the, well, sometimes it's often the local minimum
if you're trying to, Minimize the the loss function, but the
in basing inference, if you're trying to maximize the probability, whatever, you're you you get
stuck stuck in one of your next which was Your name is Max. So Yeah.
Exactly. Right. Right. That's the first one. That's the second one. And then, you know,
I I worked on location data a lot, so it's kind of a a triple
meaning. And so, I've
been doing that for for quite a while. You can go back into kind of
a a really extensive library there. And, I have
the website local max radio.com. I have.
If you go, I have local maximum labs. If you go to local maximum local
max radio.com/labs, I have a
bunch of papers And, you know, kind
of works that I've done, which, you know, includes some
discussion of machine learning, like kind of the math mathematics behind bias correction,
but But also something kind of fun that I did, like, with the podcast last
year, which is, like, I just rewrote the US constitution, fixed a bunch of
things just because I I felt that was fun. I was Taken aback by how
mad people get when you when you do that, it's not like I was actually
trying to, you know, run a political campaign for it. I just thought it was
a a fun project, and I learned a lot. But Some once you venture into
the political, people start treating things different. People get angry pretty quick.
Yes. That's true. Yeah. Yeah. I I I The
I I I love to hear criticisms on it. I wanna hear what what what
people think. But The one criticism
that I get a lot, which I really hate is, like, how dare you spend
your free time on this, which I I just don't get at all.
Yeah. But, which, you know, whenever I put
out some kind of math paper, even if it's like and there there is one
called relative probability, which is, you know, sort
of an abstract paper where it's like, okay, a reimagined probability
theory as, okay, let's say you can't talk about The probability of something
happening. Let's say you can only talk about 1 probability relative
to another. What, what does that look like? And I just stated some basic facts
and, you know, Not that many people gonna use it. Maybe people
won't use it for for a while. I I feel like it's an interesting idea,
and I feel like it will have uses eventually. But,
You know, nobody criticized me for that. For like, how dare you spend your free
time on that? Exactly. They they pick on you for the other
stuff. Yeah. I mean, I I look at people. I mean, you spend your free
time yelling at people on Twitter. I mean, what's the difference? I was gonna say
you can you can look at TikTok, and you can find far more destructive uses
is of Exactly. Exactly. So that's so that's that's my
main thing. I I think maybe with the, with the Constitution, I think people have
their sort of ideal society in mind. And if If your thing doesn't wind up
with that, they they kind of perceive you as a threat. Like you're trying to,
like I was trying to revitalize democracy, but some people are saying, no, you're
backsliding on democracy. Alright. Like, let's talk about it. But, yeah, it's it's
people get you know, people get different. We need to have you back and talk
about that more. Yeah. For sure. For sure. Talk about that. Absolutely.
We'd love having you. Both Andy and I, however, do have a hard stop, and
I would love this This covers you to go out for a couple hours, and
we'll talk to you more. And I just had a a conversation last night with
my cohost that went a couple hours. I know how it goes. Yeah. Yeah. Yeah.
We ended at 1 AM, and I was like, oh my god.
Well, those 1 AM conversations. I know what you mean. You got it. So
With that, we'll definitely make sure. Send us all your links, and
we'll make sure we get them in the show notes, and we'll let Bailey, our
semi extension AI host, Co host, 3rd
host, wrap up the show. And thank you, dear
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