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The AI Engineer’s Dilemma: Mastery or Versatility? | The Pair Program Ep37
Episode 37 • 9th January 2024 • The Pair Program • hatch I.T.
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The AI Engineer's Dilemma: Mastery or Versatility? | The Pair Program Ep37

Join us in this episode as we dive deep into the world of artificial intelligence through the eyes of two industry leaders. In this episode, we're joined by Maggie Engler, a Technical Staff member at Inflection AI, and Andrew Gamino-Cheong, the CTO and Co-Founder of Trustible.

Maggie and Andrew share their first-hand experiences of working in the AI engineering space, offering insights into the challenges and innovations that shape their daily lives. They explore the critical question of whether AI engineers should strive to be jacks of all trades or masters of one specific skill, shedding light on the diverse skillsets demanded by this dynamic field.

But that's not all – this episode also delves into one of the hottest topics surrounding AI: ethics. Maggie and Andrew engage in a thought-provoking discussion about how AI should be used, the ethical dilemmas faced by engineers, and the inherent limitations in AI's application.

About the guests:

-Maggie Engler is a technologist and researcher focused on mitigating abuse in the online ecosystem. She currently works on safety for large language models at Inflection AI.

-Andrew has spent his career working at the intersection of policy and AI. Prior to founding Trustible, Andrew was a machine learning engineering tech led at FiscalNote. Now, at Trustible, he's flipped the script and is working to apply policy to the AI landscape.

Sign-Up for the Weekly hatchpad Newsletter: https://www.myhatchpad.com/newsletter/

Transcripts

Tim Winkler:

Welcome to The Pair Program from hatchpad, the podcast that gives you

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a front row seat to candid conversations

with tech leaders from the startup world.

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I'm your host, Tim Winkler,

the creator of hatchpad.

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Mike Gruen: And I'm your

other host, Mike Gruen.

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Join us

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Tim Winkler: each episode as we bring

together two guests to dissect topics

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at the intersection of technology,

startups, and career growth.

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And welcome back to another

episode of The Pair Program.

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I'm your host, Tim Winkler,

joined by my co host, Mike Gruen.

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Mike, uh, given our episode today is

centered around AI, I was inspired

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to think Um, back through some of the

greatest AI references in Hollywood

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over the years and some of the, some

of the names that kind of popped up,

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some of the characters that popped up

or agent Smith from the matrix, um,

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Skynet terminators from the Terminator

data from Star Trek and then Wally.

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So my question for you to kick

things off, if we're going to set up

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a battle Royale between these four

AI characters, who would be the,

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who'd be the last bot standing in

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Mike Gruen: the ring?

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I mean, Skynet, they time travel.

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I think that trumps.

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I think that trumps everything.

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Andrew Gamino-Cheong: Data time

traveled once or twice, though.

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No.

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Yeah.

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Mike Gruen: But through like wormhole

like that, I feel like that was

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more discovery, not invention.

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But I would like to point out, and

I think I've mentioned that you

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left off that list, Whopper, the

original Skynet from War Games.

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So it's good to start.

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That's not really a correction.

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I'm not correcting.

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I'm just adding to the list.

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But just adding.

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Yeah.

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What about you?

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Which one do you think

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Tim Winkler: wins?

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Well, I wanted to kind of keep on,

on theme, so I just plugged it into

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chat GPT and the answer was in a, in

a straight up battle royale, my money

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might be on Skynet Terminators due to

their combat focus and relentlessness.

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Agent Smith could be a close second

if he manages to take over the other

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bots or manipulates the environment.

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Data and Wally are strong contenders,

but they might be limited by their

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programming and lack of aggressive intent.

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But then again, Wall E might

just charm everyone into laying

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down their arms, who knows?

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Interesting.

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Andrew Gamino-Cheong: That's

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Mike Gruen: a good answer.

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That's a much better answer.

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Uh, well, more, well,

much more thought out,

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Tim Winkler: clearly.

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Yeah.

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But yeah, I kind of like the

Wall E just charming folks, like,

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let's just, let me win this one.

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Nice.

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Um, good stuff.

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All right, well, let's let's give our

listeners a preview of today's episode.

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So today we're going to dive into a

discussion around AI engineering and the

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debate on should AI engineers kind of be

jacks of all trades or masters of one.

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Uh, and we'll tackle everything

from, you know, career paths to

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ethical kind of quandaries and,

and, uh, and a few other things.

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But, uh, we've got two very qualified

guests joining us, uh, Andrew

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Gamino-Cheong and Maggie Engler.

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Andrew is the CTO and co founder of

Trustable, uh, an AI governance startup

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has over seven years of experience

as a AI ML engineer and architect at

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Fiscal Note prior to starting Trustable.

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And he's a very passionate about

the intersection of AI and policy.

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Maggie is an experienced AI engineer,

currently at Inflection AI, an

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AI studio based in San Francisco,

creating a personal AI for everyone.

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And prior to Inflection, Maggie

spent two years at TwitterX building

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models to detect policy violations

and has taught data science courses

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at UT Austin and Penn State Law.

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So, before we dive into the

discussion, first off, thank

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you all for joining us today.

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But we will kick things off with

a fun segment called Pair Me Up.

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This is where we pretty much go around

the room and give a complimentary pairing.

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There we go.

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Teed up.

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Um, Mike, you always kick us off.

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What do you got for us today?

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Keeping it

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Mike Gruen: nice and simple.

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Going back to the basics with

food, hash browns and sour cream.

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Um, I was recently reminded that

there was a restaurant in, um,

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well, Rockville, South Rockville,

North Bethesda, but really South

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Rockville, uh, called Hamburger

Hamlet, uh, for a long, long time.

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When I first moved down here, that

had, that was, it was called those

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potatoes or something like that.

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And it was a hash browns

and sour cream and it was.

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Went excellent with the burger

or whatever you're having

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Tim Winkler: for dinner.

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Yeah, I wasn't seeing that coming.

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The sour cream threw me off a little bit.

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The hash browns.

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Ketchup maybe.

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Uh, sour cream.

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And I'm

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Mike Gruen: not even

a huge sour cream guy.

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But sour cream and hash browns,

it was, it's a nice pair.

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Tim Winkler: Okay.

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I'll have to take your word on that.

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I love sour cream too.

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I've never tried it on hash

browns, but we'll give it a shot.

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I'm

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Mike Gruen: talking like the potato.

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I'm not talking like

the round potato ones.

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I'm talking like the like.

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Where it's the the hash browns

are like the breakfast, like

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the skinny, shredded potato.

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Tim Winkler: Yeah, yeah, I gotcha.

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Yeah, I'm following you.

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I'm just thinking of a McDonald's

hash brown for some reason.

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That's what's coming to my mind.

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Andrew Gamino-Cheong: I see.

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I'm getting hungry now.

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Mike Gruen: Good thing.

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It's Friday afternoon.

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Yeah,

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Tim Winkler: I'll, I'll, I'll jump in.

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So, um, my, my parents going

to be fitness and fellowship.

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So earlier this year, I joined this

kind of workout community in my town.

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It's called F3.

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It stands for faith,

fitness, and fellowship.

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Um, it turns out there's actually

hundreds of these kind of small

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outposts across the country and actually

some, some global locations as well.

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But essentially what it is, it's small

workout groups of Anywhere from like

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three to 20 guys that meet up a few days

each week, usually pretty early in the

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morning for like a 45 minute hit workout.

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And for me, it's kind of been a valuable

way to combine community and exercise.

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And so like the fellowship piece

of it, it makes the fitness part,

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in my opinion, more enjoyable

and I guess more sustainable.

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You kind of get a guilt trip if you

don't show up or people, you know,

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kind of rag on if you're, if you keep

missing a couple of days consecutively.

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So.

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Um, another perk of it for

me has been to my family.

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We live in Northern Virginia, but we We

have a place in Chattanooga, Tennessee

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that we try to come to, you know, a couple

of months out of the year, and especially

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where I'm recording from today, um, but,

uh, there's little F3 post here as well.

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And so it's a way to just kind of quickly

plug right into the local network here,

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kind of get a workout in, um, but also

kind of meet some, some people as well.

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So.

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That's, uh, that's my parents,

um, fitness and, and fellowship.

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Um,

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Mike Gruen: definitely agree.

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I went to the gym the most when

I had a coworker, he and I,

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it was like this, like, he was

like, all right, tomorrow at 6am.

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I was like, I, I have no choice.

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Like you've already,

we've set the challenge.

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So yeah.

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I'll see you there tomorrow at

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Tim Winkler: six.

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Yeah.

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I mean, Jim's capitalized on

that community style, like

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Orange Theory is another one.

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Like people want to.

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Get those flat points.

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They want to, you know, post it

through their, the little, the little

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network, the app and everything.

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So it's, it's, it's

pretty, it's pretty genius.

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Um, but, um, definitely something

that kind of keeps me motivated

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and keeps me coming back.

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Um, cool.

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So let's pass it along to our guest.

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Um, Maggie, I, I'll start with you if

you want to give us a quick intro and

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Maggie Engler: tell us your pairing.

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Um, well, I'm Maggie.

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I'm in Austin, Texas, and y'all

already introduced, uh, me a little

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bit, but my pairing, I'm also kind

of moving into this fall mode, even

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though, um, it really has only just

started cooling off, uh, down in Texas.

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Um, and so I was thinking about also,

uh, food related, um, my pairing is

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coffee and pie because I love, uh, like

Thanksgiving, obviously, and having food

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and family, but what I really love is

like leftover pie and then having that

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for breakfast, uh, with like black coffee.

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To me, that is like the perfect breakfast.

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That's

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Tim Winkler: awesome.

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Now I'm salivating because pie is

one of my favorite desserts of all

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Mike Gruen: time.

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Dessert for breakfast is great.

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This morning, I had my leftover

bread pudding for breakfast, so.

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Tim Winkler: So do you, your coffee,

you just go in straight black

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coffee or do you do like a flavor?

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I'm just going straight black coffee.

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Maggie Engler: Nice.

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Yeah, and I actually, I don't

really do like iced coffee,

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which is kind of unusual.

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Uh, especially when it's hot out,

but I'm pretty much just, uh,

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yeah, old fashioned that way.

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Tim Winkler: I love it.

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Coffee and pie getting

geared up for Thanksgiving.

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Um, awesome.

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Well, thanks again for joining us,

uh, Andrew, how about yourself?

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Quick, uh, intro and you're pairing.

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Andrew Gamino-Cheong: Yeah.

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Uh, my name is Andrew.

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Really excited to be here.

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Um, I'm actually calling in from DC.

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So a close, uh, I know for some

of the other places you guys were

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talking about for me, I'm actually

going to go with like a really great.

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a competitive strategy board

game and a craft cocktail.

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There's a lot of places that I love

to go to in college that had like,

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you know, board games and drinks.

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And then during the peak pandemic,

my wife and I, I think bought

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every good competitive two

player board game out there.

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And then some of those pandemic

nights where you couldn't go out

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and do anything, we'd make cocktails

and play those for hours on end.

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So I think something about the

pairing of those that just work really

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well, getting vibes on both sides.

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So

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Tim Winkler: nice.

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That sounds awesome.

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You have a favorite cocktail or board

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Mike Gruen: game, board game,

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Andrew Gamino-Cheong: board game.

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There's one, um, seven wonders duel.

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It's a dedicated version for two players.

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Uh, my wife and I got competitive in

that real fast and that was amazing.

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Mike Gruen: Nice.

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Cheers.

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My wife and I was, uh, Starfarers

of Catan is our, uh, was

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our go to for a two player.

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So, yeah, love that.

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Tim Winkler: Yeah, we were having a

debate, uh, at Hatch, uh, not long ago

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about the, the goat board game out there.

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And, um, Clue came up quite a bit.

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Clue was, was one of, uh, uh, group

favorite, but it's not really a

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two person thing, but Catan was

definitely also top of the list.

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All right, good stuff.

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Uh, well, again, thanks

for joining us, Andrew.

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And, uh, we'll, we'll transition into,

uh, the heart of the, the topic here.

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So, uh, as I mentioned, we're going to

be talking about, you know, depth versus

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breadth as it relates to AI engineering.

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Um, and we want to kind of tackle this

from a few different perspectives.

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Um, Andrew, you know, why don't

you kind of lead us off here?

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You know, you were a double major

on computer science and government.

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Sounds like it played a part in

your career path as an engineer.

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So what are your kind of thoughts on

the topic of specialization versus

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Andrew Gamino-Cheong: generalization?

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Yeah, happy to dive into that.

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So as you mentioned, in undergrad,

I double majored in both political

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science and computer science.

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Honestly, at the time, I was unsure

whether I wanted to become a lawyer

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doing stuff in the tech space, or

if I want to go into the tech space

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and do stuff related to the law.

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I ended up choosing more of the latter

because I always had these dual interests.

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Um, yeah.

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Really partly informed by, you know,

the kinds of things I did growing up.

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You know, I was the biggest fan of the

West wing really shaped my whole view

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and started to come to DC for college.

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Um, but I also loved watching

every sci fi show out there.

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You know, I watched all of Star Trek

and watch data and all these cool

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ideas about AI, um, and the impacts

of those could have on society.

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And so what I was always thinking about is

actually how could we apply these awesome,

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powerful ideas in AI to this space?

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You know, I always saw a lot of.

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Similarities in the kinds of logical

things that actually are embedded in

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laws, you know, these policies, these

ideas, there's logical principles,

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there's interpretations and how that

could actually be perhaps assisted by A.

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I think like a lot of people,

I always tried making my own

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like, Hey, could you create an A.

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I that could actually interpret laws,

make recommendations based on that.

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I think now I've got a much

better sense of the ethical or

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safety challenges around that.

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Um, but my advice sometimes is actually

to when I talk to the former students,

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you know, pick two things, pick one

thing that will give you some technical

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skills, pick another that's really

piques your intellectual curiosity.

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And you can find a really, really great

career path working at the intersection

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of those two, because you can basically

be always understanding the latest

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technologies and ideas and applying it

to the problems in your other space.

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And you can do that in both directions.

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And that's where I think.

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We see innovation happen the most

and you're taking ideas and solutions

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that have been developed in one

space and applying them to another.

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I think my career has been

really successful doing that.

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Um, that's definitely something

I recommend to everyone else.

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Tim Winkler: Yeah, that's

really sound feedback.

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Um, and advice, you know, this is

probably a good jump off to also like,

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you know, explain a little bit more about

trustable because obviously it sounds like

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this played a big part in you building

this, this business and what you all,

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the problems that you all are solving.

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Andrew Gamino-Cheong: Yeah.

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So right before I started Trustable,

I was working for a company

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that basically was using AI to

understand the policy landscape.

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We'd scrape every piece of proposed

regulation legislation initially in the U.

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S.

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and then globally.

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Use AI to try and identify what was

relevant, you know, which legislation

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was more likely to pass or not based

on individual voting preferences.

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You know, which things were more likely

to be significant to different industries.

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One of the biggest trends we saw

was on regulation for AI itself.

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So I remember, for example, reading

the first draft of the EU's AI Act

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almost two and a half years ago

when it was proposed and immediately

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starting to think through how would

this impact actually my life, right?

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I was on a day to day basis

proposing new ideas for AI.

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I was never having to go through our

legal team, though, to discuss them, to

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understand the compliance requirements

or the legal context around that.

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So I was literally starting to

think through, actually, how could

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I make sure that I don't have to

spend all my time dealing with

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just compliance and legal teams?

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Like, could I give them all the

information they need up front

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to help do the kinds of risk

assessments that these laws require?

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That was then the origin of Trustable.

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You know, our, our goal is to make

compliance with all of these AI focused

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regulations as easy as possible.

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Where that's understanding even what

risk category use case of AI falls into

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for the AI act, conducting some of the

workflows to do like risk or impact

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assessments on individual risks, and

actually helping to just, um, helping

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organizations adopt ethical principles

and helping them actually document all

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the ways in which they are being ethical

with AI in a provable way so they can kind

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of build trust with their stakeholders.

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So this is actually, and we ourselves

are using AI as part of this, right?

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We've actually done a lot of

research now on AI incidents.

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We also have a large language model

system that can help, uh, kind of teach

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some of our customers the different

requirements are and help them interpret

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and even document things in a smarter way.

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We won't use generative AI to

write your documentation, but we

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will actually evaluate how good

your documentation is with AI.

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Tim Winkler: Yeah.

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And it's a, another topic that we'll,

we'll get a little bit deeper into, uh,

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later on in the conversation because

you had a, you know, some interesting

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perspective on like the, the doomers

and the utopiast and fun, um, no

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ground there as well when it comes

to AI, but, um, let's, uh, let's get

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Maggie's perspective on this as well.

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So, um, Maggie, I guess, uh, your,

your initial thoughts when you,

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when you think about AI, when it

comes to, you know, engineering

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as a specialist or, or generalist.

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Yeah.

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Maggie Engler: Yeah, I think that's,

um, I think there's room for both.

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Um, and, um, just thinking back,

I, it's super interesting, Andrew,

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that you are SPS in government.

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Um, I was, I think, pretty much throughout

school, pretty much a specialist.

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Uh, I actually don't have a SPS degree.

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Uh, I was, uh, did a bachelor's

and master's in electrical

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engineering, and I was focused on

statistical signal processing cuts.

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So kind of very like, Applied math,

um, focused, not really at that point

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with too many, um, kind of practical,

uh, applications, um, but the first,

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uh, role that I had in industry, I was

working on a, um, cyber security platform.

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So doing malware detection,

um, with machine learning and.

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I think that, um, from that point

on, I was kind of like, Oh, well,

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first of all, um, just from a purely

academic standpoint, like the data

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science and machine learning world, uh,

aligns really well with my skillset.

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Um, but then also working in that, um,

field, uh, kind of by accident, really,

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uh, I found that cybersecurity was super.

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Interesting to me on a personal level,

I became really interested in how, uh,

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responsive different machine learning

systems are to, uh, different types of

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attacks and how, um, there's kind of this,

um, cat and mouse game, uh, where as soon

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as you sort of harden a model to some type

of attack, like you then start to see,

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um, novel things come up and, um, um, For

me, like that sort of adversarial nature,

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um, meant that it was, it was always

kind of fresh and felt, um, um, like

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that I, uh, that I was always learning.

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And so, um, ultimately I think we

kind of ended up at the same place,

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even though I was certainly not as

broad as Andrew when I was in school.

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Um, in that I kind of.

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Uh, selected, um, my career opportunities

boards, uh, first sort of explicitly

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cybersecurity information security.

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And then, um, after that, much more

towards trust and safety more generally.

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Um, so I worked at, um, you already

mentioned I was, I was at Twitter and, uh,

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now X, uh, for, um, over two years working

on their, in their health engineering

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organization on policy enforcement.

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And in my current role, I took a

lot of that, um, background to an

362

:

AI startup, uh, where a big part of

my job is just trying to understand,

363

:

uh, and improve, uh, the safety of.

364

:

Um, our large language model product.

365

:

And so understanding, uh, what are

the risks, um, associated with, um,

366

:

what generations the model produces

in these different conversations.

367

:

Um, how can we measure that?

368

:

And how can we kind of prevent, um,

unsafe generations from happening?

369

:

Um, so I've also kind of somewhat narrowly

focused in on on a certain problem area,

370

:

even though obviously, data science

machine learning is is super broad.

371

:

You can do almost anything with that.

372

:

Um, and so I really like, um, uh, this

proposition around like, If you can find,

373

:

um, uh, a broad enough skill set, but

also narrow in on like a particular area

374

:

where you're interested in applying it,

um, that seems like a recipe for success.

375

:

Tim Winkler: Yeah, it's

really fascinating.

376

:

Um, Mike, I'm kind of curious on your

input on this too, just kind of, you

377

:

know, I mean, it's a very similar, a lot

378

:

Mike Gruen: of, yeah,

it's just, it's funny.

379

:

Cause, uh, as.

380

:

Um, as you were talking, I was thinking

about my own story, my own journey

381

:

on like how to, I went into natural

language processing and then I was on

382

:

a cybersecurity product where we're

using inferential statistics to try and

383

:

find bad actors and stuff like that.

384

:

And I, and like the whole idea of, for

me, I went to school for computer science.

385

:

I minored in English.

386

:

Basically poetry.

387

:

There's not a good intersection there.

388

:

Um, but, um, I do agree with the idea of

like if you can find certain areas that

389

:

like you're really passionate about.

390

:

Like when I was doing the stuff

with the natural language processing

391

:

and we were, um, it was it.

392

:

It was really for intelligence

communities to find like bad actors,

393

:

uh, looking at different things.

394

:

Um, passionate about that and being

able to apply my sort of software

395

:

engineering expertise to that.

396

:

So I agree with that sort of

like, if you can find that, those

397

:

niches that really interest you.

398

:

And I don't know that you need.

399

:

I think it can change over time.

400

:

I'm, I've been working for a long time and

I've moved from thing to thing to thing.

401

:

Um, I think that's an important part

of a career is find, you know, being

402

:

able to find the next thing that you

want to work on or the next area.

403

:

Um, once you start maybe losing interest

in a particular area, I know other

404

:

people that have like stuck in the same

thing for years and years and years,

405

:

and they've never lost interest in it.

406

:

And I can see that also happening,

but it's also, I think having a broad

407

:

skill set that you can apply to very

specific problems is a, it's a good way

408

:

Tim Winkler: to go.

409

:

Are there any verticals that you

would say, like, you know, being a

410

:

specialist is, is preferred, right?

411

:

Um, uh, healthcare, anything, anything

that comes to mind, uh, that you all would

412

:

say that you've heard from colleagues or

friends that have been truly very dialed

413

:

in and it's proven beneficial for them?

414

:

Andrew Gamino-Cheong: I know, um, a

couple of people from some grad school

415

:

times who have kind of combined a lot of

background in the medical space with that

416

:

deep understanding of computer science.

417

:

And there's so many amazing applications

actually of, um, even taking computer

418

:

science concepts that are now almost

a decade old and they're still

419

:

actually quite novel when applied

to computational biology problems.

420

:

Um, you know, like sequencing of different

things and now looking at applying some

421

:

stuff around like DNA sequencing and the

algorithms developed to that sequencing

422

:

actually stuff in tumors themselves.

423

:

And, you know, what you realize is

that there's actually so much good work

424

:

to be done there that isn't, it's not

viewed as cutting edge anymore, right?

425

:

Even the idea, I knew someone

who is taking the concept of word

426

:

embeddings should now feel ancient

in terms of NLP technologies.

427

:

Then we're applying that actually

to like sequences of DNA that

428

:

they were collecting, right?

429

:

Because if you're talking about the

context of things that are next to things

430

:

and how that can actually represent stuff.

431

:

They're actually able to learn a lot

and improve their own, um, topic model

432

:

kinds of algorithms in the computational

biospace using what is practically

433

:

ancient technology now in the NLP space.

434

:

And I found that to be like

a fascinating application.

435

:

What about you, Maggie, anything

436

:

Tim Winkler: that comes to

437

:

Andrew Gamino-Cheong: mind?

438

:

Maggie Engler: Um, that is fascinating.

439

:

I think that, um, I have seen, uh,

certainly sort of very specialist,

440

:

uh, individuals, usually like PhDs,

uh, be extremely successful at sort

441

:

of the cutting edge of AI research.

442

:

Um, I think that, um, uh, I worked with

in the past people who have done, for

443

:

example, like large language models,

model research for, you know, at this

444

:

point, much, much longer years longer

than most other people in the field.

445

:

Um, and I think that does give

you an edge, but it does seem to

446

:

be like a very small slice of the

sort of total opportunity that I

447

:

think in health is a great example

of a vertical where all of this.

448

:

Specific domain knowledge is

really, really helpful and you

449

:

can use, um, like Andrew said, um.

450

:

Uh, just the application of even

a technique like word embeddings,

451

:

um, could produce novel knowledge.

452

:

Um, so I think there's, I think there's

a mix and I think, uh, that was what I

453

:

was trying to get at at the beginning,

uh, is that I think that in a lot of

454

:

cases there's room for specialists and,

um, folks who are, are, have that more

455

:

broad, um, knowledge, but in particular,

uh, sort of the cross pollination of

456

:

ideas, um, seems to hold a lot of value.

457

:

Tim Winkler: Yeah.

458

:

And I think with the, you know, the,

uh, generative AI models, like the, some

459

:

of these foundational models that have

come out of the last year, um, these are

460

:

really kind of spicing things up, I think,

and, and adding, adding to this debate,

461

:

because, and Maggie, maybe we were talking

about this on our initial discovery call

462

:

about, you know, Um, The power of these

tools and how they can be applied to

463

:

not just a computer science, you know,

graduate, but like folks in marketing or

464

:

folks in finance or something there where

they can now consider themselves going

465

:

down this path of a, of an AI opportunity

that maybe wasn't quite as present before.

466

:

So I'd love to just kind of pull on

that thread a little bit and, and, and

467

:

maybe, you know, starting with like how.

468

:

How these have impacted your all's work,

or, you know, how, how do these kinds

469

:

of models like limit or enhance career

prospects, especially for those folks

470

:

that are coming out of school and, um,

you know, exploring that next opportunity

471

:

Andrew Gamino-Cheong: for themselves?

472

:

I'll let you go ahead, Maggie,

first, since I went first on

473

:

some of the last few questions.

474

:

Maggie Engler: Uh, yeah, I think it's

as we talked about, I think it's a.

475

:

Really interesting time right now

because of these foundation models.

476

:

Um, one of the things that strikes

me, so in my own workflow, I've

477

:

started integrating coding assistants,

things like that, um, that.

478

:

Don't necessarily produce anything

for me that like I, I wouldn't have

479

:

known how to do, but can, uh, make

things more efficient, um, and make it

480

:

faster to do things like documentation.

481

:

Um, but for me, the big question,

right, is, uh, how will this

482

:

change kind of future work

opportunities and even in field?

483

:

I do think that the argument that I

think I've made at that point in time is.

484

:

Um, that it will not necessarily,

um, replace entirely a lot of a lot

485

:

of the professions that people are

kind of worried about, um, losing,

486

:

um, but that it is always going to be

sort of an advantage, like any tool.

487

:

Uh, to be able to use generative AI

well and, um, understand its limitations

488

:

and understand its capabilities.

489

:

Um, actually my , my, um, uh,

colleague from Twitter, uh, NMA Dimani

490

:

and I, um, have a book coming out.

491

:

Uh, actually in a couple weeks with

Manning on introduction to generative AI.

492

:

Um, but, uh, shameless plug.

493

:

Um, but in that where we talk a lot

about, um, sort of the things that

494

:

people, uh, do use it for already.

495

:

And then like things that they

really shouldn't be using it for.

496

:

There was a super famous example of.

497

:

A lawyer who, um, submitted a legal brief,

uh, with chat deep written with chat,

498

:

GPT, and didn't really, uh, or didn't

even back check it, um, and so it caused

499

:

this whole kind of delay in the case.

500

:

And I think he was penalized

professionally in various ways.

501

:

Um, because ultimately, uh, people

are still going to have sort of the

502

:

responsibility to ensure quality of

their output, but if you're able to, uh,

503

:

produce, uh, writing that is, um, You

know, to the quality that it needs to be.

504

:

And, um, you're able to do that

much faster, much cheaper, and

505

:

that's always going to be an edge.

506

:

I

507

:

Mike Gruen: mean, I think just

jumping in a little bit on that,

508

:

like the, I think back to the

nineties when the web was starting.

509

:

Um, I think what we've seen is really

an enablement of certain career

510

:

opportunities that didn't exist.

511

:

So like when I first started.

512

:

You had some artists and some graphic

designers on staff that were sort of

513

:

helping to do things, but now you,

like, I've worked with people who

514

:

are just straight up graphic designer

artists who can now do a whole web

515

:

application front end the whole, you

know, and most of the logic to it.

516

:

And I think that like, right, we, the

software engineers, computer science,

517

:

we, we build these tools that then

enable others to use their special

518

:

talents, their, whether they're

artists or whatever, to be able to

519

:

sort of take it to the next level.

520

:

And I think that that's what AI is

going to be able to do is sort of

521

:

make some of these like have impact

to other careers that we can't even

522

:

think of, um, and enable them to

be more efficient in their jobs.

523

:

Andrew Gamino-Cheong: You know, one thing

that I always find funny is that we call

524

:

it prompt engineering, but so oftentimes

it feels more like prompt art, right?

525

:

It's more like there are some funky

things that can happen depending

526

:

on what prompts you put in there.

527

:

I know Maggie, this is like

the big problem that you're

528

:

trying to actually solve for.

529

:

But I think it is amazing because

I've seen some very incredibly

530

:

intelligent people who don't have a

technical background do some really

531

:

amazing things with these algorithms

and these generative AI systems.

532

:

I do think though, the limitations

of them aren't well understood.

533

:

You know, some people it's

like, Oh yeah, I had it like

534

:

calculating all this stuff for me.

535

:

I'm like, Oh, it really doesn't have

actually an understanding of math.

536

:

And like, if you didn't check

the math, you could get into

537

:

real trouble in doing that.

538

:

I think that's one of the biggest

challenges people have, even on

539

:

like their day to day basis, right?

540

:

It's knowing like, what are

the things that it can do?

541

:

What it can't do?

542

:

You know, if you ask him stuff,

most of its information, its

543

:

core training data set for chat,

GBT only goes up to:

544

:

It has some other ways of adding in some

other context about some things, but like

545

:

that itself could be a huge deal for some.

546

:

Use cases and they've got a small

disclosure now and like the left hand

547

:

corner for it, you know, our point of

view and is always that, you know, you

548

:

need to be doing a lot more thoughtfulness

about what tasks it can, can't do.

549

:

And I worry that a lot of people,

they don't understand it well

550

:

enough in those limitations, um,

that itself can bring some risks.

551

:

Tim Winkler: Yeah, it's,

it's an interesting space.

552

:

It's like handing, you know, somebody

the keys to like a Lamborghini and

553

:

not knowing exactly, you know, it's

capable of a lot of things, but you

554

:

know, half of half of the bells and

whistles you don't even know about.

555

:

So it was still so early on just to kind

of understand like some of the hacks

556

:

and the tips, how to best use the tools.

557

:

Um, so it'll be interesting, but I

think with that, You know, with, with

558

:

where it's at right now, I, I, I'm

curious to know, um, you know, we'll

559

:

say for like data science bootcamps

and things of that nature, right?

560

:

Are those already being crafted, uh, you

know, in terms of like really emphasis

561

:

around AI and have you all seen any of

that or, or, or just generally in, in,

562

:

in pure academia at large, um, are you

seeing these programs being built around?

563

:

Career paths within AI and

what does that look like?

564

:

Andrew Gamino-Cheong: I know

there's a lot of focus on training

565

:

programs to learn how to use them.

566

:

I haven't necessarily seen that,

um, in like academia itself.

567

:

There's still very much desire to teach

how these systems work under the hood,

568

:

partly because there's now so much

focus on how to mitigate the risks.

569

:

And you really only do that once you do

understand the underlying levels and like.

570

:

You know, these models aren't yet

explainable and yet the necessity

571

:

potentially legally for them to be

explainable for certain use cases.

572

:

So high.

573

:

So I do suspect that'll be one of

like the biggest areas of focus.

574

:

Um, on that research side of things, I

think one of the, the challenges in one

575

:

area as well, why sometimes I recommend

like explore a multidisciplinary

576

:

approach is that there's fewer and

fewer orgs who are working out at

577

:

the kind of cutting edge of things.

578

:

And that's partly because these

models are so large, you need so much

579

:

data, so much compute, that there

is kind of a concentration, right?

580

:

If you want to work on a truly large

language model, you need a billion

581

:

dollars or at least a hundred million

dollars in funding to be able to.

582

:

Really support that kind of

stuff, and that's only going to

583

:

be accessible to a smaller and

smaller number of organizations.

584

:

You know, I actually knew some professors

in grad school who they used to be some of

585

:

the world leaders in machine translation,

but they no longer have access to actually

586

:

the algorithms and the data and the

compute to do that still cutting edge work

587

:

without actually then just associating

with a lab working in big tech.

588

:

So I think that itself can pose

challenges to accessibility

589

:

for like those specialists in

academia versus in big tech.

590

:

Is that because

591

:

Mike Gruen: I'm sorry, is that because

the what was cutting edge now just record

592

:

the new cutting edge just require so

much more compute and the access to it?

593

:

Or is it more nefarious is I guess is it

more that big tech is actually gobbling

594

:

it up and preventing it from being done

at academia or something like that?

595

:

You'd rather not

596

:

Andrew Gamino-Cheong: say I don't think

they're deliberate in gobbling it up.

597

:

Like they're not trying to be, I'll

say predatory in that sense, but they

598

:

are the only ones who can literally

have like, Oh, we can spend a hundred

599

:

million dollars to train GBD five.

600

:

Right.

601

:

Right.

602

:

No university.

603

:

Can throw that kind of resources at that.

604

:

Tim Winkler: Sorry, Maggie.

605

:

I think you were going to say something

like rudely interrupting them.

606

:

Maggie Engler: No, um, I was just going

to add that, uh, that is absolutely true.

607

:

And I do think that is a problem,

um, for the field having.

608

:

Thought a lot around, um,

sort of the I safety space.

609

:

There is a lot.

610

:

Well, first of all, I guess the point

one is that it is harder to do cutting

611

:

edge work because of the resource

constraints that entry brought up.

612

:

I think that's starting to be, um,

remedied a little bit through like

613

:

the sort of open source development.

614

:

Um, an ecosystem.

615

:

Um, so you're not going to be able

to, or it's going to, at least for a

616

:

long time, cost a huge amount of money

to do foundation model development.

617

:

But, um, I've seen so many cool things

around like, um, um, replicating

618

:

performance, the performance of

these cute large models in a smaller

619

:

model, um, on, you know, 100 worth of

harm hardware and things like that.

620

:

So I think, um, it's, yeah.

621

:

We're starting to move in an inner

direction where it's slightly

622

:

more accessible, but not for

the not if you want to do the

623

:

sort of cutting edge research.

624

:

Um, and so, like, it's very going to

be very accessible for, like, building

625

:

applications and things like that, but

not necessarily the type of work that, um,

626

:

that you'd like, um, sort of professors

to be working on with respect to, um, some

627

:

of the safety risks and things like that.

628

:

Um, The other thing that I was going to

mention is that I think for these big

629

:

companies, it becomes Sort of a there.

630

:

There is kind of a situation where they're

all racing for different resources.

631

:

And that does, yeah, um, drive up the

cost of development for other folks.

632

:

Um, and I know that some leaders in the

space have, um, proposed things like

633

:

licensing, uh, for the, if you want to

have a model that's You know, at GPT 4

634

:

level or higher, like, um, having some,

uh, you would need to get approval,

635

:

um, or a license for that, um, which

is, I mean, I guess a good idea from a

636

:

safety perspective, um, because you just

have fewer people, um, at least legally

637

:

developing them, but I, even, even as

a person who works in AI safety, I, I.

638

:

Very much have like a reticence towards

like any type of limitation around like

639

:

who is allowed to, uh, develop them.

640

:

And so, uh, that I was just going to, uh,

sort of, um, also reference that proposal

641

:

because I think it is interesting to

see and, um, Andrew, you're trustable.

642

:

I know is super involved with this,

but, uh, in thinking around this, but

643

:

like, it will be very interesting to

see, I think, how the AI governance

644

:

develops over the next few years.

645

:

Andrew Gamino-Cheong: Yeah, there's really

good questions on like the liability,

646

:

right, who owns that a big thing that, you

know, we focus on is it's really important

647

:

to have models like the ones that you

guys are building an inflection, you know,

648

:

disclose what the risks are for something.

649

:

But then you can't, there's no

way you guys can really understand

650

:

all the ways that can be used.

651

:

Right.

652

:

And that itself presents challenge.

653

:

So even if you license out

actually that, yeah, you're

654

:

allowed to build these models.

655

:

It's there still has to be a lot

of responsibility on the groups who

656

:

are actually deploying it to make

sure that they're doing an ethical

657

:

decision like, Hey, are the benefits

outweighing the risks, right?

658

:

I can look at the risks declared by

my model and then I need still need

659

:

to decide whether those risks are

appropriate or not for my use case.

660

:

And how that kind of maps out.

661

:

I do think one of, to tie it back to

just what we were talking about just a

662

:

second ago as well, you know, academia,

they love open source stuff because

663

:

then they can get access to actually

do things at the edge of this model.

664

:

But the danger there is actually,

um, all of the, I think the worst

665

:

uses of AI that we're worried about.

666

:

They're not actually going to come

from like open AI system that has a

667

:

trust and safety team with a 10 million

dollar a year budget looking at stuff.

668

:

It's going to come from

the open source systems.

669

:

If you want to run a misinformation

campaign, do some illegal

670

:

shit with AI, you're going to

use the open source models.

671

:

And then the problem is like,

who's responsible for that?

672

:

You know, what are the conditions there?

673

:

And so there's been a couple of policy

papers that came out earlier this week,

674

:

recommending that large frontier models

actually not be open sourced at all.

675

:

And the government's forbid that, which

actually could again, impact the ability

676

:

for academia to be able to do some of

their own frontier research on that.

677

:

And there's a good kind

of trade off there.

678

:

I mean, I think it's,

679

:

Mike Gruen: as you guys were talking, I

was sort of thinking about how it's so.

680

:

In the past, these types of big,

expensive types of endeavors and new

681

:

frontiers, space, nuclear technology,

whatever, all started in the government.

682

:

The government was the only ones who could

possibly have the budgets to do this.

683

:

There wasn't an immediate commercial

application for X, Y, Z with A.

684

:

I.

685

:

There's an immediate commercial.

686

:

Use, and that's what's driving business

to sort of be at the forefront of it.

687

:

And therefore I think government is

playing catch up as opposed to in the

688

:

past on some of these like, right?

689

:

Like what stops somebody from

building a nuclear bomb in the past?

690

:

Like we we figured it out the government

funded all that they put in all these

691

:

regulations to make it really really

difficult for Someone to do this but

692

:

for on AI that's just not the case the

the forefront is commercial application

693

:

So I think it's an interesting as you

guys were talking sort of some things

694

:

click there that I hadn't really Thought

695

:

Tim Winkler: about in the past, I think

it's a good segue to, and, uh, Andrew,

696

:

in our initial disco call, you were, we

were kind of talking a little bit about,

697

:

you know, the, the doomers out there,

the utopias, and then you had a third

698

:

one, the AI pragmatists, you want to

kind of expand on that, uh, just kind of.

699

:

Explain a little bit more

of what you mean by that.

700

:

Andrew Gamino-Cheong: Yeah.

701

:

So, you know, like any media thing,

media loves really, you know, uh, eye

702

:

catchy headlines, like AI is going

to create, you know, solve all of our

703

:

problems and you can read blogs from

famous VCs about how AI is the solution

704

:

to all of our problems and also read,

you know, we talked, we began this

705

:

podcast talking about Skynet, right.

706

:

AI is going to kill us

all kinds of things.

707

:

Those are great for headlines, but the

danger is that that kind of distracts

708

:

from actually trying to solve some of

the real problems out there, right?

709

:

You don't need to have military

AI to still have AI harms.

710

:

One of the first instances that almost

set off this entire industry now of

711

:

AI safety research was around use

of AI to recommend prison sentences.

712

:

ProPublica did a great expose

about like Hey, this is biased

713

:

towards a certain group.

714

:

Underlying that actually was a

discussion about how you measure

715

:

fairness in an algorithm and arguably

an ethical debate about what fairness

716

:

was used to optimize things, right?

717

:

AIs are trained to maximize some value.

718

:

If that value is Arguably has an ethical

aspect to it that needs to be discussed.

719

:

You know, the truth is that we're never

going to be able to pause all of AI,

720

:

nor should we really assume that AI

can really solve all of our problems.

721

:

Cause there's a lot of things

that frankly are beyond its realm.

722

:

And so the question is really,

let's assume AI is going to

723

:

be everywhere pretty quickly.

724

:

You know, how do you actually set up the

right conditions to do that responsibly?

725

:

You know, we can't really prevent it.

726

:

And so what are the policies that

should, that we should adopt instead?

727

:

One example of that, and you know,

this may sound a little bit cynical,

728

:

is it's always going to be cheaper and

faster to generate content with AI.

729

:

And so trying to say we're going

to watermark everything, it's

730

:

going to be really difficult.

731

:

And also again, with any open

source system, any watermarking

732

:

things can be evaded.

733

:

And so instead, I also say like,

let's look at what is quote

734

:

certified human content look like,

you know, it's like the equivalent

735

:

of an organic label on something.

736

:

Let's define the criteria for that and

actually get that set up because there's

737

:

going to be a lot of interest and demand

to say like, yeah, I, I will only buy

738

:

journalism that's certified human content.

739

:

Right.

740

:

Or like certain unions will

want to enforce a certain

741

:

level of that kind of stuff.

742

:

Um, you know, that's just facing

the reality that probably the

743

:

majority of content that we'll see

coming out within five years is

744

:

maybe even kind of conservative,

is, uh, will be AI generated.

745

:

Maggie Engler: Yeah, I think it's also

so important to, right, think through

746

:

kind of the context in which all of

this content is appearing and Um,

747

:

what we really need to do as kind of

a society, um, in order to respond.

748

:

Um, I guess that, that might be your

definition of pragmatism, um, but it

749

:

reminds me I was recently, um, at an

event, uh, organized by the, uh, give CT

750

:

global internet forum on counterterrorism

and talking about a lot of this stuff,

751

:

generative AI and how, um, we've already

started to see sort of deep fakes from,

752

:

or of political figures and things

like that being used for various.

753

:

Um, purposes.

754

:

And when it comes to something like

watermarking, I think it, that to me

755

:

strikes me as like, um, an example of

a technocratic solution where, right,

756

:

like, even if you're saying like, okay.

757

:

Setting we're setting aside open source

models like all of the big AI generation,

758

:

uh, models have agreed that they're

going to all watermark their content,

759

:

but then ultimately, like how many

people who are scrolling through X or

760

:

or like other social media platforms are

going to be like, Oh, I wonder if like.

761

:

This clip of President Biden is is real.

762

:

Like, let me go just check it against

all these different waterworking systems.

763

:

No one's going to do that.

764

:

You know, 1 percent of people less

than that are going to do that.

765

:

And so I do think like what I'm most

interested in is how it is exactly what

766

:

Andrew is getting at, like how we can

set ourselves up for this, um, in the

767

:

way that in a way that is, um, kind of

the most, um, productive as possible

768

:

and the most Um, uh, sort of realistic

around what is, what has already

769

:

happened and not trying to stuff the

genie back into the bottle, so to speak.

770

:

Andrew Gamino-Cheong: One of my

favorite, um, I'll say like pragmatic

771

:

AI ideas I heard out there was instead

of schools and teachers trying to

772

:

prevent people from using, you know,

GPT to generate their stuff, which is.

773

:

You know, that's a,

that's a losing battle.

774

:

It's going to be impossible to

ever like truly restrict it.

775

:

They said, all right, you have to turn

in one copy that is generated by GPT

776

:

and you have to disclose what your

prompt was and all the stuff you did.

777

:

And you also have to hand in

the handwritten version as well.

778

:

You know, that shouldn't

necessarily reflect that.

779

:

I thought that was like really pragmatic

because actually they'll end up

780

:

with a whole corpus of like 30 plus

essays written by GPT to then compare

781

:

against all the ones that weren't.

782

:

And it's kind of like.

783

:

You know, use this as a tool,

but still have to like show that

784

:

original and creative side of things.

785

:

Those are the kinds of solutions

I think we just really need to be

786

:

talking about more instead of just like

banning this because I think that'll

787

:

be a just a waste of time and effort.

788

:

Yeah,

789

:

Mike Gruen: the one that I saw that

wasn't also classroom was the idea

790

:

of like, we're just going to change

what's homework and what's classwork.

791

:

Rather than going home and writing this

paper on your time, we're going to use

792

:

the class, like read the book at home, if

you want to use chat GPT to whatever to

793

:

come up with ideas or whatever, but like

we're going to actually use class time

794

:

to write the paper, which I thought was

an interesting way of doing it to make

795

:

sure that people get the concepts and

796

:

Tim Winkler: stuff like that.

797

:

Yeah, I think it's all a pretty

fascinating conversation at large.

798

:

I mean, it, yeah, everybody's

gone through that, that point.

799

:

Uh, probably at one point in

the last six months or years is

800

:

my job is my job in jeopardy.

801

:

Is my role going to be

one that's replaced?

802

:

And, you know, I think one of the

biggest things that we've always kind of

803

:

preached is just like, it should happen.

804

:

Be a part of everybody's job.

805

:

Just a matter of like, how do you

use it as a tool in your tool belt to

806

:

become more efficient or what have you?

807

:

But, um, yeah, it's just, it's still

so early to, I'm very excited to see

808

:

how everything's, how things play

out over the years, but, um, this

809

:

is a great kind of starting point

to keep the conversation moving.

810

:

I love the pragmatic outlook on this too.

811

:

I think it's a, it's a really fascinating,

uh, addition, Andrew, but, um, yeah.

812

:

Why don't we, um, put a bow on

it and transition over to our

813

:

final segment, uh, of the show.

814

:

So this is going to be the five second

scramble where I'm just going to ask

815

:

each of you a quick series of questions.

816

:

Uh, try to give me your, your

best response within five seconds.

817

:

Um, some business, some personal,

I'll start with you, Andrew, and then

818

:

I'll, I'll jump over to you, Maggie.

819

:

So, um, Andrew, you ready?

820

:

Yeah, let's do it.

821

:

All right.

822

:

Uh, explain trustable to me

as if I was a five year old.

823

:

Okay.

824

:

Okay.

825

:

Okay.

826

:

Andrew Gamino-Cheong: We help you

do all the legal paperwork for AI.

827

:

How would you describe

828

:

Tim Winkler: the culture at

829

:

Andrew Gamino-Cheong: Trustable?

830

:

I mean, we're an early stage company,

so it feels like a family of friends,

831

:

family of friends working together.

832

:

I don't know if that makes sense, but

833

:

Tim Winkler: I got it.

834

:

What, uh, what kind of technologists

would you say thrive at, at Trustable?

835

:

Andrew Gamino-Cheong: One

who is comfortable kind of

836

:

learning stuff on their own.

837

:

There's a lot of unknowns for what we're

doing on the regulatory and AI front.

838

:

Very cool.

839

:

Tim Winkler: And what would you say,

uh, are some exciting things that folks

840

:

can gear up for, uh, heading into 2024?

841

:

Andrew Gamino-Cheong:

Yeah, I mean, be ready.

842

:

Uh, the number of new applications

of AI we're going to see is

843

:

going to be explosive, I think.

844

:

Tim Winkler: Nice.

845

:

If you could have any superpower,

what would it be and why?

846

:

Andrew Gamino-Cheong: Ooh.

847

:

I'd have the ability to, um, go back

in time and re even just to re observe

848

:

things that happened in the past.

849

:

Nice.

850

:

Tim Winkler: All right, kiss, marry,

kill, bagel, croissant, English muffin.

851

:

Andrew Gamino-Cheong: All right,

kill English muffin, uh, kiss

852

:

a bagel, marry a croissant.

853

:

Tim Winkler: Um, what's something that you

like to do, but you're not very good at?

854

:

Andrew Gamino-Cheong: Ooh,

um, probably bike rides.

855

:

I, I love to go on some

trails, but I'm like.

856

:

I'm not particularly fast

or athletic about it.

857

:

So keep, keep that helmet on.

858

:

Yeah, I crashed a lot.

859

:

What's,

860

:

Tim Winkler: what's a charity or corporate

philanthropy that's near and dear to you?

861

:

Um,

862

:

Andrew Gamino-Cheong: my wife

and I have volunteered at a

863

:

dog shelter, um, here in DC.

864

:

Cool.

865

:

Very

866

:

Tim Winkler: nice.

867

:

What's something that

you're very afraid of?

868

:

Andrew Gamino-Cheong: Ooh,

something I'm very afraid of.

869

:

Uh, dairy.

870

:

Definitely afraid of dairy.

871

:

Tim Winkler: All right.

872

:

I appreciate the honesty.

873

:

Um, who is the greatest superhero of all

874

:

Andrew Gamino-Cheong: time?

875

:

Greatest superhero of all time.

876

:

Uh, I've got a soft spot for Iron Man.

877

:

Nice.

878

:

Tim Winkler: That's the first time

I've heard Iron Man on the show.

879

:

That's good.

880

:

I like

881

:

Andrew Gamino-Cheong: that.

882

:

Tim Winkler: All right, that's a wrap.

883

:

Uh, Andrew, Maggie,

are you, are you ready?

884

:

I think so.

885

:

All right, perfect.

886

:

Uh, what is your favorite part

about the culture at Inflection?

887

:

Maggie Engler: Uh, I think my favorite

part is that, um, Because this area

888

:

is so new, like there's a lot of

just openness to experimentation

889

:

and, um, trying different things out.

890

:

Very cool.

891

:

Tim Winkler: What kind of

technologists thrives at Inflection?

892

:

Maggie Engler: Uh, quite a range.

893

:

Um, but definitely people who are open to

Um, iterating fast, but also kind of, uh,

894

:

robust evaluators, um, and, and, uh, like

to borrow a term from, uh, cybersecurity,

895

:

really like pen testing and, and kind

of relentless in terms of, um, trying

896

:

to find all the chinks in the armor.

897

:

Tim Winkler: Nice.

898

:

Red, red team stuff.

899

:

Um, what, uh, what can our

listeners be excited about with

900

:

inflection going into 2024?

901

:

Maggie Engler: Oh, uh, I think we'll

have a lot of, uh, improvements

902

:

on the model side coming out.

903

:

Um, So yeah, I don't want to,

I can't say too much about

904

:

it, but definitely stay tuned.

905

:

Um, and, uh, the product, uh, pie,

um, will be, it will be, we'll be

906

:

continuing to iterate on our, on our

907

:

Andrew Gamino-Cheong: product.

908

:

Tim Winkler: Cool.

909

:

Excited for that.

910

:

Uh, how would you describe your

morning routine in five seconds?

911

:

Um,

912

:

Maggie Engler: I usually work out, uh,

Peloton and, um, have like some kind

913

:

of breakfast, like toast, simple, uh,

toast, peanut butter, anything like that.

914

:

What do you love

915

:

Tim Winkler: most about living in Austin?

916

:

Maggie Engler: Oh, I love Austin.

917

:

My family's from Central Texas.

918

:

tailgating at UT, lots of

sand volleyball, fun town.

919

:

Tim Winkler: Cool.

920

:

I'm going to flip it

from what I asked Andrew.

921

:

Um, what's something that you're

good at, but you hate doing?

922

:

Oh,

923

:

Maggie Engler: um, that is interesting.

924

:

Um, let's see.

925

:

I'm always, I'm, I'm very good at, there

are certain like household chores that

926

:

I have like, um, kind of a systematic

approach to, but I don't like enjoy doing.

927

:

So.

928

:

Um, like, I don't know, um,

like big loads of laundry.

929

:

I guess.

930

:

I

931

:

Tim Winkler: hate laundry.

932

:

Um, what, well, if you could live in

a fictional world from a book or a

933

:

movie, which one would you choose?

934

:

Hmm.

935

:

Maggie Engler: Wow.

936

:

Um, I.

937

:

Would love to live in, um, like the

kind of Gabrielle Garcia Marquez,

938

:

like magical realism, um, based,

so like kind of a South America

939

:

tropical area, but like with magic.

940

:

Tim Winkler: Sounds awesome.

941

:

What's the worst fashion trend

that you've ever followed?

942

:

Maggie Engler: Oh gosh, um, crepe pants.

943

:

Tim Winkler: Well played.

944

:

Um, what was your dream job as a kid?

945

:

Maggie Engler: I was actually just

talking about this with someone.

946

:

I really wanted to be a farmer,

uh, for a long time as a kid, kid,

947

:

because, um, my grandpa was a farmer.

948

:

Um, and I thought like pigs and

sheep and all that was really

949

:

cute or were really cute.

950

:

Tim Winkler: That's

such a wholesome answer.

951

:

Um, and we'll end with your

favorite Disney character.

952

:

Maggie Engler: Um, probably Mulan.

953

:

Uh, I feel like she was early to the,

like, strong female lead, uh, game.

954

:

And, um, yeah, is just a badass.

955

:

Tim Winkler: Yeah, she's a badass.

956

:

And great soundtrack too.

957

:

Great soundtrack also.

958

:

Alright, that is a wrap.

959

:

Thank you all both for participating

and, uh, joining us, uh, on the podcast.

960

:

You've been fantastic guests.

961

:

Uh, we're excited to keep tracking

the innovative work that you all

962

:

We'll be dealing with your companies

and building in the AI space.

963

:

So appreciate y'all spending

time with us, uh, on the pod.

964

:

Thanks for having

965

:

Andrew Gamino-Cheong: us.

966

:

Thank you.

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