Judith: Welcome to Berry's In the Interim podcast, where we explore the
cutting edge of innovative clinical trial design for the pharmaceutical and
medical industries, and so much more.
Let's dive in.
Scott: All right.
Welcome everybody.
Back to In the interim.
This is actually our 60th episode of In the Interim, I'm your host, Scott
Berry, and I'm joined by two other senior statistical scientists at Barry, Dr.
Nicholas Berry.
And Dr.
Joe Marion.
Welcome back to In the Interim.
Nick Berry: Thanks.
Joe Marion: Uh, thanks Scott.
Happy to be here.
Scott: So the 60th episode of, of in the interim 60 interims.
Interestingly, uh, Joe, it was just recently involved in the 60th
interim analysis of a clinical trial.
And we won't say what trial that was, but you're doing double duty on the 60.
Joe Marion: Yeah, it's, uh, it's funny how that number creeps up on you, right?
People always ask me like, how many interims can you have in a clinical trial?
Or like, you know, sure.
Surely, you know, you can't do too many of these.
In a sky's the limit as, as far as I can tell.
If I think, if I stayed at Barry for another, uh, 30 years, you know, we'd be
at what, how many interims would that be?
It'd Be a lot.
Scott: Be a lot.
Joe Marion: lot.
Yeah.
Nick Berry: joke about how you, uh.
Spend your alpha at Barry.
Sometimes when you do an interim, you're spending your personal alpha.
So 60 interims is a lot of alpha for you to spend.
Joe Marion: I am all at Alpha man.
Well, I mean, if you believe that the alpha spending literature,
every time you get a failure, you get to recover some alpha.
Right?
So maybe I've got plenty of alpha, you just dunno.
Yeah, yeah.
Yeah.
Scott: Alright, stay.
There's nothing better than adaptive design humor, uh, for,
Joe Marion: That's
Scott: our episodes.
Yeah.
Joe Marion: People love it and
Nick Berry: Yeah.
Joe Marion: invite us to parties because they want to hear it.
Nick Berry: Hmm.
Joe Marion: Yeah.
Scott: Okay.
So today's episode, I, I.
When we come up, we've done 60 episodes of this, and we're not running out of ideas,
just like we're not running out of alpha.
We have lots of ideas, and I'm asked all the time when I go out, what, how
is AI affecting Berry consultants?
I'm asked by statisticians.
I'm asked by clinicians.
I'm asked by people who don't know really what we do, but we know we have
a company, uh, in, in the tech space.
So today's episode is.
AI at Barry.
How do we use it?
What, what do we know about it?
What do we think where the world is going?
And today, I, I have Nick and Joe on, and they, they're part of our
committee that is exploring ai and it's a incredibly rapidly moving thing.
And we're, we're, we're keeping track of this, so we're gonna
talk about today, AI at Berry.
I think it's important to set up what Barry does.
So statisticians within the clinical trial space do lots of different things.
Some of those things we don't do.
Some things we do, and I think it's important in the role
of AI in those, those things.
So Berry Consultants at its heart is a clinical trial science company.
We have clinical trial scientists that work at Berry.
Most of them are statistically trained.
We do have clinicians, we have mathematicians, computer scientists,
but they are clinical trial scientists.
We do really three major, major things, core competencies, and the first
is trial design and trial strategy.
So the science of clinical trial development and how the trial fits
into that, the science of it, we most of the time were involved in designs
that would be considered innovative.
And from that meaning.
Not typical, not fixed trials.
So we do adaptive trials, seamless trials, adaptive enrichment trials.
We incorporate external data within a clinical trial, more complicated analysis.
In a trial platform, trials, basket trials, all of these
fall under this umbrella of innovative clinical trial design.
So we spend a lot of times in the design of these trials.
The implementation of these innovative trials, whether it's carrying out the
analysis, doing 60 interim analysis in the trial needs, additional expertise,
and so we have a group that are experts in implementing innovative trial designs.
So that's another core competency here at Barry in, in doing that.
Clinical trial simulation plays a critical role in the design of innovative trials
simulation, guided clinical trial design.
We do it on individual projects.
We might be writing custom code for individual projects.
We also have software that we license to pharmaceutical,
academics, government, CROs.
For clinical trial simulation.
And so we have a product that does that.
It's another core competency at Barry in, in what we work on
and what we do on a daily basis.
So thinking about what is the role of AI in these various activities?
What we don't do is we don't do a process stuff in trials.
We stay out of the quote unquote CRO space.
We don't create databases for trials.
We don't do data management in trials, generation of the tables,
listings and figures, the the SAS programs that generate these.
Creation of CRFs.
We don't do that.
This is CROs are really good at that and that's not part of this strategy,
part of clinical trial science.
SAPs play an interesting role and maybe an interesting role in the whole I.
Role of AI in all of this.
We do contribute to statistical analysis plans.
We don't create the huge tomb of the, the all of the tables, listings and
figures, concomitant meds and all of that, because the, the group doing
the TFS should be involved in that.
But we play a really.
Contributing contr critical role to the analysis, the objective within sap.
So we work on SAPs, but we don't sort of generate them as a core competency.
The last thing is we are not experts in ai.
Uh, now we're interesting conversation.
Should we be calling ourselves experts in ai, uh, in that, but
we don't consider ourselves.
So in this question of ai, we're not selling clients that we're ai, but we're
utilizing AI and how we're using it.
We're learning about that, and that's what we're gonna talk about today.
So maybe I'll, maybe I'll throw it to Nick now.
Nick, you're, you, you, you do design, you do implementation.
Uh, you, you, you spend the majority of your time on the software team.
How do you use ai?
Nick Berry: Yeah, I mean this is maybe the most straightforward, um,
use case where I think it's pretty generally accepted that AI is very
good at coding, especially if you.
Limited to things that are, you know, all over the internet.
Like me, it's incredibly good at creating user interface interfaces.
Uh, it's better than I am at modifying visual interfaces, connecting,
um, buttons to effects and making sure all the data is propagating
to the right places and tracking.
Data storage models and things like that, it's incredibly good at that.
Um, and I, definitely use it.
Uh, for that.
We have this fax that is now, I dunno, 17 years old is the name of our software Fax.
It was created obviously before ai?
the desktop app, the fax desktop app has.
I mean essentially no, uh, LLM generated code in it, right?
It's written predating it.
It's is, yeah.
Um, we are working on new technologies, uh, developing new fancy versions of,
of facts and other applications and it's comes in handy, in that, with that
said, like with the user interfaces thing that I love using it for, I.
Am by now familiar with some of the pitfalls of using it.
Um, probably makes me better at using it in general, but it's not
incredibly good at writing technical statistical code in my experience.
Um.
Some things it does really well.
Uh, you know, I think if you're simulating a trial and you want it to, to generate
an accrual of patients for you, like it knows it's gonna do the cumulative
sum of exponentially distributed patients, and it does that every time,
that that's not what I'm talking about.
Um, yeah.
Predictive probabilities is a case where I've seen it, you know, it thinks
it knows what it's doing, and, uh, even says something really, you know.
Reassuring about what it's doing and then lo and behold, it's not
doing what you think it's doing.
And so some of the more, you know, maybe nuanced statistical concepts,
I think it, it struggles at.
But in general I think really good.
Even, you know, in simulations writing simulation code, it will
give you a backbone, it'll give you a nice flow through your app
and, and help you write stuff.
And so, I think we use it for a lot of like non-technical coding.
I tried to, to get help our, we, we are adding analytical calculations
for a lot of designs to facts.
And, you know, this is a numerical integration.
It's, it's a,
it's really computer science heavy code, not so much like statistical
heavy code in a lot of ways.
And in that case.
It, it
Joe Marion: Is it good?
Nick Berry: away, like, oh, you're using this person's integration algorithm.
These are your weights, so on, so on.
And then it randomly slips in something you don't want to be there.
And it's hard to, to catch that in a lot of cases because, you know, those
algorithms are not like intuitive to look at and say, oh yeah, okay,
I see exactly where it went wrong.
And so we had some, some mixed, uh, mixed results in, in like
that heavy computation area.
Joe Marion: Is, is the cost of debugging worth the cost of using it?
Like, that's what's, what's the trade off in time?
And then like, I don't know, what's the trade off in enjoyment?
Like what, what would you rather be doing?
Yeah,
Nick Berry: for me it, it was, it was worth it.
Um, the,
yeah.
that, that's not like a dopamine hit for me.
Right.
Like writing numerical integration code didn't, actually,
Joe Marion: No,
Nick Berry: that doesn't keep the lights on like mentally for
Joe Marion: not coming over to my house, that's for sure.
away on Friday nights.
Yeah.
Nick Berry: that interestingly, the, the UI stuff, watching it make big
changes and like, oh, I, I feel like I have a really nice flow for this.
Like, I'm gonna help people understand what they're inputting in
our app and it's gonna be so nice.
That is a dopamine hit and the amount that it can do sort of in
one shot is kind of incredible.
And it's low stakes, right?
A lot of times I'm just, you can look at it and say, Yeah.
it did the right thing.
And I can look at the, the database and say, yeah, all the
stuff's where it needs to be.
other things that are higher stakes, like my group sequential probabilities
being calculated correctly and am is the type one error that I'm reporting,
actually a type one error and things like
Joe Marion: Okay.
Nick Berry: yeah,
Scott: So let's talk a little bit about efficiency, and you sh you demonstrated
to me an example the other day, and I, I think, by the way, I, I can ask, what's
your favorite AI for code generation.
Nick Berry: I, I use.
Claude, um, I use
Scott: Okay.
Nick Berry: as of today.
4.7.
Um, is the model
Joe Marion: Extra thinking.
Right?
Or maximum thinking How?
How much thinking do you think?
Yeah.
Extra high thinking.
Yeah.
Nick Berry: places to be.
I can't use Max.
Yeah.
Scott: Okay, so you provided a demonstration of a pretty simple clinical
trial design, but simulations it made in our shiny app, really beautiful app
for exploring things and all of that.
And, and I know you kind of knew what you wanted to do going into this, but largely
you could write this code in 10 minutes.
Uh, it was the demonstration of this with Claude, with Claude.
Nick Berry: right?
Joe Marion: the Claude writes the code in 10 minutes.
Scott: yeah.
No, I, yeah, I meant using AI where if you sat down from scratch in writing
this, it's, you know, it's 10 days.
Uh, I, and, you know, don't know the magnitude of that.
And now, yeah, now I just, I just challenged Nick about how long he
could do it for that, but, but.
Nick Berry: I might never need to write reactivity code in shiny ever again.
I mean, I'm not a shiny developer, right?
I'm not even really a web developer, even though I do It to some extents.
But I mean, I'm a statistical developer, uh, in, in most regards.
so, Claude
Scott: was
Nick Berry: good at doing the things that I don't really.
Know that well, right.
These are the fringe cases of my job that I've had to learn and I've had to
go and, you know, take courses online about how to do web development and now,
um, passing off a lot of that skillset, um, which is great as long as it's doing
a good job, which it seems to be doing.
Um, but the skillset that I'm sort of an expert in, which is the more
statistical computation writing MCMC code, things like that.
I'm doing that still.
Right.
That's not something I'm passing off to, to AI at this point.
Scott: So we we're putting together a development plan.
So you, you mentioned fax as a desktop product, for example.
We're making it a cloud-based product.
We're redesigning the GUI to this.
We're adding a, a, a number of really nice things.
Had we done this five years ago, we would've had.
More developers and it would've taken significantly longer.
All of a sudden with what AI has done in the last year,
we need less goey developers.
Within all of this, Nick, is the productivity maybe of three
developers years ago in terms of time.
This is, uh, this is game changing, enormous ability for us to do
this much more efficiently.
Nick Berry: Well, in my mind it's like the perfect use case.
I, I'm not gonna say like, oh, we 10 XD our development.
That's not,
Scott: Yeah.
Nick Berry: a mature app that we've spent 15 years with
statistical programmers developing.
We've always had The engines in the background, like
the, the crown jewel, right?
This is the multiple amputation
Joe Marion: part, right.
Nick Berry: response modeling, all the good stuff that drives facts
and makes it do stuff that no other software can do that is there.
It's essentially unchanged.
The only thing we have to do for this web development is create a facade around it
that takes in inputs, converts them to parameters, and passes it into engines.
So I think it's like the perfect use case, right?
The, the tests that we use for current facts carry over.
They go to web fax, like, you know, our testing.
We can run these on the cloud, we can run them on the desktop
and make sure the results match.
Like we have so many protections while we develop this, that it's
like the perfect use case, uh, for AI in for, for, you know, this.
AI as a coding agent, at least in my mind.
And so we've gotten really nice returns from it as like
Scott: Yeah.
Nick Berry: UI scaffolder.
Joe Marion: Scott, let me, let me ask you a question, Scott.
now Nick is doing the work of three nicks.
I mean, we're making up the number three, but let's just say it's three
Scott: Yeah.
Yeah.
Joe Marion: So I mean.
To me that says maybe you want more K Nicks, right?
Like he's, in some sense, you're getting three times the
value for the, the same person.
Like maybe you want even more like, like maybe the, maybe it's not that
we're more efficient, but like, I mean, maybe you'd want nine, nine
K Nicks worth of capacity now.
Right.
And
Scott: Yeah.
Joe Marion: I
Scott: Yep.
Joe Marion: the margin, it's kind of changing the value of what we can do.
So.
Scott: Yeah.
From a business perspective perspective, this is really interesting.
Is, is.
Do I have the work to support nine times, nine nicks, you know, sort of
thing within that in the development.
And so we, as a company we've talked about, we, we are.
We, we've got a good amount of business.
Barry's doing very well.
We could expand people, we could add more people to Barry, it's an
interesting question of, in five years will all of the individuals
at Barry be three times themselves, five times themselves, and all of a
sudden, you know, the, the work needed?
Do I need more people?
I, it's a really interesting thing from a business perspective and
software is different than design.
Joe Marion: Yeah, software is, I'm thinking more on the software
side of the house as well.
This is like,
Scott: right,
Joe Marion: you know, you, you could, you could do things.
It would've been really expensive before and now may.
Now maybe they make sense, Right, Like now they're,
Scott: right,
Nick Berry: the skillset that makes me good for the software team right now
is not ability to develop software.
It's.
It's a little bit of that, but mostly it's me being able to
see what I want and have ideas
Joe Marion: I.
Nick Berry: I.
want from, you know, we've, we've learned a lot of lessons
with fax desktop over the years.
We're trying to improve the, this cloud app to resolve all the things we
wish we'd resolved, but being able to know what I think people would expect,
what I want this, this new app to do.
And then precisely state, and this is where my software skills
come in, is tell the, the.
AI agent exactly what I want it to do.
And a lot of times that involves speaking in sort of software terms and
using things that, uh, aid me in being precise about specifying what we want.
Um, and especially for like the front end.
And for things like that, it's mostly just having a vision and
being extremely specific about how you want to implement it.
Joe Marion: Yeah.
And having good taste.
Having a good vision,
Nick Berry: vision.
Scott: Yeah.
Nick Berry: A good
Joe Marion: yeah.
I think both of those things matter, right.
Nick Berry: Yeah.
Yeah.
Joe Marion: It's an incredible time to build things like.
Scott: So this is gonna come back.
As in you, you brought up the really important issue that
you have this imagination and ability to know where to go.
You're consuming lots of information and making these really good decisions.
And then you can tell AI to do something and you know what it is.
And AI's really, really good at taking those instructions and creating something,
and you're iterating it and you're, you're on top of it and you're controlling it.
Nick Berry: It's been fun because I can ask it to do three things and I
can pick which one I like the best.
Whereas normally I, I don't have time to do all three things and look at
Joe Marion: Yeah, you're not gonna do three things,
Nick Berry: things.
I can look at
Scott: Yep.
Nick Berry: I can say, oh, this one actually turned out the best.
Let's do that one.
And like that's, yeah, so
Scott: Yep,
Nick Berry: amount is actually get
Scott: yep,
Nick Berry: at the options live in the app and going so,
Scott: yep.
Joe Marion: And I,
Scott: Joe, how.
Joe Marion: for general use too, right?
Is like,
Nick Berry: sure.
Joe Marion: ask for multiple things, give it feedback on what
you like, like, you know, the labor is in some sense cheap on its end,
Scott: Mm.
Joe Marion: uh, yeah.
it and interact with it.
Yeah.
Scott: Okay.
So that, that, maybe we should make sure we touch on how to use it well.
But that sounds like that's an important role of it.
So how do you, uh, Joe spends more of his time in the design of clinical trials.
He does implementation 60 interims in a particular trial.
Does implementation, um, what, how do you use AI at Barry?
Joe Marion: Yeah, I mean, I think there's, I with what Nick does of course, right?
Like I think.
Coding is where these things are their very best.
So, you know, we're writing simulation code.
Um, we do a lot of visualization.
It's good at visualization, but of course, you gotta be very specific, right?
You have to know what to tell it that you want.
It'll, it'll produce plots, but they're often like, you know,
not quite of the right quality.
And so, You know, things you can do is like, you can have like a style guide
for how you want plots to look and that kind of thing, and that helps it sort of
one shot those tasks a little bit better.
Um, things I'm really excited about.
Um, well, I, one application that I think is really cool there's
this, this common like, step of regulatory interactions with.
Where, where when you're preparing for a briefing with the FDA, I feel like
every company ever that's doing this preparation, one of the things they
do is they think about what could the FDA's response be to our questions?
What should we be prepared to answer?
How do we do that strategizing?
I, I sort of always, I don't know.
I mean, I felt it was necessary, right?
But I hated writing those questions down.
I kind of knew what they were.
it's tremendously interesting to get the AI critique of like a sanitized document.
What is the regulatory perspective gonna be?
And I think it's really valuable to us at Berry because you know, you know,
based on the things it's trained on, it's gonna give you a pretty traditional view.
And I think that can really help you sort of understand what that is.
It's not the argument we're usually articulating because of, you know, the
kind of work that we do, but uh, I love that ability to get critique from it.
I think it's tremendously fascinating.
Nick Berry: I, in my experience when doing something like that, it's usually
like a numbers game in getting a lot of different things from the, the,
the AI model where you can pick and choose the things that are actually
intellectually interesting and.
and like you said, I kinda wanna emphasize we work at Berry Consultants.
We tend to have, we're not, obviously not a homogenous population of
people, but in general we're designing adaptive trials, often Bayesian
interim analysis, things like that.
And the literature, you know, everything that's posted online, the vast majority of
it is traditional and frequentist and not as innovative as a lot of things we do.
So the LLM.
Thinks like that, right?
So you're almost getting a, you, you are getting a conservative
perspective when you ask it questions.
Joe Marion: Yeah, complimentary review and then you can think
through how you'd answer them.
It's not very good at answering its own questions 'cause you know,
we've got a particular house view that's like really underrepresented,
but you know, I think that that.
It's like a pretty exciting thing.
And yeah, like Nick said, you want a bunch, like if you get 10 criticisms,
you might like three or four of them might really be worth, worth,
interesting, and worth addressing.
But it's still good value.
Yeah.
Nick Berry: told it?
Uh, give me feedback like you work at Berry Consultants.
Joe Marion: Yeah.
Not, well, actually, I think one of the, I've, I haven't tried this in a
while, but I used to tell it to like.
You know, write like Kurt Veley, right?
Or like, give me the Kurt Veley arguments for this kind of thing.
And like, not a very good Curt, I wonder if it's a good curt now, I
Nick Berry: yeah.
Joe Marion: that would be interesting to me.
Nick Berry: Yeah.
Joe Marion: I, I kind of think it probably isn't.
Um, it, it can do other public figures reasonably well, but I don't think it
does any like statistician super well.
Nick Berry: Yeah,
Scott: Okay, so this I,
Nick Berry: on Twitter a lot, right?
He has
Scott: yeah.
Nick Berry: Twitter about different things, and he may probably not, you
know, he has a lot of public visibility on some of these topics because
Joe Marion: He has some chance of being out there, right?
Yeah.
Um, other stuff that I think AI is cool for.
Um.
I, produce way more shiny apps than before.
because the, the cost of doing so is really low.
And what's great is that you can pass them to clients and, and then that
really gives them like a hands-on tool that, that allows them to interact
with the decision they're making to tweak all the levers themselves to
get that kind of firsthand experience.
you know, sometimes in like a rare disease trial, they'll want to
know it's, you know, I have 50 end points, which one should I use?
Right.
And the ability to hand a tool over instead of like a ton of
plots is, is tremendously useful.
Absolutely improves the communication.
I'm hopeful like that.
This will be good for facts too, right?
You can imagine a plain, plain language version of facts where
you describe in text what you want the design to be, and then it knows
how to set up a FACTS file, like.
You know that that's, you know, probably a little bit of a ways away, but it's
not like a pie in the sky dream anymore.
I think five years ago that would've been a tremendously complex endeavor.
Yeah.
Nick Berry: course.
Every single person that we show this cloud app to their mind initially
goes like, it makes so much sense.
This has to be something that we can do.
It's so cool and.
The parameters that go into facts are known, right?
There's, it's not like it has to write simulation code that
can do infinitely many things.
There's a set of parameters that are predefined.
The storage mechanism for those parameters is predefined.
We have documentation for that.
Presumably, you give that to an LLM, someone can describe their trial that
they want, not design me a good trial.
I don't, we can get to this later, but it's not gonna do a good job.
It's not gonna do what you want.
It's not gonna do a good job in that case.
But I want a trial with three interims.
I want Goldilocks stopping style, um, decisions at interims with
these predictive probabilities.
Um, and I want to figure out what p value threshold gives me.
A good type one error.
Like now it's got the parameters it needs, and it can fill in, can say, oh,
when do you want your interim scheduled?
And things like that.
And then all it has to do is say.
Interim analysis schedule Vector comma 400, comma 500, and you
know, it's, we have to teach it.
It's guardrails and then I think it's like Yeah.
Joe Marion: But it'll not.
Nick Berry: of of doing, of, of figuring out exactly, you know,
what prompt gets it to do the right thing and things like that.
But, but it's
Joe Marion: Yeah.
W what's the right like iteration loop that, that lets it learn that, right?
yeah.
Scott: So as, as of this point, so AI is being used, the LLMs where.
You're asking it to write code or you're asking it to do a task, and it's making
us much more efficient, even better at that task, you said, uh, typically you'd
walk in historically with a set of seven slides that you're presented to a client.
Now you're walking in with an app that has presumably.
Hundreds of potential things that they could look at.
So you're, you, you're, you're increasing things, but largely you're using it
to be more efficient and maybe a bit more broad about what you're doing.
What you just described is a whole different thing where we're training
something and a generative AI that we're now creating a tool that's using AI
for somebody to use facts or somebody to use it, which we haven't done yet,
but is, is an intoxicating thought.
Joe Marion: Yeah.
Really democratizing in some sense, right?
You still have to have good taste to use that tool, right?
You still, you still would have to know what to tell it, right?
It.
It's not a self-governing process, but I think it cuts
so much of the learning curve.
Some, so much of the uninteresting learning curve out maybe.
Yeah.
Nick Berry: Yeah.
Scott: Okay, so let's, let's dive into some of that.
I, I, but let's talk about things that are happening at Barry with, with ai.
I think.
You can't do a search engine now without it being ai the default in all of this.
And that's, that, that's pretty common.
I think everybody's doing that.
The writing, you talked about writing, uh, the ability for it to, to read your
writing, to critique it, to, to update it.
Every one of these podcasts goes into AI and it creates social
media, blurbs, and a blog.
Joe Marion: sense.
Scott: And a blog.
Now, I read every single one of the social media things.
I edit it, I read the blogs.
I, I will go in and read some of these and there will be a paragraph that
I just don't understand, and it just didn't sort of work and all of that.
I don't mean that to be critical of ai.
I, I think that that's, that's.
Joe Marion: the technology is sometimes, right?
Like
Scott: Yep.
Uh, and maybe it was because I did do a very good job on the podcast
of explaining it, and that's what AI came out with, with the meaning of it.
Uh, we did, we did it, we did one on the award, five trial, that was the
acronym of the trial, and it kept saying the award winning Trulicity
trial, you know, sort of thing.
Um, which was great.
But those things, if I were to produce them myself.
Is maybe four hours to write the blog and the social media stuff.
I get those and I edit them in an hour.
Uh,
Joe Marion: Okay.
Scott: is, this is productivity and it, it does some neat stuff,
uh, to it that, that, that I think makes it better than if I had to
self generate it entirely by myself.
It's a different sort of view on it.
We're, we're exploring things like we write, we write
hundreds of proposals a year.
We have a bank of proposals.
Um.
And I, I write many of these and these take, you know, a couple
hours to write each one of these.
All of a sudden, AI does a really nice job, uh, at, at that.
So these are natural things that, that, that this does, uh,
within it and, and the code.
Uh, other things currently at, at Barry that we can say we currently do with ai.
Nick Berry: I think we need to talk about writing.
Um.
Joe Marion: Mm-hmm.
Nick Berry: You, you kind of, this could be considered one of our,
you said our three pillars are like design, implementation, and software.
I, think you could essentially add, you know, publication and research to that as
something that we do a lot of at Barry.
Uh,
Scott: I, I'll push back on that a little bit though, and what AI does on that.
'cause I a, a simple little thing where,
Nick Berry: I think too, but.
Scott: okay.
Joe Marion: Yeah, I think we are gonna disagree, so this should be
Scott: Oh, that, that makes for a good podcast.
Uh,
Nick Berry: Oh
Scott: an interesting blog when it comes out and describes,
Nick Berry: I, I might disagree with both of you, but I'm, think AI sucks
at writing things from scratch.
think it's obvious when it writes something.
I, I, maybe this is but I feel like I can tell, and obviously the, the.
Other side of this is that, think of all the times I've read something
and haven't said, oh, that's ai.
And when it actually has been.
So obviously I don't know my error rate, but
Joe Marion: Well, there, there's some science behind that.
If you're a heavier user, you tend to know to be a better identifier.
Nick Berry: okay.
Joe Marion: That's a, true thing.
That's a, that's a, that's a studied fact, but yeah.
Nick Berry: think it's, it's vacuous and non precise when it writes, especially
when it's writing from scratch.
even, you know, I go on LinkedIn where this a, this podcast will be
advertised on LinkedIn and I'll see it.
single post on LinkedIn looks the same.
They're all the same.
it's like header, five bullet points with emojis footer.
And it's like people just over and over and over write this.
So I think it's bad at writing things from scratch.
It uses similar patterns over and over.
And when I notice that pattern, I immediately.
Start discounting the ideas in the thing that I'm reading, So I, maybe I'm, I'm
being a little too negative, but this is like kind of how I go, you know, I
will not as much heed in what is written if I think I detect AI being the main
author of the thing that I'm reading.
I think it's
Joe Marion: I bet a lot of people feel that way.
I think, I think that's like a, I think that's a correct view.
Yeah.
Would wouldn't disagree that that's how people perceive that kind of writing.
Right.
I mean, I think so.
One.
You know, there are lots of kinds of writing that aren't, that
aren't necessarily about getting lots of good character or an
interesting structure into it.
Right?
There are lots of ki, there's a lot of, there's a lot of writing that's about
just like sort of clearly presenting what you're describing, laying
out the facts, that kind of thing.
Um, I think, you know, the idea of voice is less important in that kind of writing.
Um, but I don't, I don't think it's like a standalone thing that, that you
should let write something by itself.
Right.
Um, I.
I think a really good use case of it is like, you know, you always used
to get that writing advice, like just write down your ideas as fast as you
can and get a first sloppy draft out there and just like make that happen.
And I think this is a much more pain-free way to, to get that first draft of
a paragraph or something like that.
It's like definitely not the final one.
But you know, getting words on the page is a huge.
A huge, uh, un blocker in a lot of situations.
Um, and then I like that once that's been done, I can be very critical, right?
It puts me in the mode of a reviewer when I'm reading that text.
you need to be right, 'cause you need to be critical of it.
But when you're seeing it, then you can think, okay, don't
have any buy-in in this text.
There's no sentences I love in here.
Like, let's, let's really dissect this and pick it apart and make it
into something that's worth doing.
will say like a lot of it's like.
It often like Um, it gets like the subtleties way wrong when it writes
and, and so that it's a kind of like sloppiness or imprecision in, in the,
in the way that it's communicating.
And so that I pay a lot of attention to, if you just kind of breed through
it, it's like, oh yeah, all this, those words sound kind of nice together.
It like kinda, it gives you a warm fuzzy, but then when you think
real deep about it, like, oh, maybe I don't like that at all.
So.
But I love, I love the ability to do that first drafting.
then I love the ability later on to do tweaking little things like, Hey, I
don't, I know this sentence is wrong.
Here's what I don't like about it.
Gimme five different options.
And then you get a sentence and then you tweak that again.
And then like, I don't, I like that iterative, you know, version of writing.
I'm sure it comes real natural to someone who's written a very, very large amount.
Yeah.
Nick Berry: I
Scott: I think,
Nick Berry: editing and the, the fine tuning
Scott: yeah.
Nick Berry: the ability to, to ask it, to edit your text and then review things
as it tries to make changes where it's not just now sending you back a huge
paragraph with stuff change, like, you know, it highlights where it made
a change and what's new and things
Joe Marion: What's different
Nick Berry: walk through.
see what it's actually changing without having to
Joe Marion: and it'll tell you why sometimes too, which is nice, right?
Yeah.
Nick Berry: I think it definitely, it, it's a good editor, uh,
in a lot, a lot of cases.
Scott: So I don't think we have the huge disagreement
that we thought we would have.
Nick Berry: Okay.
Scott: I agree.
Agree entirely.
Yeah.
Um.
Joe Marion: What?
are
Scott: Yeah.
Joe Marion: for?
Yeah.
Scott: Yeah, that's right.
Right.
That, that's right.
Now, but the interesting thing in all of this is not to become,
uh, you know, critical of ai.
And, and somebody wrote me a, a thing where they largely created, they took
25 references and said, write me a paper based on these 25 references.
And I, I hated it.
Uh, there, but the, the, there's nothing new.
It wasn't written very well.
I didn't, you know, it, it was, it was, but this is gonna get
better and better and better.
I, the, the idea that AI will never do this, I mean, imagine how good
it's gotten in the last few years.
So this is a current status of it, but I agree.
It's, it, I, I don't like it, but it's an, it's an amazing.
Editor tool.
It's amazing thing that if you are actively working with it and you're
writing it, getting suggestions, this is an incredible tool.
Nick Berry: Yeah, a couple years ago, which I, I mean a couple years
ago, like the very onset of chat GPT, when it became public, someone sent
a, uh, email to Don Don Berry, our.
Founder, um, been on the podcast a bunch of times saying, if you give me $5,000,
I'll write a book, a biography about you.
And Don asked for a snippet, said, okay, send me a snippet,
or blah, blah, blah, blah.
And it wrote Don's biography and they sent it to him in a lot of
things and he read it apparently and said it got a lot of things right.
So he replied and said, if you can make it do this, this, this,
this and this, I'll pay you.
And I think they became impossible for AI to do, um, especially at that time.
But, so we never paid $5,000, but you know
Scott: Yeah.
Yeah.
Nick Berry: yeah.
So.
Scott: It's interesting.
For example, could I dump my 60 episodes of, in the interim into AI and say,
write me a book on in the interim, or,
Nick Berry: at that point, but yeah.
Scott: yeah, Or, or, you know, what would Scott say?
Joe Marion: out there for sure.
Scott: What would Scott say to the following?
Does it know who I am now?
Obviously it, it's gonna do really well at knowing, you know, what would
Trump say, what would Obama say?
What would others say?
'cause there's such a wealth of stuff out there.
Uh, but that's interesting.
But let's, let's move to something that, that concerns me as a company.
So.
Where when we're working with a client, we really need to
worry about confidentiality.
So what does that have to do with ai?
Uh, if we use ai, we need to make sure that the confidential information that
it's using to write something doesn't then go outside and become part of,
its its wide world that it's using.
To write other stuff where all of a sudden it's using that for somebody else.
So security's a huge deal.
Um, what, what, what do we know about security?
Nick Berry: Do you want this,
Scott: come on.
Nick Berry: So
Scott: Okay.
Nick Berry: the
Joe Marion: I'll, start.
Yeah.
Scott: You've looked into this.
Nick Berry: Yeah.
Joe Marion: So I mean, we know some things, right?
So there are things that are, that are, that they say that, you know,
for example, our data is encrypted in transit and it rest, that means
that there's not really a point in the process when it's human readable.
So you have to be able to decrypt it to read the data.
Um, they talk about,
Uh, they, they say, you know, the, uh, business versions of the paid
versions of these softwares say that they won't train on your data.
We learned yesterday that that is, has at least some known caveats around it.
uh, in particular, if you give them feedback, did you like or not like
a response, well then that actually gives them permission to look at that.
So, you know, that's kind of concerning, you know.
It's a little bit harder to parse the implications of that statement, right?
Like it's, it doesn't, won't train on your data.
I mean, what, what does that mean exactly?
And how would a lawyer understand that?
It, it's harder.
I don't know.
I, think in some sense I do think about this in some ways at least.
Um, and there are absolutely exceptions to, there are absolutely reasons why,
this is not a good analogy, um, which we can go into, but I think in some ways.
It's a little bit analogous to email, right, in that you have your
confidential information, uh, it's stored in someone else's, uh, it's
stored in someone else's servers.
It's not local.
It is potentially, it's encrypted.
Um, but you know, you're kind of relying on their guarantees that
they're treating that data in a way that's fair and reasonable.
I don't know.
Nick, what do you, what do you think?
Nick Berry: Yeah, I mean, I think I use that as a mental model in the same way.
And, uh, there are things that we don't email right at Barry.
Certainly, you know, uh, level data doesn't go through email.
Interim analysis reports for trials that are still unblinded, that
are still blinded to the public.
Don't go through email and we have different like SFTP
protocols that we use for those.
there's, I mean, we are not AI policy lawyers.
There are AI policy lawyers looking into these things, but that's not us.
Um, I think in because of that, we.
Have taken a sort of conservative approach at a lot of things.
And even though we have this like business plan that says we're not
gonna train on your data, like, that doesn't give us free reign to upload
everything and to put everything in there.
So we, we are tasked with, and we don't have an SOP yet, uh, we don't have this
like put together, but Joe and I and the committee that we're on are tasked with
trying to come up with, uh, strategies for this and, um, you know, they're obvious.
Red line, don't do this.
And there are gray areas, and we don't have all the answers
to this, but, uh, conservatism
Joe Marion: Yeah.
and I think people generally don't have the answers.
Right.
And, but yeah, I agree with you.
We're we're trying to take a conservative, kind of measured approach.
Nick Berry: so
Joe Marion: Yeah,
Nick Berry: data blind.
Uh, documents that you upload, um, you know, you can give the structure of a
data frame without needing to give the data set to, to AI and things like that.
And it's, it's hard because each, we're not all, like Barry is not
a homogenous group of AI users.
Every person feels differently, thinks differently, uses it differently,
and at the end of the day, we don't get to, to watch people.
Interact with ai.
And so we kind of have to have some trust in the people at Barry, and we
tried to do this at our meeting, which is sad, is we have to instill some
sort of working knowledge about AI in the entire company because everyone's
gonna be using it and we want everyone that's using it to be at least informed
in some, in some regards about how they should and shouldn't interact with it.
And so.
Um, it's, it's hard, but we laid down some like obvious do not dos,
and I feel like that helps a lot, uh, to, to, to clean up some of
Scott: Yep.
Nick Berry: But
Scott: Yep.
Okay.
Nick Berry: areas.
Scott: Oh, okay.
Now another one that bothers me and is AI that's generating a statistical model.
And interestingly, if you ask AI to create you a piece of code, you have
the code and that that's the product.
And you can, you can do that.
Uh, unlearn, for example, talks about generating digital twins, and it's,
they don't tell you what it does.
It's ai.
It's a black box.
And by the way, it's also a hype thing.
It can't be wrong because it's AI sort of thing.
So I bristle at this a little bit because behind this, if AI helped
you create a model, there's a model and we could talk about the model
and the data that goes into it.
So I really worry this becomes both a hype thing.
A black box thing and takes away from the value of us as
statisticians, as model builders.
So I don't think at Barry we've ever used AI to create a model.
Nick Berry: To fit a model to get results from a model, like,
Scott: Yeah, I'm, I'm not even sure what that means nowadays, but it is certainly.
Nick Berry: density of the the treatment effect parameter.
Scott: Or predict what this patient is gonna be like in six months.
Don't tell me how you did it, but, you know, predict it kind of thing.
Joe Marion: because I've definitely done, here's the model I want right.
In Stan.
I mean that, that's no issue,
Scott: yeah, yeah.
Joe Marion: not what you're talking about.
Right?
Yeah.
Scott: no, no.
Uh,
Nick Berry: would
Scott: not that so,
Nick Berry: model, fit your stand model, make you a density plot,
and do all that for you now,
Scott: so I, I really worry about that.
Joe Marion: to
Scott: Yeah.
Joe Marion: separate experiment.
Yeah.
Scott: Though, if I ever want, if I wanted to sell Berry, I think if I changed its
name to Berry ai, much like what is this shoe company, Allbirds or something?
Who did this?
Joe Marion: uh, birds.
Scott: Yep.
Yeah.
Joe Marion: AI is,
Scott: somehow this hype all of a sudden Barry's worth five times what
it is, you know, sort of as a thing.
So I, I, I bristle at that.
We talked a little bit about, about, about generative ai and
we haven't jumped into that.
I think a real, let, let's sort of finish this a little bit with the
question of will AI take our jobs?
Within the Senate?
It's a, it's a fair question.
I think certain people in the statistical community, it's a,
it's, it's, it's a, it's a worry.
And it's, it's interesting.
I, it, the, the people whose jobs are threatened is more than the people
who think their jobs are threatened.
Oh, AI can't do what I do.
Uh, you know, eventually AI is gonna do what we all do.
But you know, how long is it, do you feel like AI threatens our jobs?
Joe Marion: I mean, I, I hope it threatens part of my job.
I, I hope that pretty soon we're using AI just like fully
for validation of code, right?
I think we would do a lot more validating, which I think would be valuable for us.
And I would personally be doing less code validation.
And if it took that part of my job, I wouldn't shed any tears.
Right.
Um, is it gonna take my job overall?
I don't know.
I think it's quite hard to say where things are going.
The robots are building themselves right now.
Right.
And hard to know like what the end state of that is.
Um, yeah.
Nick, what do you think?
Um.
Nick Berry: Yeah, I mean, I do more software development than,
than both of you, so that puts my job immediately at more risk.
I think, um,
Joe Marion: Yeah,
Nick Berry: you,
Anthropic, just you, you're talking about the hype of ai.
Anthropic just released that their new model mythos
can't be released to the public
because it's too dangerous because it found zero day security issues
in every major operating system.
I, it's probably hype, like right?
If you want to advertise your new model called Mythos, you say it's
too dangerous to be released and
Joe Marion: Oh, absolutely.
Just like fax Cloud.
Too dangerous.
Nick Berry: gotta think of a better name with Greek origins than Fax Cloud.
We, we did talk about, uh, I can't remember something, some cloud
name, uh, Cirrus or something like that as our fax cloud or something.
That probably works,
Joe Marion: What's the word for storm cloud in Greek?
That's what we want.
Uh.
Scott: Yeah,
Nick Berry: yeah.
Scott: it's, yeah.
Nick Berry: So my job might, at least my.
Pure programming.
Part of my job is probably more at risk, but like I talked about earlier,
Scott: Yeah.
Nick Berry: the part of my job that I'm retaining completely is
the decision making part and the planning part and the vision part.
And it's hard for me to see with the, you know, generation
of models that being at risk.
Uh, what happen eventually is that my decision making turns into more of a.
Clicking.
Yeah, that's good.
And it's iteratively making a bunch of stuff or you know, it makes 10 decisions
and I choose the good ones and it builds itself, or something like that.
So, I mean, obviously we're doing, we're stupidly conjecturing about something
that we could can't possibly know and aren't that informed about, but.
I don't know.
I feel like the decision making that we have is safe and the, the planning
and telling the story in the right way, which is something we do so well,
and so and so purposefully is safe.
But you know, a lot of the, you know, the technical things, eventually it's gonna
be able to do the technical things that I said it's not good at doing right now.
Joe Marion: The, the parts of this job that are about communication and
especially relationship building, I don't think those will go away.
Scott: Okay.
Joe Marion: Like, they're never gonna send Claude to go talk to the
FDA, they're gonna send Scott Berry.
Right.
Like, you know, that's, the AI is just not gonna have the same weight.
Right.
Same credibility, like, yeah.
Scott: The, the whole strategy part that even people are using it to design trials.
You could go into to one of these and say, design me a trial.
But your it, it's gonna do what you tell it to do.
Three arms, this and all that.
Not asking the questions, is this the right thing in your development?
What are your uncertainties?
Asking the right questions to understand why are you doing the trial?
This whole part before you get into, give me the calculations of the
following are the huge part to this.
And, uh, AI can't do that yet.
Maybe eventually it's, it's doing those parts, but it's not there yet.
Okay.
The, um, uh, I, you know, I, I, I harken that to, um, I was, I went in for.
Dizziness a couple days ago, and I saw a real clinician and she asked me a
couple questions and she knew immediately what it was, uh, positional, vertigo.
And not only that, she said, do this Epley maneuver.
And it was a physical thing of moving your head into position
for three minutes and it was gone.
Nick Berry: Okay.
Scott: It was a cure.
So, you know, maybe AI's gonna get there and it could ask me the right
questions and did the right thing, but.
Joe Marion: That's crazy.
Scott: Uh, I, I did, and, and your mom spent more of the time
asking what was wrong with me.
I think she, she asks Claude that quite a bit.
What is wrong with my husband?
Joe Marion: with my husband?
Scott: Yes.
Yes.
Joe Marion: Oh man.
Scott: All right.
We will end it there.
We appreciate you all joining us for our 60th episode, and until next
time, we'll be here in the interim.