Join us for the S3 premiere of The Human Odyssey™: A Human-Centered Podcast!

On this episode of The Human Odyssey™ join Rashod Moten, Human Factors Specialist, and Dr. Jennifer Fogarty, our Director of Applied Health and Performance, as they discuss the various ways in which Artificial Intelligence intersects with Human Factors and Applied Health & Human Performance.

This episode of The Human Odyssey™ was recorded on March 23rd, 2024.

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Welcome to The Human Odyssey, the podcast

about Human-Centered Design.

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The way humans learn, behave, and perform

is a science, and having a better

3

understanding of this can help improve

your business, your work, and your life.

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This program is presented

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by Sophic Synergistics,

the experts in Human-Centered Design.

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So let's get started on today's

Human Odyssey.

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Hello, and welcome to The Human Odyssey

Podcast.

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My name is Rashod Moten.

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I am one of Sophic’s Human Factors

Specialists.

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I'm joined here today

by our guest, Jennifer Fogarty, Sophic’s

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Director of Applied

Health and Human Performance.

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Hello. Hi.

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Thanks for having me.

And thanks for joining us.

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Sorry I got hung up there for a second,

but I wanted to ask

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just a bit more about your history

and your background.

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Sure.

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Yeah.

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So, I started out,

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PhD in Medical Physiology,

so I was studying cardiovascular disease.

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Very passionate about Human Health

and Performance.

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I'm an avid, avid exerciser and,

someone who believes that,

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you know, we can actually do more

for our health through - through things

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like exercise and eating well, like,

and how do we prove it to ourselves?

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So I was really focusing on that

and actually had a model where we showed

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that exercise indeed

grows collaterals in a heart

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when you have a blockage,

like you naturally can build

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em and I was fascinated by the process

behind that.

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So I started doing some molecular work

in my postdoc to partner

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with the functional work.

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At that time, I had an opportunity

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to be part of a mission

that had NASA science.

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It was one of my committee members.

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He really liked the way I operated

so he said, could you come run my lab

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at Kennedy Space Center while we have our,

It was a rodent mission.

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It was the last Space Life Science

mission.

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And unfortunately, it was the Columbia

107 mission that didn't return.

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So in about a three or four month

span of working

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at Kennedy Space Center,

I got to learn a lot about, aerospace.

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My family has an aviation background,

so I was familiar with high performance

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jets, people who do, extreme

things and acrobatic flight.

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And I always thought I would end up,

like, studying that in some way

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or incorporating it but didn't have a

directory there at the time.

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Went through the Columbia accident

at Kennedy

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and, was really just blown away

by the environment and the culture and,

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you know, at that time, you know,

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the ultimate sacrifice

these people and their families

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had made to to try to do things

that have never been done before.

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And, came back to Houston.

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I was in College Station at the time

and was looking for a position

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outside of academia,

and one came up at a contractor

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which was Wyle Life Sciences,

and sure enough,

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they had a role for cardiovascular

discipline scientists.

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So it kind of lined right up and,

I started working at Johnson Space Center.

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Quickly

moved on to a civil servant position

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because I had a unique set of skills,

having done clinically relevant research,

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was highly translatable

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and the physicians of flight surgeons

who support the astronauts

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really needed some folks who understood

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what was coming up

through the research pipelines and how to,

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how to potentially translate

it really for their purposes.

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So I was working background of, evidence

base, what was going on in research,

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how it might apply to the needs

that were happening in spaceflight,

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because spaceflight

really puts a premium on prevention.

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Right.

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The best way to manage

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medical care is not to have

a medical incident.

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That’s fair.

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Like, can we really avoid these things?

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Can, can we know that we're not going

to have bad outcomes during a mission,

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a variety of different durations?

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At the time, it was shuttle focused,

but it was the earliest stages of ISS

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where people were now living

instead of two weeks in space on shuttle,

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they were living four

and five months on station,

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and that was a very new experience

for all of the space programs,

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except for shorter stays, that were done

previously.

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There was Skylab and there was NASA,

there was Mir and then NASA Mir.

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So there was a

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little bit of an end of like five

and ten people who had experienced this.

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But it was really the start of, of the

world, of the International Space Station.

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And that was a remarkable

time to be around in science and

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and with the flight surgeons

supporting them.

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So kind of wove

my way through different NASA jobs.

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It was similar, like,

I'm a utility player there.

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I love solving problems

so if there was an opportunity

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to have a role where I was involved

in making a difference in operations

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while I was helping to guide

what research needed to happen

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that was really kind of the best combo

for me.

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But ultimately,

toward the end of my career with NASA,

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I was the Chief, Chief Scientist

of the NASA Human Research Program,

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which when you start getting into those

roles, there's a lot less fun.

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I can imagine.

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But as one of the Russian,

I was interviewed in Moscow

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and we had done one of the isolation

campaigns in their, their NEK Facility.

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They came up and they said, you know,

why don't I smile a lot or something?

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And, and I said, well,

I am a serious person.

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And usually I'm listening and thinking,

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so I don't think about

what my face is doing,

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you know, when I'm on camera

or something along those lines.

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And he asked me,

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through an interpreter, is it

because you think you're the big boss?

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And, I was like, well, actually,

I am the big boss.

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I don't have to think it.

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I'm in charge of a pretty big program,

and I have to be responsive

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to the taxpayers and Congress and,

you know, NASA Headquarters.

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I said, so, yes, I'm

a very serious person.

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I can laugh about it

now, but at the time I was very like,

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what do you mean, the big boss?

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What do you think

I’m the big boss? I got the title.

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It was a lot of

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responsibility,

but it was also very, gratifying.

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Right? Just in a different way.

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So after a couple of years of that,

I decided to step away from government

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work directly, being a civil servant,

and go into industry

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and that's when I joined

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Sophic as the Applied Director

or the Director of Applied Health

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and Performance.

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You're not the only one who doesn’t

remember my title.

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So it's, it's just an opportunity

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to work with a variety of spaceflight

providers, people who do medical

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hardware,

people who are going to do extreme

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environments, other than spaceflight,

we can get involved with.

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So it was applying my skills

to problems again

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and being part of building solutions

and seeing them applied.

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So it's an exciting

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time to be in aerospace, obviously,

you know,

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with the Artemis commercialization

of low-Earth orbit and potentially even,

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you know, lunar missions and then Mars

missions, there's a lot going on.

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There's a lot of companies

that are starting out, people

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who want to engage and really need help

with Human-Centered Design

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and as, as we talk about more

in the government/industry side,

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Human System Integration,

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and then the concept of keeping people

healthy before they go into space.

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And while they're in

and on those missions, which

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right

now, descriptively, are incredibly varied.

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Yeah, I Imagine.

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So, yeah,

the variables are almost limitless.

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Thank you for having me.

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No, no Love the conversations.

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Oh, same, same and honestly, thank you.

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Your background.

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I, I didn't want to provide,

you know, just give an intro,

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brief intro because I knew I wouldn't

do it justice so I really appreciate that.

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Now, for

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today's topic, we’re going to discuss

artificial intelligence.

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You know, it's a hot topic today.

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It's just in, in society, you have,

of course, everyone under the sun

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speaking about positives

and negatives, fears

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you know, any even you know,

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for optimists, you know,

they're thinking about where it could be.

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So today, just want to primarily

just focus on artificial intelligence

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with regard to your background itself.

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But before we do that, I do want to ask

you mentioned College Station.

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You wouldn't happen to be an alum of,

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[Texas] A&M.

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Well well this kind of.

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The, the reason I hesitate

is it's an interesting story.

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So when I joined the College of Medicine,

it was the Texas A&M University

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College of Medicine.

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While I was there,

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the College of Medicine and other schools

associated with the Texas A&M system

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kind of pulled out and became the Texas

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A&M System Health Science Center.

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That existed,

I think, on the order of a decade.

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So my degree actually talks about coming

from the Texas A&M Health Science Center,

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and my, my class in particular,

because of the date

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we started,

typically we would have gotten Aggie

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rings like that

was the model even for graduate students.

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I was, I'm from New Jersey, so.

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It's was quite a culture shock

and I didn't understand the whole thing.

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But, nevertheless, there were people that

were in my class were very disappointed

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that when that all that shift happened,

there was a hot debate

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about whether the graduates

would actually get Aggie rings.

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Yeah.

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And some people were,

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you know, obviously sad, very sentimental

and very went down that path.

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I didn't really understand it.

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So I kind of was over my head.

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But yeah,

I mean, I've had this strong association

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with A&M and the College of Medicine

in particular,

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and I did a lot of work at the Large

Animal Clinic

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at the Veterinary School,

which I tell you is just stunning.

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I mean, both the capabilities are amazing.

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The amount of funding they have, the work

that they do, world class.

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but yeah,

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that's the stuff I experienced

in the, experiences I was able to gain

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because of the remarkable research

they did, really sets you up

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when you leave to be well-versed

both breadth and depth.

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The opportunities are kind of limitless

there

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if you're willing to do work 24 hours

a day.

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As a grad student,

sometimes that is required. Yes.

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That’s my, that's my A&M story.

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I try to be very careful

because I'm like, technically,

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if you saw my degree,

it doesn't say those words, but yeah.

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Just wondering,

you mentioned College Station in the A&M

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as a huge presence here in Houston,

specifically in the health care field.

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So just wanted to Yes for sure.

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Yeah, very strong. All right.

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Well just to get back again.

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Thank you again, but to get back to

of course AI, before we,

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you know,

dive deep into a conversation about it

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I do want to ask, how would you define

artificial intelligence?

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Just based on your understanding

of it. Sure.

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Which, which as someone with a degree

in Medical Physiology, I’m not,

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not the highest qualified person

to comment on this,

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but as someone you know aware of it,

you know,

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that might actually answer your question

with the question.

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So, so I think I understand, the,

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the use of the terminology,

“artificial intelligence,”

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and it's usually coupled in my

world with machine learning.

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I'm a little

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more accustomed to understanding

in a very tangible aspect

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of machine learning,

and the development of algorithms

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that can go into massive data sets

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and kind of evaluate patterns.

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Right.

Particularly in medical data. Right.

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And the machine can learn things now

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to transcend that.

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You're like, at

what point do we go from algorithms

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that can be used to interrogate data,

find patterns,

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and then check against reality,

which it says, are these patterns real?

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You know, and do they are they meaningful?

That was the other part.

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Like in the medical domain, you're like,

doctors would never do

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it, should not do tests that do not have

a positive predictive value.

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Meaning when you get the answer,

you know what to do with the answer.

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Whether it's a zero,

you didn't have a problem,

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or a one, you have the problem

that test has meaningful interpretation.

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Yeah. If you don't understand

where you're going with it, don't do it.

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And that's where AI, for us sits right now

in this gray zone of

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it does not necessarily confidently

deliver a positive predictive value.

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It delivers new insights

because it can go,

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these algorithms can go so much more

broadly than the human mind can right.

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Assimilate data that comes from a variety

of sources and the abundance of data

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that's available for,

for a variety of different environments.

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But for me, it's really the concept

of going artificial intelligence is,

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it transcends mathematical equations

that are algorithms,

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to it itself starts to build new

algorithms, right, based on the patterns

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it is or isn't seeing,

or it is actually determining

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whether it's got the tools.

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Yeah. Yeah.

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Right and to be honest with you,

it's like

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with the most current manifestation

I think people would be,

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experienced with is ChatGPT,

you know, which they call it, and recently

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I very much appreciated the terminology,

like artificial intelligence in the wild.

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Yes. This capability has been unleashed

and people are playing with it

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and when you play with it,

you train it, right?

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Not different than children

or dogs, or cats.

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Which obviously has a variety

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of different outcomes depending on who's

doing the training and what you've got.

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Yeah.

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But watching it deliver unique insights

based on the direction it's been given,

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that kind of transcend

anyone's person, person's capabilities.

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So the way I think of it,

it is almost like the personification

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of the diversity of people

who make up the contributors.

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Right?

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But instead of trying to figure out can I,

can I understand what you are saying

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and use your intellect

in your experience base?

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It is pulling the salient points from you

in some way,

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putting it into the pot of options,

reconfiguring them,

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kind of doing like a lot of what I know

is probabilistic modeling.

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Like, let's try this permutation. Yeah.

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And comes back a million times

later and says,

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when we've looked across all the patterns,

this is this new insight I can give you.

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Yeah.

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and so for me, I like,

you know, the idea of it being sentient.

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I don't think it's true.

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It's not thinking, but it's using math

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with respect to a variety

of different data sources and the idea of

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recognizing, having pattern recognition

or recognizing lack of pattern,

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you to then reconfigure itself

to do another query.

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Yeah.

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So it's not quite,

it's beyond the algorithm level

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where someone's writing the code

to make it do a task.

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It itself

creates its own tasks and I'm like, well,

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that is pretty powerful

and I understand why there's all sorts

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of emotions

and concerns wrapped up in that.

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But I do think of it, I'm a little bit

of a centrist in a lot of things,

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which is it's a tool,

and we get to decide how we use that tool.

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And if we want to let it run free

and tell us to do things,

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that is a choice that is made versus

I'm going to use it to help me understand

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things, and I can use it as a tool

that can give me information

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that otherwise

I would never be able to perceive.

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so I'm a knowledge is power

kind of person, so I don't fear it.

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But I do understand

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people's concerns about other people's

choices, about the utilization of it.

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Yeah.

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No, that makes perfect sense

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of course, you know

always hear the, analogy to say Skynet.

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You know? Yeah.

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That's generally where most people’s

fears come from.

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Yeah, the entertainment

industry you know, when art,

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you know,

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portrays

a reality and then we start to live it,

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you know, that has already defined

where it could go.

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Yeah.

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Right, and so it would be good to have

some more positive representations.

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Yes, yes.

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Which I think are out there,

but maybe not as interesting in the world

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of social media and different modalities,

they're not as clickbait susceptible.

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Right?

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That's something I've seen.

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I think, even YouTube,

just in understanding, of course.

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So, as far as my background, you know, my.

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[Inaudible] A small snippet,

I have a grad degree,

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you know, in Psychology, Human Factor

Psychology, with Human Factors focus.

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That being said, I remember back

in one course, my favorite professors,

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you know, we started talking about

algorithmic thinking and modeling, right?

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And of course, that naturally led

to neural networks

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Right

as they pertain to, to the software side.

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And then the development side.

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And you know, after that, that course,

I think it was maybe that summer

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I go back and went, you know, headfirst

into just learning about AI

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and how these models

are not only developed but

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also implemented within the systems

and whether or not it's truly like,

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you know, as you said,

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data in, data out, you know,

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you have a subset of inputs capture

big data sets, gives you an output.

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Right? I wanted to learn more about that.

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And what I found was, you know, just

in clicking even in educational videos

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or educational blogs, reading journal,

not just journal articles,

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but articles, many articles, a lot of them

would highlight neural network

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but then would not actually explain

how neural networks

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are actually implemented in -

on the software side.

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So having it's having the idea,

giving the idea in the background

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and understanding that, you know, the

neural network is literally a 1:1 ratio.

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I'm giving you this one

input, this or the guidance.

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And it's looking for this

particular subset of data.

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Right? That was really interesting.

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So yeah, no thank you for actually

explaining that and not

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I say

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not buying into the fear

I appreciate that.

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Well and as you know, comes up regularly

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like panicking and fear

don't make things better.

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Yeah Yeah, so helping the audience

and folks who don't understand it,

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you know,

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have some sort of frame of reference,

and in a way that they can understand it,

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to the best of our ability,

to just kind of

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settle and calm everybody down

because then you can think about it.

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Yeah.

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If you're already in fear mode, that's -

we know on a, on a neurobiology level,

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you've already obstructed

some elements of clear thinking,

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because now, you know, in my world as

physiologists like that's fight or flight.

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So your body is already redistributing

blood a certain way.

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It's already prioritizing actions

in a certain manner.

344

And so that used to serve us

very well, right?

345

When not reacting

was likely to lead to death.

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Right?

347

So Yeah but right now we are just

bombarded with issues that set off

348

our fight or flight syndrome because it's

so intrinsic to how our brain operates.

349

Like it's, it's not quite binary,

but it can be pretty close.

350

And then our behavior

and kind of our learned systems

351

potentiate the fight, or fight and flight

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over the calm and that's run by two

different parts of the nervous system.

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And so then you have to be very deliberate

about doing things, for yourself,

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like calming yourself down - self down

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to potentiate the calm side

of your nervous system. Yes.

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That then again

357

allows your brain to work

more optimally to be calm

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and think through an issue

rather than physically react

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and then reflexively react and of course,

it's not always a physical thing,

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but verbal like the, “No”, and, “I know,”

“Absolutely not.” And, “you're wrong”.

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And you know. All that's all the.

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keyboard warrior stuff that happens.

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Yes, yes.

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That tends to be relatively unproductive.

365

So Yeah absolutely.

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Now you know,

367

given your background

and you did touch on this earlier,

368

but I wanted to kind and get your thoughts

on how you think,

369

AI artificial intelligence

370

is not only being implemented

in health sciences and human performance.

371

whether that be at the research level or,

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you know,

373

whether you're looking at practitioners,

how it's being truly being implemented,

374

but also has an impact on the industry

at all?

375

Sure. Yeah.

376

There's a couple areas

377

where it's really

378

been on the leading edge of coming in,

and it started with definitely

379

the machine learning side.

380

So one is radiology.

381

you know, I don't know how many people

might have experienced this,

382

but often if you get a biopsy

and it gets sent off, and, you know,

383

clearly there's something potentially

seriously very wrong if you're getting,

384

like, an organ biopsy or tissue biopsy,

you know, from a clinician,

385

not a research protocol.

386

it can be weeks

before they expect a result.

387

Right?

388

So now you get to like the the heightened

sense of, “I need an answer.

389

Like the answer could be anything.

390

And then I know what -

then we can have a plan.”

391

But just the waiting

is a torturous process.

392

Well, the question is, “Why

does it take so long?” Well, it's a very,

393

expert and human dependent activity,

394

and the people who specialize in that

get bogged down

395

in a lot of false positives

and a lot of just negative samples.

396

So the question was for that sort of tool,

397

does it always require the human eye

398

or could we have the experts train,

399

you know, a

400

machine learning algorithm

to know what to look for.

401

Not so it could do the triaging

and you could speed up the process.

402

So in the radi- the domain of radiology,

403

more and more,

404

interrogations,

whether it be MRI, CT scan,

405

biopsy - are being triaged by machine

learning algorithms

406

which may have already crossed over

into what may be artificial intelligence,

407

that the machine is now recognizing that,

hey human,

408

you forgot to tell me these things.

409

Like this pattern.

410

I see this too.

411

It goes into another category

like you are looking for

412

cells of a certain type

would have indicated a disease process.

413

I didn't see them,

so it's a negative for that

414

but I saw this other thing

that you should look at now.

415

So it flags the specimen to be reviewed

by a human for a particular reason,

416

and changes the prioritization of - of how

they look at it.

417

So the most critical cases can go

to the top of the line, to the human

418

who really needs to do the high level

subject matter expertise work.

419

My experience with, with spaceflight in

420

particular is with, imaging of the eye

421

in particular.

422

We've got an issue going on

with astronauts that's hard to explain.

423

Very concerning.

424

And, a lot of energy

putting into studying that.

425

But one of the areas that's really been

remarkable is in, nuero-ophthalmology

426

and imaging

with respect to AI in the mental now

427

and that is a clinical, a clinical tool.

428

A lot of clinicians are using that.

429

The confidence,

the verification has been done.

430

There's a lot of certainty.

431

There's

constant quality control being done.

432

and that field just continues to grow,

you know, and there was some fear

433

not only that it could be like, “Is

it wrong?” You know, constantly like, “Is

434

this good quality?”

So a lot of that work continues

435

in the background to continue

to assure that this as is as good

436

or better

than if a human did the first pass.

437

But the other element was people

having fear of being replaced. Yes.

438

What do I do now if I'm not looking at,

you know, 30 slides a day

439

or sitting in a dark room

staring at a screen at MRI images all day?

440

And it was.

441

Yeah, like we

we don't have enough doctors right there

442

that in that case,

there was no reason to fear.

443

We just shifted your role

to the higher level expertise

444

and applied it differently.

445

So I think radiology

has gotten comfortable with the idea

446

of using it as a tool

and really potentiating their value.

447

More broadly, it is not applied

for the reasons I mentioned.

448

Like the validation is not there,

the confidence is not there.

449

Overall, the data to support

a diversity of people is not there.

450

And a lot of medical care,

you have a selection bias based on people

451

who can afford the insurance

or afford the test.

452

So there was some work done recently,

it was actually on

453

an immune therapy for cancer,

that they thought they had a pattern

454

and they had a treatment regime

based on the pattern.

455

And, they started delivering

that, treatment more broadly.

456

And it turned out that for people of color

and Asian people,

457

that it was a worse choice.

458

And it turned out because they weren't

part of the select in pool

459

when the study was first done,

those findings were not present.

460

So they took a more narrow population,

extrapolated that this would be good for

461

everybody based on the cancer criteria

when it turned out that it's

462

not just a cancer criteria,

463

but some other genetic underpinnings

that have to also be present.

464

It's just that

465

genetic underpinning

wasn't diverse enough to pick out

466

that it didn't work for everybody.

467

Yeah and I have to ask, as far as,

you know,

468

in instances like that, you know,

whenever you do see at least

469

some signs of bias within the outputs

itself, are you finding -

470

of course, you know, you mentioned that

it's not really being implemented broadly,

471

you know, across the industry

but whenever that does come up,

472

are you seeing that heighten

that level of concern

473

a bit more

or is it kind of just triage the issue

474

on its - in the, in that

silo and then saying, okay,

475

after further

476

assessment, we'll decide

whether we want to implement this later?

477

I think in the, in the clinical

478

and research, clinical research

domain, it's just heavy, heavy skepticism

479

and particularly for the ones

that have shown to not be beneficial

480

use of it or are limited by

something like, selection bias, data bias,

481

people have pulled a little back

482

and said, okay,

you know, from an industry standard.

483

And this isn't just like government

regulation.

484

This is, you know, the clinical industry,

485

the insurance industry is involved,

as you might imagine.

486

Now, that's not today's topic.

487

So that that's all I’ll say on that.

488

I think we both had the same reaction.

489

It's like four podcasts.

490

Yeah.

491

Yeah. It's -

there’s a lot to unpack there.

492

Yeah. Yeah.

493

But no

494

they, they really have kind of pulled back

and said we need to do better.

495

And that has been, the benefit was

it was the push recognizing

496

that we don't have

a diverse enough data set.

497

And that actually revealed

498

other issues with it

which is inequitable care, access to care.

499

Why is it?

500

Why don't we have these people

in our database?

501

You know, like what - how do we do this?

502

I mean, we have to write this thing.

503

So I think it has resulted

in some good things, but it will delay

504

the product, which in that sense,

going back to, like, it's okay,

505

you know, for any job I've ever worked,

and I get a lot of pressure

506

to do things fast, light-rush,

like there's a lot of urgency.

507

Yeah, yeah.

508

Real or not,

like we want to make progress.

509

And I get that.

510

But my phrase is always like, “I will only

go as fast as good will allow.” Yeah.

511

And if I don't think something is good

and I know that’s a generic phrase,

512

but that means, you know, credible,

valid, evidence-based,

513

you know, as you know,

quality of data, diversity of data.

514

But you start ticking down the list of,

of what means good.

515

Until it has those things,

we're not going to production.

516

Yeah.

517

Like and we can explain

why, there's solid rationale,

518

you know, but that, that definitely

gets a lot of angst when you're working

519

on the business side of the house

so Yeah, no, I can imagine.

520

But - but there are fields advancing it.

521

I think the other ones,

the more generalizable where the,

522

the diversity, it's a very broad gradient

of both the medical conditions

523

and the medical treatments,

those are incredibly complex.

524

And those are going to take longer

where something like

525

machine learning algorithms to

AI start to make sense to us.

526

And the field believes

it's the right thing to do

527

and it's showing benefit.

528

Yeah.

529

You know, surpassing

what standard of care is today.

530

It is more prevalent

in the performance world

531

because again, and mostly it's a risk

- that risk:benefit ratio.

532

Yeah.

533

And when you're talking about potentially,

potentiating elite athletes or,

534

or people who are considered

occupational athletes, people

535

who go into hyper extreme environments

like the Everest climb or,

536

you know, things of that nature

going to Antarctic, high altitude work.

537

That's when you're saying, like, “Well,

hell, I got nothing to lose here.

538

Like, like if it can help me do it

better, let's, we're all for it.”

539

So a lot of data gets gathered,

a lot of biomedical monitoring is going

540

on, and then you're dealing with super

deep data on an individual

541

that you can do a lot of work on them,

baselining them

542

and figure out, like, are there ways

to potentiate them and who they are in

543

a, a pattern we couldn't have seen, other

than throwing these algorithms at it.

544

It’s like a signal to noise issue, like

we're going to gather a bunch of stuff,

545

we're going to know to

546

look at some things, you know, that we

typically have have done for decades now.

547

But there's a lot of potential signal

in all of this noise.

548

We just don't know how to find it.

549

Yeah.

550

So the ML AI process

kind of draws the signal out

551

and I always tell people my approach

is that signal just becomes a clue.

552

It does not tell me what to do yet.

553

Now the work happens.

554

Like let's go verify that signal.

555

Let's verify

what we would do with that information.

556

And if it belongs in the operational

domain, does it belong on Everest?

557

Does it belong in high altitude?

558

Does it belong in space spaceflight.

559

You know? Yeah.

560

It's interesting because of course

I've seen that, you know, whether

561

when it comes to performance coaching and,

of course,

562

athletes

and definitely extreme athletes as well.

563

and another tidbit about my history.

564

I was in the military as well.

565

Yeah, that’s another area, they’re

very interested in all of us for sure.

566

As you might know.

567

Yeah, yeah.

568

And that's something, you know,

I had the pleasure and honor of,

569

working in Bethesda

and got to see one of their labs there,

570

or work - work with one of their labs

for human performance.

571

And it was actually, really amazing

to kind of see exactly

572

how we're not only tracking performance,

573

but also increasing performance,

improving performance.

574

And that was my first time

575

really seeing any semblance of machine

learning, you know, being used.

576

And it was

it was enlightening to me, to myself.

577

That being said, you know,

578

you do want to make sure that the - that

the data is good.

579

Yeah.

580

And any of your modalities

that you're, implementing

581

you want to make sure they're good.

582

How long typically does it take?

583

You know, at the - from the research

level of, let's say

584

research

has been validated, peer reviewed and

585

industry, specific

industry, let's say those coaches,

586

you know, how long does it take for that

information to not only get trickled down

587

but also used, and then for,

588

on the backside, how long does it take

for that data to get sent back up and say,

589

“Hey, we're using this.

This is actually great.

590

You know,

we think we should actually improve

591

or increase our use of AI, any AI system.”

592

Yeah, I think it's a,

it's still a question of,

593

it has variable lengths

depending on who the user is.

594

Yeah, that makes sense.

595

So you see people who are

596

I will say another group who's avid

users of this,

597

are people interested in longevity.

598

You know, a ton of data is coming out

599

on biochemical pathways,

molecular pathways that get turned,

600

get turned on, get turned off Over time,

601

you know, chronology affects biology.

602

but then lifestyle factors,

you know, who are you?

603

What and how have you been living?

604

Where have you been living?

605

Very important.

606

What are your leisure activities?

607

So that in a composite

ends up creating the version of

608

what are your exposures and exposures

times kind of your genetic

609

vulnerabilities versus robustness

lead to your outcomes over time.

610

And some can behap more quickly

versus happen later.

611

But if someone wants to be an architect

of their biology,

612

you're going to have to dig pretty deep

into the molecular world.

613

And as your body translates from,

you know, your DNA code

614

into the RNA, then to a protein

and a protein to function,

615

and then the function

to how your body operates.

616

Right?

617

That's where the rubber meets the road.

618

Like do you run faster?

619

Do you live longer?

That's the end question. Yeah.

620

And those people,

they call it biohacking now.

621

I would say the biohacking community

is willing to use

622

just about any tool possible,

and they'll take any clue and try it.

623

That is terrifying.

624

It really is.

625

And but in this day and age,

626

if it doesn't require a clinician

to prescribe something,

627

you have the freedom to go acquire stuff

and people will leave the country,

628

to go get access to tools,

629

meaning therapies, medications, whatever.

630

There's a laundry list of things

under that headline.

631

But,

yeah, that moves very rapidly, right.

632

Because the clue happens

and they want to go try it.

633

They are their own experiment

over and over again.

634

And there are people who have suffered

the ultimate consequences of,

635

using themselves

as, as a science experiment.

636

that when strong validity is not there

637

because a lot of it is like,

who knows how wrong they were?

638

We'll never actually know because of how

they, did they document what they did?

639

Can we repeat this experiment of one?

640

It could be what I call fail

for the wrong reason,

641

which is you have the right tool

but the wrong amount of the tool.

642

Right.

643

Dosing can be dependent.

644

Timing can be dependent.

645

So that's why a real science protocol

would help you know

646

if something has the potential

to be a tool that could be more

647

broadly used or prescribed in a way

that could make sense when people need it.

648

You know, and I, given my background

and where I've spent the past

649

20+ years doing, I do lean a little bit

right of center when you talk about,

650

having rigor in that process

651

and then having clarity about what we know

and how much we know about it,

652

not to obstruct freedom of choice,

but your freedom of choice

653

is, obfuscated by the idea of,

you don't know what you're choosing.

654

Yeah.

655

So in the world of human research,

we have informed consent.

656

So when it comes to something

like participating in something

657

that's - is using AI to give you insights.

658

Part of the informed consent,

and that this is not literal, but how

659

I would approach it.

660

There are

661

other analogies to this is uninformed,

informed consent.

662

What you're going to be told is,

we don't know what risks

663

you're really accepting here,

but you're willing to do it anyway.

664

And what parallels

665

this is people who are willing to sign up

for a one way flight to Mars.

666

You know, there are different companies

out there trying to get lists of people.

667

This happens pretty regularly.

668

not the credible companies in terms of,

they have a vehicle and stuff ready.

669

They're trying to get funding,

you know, crowdsource

670

funding to go build a something.

671

but with the caveat that, hey,

we don't think we can get you back

672

and they’re like, “I'll go anyway!”

673

You get hundreds of thousands of people

signed up.

674

And so clearly there's

no impediment there.

675

Yeah.

676

But they're - they're informed

if you just, if like, “Do

677

I have to protect you from yourself?”

Sometimes you know it's very parental.

678

That's what people don't like.

679

That tends to be

you know what regulatory bodies do.

680

So Yeah.

681

So AI is sitting in that space where,

it has a lot of potential.

682

It's in the wild to some extent,

and you can play with it,

683

but I, and you don't need a prescription

for it, so government’s not regulating it

684

they're struggling with what that means.

685

I don't think they really should.

686

They're not good at it.

687

[Laughter]

688

But what do we do

as a, as a culture, as a civilization,

689

where do we give ourselves some boundaries

so that we can ensure people are safe?

690

Because I guarantee you as what we see

even in the pharmaceutical industry,

691

you know, in the recreational drug

industry,

692

you can be very upset

if you suffer bad consequences

693

from something that you were -

you were trying to use

694

and you thought could give you benefit

and now, now you want someone to blame.

695

Yeah.

696

So, you know, you do want to set up

a structure where there's some boundaries

697

that says, “You go outside

these bounds, you're on your own.”

698

But for what we know,

it has legitimate purpose

699

and - and has verification

and validation of it.

700

We think we could apply it and do better.

701

We can do better.

702

How we do today because it gives

us insights we couldn't have had before.

703

Yeah.

704

It actually touches on

one of my final questions as well.

705

You know, earlier

you spoke about OpenAI, ChatGPT, and,

706

you know, in speaking about

707

not only how AI actually captures

information and how it is being used,

708

but also specifically

when it comes to health and biohacking.

709

You know, you've seen,

we've seen obviously,

710

those apps

where you can sign up, I'm guilty of it.

711

I think I signed up for when the Wim

Hof Method at some point as well.

712

You know, we're all

if you're somewhat health conscious,

713

there's

something that you're actually interested

714

in, but the one question I never thought

to ask, you know, whenever I'm actually -

715

whenever I'm going into the apps

and I start putting in my information

716

is how my information, so my, my actual

personal information is being used.

717

Are you seeing any concern

within the industry,

718

with regards to privacy protections

when it comes to AI?

719

A lot of concern,

720

and clearly in the,

in the clinical domain, particularly

721

in the United States and Europe,

has some pretty strict laws, right?

722

They actually have stricter laws that,

at some point when I was dealing with my -

723

my job at NASA, I had

international partner, you know, work and,

724

yeah, one of their laws,

about electronic data, pretty much like,

725

shut everything down for a little bit

until the lawyers

726

figured out, like,

how do we implement something?

727

What does it really mean?

728

So the GDPR was - was something to assure

729

that they had the best of intentions,

but it just like, the wheels grinded

730

shut for a couple of months until, cause

we had test subjects

731

in a very expensive study

and suddenly like they were like, well,

732

we can't send you the data

from Europe to the United States.

733

Yeah.

734

I was like, well,

considering we're paying customer like

735

and then they're consented, I'm like,

you're going to have to figure this out.

736

And - and

737

they did but they

just they didn't know how at the moment.

738

And that didn't

have to do with AI in particular.

739

But that just gave you like the

740

ultra conservative, like it's an all stop

until we figured it out.

741

So since so few,

742

clinical tools depend on AI,

743

you won't see it in a disclaimer

right now, but you get the HIPAA release,

744

you know, the Health and Insurance

Portability Act release, which tells you

745

we can only send your data to other people

who are going to do X, Y, and Z with it.

746

And otherwise, you know, it's - it's

secure, it's behind these firewalls.

747

You know, they try to give you some

information about your actual privacy.

748

So that structure is in place,

but I don't see anything

749

coming out in releases

talking about using AI tools.

750

Usually those are going to come out

in a separate consent

751

because essentially

that would fall under research.

752

Yeah.

753

So clinicians do do research

and there is clinical medicine

754

using people's data to go train

AI and then surveil AI on the back end.

755

Is it

delivering results that actually happen

756

because you have the medical results

in the medical record,

757

and even it is allowable under HIPAA

758

in the United States for your data

759

to be used without your consent,

if it can be anonymized,

760

meaning like some of your

761

demographics will not be moved

along with you, your name,

762

your Social Security number

or your insurance information,

763

none of that will move, but it will say

like male between 25 and 50.

764

You know, it might,

depending on the requesters

765

request, it may have something like body

mass index, some - some of the metadata

766

that would help them understand

and then would say like, did you have,

767

you were normal, healthy,

768

not hypertensive,

not two - not type two diabetic

769

because then they want to compare you

with people who are sort of like you,

770

but type two diabetic

and have BMIs that are high and say

771

can AI predict, could have I predicted

who was going to be who.

772

So they don't give AI all the information.

773

They kind of give them the left side

of the block of information saying what -

774

who were you if we knew you from,

you know, ten years old to 25

775

and then we map you from 25 to 50.

776

So we already know who you became.

777

But if we only gave AI the upfront data,

the earlier

778

part of your life could it have known

you were going to become that person.

779

Could it have tracked essentially

your biomedical information in a way

780

that said you had risk factors

we couldn't see?

781

And -

and that is the good use of AI, right?

782

Because then we can go to the 10

to 25 year olds and say,

783

how do we really do prevention?

784

How do we stop you

from becoming a type two diabetic?

785

How do we stop you from becoming,

you know, the - the heart attack victim

786

or the stroke victim like that

is the goal of using AI in medicine.

787

So HIPAA has that built in,

which is a great tool

788

because it's a phenomenal database,

but it does protect your privacy.

789

Outside of clinical world,

790

if you're engaging in these apps,

791

you have no guarantee

what's happening with your data.

792

You can go into the fine print,

and I would always recommend

793

downloading the terms

and reading them later.

794

Like, we all get it.

795

I mean, I've signed up for stuff,

I get iTunes.

796

I don't, I'm not a lawyer.

797

I don't understand most of that.

798

Like I have a pretty high degree

and still I'm like,

799

I don't understand my phone bill.

800

Yeah. My cell phone bill. Yeah.

801

I mean they've had - they've done joke

like not joke but like, can a neurosurgeon

802

and a brain, you know, can a brain surgeon

and like a nuclear engineer

803

figure out your cell phone bill,

like what am I being charged for here?

804

And then like the iTunes agreement,

like, no.

805

We just, we want the iTunes.

806

Like just, let’s move on.

807

But I

808

also don't want to sign away my rights

and give you stuff.

809

Yeah.

810

So the warning is,

is that a lot of times in these companies,

811

you are the commodity.

812

They are using your data

to build their business case,

813

and they are using it to refine their,

their offering, their product.

814

So in some cases, if you engage,

you are being provided something of value.

815

That's why you did it, right?

You wanted something from them.

816

Well, they need to build

their business case of the future.

817

So they're going to use your data

and you as a participant to get there.

818

And so then the - there's a mutual benefit

819

if you understand what you signed up for.

Yeah.

820

There are some apps,

and I, I was told this a long time ago

821

and I think I,

my son is 16, on the internet,

822

in the wild.

823

Very nerve-racking.

824

And I'm just training him

to be the best critical thinker he can be.

825

It's like,

shutting all that off is not an option.

826

But, he's faced with a lot of choices.

827

He may not really understand. Not,

he doesn't have the life experience.

828

So he thinks he knows it all,

but he does not have the life experience.

829

So, but I say the one thing you gotta know

is if something is free,

830

you are 100% the commodity.

831

Anytime someone's having you sign up

and you got to give them your email,

832

you know, the texting is terrible

now, the phone number they want, but

833

you know, the email and your metadata,

and they may be watching

834

you and your habits, like, you know,

some of these shop apps and stuff,

835

like they're watching everything

you buy and search and just know that for

836

whatever you thought was worth getting,

that they're getting a whole lot.

837

Yeah.

838

And you have signed away

your rights to know what that is.

839

And that's

840

where it's very dangerous,

and that's why people go to DuckDuckGo.

841

And, you know, which I get.

842

I don't know what to do about it.

843

I have no answer.

844

I'm, I'm a little, like,

willing to try stuff myself,

845

but I think that the warning is like,

be skeptical.

846

That's healthy. Go educate yourself.

847

That's in your power.

848

Right?

849

Try to understand the

the sources you're getting educated by.

850

That's the other one. Yes, yes.

851

There's a lot of fake news out there.

852

Yeah, yeah, yup.

853

It’s a daily conversation sometimes.

854

But, you know,

I don't think this is Skynet.

855

I think, like, I was there with Y2K,

856

I was - I was,

857

interesting things

I've seen happen, and predictions

858

that - the world still hasn't ended.

859

Yeah. 20 times now.

860

Yeah.

861

I don't think it's going to do that,

but we - we got to keep an eye on it.

862

Don't - don't be naive about it

and then think about your data

863

and yourself as, protect it like,

864

like it's your most precious resource,

you know, ask the hard questions.

865

And if, if something you want to engage

in is not being honest with you,

866

maybe it's really not worth engaging in,

especially on the internet.

867

It's a lesson for life.

868

There we go.

869

It’s a hell of a podcast.

870

Oh yeah. It was fun.

871

Thank you Jennifer,

that was my last question.

872

Did you have anything that you wanted

the listeners

873

to know as far as about yourself,

anything upcoming?

874

I think, yeah,

there's a lot of exciting things

875

going on in the aerospace domain

and commercial space and my big message

876

to people is that we get a lot, you know,

why do we do this?

877

Like, we have a lot of problems to solve,

you know?

878

You understand

879

when you look around you like,

it can be overwhelming at times, right?

880

And, can be a lot,

881

you know, very hard on your mind

and your heart on any given day.

882

But for people who engage in

something like spaceflight

883

it itself is - is not the reward.

884

The reward is the accomplishment

of getting solutions

885

that are going to

change how we live on Earth

886

because they have to be

887

stripped down of all the things

we take for granted, and we have to

888

accomplish things that we just won't solve

for ourselves here on Earth.

889

And while I understand

people are talking about

890

why we have to leave Earth potentially

one day, and I hope that's never true,

891

I am working in a domain

where I want to bring these solutions back

892

to Earth and improve dramatically

the equity in access to health care.

893

I want to make differences in women's

health and early screening and mental.

894

I mean, it's just like today

895

even it's just on the top of my head

about some of the women's health issues,

896

and that -

those are my goals using spaceflight.

897

So while I accomplish one thing,

898

I'm going to accomplish these other

and you don't have to pay twice.

899

That was the goal.

900

Yeah, yeah,

I - it's a huge forcing function to solve

901

some really, really hard problems that we

just have not solved for ourselves here.

902

And the last thing, it's

actually an African proverb.

903

It always makes me

just a little emotional, but

904

“The Earth was not given to you

by your parents.

905

It is on loan to you by your children.”

That’s beautiful.

906

Yeah.

907

And I, this is a stunning revelation

and perspective on how we treat things.

908

And when you are loaned

something, it's a much different concept

909

than when you're given something.

910

So take care of it

and it'll take care of you.

911

That's it.

912

That's awesome. Well, thank you again.

913

Thank you so much.

I loved the conversation.

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I’ll come back for more.

915

Yes. Please do. I will.

916

All right. Well, to our listeners,

thank you guys for tuning in.

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This has been episode one of season

three of The Human Odyssey Podcast.

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Once again, my name is Rashod

Moten and again, we're here with Jennifer

919

Fogarty

and as always, please join us next time.

920

If you do want to provide any feedback,

reviews,

921

anything like that, please

visit us on any one of our platforms.

922

We're on all social media platforms

and feel free to drop a like and a review.

923

Thank you so much. See you next time.

924

The Human Odyssey is

925

presented by Sophic Synergistics,

the experts in Human-Centered Design.

926

Find out more at SophicSynergistics.com.

927

Get Smart, Get Sophic Smart.