January 8, 2025 • 40 mins
In episode #122, discover the inspiring journey of Ria Cheruvu, a prodigious AI architect at Intel, who challenges the status quo with her groundbreaking work from a young age. Ria's incredible story takes us through her accelerated academic achievements and dedication to security, privacy, and fairness in AI systems. We explore her passion for the convergence of neuroscience and cognitive computing and her advocacy for women in STEM, showcasing how she is shaping the future of technology with her innovative mindset.
Ria shares her inspiring journey as a young AI architect at Intel. She offers insights into her career path, the importance of mentorship, and the evolving landscape of AI. She encourages women in tech to overcome challenges, embrace growth, and leverage community support while exploring opportunities in this transformative field.
Here are some topics covered:
• Ria's journey from a high school prodigy to an AI architect at Intel
• The significance of mentorship and community in overcoming challenges
• Exploring AI's intersection with neuroscience and technology
• Ria's focus on security, privacy, and fairness in AI systems
• Encouragement for young women to pursue careers in STEM
• The necessity of communication, confidence, and rest as key skills
• Recommended resources for learning about AI
• The potential of AI to reshape career opportunities and ethical considerations
Tune in to gain a deeper understanding of building a career in AI, where both technical and non-technical skills are essential.
AI resources for AI enthusiasts:
Ria’s Profile linkedin.com/in/ria-cheruvu-54348a173
Websites
- scholar.harvard.edu/riacheruvu (Portfolio)
- researchgate.net/profile/Ria_Cheruvu (Portfolio)
- riacheruvu.github.io (Portfolio)
- https://riacheruvu.medium.com
- https://m.youtube.com/@riacheruvu555
Leaders to follow
- Fei-Fei Li
- Yejin Choi
- Sebastian Raschka
- Tom Yeh - AI By Hand - https://aibyhand.substack.com
Ria’s courses
- https://www.pluralsight.com/authors/ria-cheruvu-53
- https://www.udacity.com/course/discovering-ethical-AI--cd13462
- https://www.udacity.com/course/data-analyst-nanodegree--nd002
See the full library of Women in Business and Tech Career podcasts
Ria Cheruvu's Career Tips for Women Transcript
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:01):
Hi there, my name is Mary Kiloalea.
Welcome to the To Be Bolderpodcast providing career
insights for the next generationof women in business and tech.
To Be Bolder was created out ofmy love for technology and
marketing, my desire to bringtogether like-minded women and
my hope to be a great role modeland source of inspiration for
my two girls and other youngwomen like you, encouraging you
(00:22):
guys to show up and to be bolderand to know that anything you
guys dream of it's totallypossible.
So sit back, relax and enjoythe conversation.
Hi, thanks for tuning in.
Ai is one of the hottesttechnologies in the world today
and a major driver of careergrowth and opportunity.
Our guest today is a youngwoman with exceptional talent.
(00:45):
Rhea Cheruvu is an AI architectat Intel, machine learning
engineer, data scientist,industry speaker and an
instructor.
Rhea holds a master's degree indata science and a bachelor's
in computer science from HarvardUniversity.
She's been featured in numerouspublications on AI, holds
multiple patents and has spokenat prestigious events such as
(01:07):
the Women in Data ScienceConference, tedx and other top
industry events.
Rhea is passionate about theimportance of open source
communities, women in STEM andcontributing in disruptive
technology spaces.
Her area of expertise includessolutions for security and
privacy in machine learning.
Her area of expertise includessolutions for security and
privacy in machine learning,fairness, explainable and
(01:28):
responsible AI systems,uncertain AI, reinforcement
learning and computationalmodels of intelligence.
In addition to her technicalwork, rhea is a published poet,
children's book author and aneuroscience enthusiast.
Rhea, thank you so much forbeing here.
Speaker 2 (01:46):
It is such an honor to have you on the show.
Thank you, Mary.
I'm so excited to be here todayand have a great conversation
with you too.
Speaker 1 (01:52):
Awesome.
Okay, well, you have achievedso much at a young age, and so
here's some facts.
I don't know if our listenersknow, but I read that you
graduated from high school at 11, became the youngest ALB
graduate in Harvard history andstarted working at Intel at 14.
(02:12):
That is incredible.
Can you share your journey andwhat it was like to land your
job at 14 at Intel?
Speaker 2 (02:21):
Oh, thank you so much , and definitely I think it was
a fantastic journey that wasfilled with lots of great
moments and helping hands too.
So I graduated high school whenI was 11, as just mentioned and
you know it was basically a lotof the kind of planning, I'd
say in the forethought that mymom put forward as part of my
(02:43):
curriculum.
She was my learning coach andthere with me every step of the
way and, you know, basicallyhelped me design a journey that
was accelerated, that wasmeaningful and that was also
really kind of exciting, to kindof pursue challenges.
And then, you know, after that,I enrolled as part of, you know
, the computer science programsat my at the university and
(03:05):
basically, you know, startedthere.
And after I had graduated withmy undergraduate degree in
computer science, I joined Intelas an intern and at that time
one of my mentors helped onboardme into the process.
So I basically interviewed withthree different teams that were
all related to AI and I had theopportunity to, you know if
gotten a go from all three teamswhich I'm grateful I did to
(03:27):
basically select the team that Iwas interested in and I went
ahead and selected a team ondeep learning and architecture.
And then again I'm so gratefulto my mentor.
He's the one who kind ofcompleted all the onboarding
forms and the documents andthings like that to help me get
my first internship at 14 atIntel right, which was super
exciting.
Right before that I actuallyhad an academic internship at
(03:48):
Yale at the Clinic ofNeuroscience Imaging Center, so
I had some experience with youknow what it means to kind of be
in that environment and, to youknow, start having deliverables
and pursuing them in the CSspace.
But working at Intel hasdefinitely changed my career and
my life for the best way.
So that's a little bit about meand my journey.
Speaker 1 (04:07):
That's incredible, and I don't know what the other
options were, but for you tohave had the foresight, to know
that AI was going to explode andbe so relevant, and what a
great career path you choseearly on, is amazing.
Speaker 2 (04:24):
Thank you.
Great career path you choseearly on is amazing, thank you.
And again, I think full creditgoes to my parents and also
mentors for that one, mary,because both of my parents are
computer scientists by trade andhave worked in a lot of
different, really interestingroles.
Right, my mother was really onthe data analysis side and now
she has, you know, her degree inphilosophy and does a lot of
(04:44):
work on that side.
And then my dad is kind of moreon the security side, so very
specialized and embedded, and,you know, firmware, et cetera.
So one of the things that my momand I were very interested in
while I was growing up, andsomething she imbibed in me, is
kind of an interest inneuroscience, which is a really
fascinating still to this day,and that naturally kind of led
to an interest in neuroscience,which is a really fascinating
(05:04):
still to this day, and thatnaturally kind of led to an
interest in AI.
Right, it's the perfect blend ofdisciplines between
understanding neuroscience andthen using AI as a way to kind
of mimic neuroscience.
So that's kind of how itstarted, and then it just kept
bubbling over.
And now, at this point in time,I've been at Intel for six
years.
I've been working in AI forabout eight years, counting the
time that I was working onprojects during my degree and as
(05:27):
part of my internship at Yale.
I'm returning to a point in mycareer where I'm excited to get
back potentially intoneuroscience and psychology.
So I think it's a really nicekind of roundup of using AI and
tying it together with cognitivecomputing and neuroscience, and
I really have to thank myparents, my mom especially and
also my mentors, who were, youknow, right at the get-go, were
(05:50):
saying you know, ai is a boomingfield.
This is the perfect place tokind of transform and explore
challenges and solve them.
Speaker 1 (05:58):
That's so fascinating .
How have you navigated thechallenges of being the youngest
person in many businesssettings or academic settings as
well?
Speaker 2 (06:07):
Yeah, I mean, I think there's.
I'm personally blessed to nothave seen a lot of significant
challenges or challenges at allwhen it comes to discrimination
and things like that.
I've never kind of experiencedthat at Intel, which I'm very
grateful for, and also atHarvard too, so I think it was a
pretty seamless experience.
Everyone is always verywelcoming, makes space to kind
(06:29):
of explore ideas and you know,basically again, it's when I say
challenge you know pushing backand forth on assumptions and
being able to kind of push theenvelope right and keep
innovating on better things and,you know, creating better
solutions.
So I definitely say that verygrateful for for that kind of
pocket or envelope of, of havingthe opportunity to explore
(06:52):
those things.
Of course there's generalworkplace hiccups that happen at
any point in time and I think,again, you know I am kind of
back to relying on my communityand the folks that I can always
reach out to and ask for adviceand and help and you know,
connect with to understand, hey,what is the next best step for
me to take in my career, right?
(07:13):
Or what can I do to overcomethis particular hiccup or hurdle
?
Or maybe, you know, lack ofinterest, right?
Or maybe a need to getchallenged further, right?
So I think that it's acommunity has been a really big
aspect for me personally to getpast that.
Speaker 1 (07:27):
That's such a good thing to call out for the
younger people entering theworkforce.
Many wait to build theircommunity, but having it at the
get-go adds so much clarity orguidance.
I guess you could say AI coversso many areas.
Tell us about your primaryareas of focus and interest
(07:47):
within AI.
I know I mentioned a few in theintro, but why you chose those
and what leads the passion there.
Speaker 2 (07:55):
Yeah, so I think there's a couple of disciplines
in AI that really speak to mepersonally.
So I think it's changed overthe years.
When I first kind of startedoff my career path, mary, I
think it was really focused onagain that connection to
neuroscience, right.
So a lot of it was onstatistical data analysis, like
of fMRI images, and then theother part of it is something
(08:18):
that I still kind of smile aboutwhen I think back about
something called neuralcryptography, which was a really
interesting blend of usingneural networks and security
algorithms, and I hope that thefield gets revived.
It was a very niche field thatyou know.
It's still only a littleattention is given to it.
But I'm sure as AI modelspopularity and complexity
increases, we'll get back tousing neural networks, for you
(08:40):
know encryption protocols andother elements, and quantum
computing is definitely going toaccelerate that right.
But that's kind of where itstarted.
In the middle of my journey Ikind of, at Intel, got
introduced the idea of theend-to-end stack, right.
How do AI models and in general,you know the things that we
interface with work from thechip level all the way to?
(09:01):
You know the front-end webinterface level, like how voice
assistants are working insmartphones or how our laptop
and blur our background, right,and all of these really fun,
interesting facets of thetechnology.
And then I'd say after that,sorry, it's just, you know, a
progression, but there's alsokind of interesting technologies
around, you know, cognitivecomputing and reinforcement
(09:21):
learning, the idea that AImodels can scale up and learn
and, you know, get feedback fromtheir environment.
And that's led me to my currentinterest today around
human-centered AI and AI that iscapable of learning and
adapting to differentenvironments, right.
So I'd say that's kind of beenmy progression of interests and
areas of AI and that allows alot of AI developers and
(09:41):
programmers to touch all ofthese different areas from, you
know, basic foundational neuralnetworks and machine learning
models and data analysis, allthe way to these complex
paradigms.
Speaker 1 (09:52):
It's so fascinating.
As an influencer and anadvocate for AI.
What do you hope to teachothers most about this field,
because you know there's so manythings worrying people about AI
today.
What is it that you hope to, Iguess, have an impact on the
people that follow you?
Speaker 2 (10:19):
the people that follow you.
Yeah, I mean, I think there'stwo facets to that.
The first one is that it's easyto get started and it is a
great kind of opportunity and afield to get into, even though
the hype may, you know, die down.
I know now, all of a sudden, atleast across LinkedIn and other
social media networks, the newboom is starting to become
quantum computing, with Google'sannouncement, right.
So a little bit of the focushas shifted from AI to quantum
and some hybrid things there.
(10:40):
But regardless of where the hypegoes, I think that there's a
lot of value in the AItechnology space for just
building really coolapplications that are smart and
intelligent and reactive.
So there's an increasing needfor young talent that is just
willing to break assumptionsabout the technology and start
to say, hey, what can I do withthis?
Right?
And then I think the secondaspect of that that I want to be
(11:02):
more vocal about and, I think,be able to represent better as I
grow in my career, is thatcommunity aspect as well.
Mary, you know that we discussedit earlier, right?
I think it's just that generalidea of you know what it takes
to be a, a leader in tech, andwhat are some of the decisions
you have to make, because you'regoing to get a lot of criticism
from folks that you don't knowfor making decisions that you
(11:24):
may know are right or bedifferent than what a colleague
is going to deliver, becauseit's customized or, ideally,
it's customized to yourinterests and to your passion
and problem statement, right?
So I think that those are thetwo things that I'm continuously
(11:46):
learning about, and I, you know, I always, almost every other
day, I ask my mom for her adviceon these, cause it's just so
important as a young person intech to figure out what's my
next step, what do I do that canmake a difference.
So I would encourage folks ofmy generation and of all
generations right, I think it'sa really interesting set of
questions to ask ourselves as wecontinue to grow in AI.
Speaker 1 (12:07):
Well, I think the fact that you're asking yourself
what difference can I make isby far the biggest thing that
anyone can do.
What advice do you have forwomen who are trying to find
their voice and build confidencein their careers?
Speaker 2 (12:21):
I'd say the number one thing to recognize is what
it takes to be an expert.
I think is not typically whatwe think it is.
I've had a lot of conversationswith women and colleagues in
the space right, who wanted tolearn, for example, data science
and data analysis, which is atopic, a specialization I got my
master's in.
So, you know, when you get adegree, it's generally known
(12:43):
that, hey, you know, you're anexpert in this space.
But I think that things arechanging now, right, Regardless
of certifications and degrees,right, internally, to build a
confidence, to be able to speakabout something you need to
recognize when you'recomfortable with calling
yourself as an expert and it'snot going to come at the
standards that society may putout, right, Because, again, you
know to be an expert.
(13:04):
Let's say, in computer science,maybe you need to know a bunch
of programming languages and beable to be a super efficient
coder, right, you can manage allof these tasks right.
But maybe that's not what anexpert means in, again, your
interest area, your problemfield, or what it means to you,
right?
So I think identifying theboundary at which you believe
that you, you know, have theexpertise you need to
communicate and to stronglyrepresent yourself is really
(13:26):
critical.
It doesn't mean setting the barlower for ourselves or too high
so that we can never achieve itright, but it's that balance.
Speaker 1 (13:34):
Yeah, no, I completely agree, and I think
you know.
One thing that I've I've heardfrom many women is just, you
know, not having that fear to toraise your hand in a meeting
and to speak up, even though youknow you may not be that
so-called expert, your view andwhy you're in that room matters.
So to not be quiet and sit inthe corner is, you know, one
(13:58):
action that each woman couldtake.
Speaker 2 (14:01):
Exactly.
I completely agree with that,and I'd say that that action and
the idea of implementing itmakes you an expert in certain
domains.
Right, Because it means thatnot only do you understand a
subject or a topic enough to beable to voice something about it
or ask a question, but you'reactually taking action and doing
something about it, and I'venoticed that you know,
especially in the corporateworld, and I would love to get
(14:24):
your thoughts on this too that'skind of what it takes to be a
leader, from my understanding,which is actually doing
something about.
You know a topic or an area,asking questions, and you know.
That's how you start making adifference.
Speaker 1 (14:35):
Absolutely no.
I found my time in corporatethat one when you work remote,
be on camera so that people cansee you and see that you're
engaged and be engaged but thenalso to ask those questions and
maybe you know you might knowthat answer partially, but for
the people in the room that areless willing to ask those
(14:57):
questions, I also took on thatkind of advocacy role in my time
.
Absolutely, who have been and Ithink you touched on this
earlier but beyond your parentswho have been most influential
in your life?
Speaker 2 (15:14):
Yeah, I mean I can definitely reference so many
different mentors and teachersand professors over the years.
Steve, too, is the primary kindof mentor who onboarded me to
Intel and helped me with myinternship process, and at Intel
I've had the pleasure of beingmentored by, and having
communications and networkingwith, so many brilliant leading
(15:37):
women in this space.
Lama Nachman, who leads Intel'sresponsible AI efforts, is one
of the amazing women who's kindof a trailblazer in these
efforts.
Also Huma Abidi, who has nowleft Intel, but she and her team
are always kind of have been ashining star during their time
at Intel and also, you know,continue to be like there's a
(15:58):
long lasting legacy ofbrilliance and technological
innovation that I always look upto.
And so many other fantasticcolleagues.
Dr Hal Blumenfeld from YaleUniversity, who kind of first,
you know, helped me learn aboutthe details of an internship
right, as I mentioned earlier itstill is, you know, plays a
really key role in kind ofinspiring me to to kind of keep
(16:19):
shooting for interesting newideas and although, you know,
sometimes you're not that muchin touch with certain folks just
because of time right, I thinkthey're always kind of part of
your network or you're alwaysable to reach out to them, which
I'm so incredibly grateful for.
So I'd definitely say these aresome of the few folks that are
kind of very, very close.
(16:40):
There's also a lot of fantasticyou know VPs at Intel as well
that I've had the pleasure ofnetworking with as part of
conferences, right like PallaviMohanjan and others, and they're
really fantastic women who arejust always there, right and
ready to reach out and, ofcourse, brilliant leaders.
Speaker 1 (16:57):
That's fantastic and I love that.
You've had external influencersand mentors along the way, and
I certainly don't want todiscount your parents, because
it starts with parenting.
You know the positive influence, so what do you think they did
right to nurture the love oflearning in you?
Speaker 2 (17:17):
I think I mean I've reflected on it a lot as I've
been growing up.
I think one of the fundamentalthings is you know, my parents
themselves have a love forlearning, so it was kind of like
a monkey, see, monkey do, whichis what I like to call it.
But it's just the general ideaand I mean you know, again, I
think that there's a lot ofareas where I find myself
(17:39):
stumbling to, where I kind oflook up to, you know, my mom and
dad and see how they react tothings right, and how they get
excited about, you know,learning new topics, new areas
and getting past those.
You know those problems orthose roadblocks and always
being excited to kind of explorethe new things.
I mean you know those problemsor those roadblocks and always
being excited to kind of explorethe new things.
I mean you know my mom and Italk about this sometimes and
(18:01):
you know when she was readingbooks to me when I was little,
like her excitement around, youknow, flipping the pages and
looking at the new things.
That's kind of what I think gotimprinted onto me.
So anytime I'm not feelingexcited about something, I go
and read it by her, honestly soand you know, I see, you know,
am I, you know, having a littlebit more of a lower vibe right
towards it?
Does that kind of match where Iwant to be right?
(18:23):
Because you know there's not.
There's always kind of anopportunity for us to maybe take
a step back when there's anopportunity instead of taking a
step forward, because we're notreally sure if that's right for
us or not.
So I think, having her as amentor or guide that you can
rely on and say, hey, you know,is this exciting right?
Do you see the value in this?
Or, you know, do you see valuein you know another area I think
(18:43):
that that's been absolutelycrucial for me personally.
Speaker 1 (18:47):
AI has such a broad potential, how do you see it
impacting career opportunitiesspecifically, and what roles do
you foresee being created oreliminated in the short or long
term?
Speaker 2 (18:58):
It's a great question .
I think AI and the job markethas so many interesting
conversations and corollaries.
On one end, there's the talentfor CS students and, in general,
any area that's intersecting.
You know applications right andcomputers right.
I know friends in aviation andin healthcare, et cetera, and
(19:20):
all of their fields are kind ofgetting impacted by AI, either
directly or indirectly, as partof, maybe, professors telling
them to integrate it as part oftheir projects or being
incorporated in courses.
So I think skilling up for theAI revolution is an incredible
idea and also very accessiblewith a lot of the resources
sitting on the internet, a lotof the free courses and material
(19:40):
, and then if you have thebudget to pay right, the
certifications, the degrees, thepathways, depending on the
credentials and the jobs and theroles that you're reaching for,
there's an immense opportunityon that front.
On the other front, there's thegeneral idea of disruption
right with AI and the job market.
The other front, there's thegeneral idea of disruption right
with AI and the job market, andI know it's a very hotly
(20:03):
debated topic.
So, to put it kind of verylightly, I'm personally an
advocate for you know,human-centered AI-related
developments and algorithms andmodels right, this idea that you
know, on one end, we'reencouraging AI models and the
innovation there, but we're alsobeing very careful about what
exactly is the role of human inbeing able to kind of
participate right in thealgorithm development or in the
feedback of the algorithm, rightand, you know, creating an
(20:25):
environment, basically, whereboth parties are kind of being
able to interchange and thenprovide inputs and outputs.
Again, it's, I think, anincredibly nuanced topic, from
everything from like autonomousvehicles, where maybe you want
your vehicle to do fullyself-driving so you can focus on
phone call, or, you know,managing, you know, family
members in the back of the car,right, and again it depends on
(20:48):
the perspective, right.
Or if it's, you know, again, ahybrid right, where you don't
want an AI model that'sautomatically screening your
resume to miss it just becauseyou missed a couple of keywords,
right?
So I think you know a lot ofkey nuances and I think that's
where the human-centered AItechnology definition really
comes into play.
Speaker 1 (21:06):
Are there AI tools that you would recommend people
like, more non-technical peopleuse, Like?
I know there's ChatGPT, but doyou recommend people start using
those tools today to acceleratetheir brand?
You know, getting like.
I think that's one thing that Itry to teach people because I
use it within my own business,and so what are your thoughts on
(21:30):
using ChatGPT to work on yourpersonal brand, and what other
tools would you recommend?
Speaker 2 (21:37):
I mean, I definitely agree with it, and my response
may have been a little bitdifferent, personally, a couple
months ago compared to now, butI've personally started to use
tools like ChatchBT, claude,gemini I'm still just starting
to use Gemini's as a little bitdifferent but Copilot and others
for personal brand development.
The best thing I think about AItools that are kind of writing
(21:59):
oriented or text generation, ifwe want to use the right word,
is that they can kind ofcommunicate about your personal
brand in a way that's veryobjective and fact-based that we
may not be able to convey onpaper, right?
So, again, I'll take an exampleof my mom, because I love her
and we always have these reallygreat conversations, so I
apologize if I keep reusing them, but she just started a small
(22:21):
business where we live inArizona, and one of the key
things is to write your bio,right, you want it to be catchy,
something small, right?
And Chachi PT did a fantasticjob of taking a list of
accomplishments and summarizingit into something that's
impactful, something she'sconfident about, right?
But it's kind of challenging towrite on your own, especially
when you don't know, maybe, howto articulate your work, because
(22:44):
it happens, I think, to all ofus.
So I definitely recommend theuse of these tools for branding.
Of course there's other oneslike text-to-speech right, if
you want to just kind of saysome things out loud and you
want a model to kind oftranscribe and take care of it
for you.
And even diagramming tools,right.
Ai diagramming tools can bereally helpful in just trying to
put your vision on paper orlike on a PowerPoint or
(23:05):
something like that, to kind ofget started with, you know,
defining what you want to do.
So I definitely recommend it.
Speaker 1 (23:12):
Yeah, it's amazing that all the different tools out
there I mean really, if youthink it, it's probably out
there in some type of appalready, so it's just knowing
what to look for and going tothose tools and educating
yourself.
You probably look it up onYouTube and get a lesson.
Yeah, absolutely Go ahead.
Speaker 2 (23:32):
please, no, go ahead, go ahead, please, no, go ahead,
I'd say.
The one tip that I've learnedregarding finding the right
tools though because again,there's a lot of hype out there,
a lot of tools that kind of getshoved in our face sometimes is
whatever is kind of generallymore popular, I'd say, within
the community, is definitelysomething to go for first right.
For example, I think that youknow these text generation tools
(23:53):
like Plot and ChatGPT are verypopular, and that's where we
kind of see the most value.
There are these smallerapplications, just as you
mentioned, mary right, for youknow, again, like I mentioned,
text-to-speech transcription ordiagramming, but there's so many
different applications thathave popped up there.
So I always go first towardsthe ones that are more popular
and once I've kind of examinedthem and understood the way that
(24:13):
they work further, then I kindof dive into ones that are, you
know, a little bit less used,less popular, maybe don't have
that good of a you know, videosor documentation or guidance
around them, because you kind ofease yourself into the entry of
what the tool looks like andwhat to expect.
So you're not settling for lessright, you get the expectations
and the quality of the toolthat you want to work with.
So that would be myrecommendation for non-technical
(24:34):
folks getting started with AItools.
Speaker 1 (24:38):
That's awesome because I think there's so many
people out there that arelooking and needing and wanting
to pivot in their career, soknowing where they can start to
embrace the technology, becauseduring interviews, if they can
speak to the fact that they areusing AI or are adaptable or
have that growth mindset aroundAI, I think that's a selling
(24:59):
point, because most companiesare integrating AI in one way or
another these days.
Yes, but for those who arealready technical you know,
because many women in techlisten to this what do you have
advice-wise for?
Maybe careers that they can gointo that kind of accelerate
(25:21):
their growth?
Speaker 2 (25:23):
Okay, I think it depends kind of the way that
I've started on it and I haveencouraged my friends and
colleagues to look at it.
If you're interested in AI anddata science in general is to
kind of think about the end toend stack of a problem statement
you're interested in.
That's how it started for me.
I was interested in autonomousvehicles as an example, and then
the other neuroscience side ofthings.
(25:44):
But taking autonomous vehiclesas an example, there is
everything from the sensor datafusion that's installed on the
cars, like LiDAR sensors, allthe way down to the chip level,
embedded computing right andalgorithms that you run there,
and then the AI models in theinterface that are doing object
detection, pedestrian intentestimation, right.
I think that anchoring on thespecific algorithms that seem
(26:05):
interesting and the questionsthat we get like how is this AI
model in this car able to detectthat somebody is walking next
to it, or how was it able tocreate a 3D model right Each of
those own questions opens up anentirely new career path, I
would say, or not even a path,but an interesting
specialization and a set ofskills and tools and knowledge
you can gain, for example, onthe object detection path, which
(26:27):
is very popular in the AI space, right, it opens up this idea
of you know, using differenttoolkits like YOLO algorithms,
right From Ultralytics and othertypes of you know libraries and
capabilities around you know,founding box detection and
optimization and false positivesand failure analysis, right so,
and you can create projects andtailor a resume and then start
(26:49):
to apply for jobs, let's say, inthe computer vision engineer or
deep learning engineer or AIengineer space, whereas if
you're interested in the chatbotspace, right, you're looking at
large language model,foundational model definition,
ai agents, right, lane chain,lama index, a lot of those
toolkits and you kind of targetyour focus there, learn the
skills, add it to your resumeand then keep moving forward.
(27:11):
That would be my recommendationfrom what I've learned to kind
of keep up to date with therapidly changing pace of the
tools and to kind of get thoseskills on our resume and start
looking for jobs that reallyinterest us.
Speaker 1 (27:22):
That's fantastic, because not only did you talk
about kind of the path in whichsomeone could go about doing
that, but you brought up areally good point and that is, I
think, being drawn to whatyou're interested in.
And then kind of reverseengineering how do I get into
(27:42):
that space and educate myself?
And then what did I do to learn, to learn that?
And then communicating that inyour resume.
Speaker 2 (27:50):
I would definitely say that that's the approach to
go, and in some cases you learnthat kind of the painful way
where you get involved in aproject that you think is going
to look really good on yourresume and then a couple of days
in you realize there's not alot of good documentation,
there's not a lot of, maybe,good communication, just because
teams are working on differentthings.
(28:10):
It's hard to intercept, but atthe end of the day, outside of
all of the excuses and I've donethis personally you kind of
realize that you're notinterested in it really that
much and the interest that youfelt going in was about the
potential but not about theimplementation.
And it can be kind of like arock that you keep pushing
through and you're saying, okay,I do need to do this.
But at the end of the day, ifthere's something more exciting
(28:32):
that's catching your attentionand you feel like that's
something you could easily do,right, maybe it's worth it to
take a pause, if you can, fromthis project.
That's kind of, you know, notreally your interest point and
go and solve some challenges andtackle the frustrations of what
you're interested in, whatyou're drawn to, right, because
I think, as engineers, at acertain point.
You know we get excited abouteverything in terms of the
(28:54):
potential to do and again make adifference right, but you know
that potential can wear downwhen you hit the implementation
roadblocks.
So I think it's important tokind of think about and choose
an area or look for theopportunities that come to us
about things that interest us,that allow us to kind of keep
overcoming those hurdles.
Speaker 1 (29:11):
Shifting gears a bit, speaking of things that you
love and that you're drawn to.
I think it's fascinating thatyou wrote a book Forest Mystic.
Yes, tell me what was theinspiration behind that.
I just love that you wrote achildren's book.
Speaker 2 (29:24):
Thank you, I appreciate it and thank you for
breaking it up too.
I've always kind of lovedpoetry since I was a kid.
I've stopped it recently butI'm hoping to get back into it
and you know the like reallycute stories again.
You know I have to credit mymom for the idea of the story
for this one, because it wasbasically a small tiny story
that we came up with and then wemodified the moral of the story
(29:48):
.
It was actually, you know,based on stories as a kid right
that I got into and that I heard, and then we decided to take
some pictures from, you know,vacations and trips that we've
taken, turn it into a nice cutelittle poetry book that you know
our friends' kids loved it,right, I think neighbors loved
it too.
I think it's just kind of asmall way of exploring an
(30:09):
interesting story and I think,interestingly, the moral of the
story that I recognize now thatI'm older for that book is that
you know it's important to kindof it's actually about community
, right, it's important to askfor help when you need it and
also, you know, to kind of learnfrom recognizing when help is
being given to you and what isgenuine and what isn't right.
(30:32):
So the hero of the story or oneof the protagonists is a
prankster who kind of learnsthat throughout the story and
it's just like a veryinteresting idea of why
community is important, whybeing genuine and kind and
friendly is so important to it'sso important to kind of grow.
Speaker 1 (30:54):
I just enjoy talking to you so much.
Um what non-technical skills or?
Speaker 2 (30:56):
attributes have you found most valuable in your
career?
Um, I know that there's the,the major ones that are
mentioned, which iscommunication, which absolutely
is so, so critical, um, that Istill work on right.
Uh, confidence is another thingthat I'm currently working on,
which I think it's just aroundrepresentation, and, mary, I've
seen and I learned a lot fromour prior conversations, too,
(31:16):
from you on this, and you know,I think, just reiterating that
it's so, so crucial from myunderstanding, to kind of have
that presence and that you knowconfidence while you're speaking
.
I think another key soft skillthat I've learned is rest which
is in and of itself a soft skill.
Right when you're about to givea big speech, a really detailed
(31:38):
technical demo, starting on acoding project, or starting to
map out your career path or evencommunicate with stakeholders
or anything like that, or starta new side, hobby or project,
having that opportunity to takea moment to rest, to kind of
introspect briefly before goingforward is so critical.
It's kind of like thatself-talk aspect.
So that's, that's the thirdmain soft skill I would
(32:01):
emphasize on.
Speaker 1 (32:03):
That is great Cause I don't think many people bring
that up and that is so importantfor clarity of just the mind.
Okay, with so much noise aroundAI, what are some good
resources books or podcasts orinfluencers that you would
recommend people tune into orread for future learning?
Speaker 2 (32:39):
Yes, I think I also mentioned this in a recent
interview with Mashable aboutthese two brilliant, brilliant
women leaders and pathfinders inthe space.
Then Yejin Choi again, Iapologize, I don't know if I
mispronounced her name, but sheis also an incredible kind of
leader and a pathfinder inreally interesting areas.
I think I first kind of gotfamiliarized with her research
(33:00):
in the context of moraldatabases and mapping for AI
models, which was reallyinteresting in the context of
what's happening in the AI spacegoing forward.
So I definitely recommend thosetwo amazing folks.
And then there's also SebastianBraschka, who does an amazing
set of technical resources.
I'm still trying to find timeto be able to go through them
(33:20):
all, right.
So that's kind of anotherfantastic, brilliant kind of
engineer in mind to follow.
There is also another set ofresources I'm forgetting the
name of the professor whopioneered them, but it's called
AI by Hand and I follow theprofessor and the researcher who
does those implementations onLinkedIn.
Really a fantastic deep divedown into, you know, the
(33:41):
technical mechanisms of AI whichare, you know, he makes them
super fascinating, superdigestible to understand, right,
and it's always kind of a joyto even browse through it if you
don't have enough time right.
So definitely those are thebest resources to kind of follow
.
Again, they're kind of supertechnical.
If you're looking for more highlevel ones, I definitely think
that you know some of theseorganizations that are doing
(34:04):
some good AI research that youknow we are interested in, like
maybe Microsoft or Google, oryou know OpenAI right.
Their blogs are reallyinteresting ways to kind of keep
subscribed to the latest andgreatest right.
Again, depending on whether youknow you like their
implementations, you know you'reinterested in their toolkits
and technologies, that's anotherreally great resource the
technical blogs there.
Speaker 1 (34:24):
Do you teach as well?
Speaker 2 (34:28):
Yes, I do teach.
I'm super passionate aboutteaching.
I'm actually in the middle ofcreating a course right now on
interview prep for data scienceand machine learning, but I've
taught eight courses so far inthe past two years.
It's been quite a journey, butI absolutely love instruction.
Speaker 1 (34:41):
Okay, well, of all those people that you listed, I
want to make sure that you and Iconnect afterwards and I get
that list and I'll include it inthe show notes and then, when
you publish your your newestlesson, I want to make sure I
include that, because thatsounds fascinating.
Speaker 2 (34:55):
Thank you, mary, absolutely.
Speaker 1 (34:57):
So how do you unplug from work and I guess, reboot or
recharge yourself?
Speaker 2 (35:04):
Yeah, I mean I love to hike, especially it's it's
kind of beautiful this time ofyear in Arizona, so, um, lots of
I.
I'm motivating myself to domore hiking right, and to get
more exercise.
I also love swimming Uh, it'smy favorite sport out of
everything that you know and andespecially, it's just so
wonderful to kind of cool downand to relax, but also gives you
(35:26):
a great workout.
In addition, reading is reallyfantastic.
I think now the my kind oflatest interest has been finding
these older books and novelsthat are kind of, you know,
1960s, 1970s, right, and thenjust reading them through if you
can find them in your library,right, because you know it's so
(35:47):
interesting to kind of see howthe same concepts that are
discussed in those books canstill apply today.
I think it's just sofascinating, um, with, without
you know, zero changes, honestly, um, in terms of the transfer,
the concept, so it's reallyfascinating.
And books on psychology andother really cool stuff.
Um, and games, of course, loveto play games with, uh, with
friends and um, digital,physical, it doesn't really
(36:08):
matter.
Speaker 1 (36:10):
That's awesome, okay, so what does to be bolder mean
to you?
Speaker 2 (36:15):
To be bolder means being able to understand and
create, I think, a valuedefinition for yourself and I
know that sounds a little bitcorporate-y, but the general
idea that you kind of have valueinherently in whatever you do.
You kind of bring that to thestatement whether you're tired
or you're unenergetic or you'reat your best and you're thriving
(36:37):
, you inherently have value,regardless of everything that's
happening in your life andseeing how that kind of grows as
you experience differentexperiences and circumstances
and you know whether it takes asetback or a step forward, kind
of seeing how that value growsand nurturing it.
I think that's what it means tobe bolder, which is really
seeing that value in ourselvesinherently.
Speaker 1 (37:00):
I love that unique perspective.
Where do you see yourself infive years, say, or 10 years?
Speaker 2 (37:06):
It's a great question .
Probably a CEO of amultinational corporation is the
goal, yeah, and I'm counting onmy network and my community to
keep me accountable to it.
And you know I've got a lot tolearn to get to that point.
I think a lot on leadership andyou know, again, confidence
right and stepping into spaces,but I'm always open to
mentorship and to advice and toopportunity.
(37:27):
So, yeah, I'm going to shootfor the moon and see where it
takes me.
Speaker 1 (37:30):
Well, I'll buy stock in that company for sure when
you're leading it.
Thank you, that's amazing.
Okay, before I let you go, justsome quick, rapid fire fun
questions so people can get toknow you.
And I know this interview hasbeen not focused on all the
wonderful things and deep divesthat we could take in AI, but I
(37:54):
wanted to make sure that I hadyou on our show just to talk
about careers, because I thinkthat's such a good opportunity
and you really are inspiring toso many women in STEM and I just
I think, if you don't hear itenough, thank you for that, okay
, so pizza or pasta, a pasta,dogs or cats, cats, Summer or
winter.
Speaker 2 (38:14):
Winter.
Speaker 1 (38:15):
Ice cream or cake.
Speaker 2 (38:17):
Cake.
Speaker 1 (38:18):
Comedy or drama.
Speaker 2 (38:20):
Comedy.
Speaker 1 (38:21):
All right, I'll let you go, but before I do, thank
you so much for being here andsharing your story with us.
Speaker 2 (38:27):
Absolutely.
Thank you so much for having me, Mary.
Speaker 1 (38:33):
Thanks for listening to the episode today.
It was really fun chatting withmy guest.
If you liked our show, pleaselike it and share it with your
friends.
If you want to learn what we'reup to, please go check out our
website at 2BBouldercom.
That's the number two, little b, bouldercom.
Hi there, my name is Mary Kiloalea.
Welcome to the To Be Bolderpodcast providing career
insights for the next generationof women in business and tech.
To Be Bolder was created out ofmy love for technology and
marketing, my desire to bringtogether like-minded women and
my hope to be a great role modeland source of inspiration for
my two girls and other youngwomen like you, encouraging you
(00:22):
guys to show up and to be bolderand to know that anything you
guys dream of it's totallypossible.
So sit back, relax and enjoythe conversation.
Hi, thanks for tuning in.
Ai is one of the hottesttechnologies in the world today
and a major driver of careergrowth and opportunity.
Our guest today is a youngwoman with exceptional talent.
(00:45):
Rhea Cheruvu is an AI architectat Intel, machine learning
engineer, data scientist,industry speaker and an
instructor.
Rhea holds a master's degree indata science and a bachelor's
in computer science from HarvardUniversity.
She's been featured in numerouspublications on AI, holds
multiple patents and has spokenat prestigious events such as
(01:07):
the Women in Data ScienceConference, tedx and other top
industry events.
Rhea is passionate about theimportance of open source
communities, women in STEM andcontributing in disruptive
technology spaces.
Her area of expertise includessolutions for security and
privacy in machine learning.
Her area of expertise includessolutions for security and
privacy in machine learning,fairness, explainable and
(01:28):
responsible AI systems,uncertain AI, reinforcement
learning and computationalmodels of intelligence.
In addition to her technicalwork, rhea is a published poet,
children's book author and aneuroscience enthusiast.
Rhea, thank you so much forbeing here.
Speaker 2 (01:46):
It is such an honor to have you on the show.
Thank you, Mary.
I'm so excited to be here todayand have a great conversation
with you too.
Speaker 1 (01:52):
Awesome.
Okay, well, you have achievedso much at a young age, and so
here's some facts.
I don't know if our listenersknow, but I read that you
graduated from high school at 11, became the youngest ALB
graduate in Harvard history andstarted working at Intel at 14.
(02:12):
That is incredible.
Can you share your journey andwhat it was like to land your
job at 14 at Intel?
Speaker 2 (02:21):
Oh, thank you so much , and definitely I think it was
a fantastic journey that wasfilled with lots of great
moments and helping hands too.
So I graduated high school whenI was 11, as just mentioned and
you know it was basically a lotof the kind of planning, I'd
say in the forethought that mymom put forward as part of my
(02:43):
curriculum.
She was my learning coach andthere with me every step of the
way and, you know, basicallyhelped me design a journey that
was accelerated, that wasmeaningful and that was also
really kind of exciting, to kindof pursue challenges.
And then, you know, after that,I enrolled as part of, you know
, the computer science programsat my at the university and
(03:05):
basically, you know, startedthere.
And after I had graduated withmy undergraduate degree in
computer science, I joined Intelas an intern and at that time
one of my mentors helped onboardme into the process.
So I basically interviewed withthree different teams that were
all related to AI and I had theopportunity to, you know if
gotten a go from all three teamswhich I'm grateful I did to
(03:27):
basically select the team that Iwas interested in and I went
ahead and selected a team ondeep learning and architecture.
And then again I'm so gratefulto my mentor.
He's the one who kind ofcompleted all the onboarding
forms and the documents andthings like that to help me get
my first internship at 14 atIntel right, which was super
exciting.
Right before that I actuallyhad an academic internship at
(03:48):
Yale at the Clinic ofNeuroscience Imaging Center, so
I had some experience with youknow what it means to kind of be
in that environment and, to youknow, start having deliverables
and pursuing them in the CSspace.
But working at Intel hasdefinitely changed my career and
my life for the best way.
So that's a little bit about meand my journey.
Speaker 1 (04:07):
That's incredible, and I don't know what the other
options were, but for you tohave had the foresight, to know
that AI was going to explode andbe so relevant, and what a
great career path you choseearly on, is amazing.
Speaker 2 (04:24):
Thank you.
Great career path you choseearly on is amazing, thank you.
And again, I think full creditgoes to my parents and also
mentors for that one, mary,because both of my parents are
computer scientists by trade andhave worked in a lot of
different, really interestingroles.
Right, my mother was really onthe data analysis side and now
she has, you know, her degree inphilosophy and does a lot of
(04:44):
work on that side.
And then my dad is kind of moreon the security side, so very
specialized and embedded, and,you know, firmware, et cetera.
So one of the things that my momand I were very interested in
while I was growing up, andsomething she imbibed in me, is
kind of an interest inneuroscience, which is a really
fascinating still to this day,and that naturally kind of led
to an interest in neuroscience,which is a really fascinating
(05:04):
still to this day, and thatnaturally kind of led to an
interest in AI.
Right, it's the perfect blend ofdisciplines between
understanding neuroscience andthen using AI as a way to kind
of mimic neuroscience.
So that's kind of how itstarted, and then it just kept
bubbling over.
And now, at this point in time,I've been at Intel for six
years.
I've been working in AI forabout eight years, counting the
time that I was working onprojects during my degree and as
(05:27):
part of my internship at Yale.
I'm returning to a point in mycareer where I'm excited to get
back potentially intoneuroscience and psychology.
So I think it's a really nicekind of roundup of using AI and
tying it together with cognitivecomputing and neuroscience, and
I really have to thank myparents, my mom especially and
also my mentors, who were, youknow, right at the get-go, were
(05:50):
saying you know, ai is a boomingfield.
This is the perfect place tokind of transform and explore
challenges and solve them.
Speaker 1 (05:58):
That's so fascinating .
How have you navigated thechallenges of being the youngest
person in many businesssettings or academic settings as
well?
Speaker 2 (06:07):
Yeah, I mean, I think there's.
I'm personally blessed to nothave seen a lot of significant
challenges or challenges at allwhen it comes to discrimination
and things like that.
I've never kind of experiencedthat at Intel, which I'm very
grateful for, and also atHarvard too, so I think it was a
pretty seamless experience.
Everyone is always verywelcoming, makes space to kind
(06:29):
of explore ideas and you know,basically again, it's when I say
challenge you know pushing backand forth on assumptions and
being able to kind of push theenvelope right and keep
innovating on better things and,you know, creating better
solutions.
So I definitely say that verygrateful for for that kind of
pocket or envelope of, of havingthe opportunity to explore
(06:52):
those things.
Of course there's generalworkplace hiccups that happen at
any point in time and I think,again, you know I am kind of
back to relying on my communityand the folks that I can always
reach out to and ask for adviceand and help and you know,
connect with to understand, hey,what is the next best step for
me to take in my career, right?
(07:13):
Or what can I do to overcomethis particular hiccup or hurdle
?
Or maybe, you know, lack ofinterest, right?
Or maybe a need to getchallenged further, right?
So I think that it's acommunity has been a really big
aspect for me personally to getpast that.
Speaker 1 (07:27):
That's such a good thing to call out for the
younger people entering theworkforce.
Many wait to build theircommunity, but having it at the
get-go adds so much clarity orguidance.
I guess you could say AI coversso many areas.
Tell us about your primaryareas of focus and interest
(07:47):
within AI.
I know I mentioned a few in theintro, but why you chose those
and what leads the passion there.
Speaker 2 (07:55):
Yeah, so I think there's a couple of disciplines
in AI that really speak to mepersonally.
So I think it's changed overthe years.
When I first kind of startedoff my career path, mary, I
think it was really focused onagain that connection to
neuroscience, right.
So a lot of it was onstatistical data analysis, like
of fMRI images, and then theother part of it is something
(08:18):
that I still kind of smile aboutwhen I think back about
something called neuralcryptography, which was a really
interesting blend of usingneural networks and security
algorithms, and I hope that thefield gets revived.
It was a very niche field thatyou know.
It's still only a littleattention is given to it.
But I'm sure as AI modelspopularity and complexity
increases, we'll get back tousing neural networks, for you
(08:40):
know encryption protocols andother elements, and quantum
computing is definitely going toaccelerate that right.
But that's kind of where itstarted.
In the middle of my journey Ikind of, at Intel, got
introduced the idea of theend-to-end stack, right.
How do AI models and in general,you know the things that we
interface with work from thechip level all the way to?
(09:01):
You know the front-end webinterface level, like how voice
assistants are working insmartphones or how our laptop
and blur our background, right,and all of these really fun,
interesting facets of thetechnology.
And then I'd say after that,sorry, it's just, you know, a
progression, but there's alsokind of interesting technologies
around, you know, cognitivecomputing and reinforcement
(09:21):
learning, the idea that AImodels can scale up and learn
and, you know, get feedback fromtheir environment.
And that's led me to my currentinterest today around
human-centered AI and AI that iscapable of learning and
adapting to differentenvironments, right.
So I'd say that's kind of beenmy progression of interests and
areas of AI and that allows alot of AI developers and
(09:41):
programmers to touch all ofthese different areas from, you
know, basic foundational neuralnetworks and machine learning
models and data analysis, allthe way to these complex
paradigms.
Speaker 1 (09:52):
It's so fascinating.
As an influencer and anadvocate for AI.
What do you hope to teachothers most about this field,
because you know there's so manythings worrying people about AI
today.
What is it that you hope to, Iguess, have an impact on the
people that follow you?
Speaker 2 (10:19):
the people that follow you.
Yeah, I mean, I think there'stwo facets to that.
The first one is that it's easyto get started and it is a
great kind of opportunity and afield to get into, even though
the hype may, you know, die down.
I know now, all of a sudden, atleast across LinkedIn and other
social media networks, the newboom is starting to become
quantum computing, with Google'sannouncement, right.
So a little bit of the focushas shifted from AI to quantum
and some hybrid things there.
(10:40):
But regardless of where the hypegoes, I think that there's a
lot of value in the AItechnology space for just
building really coolapplications that are smart and
intelligent and reactive.
So there's an increasing needfor young talent that is just
willing to break assumptionsabout the technology and start
to say, hey, what can I do withthis?
Right?
And then I think the secondaspect of that that I want to be
(11:02):
more vocal about and, I think,be able to represent better as I
grow in my career, is thatcommunity aspect as well.
Mary, you know that we discussedit earlier, right?
I think it's just that generalidea of you know what it takes
to be a, a leader in tech, andwhat are some of the decisions
you have to make, because you'regoing to get a lot of criticism
from folks that you don't knowfor making decisions that you
(11:24):
may know are right or bedifferent than what a colleague
is going to deliver, becauseit's customized or, ideally,
it's customized to yourinterests and to your passion
and problem statement, right?
So I think that those are thetwo things that I'm continuously
(11:46):
learning about, and I, you know, I always, almost every other
day, I ask my mom for her adviceon these, cause it's just so
important as a young person intech to figure out what's my
next step, what do I do that canmake a difference.
So I would encourage folks ofmy generation and of all
generations right, I think it'sa really interesting set of
questions to ask ourselves as wecontinue to grow in AI.
Speaker 1 (12:07):
Well, I think the fact that you're asking yourself
what difference can I make isby far the biggest thing that
anyone can do.
What advice do you have forwomen who are trying to find
their voice and build confidencein their careers?
Speaker 2 (12:21):
I'd say the number one thing to recognize is what
it takes to be an expert.
I think is not typically whatwe think it is.
I've had a lot of conversationswith women and colleagues in
the space right, who wanted tolearn, for example, data science
and data analysis, which is atopic, a specialization I got my
master's in.
So, you know, when you get adegree, it's generally known
(12:43):
that, hey, you know, you're anexpert in this space.
But I think that things arechanging now, right, Regardless
of certifications and degrees,right, internally, to build a
confidence, to be able to speakabout something you need to
recognize when you'recomfortable with calling
yourself as an expert and it'snot going to come at the
standards that society may putout, right, Because, again, you
know to be an expert.
(13:04):
Let's say, in computer science,maybe you need to know a bunch
of programming languages and beable to be a super efficient
coder, right, you can manage allof these tasks right.
But maybe that's not what anexpert means in, again, your
interest area, your problemfield, or what it means to you,
right?
So I think identifying theboundary at which you believe
that you, you know, have theexpertise you need to
communicate and to stronglyrepresent yourself is really
(13:26):
critical.
It doesn't mean setting the barlower for ourselves or too high
so that we can never achieve itright, but it's that balance.
Speaker 1 (13:34):
Yeah, no, I completely agree, and I think
you know.
One thing that I've I've heardfrom many women is just, you
know, not having that fear to toraise your hand in a meeting
and to speak up, even though youknow you may not be that
so-called expert, your view andwhy you're in that room matters.
So to not be quiet and sit inthe corner is, you know, one
(13:58):
action that each woman couldtake.
Speaker 2 (14:01):
Exactly.
I completely agree with that,and I'd say that that action and
the idea of implementing itmakes you an expert in certain
domains.
Right, Because it means thatnot only do you understand a
subject or a topic enough to beable to voice something about it
or ask a question, but you'reactually taking action and doing
something about it, and I'venoticed that you know,
especially in the corporateworld, and I would love to get
(14:24):
your thoughts on this too that'skind of what it takes to be a
leader, from my understanding,which is actually doing
something about.
You know a topic or an area,asking questions, and you know.
That's how you start making adifference.
Speaker 1 (14:35):
Absolutely no.
I found my time in corporatethat one when you work remote,
be on camera so that people cansee you and see that you're
engaged and be engaged but thenalso to ask those questions and
maybe you know you might knowthat answer partially, but for
the people in the room that areless willing to ask those
(14:57):
questions, I also took on thatkind of advocacy role in my time
.
Absolutely, who have been and Ithink you touched on this
earlier but beyond your parentswho have been most influential
in your life?
Speaker 2 (15:14):
Yeah, I mean I can definitely reference so many
different mentors and teachersand professors over the years.
Steve, too, is the primary kindof mentor who onboarded me to
Intel and helped me with myinternship process, and at Intel
I've had the pleasure of beingmentored by, and having
communications and networkingwith, so many brilliant leading
(15:37):
women in this space.
Lama Nachman, who leads Intel'sresponsible AI efforts, is one
of the amazing women who's kindof a trailblazer in these
efforts.
Also Huma Abidi, who has nowleft Intel, but she and her team
are always kind of have been ashining star during their time
at Intel and also, you know,continue to be like there's a
(15:58):
long lasting legacy ofbrilliance and technological
innovation that I always look upto.
And so many other fantasticcolleagues.
Dr Hal Blumenfeld from YaleUniversity, who kind of first,
you know, helped me learn aboutthe details of an internship
right, as I mentioned earlier itstill is, you know, plays a
really key role in kind ofinspiring me to to kind of keep
(16:19):
shooting for interesting newideas and although, you know,
sometimes you're not that muchin touch with certain folks just
because of time right, I thinkthey're always kind of part of
your network or you're alwaysable to reach out to them, which
I'm so incredibly grateful for.
So I'd definitely say these aresome of the few folks that are
kind of very, very close.
(16:40):
There's also a lot of fantasticyou know VPs at Intel as well
that I've had the pleasure ofnetworking with as part of
conferences, right like PallaviMohanjan and others, and they're
really fantastic women who arejust always there, right and
ready to reach out and, ofcourse, brilliant leaders.
Speaker 1 (16:57):
That's fantastic and I love that.
You've had external influencersand mentors along the way, and
I certainly don't want todiscount your parents, because
it starts with parenting.
You know the positive influence, so what do you think they did
right to nurture the love oflearning in you?
Speaker 2 (17:17):
I think I mean I've reflected on it a lot as I've
been growing up.
I think one of the fundamentalthings is you know, my parents
themselves have a love forlearning, so it was kind of like
a monkey, see, monkey do, whichis what I like to call it.
But it's just the general ideaand I mean you know, again, I
think that there's a lot ofareas where I find myself
(17:39):
stumbling to, where I kind oflook up to, you know, my mom and
dad and see how they react tothings right, and how they get
excited about, you know,learning new topics, new areas
and getting past those.
You know those problems orthose roadblocks and always
being excited to kind of explorethe new things.
I mean you know those problemsor those roadblocks and always
being excited to kind of explorethe new things.
I mean you know my mom and Italk about this sometimes and
(18:01):
you know when she was readingbooks to me when I was little,
like her excitement around, youknow, flipping the pages and
looking at the new things.
That's kind of what I think gotimprinted onto me.
So anytime I'm not feelingexcited about something, I go
and read it by her, honestly soand you know, I see, you know,
am I, you know, having a littlebit more of a lower vibe right
towards it?
Does that kind of match where Iwant to be right?
(18:23):
Because you know there's not.
There's always kind of anopportunity for us to maybe take
a step back when there's anopportunity instead of taking a
step forward, because we're notreally sure if that's right for
us or not.
So I think, having her as amentor or guide that you can
rely on and say, hey, you know,is this exciting right?
Do you see the value in this?
Or, you know, do you see valuein you know another area I think
(18:43):
that that's been absolutelycrucial for me personally.
Speaker 1 (18:47):
AI has such a broad potential, how do you see it
impacting career opportunitiesspecifically, and what roles do
you foresee being created oreliminated in the short or long
term?
Speaker 2 (18:58):
It's a great question .
I think AI and the job markethas so many interesting
conversations and corollaries.
On one end, there's the talentfor CS students and, in general,
any area that's intersecting.
You know applications right andcomputers right.
I know friends in aviation andin healthcare, et cetera, and
(19:20):
all of their fields are kind ofgetting impacted by AI, either
directly or indirectly, as partof, maybe, professors telling
them to integrate it as part oftheir projects or being
incorporated in courses.
So I think skilling up for theAI revolution is an incredible
idea and also very accessiblewith a lot of the resources
sitting on the internet, a lotof the free courses and material
(19:40):
, and then if you have thebudget to pay right, the
certifications, the degrees, thepathways, depending on the
credentials and the jobs and theroles that you're reaching for,
there's an immense opportunityon that front.
On the other front, there's thegeneral idea of disruption
right with AI and the job market.
The other front, there's thegeneral idea of disruption right
with AI and the job market, andI know it's a very hotly
(20:03):
debated topic.
So, to put it kind of verylightly, I'm personally an
advocate for you know,human-centered AI-related
developments and algorithms andmodels right, this idea that you
know, on one end, we'reencouraging AI models and the
innovation there, but we're alsobeing very careful about what
exactly is the role of human inbeing able to kind of
participate right in thealgorithm development or in the
feedback of the algorithm, rightand, you know, creating an
(20:25):
environment, basically, whereboth parties are kind of being
able to interchange and thenprovide inputs and outputs.
Again, it's, I think, anincredibly nuanced topic, from
everything from like autonomousvehicles, where maybe you want
your vehicle to do fullyself-driving so you can focus on
phone call, or, you know,managing, you know, family
members in the back of the car,right, and again it depends on
(20:48):
the perspective, right.
Or if it's, you know, again, ahybrid right, where you don't
want an AI model that'sautomatically screening your
resume to miss it just becauseyou missed a couple of keywords,
right?
So I think you know a lot ofkey nuances and I think that's
where the human-centered AItechnology definition really
comes into play.
Speaker 1 (21:06):
Are there AI tools that you would recommend people
like, more non-technical peopleuse, Like?
I know there's ChatGPT, but doyou recommend people start using
those tools today to acceleratetheir brand?
You know, getting like.
I think that's one thing that Itry to teach people because I
use it within my own business,and so what are your thoughts on
(21:30):
using ChatGPT to work on yourpersonal brand, and what other
tools would you recommend?
Speaker 2 (21:37):
I mean, I definitely agree with it, and my response
may have been a little bitdifferent, personally, a couple
months ago compared to now, butI've personally started to use
tools like ChatchBT, claude,gemini I'm still just starting
to use Gemini's as a little bitdifferent but Copilot and others
for personal brand development.
The best thing I think about AItools that are kind of writing
(21:59):
oriented or text generation, ifwe want to use the right word,
is that they can kind ofcommunicate about your personal
brand in a way that's veryobjective and fact-based that we
may not be able to convey onpaper, right?
So, again, I'll take an exampleof my mom, because I love her
and we always have these reallygreat conversations, so I
apologize if I keep reusing them, but she just started a small
(22:21):
business where we live inArizona, and one of the key
things is to write your bio,right, you want it to be catchy,
something small, right?
And Chachi PT did a fantasticjob of taking a list of
accomplishments and summarizingit into something that's
impactful, something she'sconfident about, right?
But it's kind of challenging towrite on your own, especially
when you don't know, maybe, howto articulate your work, because
(22:44):
it happens, I think, to all ofus.
So I definitely recommend theuse of these tools for branding.
Of course there's other oneslike text-to-speech right, if
you want to just kind of saysome things out loud and you
want a model to kind oftranscribe and take care of it
for you.
And even diagramming tools,right.
Ai diagramming tools can bereally helpful in just trying to
put your vision on paper orlike on a PowerPoint or
(23:05):
something like that, to kind ofget started with, you know,
defining what you want to do.
So I definitely recommend it.
Speaker 1 (23:12):
Yeah, it's amazing that all the different tools out
there I mean really, if youthink it, it's probably out
there in some type of appalready, so it's just knowing
what to look for and going tothose tools and educating
yourself.
You probably look it up onYouTube and get a lesson.
Yeah, absolutely Go ahead.
Speaker 2 (23:32):
please, no, go ahead, go ahead, please, no, go ahead,
I'd say.
The one tip that I've learnedregarding finding the right
tools though because again,there's a lot of hype out there,
a lot of tools that kind of getshoved in our face sometimes is
whatever is kind of generallymore popular, I'd say, within
the community, is definitelysomething to go for first right.
For example, I think that youknow these text generation tools
(23:53):
like Plot and ChatGPT are verypopular, and that's where we
kind of see the most value.
There are these smallerapplications, just as you
mentioned, mary right, for youknow, again, like I mentioned,
text-to-speech transcription ordiagramming, but there's so many
different applications thathave popped up there.
So I always go first towardsthe ones that are more popular
and once I've kind of examinedthem and understood the way that
(24:13):
they work further, then I kindof dive into ones that are, you
know, a little bit less used,less popular, maybe don't have
that good of a you know, videosor documentation or guidance
around them, because you kind ofease yourself into the entry of
what the tool looks like andwhat to expect.
So you're not settling for lessright, you get the expectations
and the quality of the toolthat you want to work with.
So that would be myrecommendation for non-technical
(24:34):
folks getting started with AItools.
Speaker 1 (24:38):
That's awesome because I think there's so many
people out there that arelooking and needing and wanting
to pivot in their career, soknowing where they can start to
embrace the technology, becauseduring interviews, if they can
speak to the fact that they areusing AI or are adaptable or
have that growth mindset aroundAI, I think that's a selling
(24:59):
point, because most companiesare integrating AI in one way or
another these days.
Yes, but for those who arealready technical you know,
because many women in techlisten to this what do you have
advice-wise for?
Maybe careers that they can gointo that kind of accelerate
(25:21):
their growth?
Speaker 2 (25:23):
Okay, I think it depends kind of the way that
I've started on it and I haveencouraged my friends and
colleagues to look at it.
If you're interested in AI anddata science in general is to
kind of think about the end toend stack of a problem statement
you're interested in.
That's how it started for me.
I was interested in autonomousvehicles as an example, and then
the other neuroscience side ofthings.
(25:44):
But taking autonomous vehiclesas an example, there is
everything from the sensor datafusion that's installed on the
cars, like LiDAR sensors, allthe way down to the chip level,
embedded computing right andalgorithms that you run there,
and then the AI models in theinterface that are doing object
detection, pedestrian intentestimation, right.
I think that anchoring on thespecific algorithms that seem
(26:05):
interesting and the questionsthat we get like how is this AI
model in this car able to detectthat somebody is walking next
to it, or how was it able tocreate a 3D model right Each of
those own questions opens up anentirely new career path, I
would say, or not even a path,but an interesting
specialization and a set ofskills and tools and knowledge
you can gain, for example, onthe object detection path, which
(26:27):
is very popular in the AI space, right, it opens up this idea
of you know, using differenttoolkits like YOLO algorithms,
right From Ultralytics and othertypes of you know libraries and
capabilities around you know,founding box detection and
optimization and false positivesand failure analysis, right so,
and you can create projects andtailor a resume and then start
(26:49):
to apply for jobs, let's say, inthe computer vision engineer or
deep learning engineer or AIengineer space, whereas if
you're interested in the chatbotspace, right, you're looking at
large language model,foundational model definition,
ai agents, right, lane chain,lama index, a lot of those
toolkits and you kind of targetyour focus there, learn the
skills, add it to your resumeand then keep moving forward.
(27:11):
That would be my recommendationfrom what I've learned to kind
of keep up to date with therapidly changing pace of the
tools and to kind of get thoseskills on our resume and start
looking for jobs that reallyinterest us.
Speaker 1 (27:22):
That's fantastic, because not only did you talk
about kind of the path in whichsomeone could go about doing
that, but you brought up areally good point and that is, I
think, being drawn to whatyou're interested in.
And then kind of reverseengineering how do I get into
(27:42):
that space and educate myself?
And then what did I do to learn, to learn that?
And then communicating that inyour resume.
Speaker 2 (27:50):
I would definitely say that that's the approach to
go, and in some cases you learnthat kind of the painful way
where you get involved in aproject that you think is going
to look really good on yourresume and then a couple of days
in you realize there's not alot of good documentation,
there's not a lot of, maybe,good communication, just because
teams are working on differentthings.
(28:10):
It's hard to intercept, but atthe end of the day, outside of
all of the excuses and I've donethis personally you kind of
realize that you're notinterested in it really that
much and the interest that youfelt going in was about the
potential but not about theimplementation.
And it can be kind of like arock that you keep pushing
through and you're saying, okay,I do need to do this.
But at the end of the day, ifthere's something more exciting
(28:32):
that's catching your attentionand you feel like that's
something you could easily do,right, maybe it's worth it to
take a pause, if you can, fromthis project.
That's kind of, you know, notreally your interest point and
go and solve some challenges andtackle the frustrations of what
you're interested in, whatyou're drawn to, right, because
I think, as engineers, at acertain point.
You know we get excited abouteverything in terms of the
(28:54):
potential to do and again make adifference right, but you know
that potential can wear downwhen you hit the implementation
roadblocks.
So I think it's important tokind of think about and choose
an area or look for theopportunities that come to us
about things that interest us,that allow us to kind of keep
overcoming those hurdles.
Speaker 1 (29:11):
Shifting gears a bit, speaking of things that you
love and that you're drawn to.
I think it's fascinating thatyou wrote a book Forest Mystic.
Yes, tell me what was theinspiration behind that.
I just love that you wrote achildren's book.
Speaker 2 (29:24):
Thank you, I appreciate it and thank you for
breaking it up too.
I've always kind of lovedpoetry since I was a kid.
I've stopped it recently butI'm hoping to get back into it
and you know the like reallycute stories again.
You know I have to credit mymom for the idea of the story
for this one, because it wasbasically a small tiny story
that we came up with and then wemodified the moral of the story
(29:48):
.
It was actually, you know,based on stories as a kid right
that I got into and that I heard, and then we decided to take
some pictures from, you know,vacations and trips that we've
taken, turn it into a nice cutelittle poetry book that you know
our friends' kids loved it,right, I think neighbors loved
it too.
I think it's just kind of asmall way of exploring an
(30:09):
interesting story and I think,interestingly, the moral of the
story that I recognize now thatI'm older for that book is that
you know it's important to kindof it's actually about community
, right, it's important to askfor help when you need it and
also, you know, to kind of learnfrom recognizing when help is
being given to you and what isgenuine and what isn't right.
(30:32):
So the hero of the story or oneof the protagonists is a
prankster who kind of learnsthat throughout the story and
it's just like a veryinteresting idea of why
community is important, whybeing genuine and kind and
friendly is so important to it'sso important to kind of grow.
Speaker 1 (30:54):
I just enjoy talking to you so much.
Um what non-technical skills or?
Speaker 2 (30:56):
attributes have you found most valuable in your
career?
Um, I know that there's the,the major ones that are
mentioned, which iscommunication, which absolutely
is so, so critical, um, that Istill work on right.
Uh, confidence is another thingthat I'm currently working on,
which I think it's just aroundrepresentation, and, mary, I've
seen and I learned a lot fromour prior conversations, too,
(31:16):
from you on this, and you know,I think, just reiterating that
it's so, so crucial from myunderstanding, to kind of have
that presence and that you knowconfidence while you're speaking
.
I think another key soft skillthat I've learned is rest which
is in and of itself a soft skill.
Right when you're about to givea big speech, a really detailed
(31:38):
technical demo, starting on acoding project, or starting to
map out your career path or evencommunicate with stakeholders
or anything like that, or starta new side, hobby or project,
having that opportunity to takea moment to rest, to kind of
introspect briefly before goingforward is so critical.
It's kind of like thatself-talk aspect.
So that's, that's the thirdmain soft skill I would
(32:01):
emphasize on.
Speaker 1 (32:03):
That is great Cause I don't think many people bring
that up and that is so importantfor clarity of just the mind.
Okay, with so much noise aroundAI, what are some good
resources books or podcasts orinfluencers that you would
recommend people tune into orread for future learning?
Speaker 2 (32:39):
Yes, I think I also mentioned this in a recent
interview with Mashable aboutthese two brilliant, brilliant
women leaders and pathfinders inthe space.
Then Yejin Choi again, Iapologize, I don't know if I
mispronounced her name, but sheis also an incredible kind of
leader and a pathfinder inreally interesting areas.
I think I first kind of gotfamiliarized with her research
(33:00):
in the context of moraldatabases and mapping for AI
models, which was reallyinteresting in the context of
what's happening in the AI spacegoing forward.
So I definitely recommend thosetwo amazing folks.
And then there's also SebastianBraschka, who does an amazing
set of technical resources.
I'm still trying to find timeto be able to go through them
(33:20):
all, right.
So that's kind of anotherfantastic, brilliant kind of
engineer in mind to follow.
There is also another set ofresources I'm forgetting the
name of the professor whopioneered them, but it's called
AI by Hand and I follow theprofessor and the researcher who
does those implementations onLinkedIn.
Really a fantastic deep divedown into, you know, the
(33:41):
technical mechanisms of AI whichare, you know, he makes them
super fascinating, superdigestible to understand, right,
and it's always kind of a joyto even browse through it if you
don't have enough time right.
So definitely those are thebest resources to kind of follow
.
Again, they're kind of supertechnical.
If you're looking for more highlevel ones, I definitely think
that you know some of theseorganizations that are doing
(34:04):
some good AI research that youknow we are interested in, like
maybe Microsoft or Google, oryou know OpenAI right.
Their blogs are reallyinteresting ways to kind of keep
subscribed to the latest andgreatest right.
Again, depending on whether youknow you like their
implementations, you know you'reinterested in their toolkits
and technologies, that's anotherreally great resource the
technical blogs there.
Speaker 1 (34:24):
Do you teach as well?
Speaker 2 (34:28):
Yes, I do teach.
I'm super passionate aboutteaching.
I'm actually in the middle ofcreating a course right now on
interview prep for data scienceand machine learning, but I've
taught eight courses so far inthe past two years.
It's been quite a journey, butI absolutely love instruction.
Speaker 1 (34:41):
Okay, well, of all those people that you listed, I
want to make sure that you and Iconnect afterwards and I get
that list and I'll include it inthe show notes and then, when
you publish your your newestlesson, I want to make sure I
include that, because thatsounds fascinating.
Speaker 2 (34:55):
Thank you, mary, absolutely.
Speaker 1 (34:57):
So how do you unplug from work and I guess, reboot or
recharge yourself?
Speaker 2 (35:04):
Yeah, I mean I love to hike, especially it's it's
kind of beautiful this time ofyear in Arizona, so, um, lots of
I.
I'm motivating myself to domore hiking right, and to get
more exercise.
I also love swimming Uh, it'smy favorite sport out of
everything that you know and andespecially, it's just so
wonderful to kind of cool downand to relax, but also gives you
(35:26):
a great workout.
In addition, reading is reallyfantastic.
I think now the my kind oflatest interest has been finding
these older books and novelsthat are kind of, you know,
1960s, 1970s, right, and thenjust reading them through if you
can find them in your library,right, because you know it's so
(35:47):
interesting to kind of see howthe same concepts that are
discussed in those books canstill apply today.
I think it's just sofascinating, um, with, without
you know, zero changes, honestly, um, in terms of the transfer,
the concept, so it's reallyfascinating.
And books on psychology andother really cool stuff.
Um, and games, of course, loveto play games with, uh, with
friends and um, digital,physical, it doesn't really
(36:08):
matter.
Speaker 1 (36:10):
That's awesome, okay, so what does to be bolder mean
to you?
Speaker 2 (36:15):
To be bolder means being able to understand and
create, I think, a valuedefinition for yourself and I
know that sounds a little bitcorporate-y, but the general
idea that you kind of have valueinherently in whatever you do.
You kind of bring that to thestatement whether you're tired
or you're unenergetic or you'reat your best and you're thriving
(36:37):
, you inherently have value,regardless of everything that's
happening in your life andseeing how that kind of grows as
you experience differentexperiences and circumstances
and you know whether it takes asetback or a step forward, kind
of seeing how that value growsand nurturing it.
I think that's what it means tobe bolder, which is really
seeing that value in ourselvesinherently.
Speaker 1 (37:00):
I love that unique perspective.
Where do you see yourself infive years, say, or 10 years?
Speaker 2 (37:06):
It's a great question .
Probably a CEO of amultinational corporation is the
goal, yeah, and I'm counting onmy network and my community to
keep me accountable to it.
And you know I've got a lot tolearn to get to that point.
I think a lot on leadership andyou know, again, confidence
right and stepping into spaces,but I'm always open to
mentorship and to advice and toopportunity.
(37:27):
So, yeah, I'm going to shootfor the moon and see where it
takes me.
Speaker 1 (37:30):
Well, I'll buy stock in that company for sure when
you're leading it.
Thank you, that's amazing.
Okay, before I let you go, justsome quick, rapid fire fun
questions so people can get toknow you.
And I know this interview hasbeen not focused on all the
wonderful things and deep divesthat we could take in AI, but I
(37:54):
wanted to make sure that I hadyou on our show just to talk
about careers, because I thinkthat's such a good opportunity
and you really are inspiring toso many women in STEM and I just
I think, if you don't hear itenough, thank you for that, okay
, so pizza or pasta, a pasta,dogs or cats, cats, Summer or
winter.
Speaker 2 (38:14):
Winter.
Speaker 1 (38:15):
Ice cream or cake.
Speaker 2 (38:17):
Cake.
Speaker 1 (38:18):
Comedy or drama.
Speaker 2 (38:20):
Comedy.
Speaker 1 (38:21):
All right, I'll let you go, but before I do, thank
you so much for being here andsharing your story with us.
Speaker 2 (38:27):
Absolutely.
Thank you so much for having me, Mary.
Speaker 1 (38:33):
Thanks for listening to the episode today.
It was really fun chatting withmy guest.
If you liked our show, pleaselike it and share it with your
friends.
If you want to learn what we'reup to, please go check out our
website at 2BBouldercom.
That's the number two, little b, bouldercom.
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On Purpose with Jay Shetty
I’m Jay Shetty host of On Purpose the worlds #1 Mental Health podcast and I’m so grateful you found us. I started this podcast 5 years ago to invite you into conversations and workshops that are designed to help make you happier, healthier and more healed. I believe that when you (yes you) feel seen, heard and understood you’re able to deal with relationship struggles, work challenges and life’s ups and downs with more ease and grace. I interview experts, celebrities, thought leaders and athletes so that we can grow our mindset, build better habits and uncover a side of them we’ve never seen before. New episodes every Monday and Friday. Your support means the world to me and I don’t take it for granted — click the follow button and leave a review to help us spread the love with On Purpose. I can’t wait for you to listen to your first or 500th episode!
Crime Junkie
Does hearing about a true crime case always leave you scouring the internet for the truth behind the story? Dive into your next mystery with Crime Junkie. Every Monday, join your host Ashley Flowers as she unravels all the details of infamous and underreported true crime cases with her best friend Brit Prawat. From cold cases to missing persons and heroes in our community who seek justice, Crime Junkie is your destination for theories and stories you won’t hear anywhere else. Whether you're a seasoned true crime enthusiast or new to the genre, you'll find yourself on the edge of your seat awaiting a new episode every Monday. If you can never get enough true crime... Congratulations, you’ve found your people. Follow to join a community of Crime Junkies! Crime Junkie is presented by audiochuck Media Company.
Ridiculous History
History is beautiful, brutal and, often, ridiculous. Join Ben Bowlin and Noel Brown as they dive into some of the weirdest stories from across the span of human civilization in Ridiculous History, a podcast by iHeartRadio.