From Childhood Linux to Red Hat
Key Points
- Mo’s first encounter with Linux came when his brother bought Red Hat Linux for a college assignment, sparking his interest in customizing and modifying software.
- He was drawn to the collaborative, open‑source community that let anyone contribute ideas and improvements, giving users a sense of empowerment rather than powerlessness.
- Concerned that open‑source tools often lack user‑friendly interfaces, Mo pursued a dual degree in computer science and electronic media followed by a master’s in human‑computer interaction to make free software more accessible.
- After completing his graduate program, he joined Red Hat to learn how the company operates and to help bridge the gap between powerful open‑source technology and intuitive user experiences.
- His ongoing motivation is the vibrant community culture at Red Hat, where shared knowledge and collective problem‑solving create a supportive environment for developers and users alike.
Full Transcript
# From Childhood Linux to Red Hat **Source:** [https://www.youtube.com/watch?v=SkXgG6ksKTA](https://www.youtube.com/watch?v=SkXgG6ksKTA) **Duration:** 00:34:11 ## Summary - Mo’s first encounter with Linux came when his brother bought Red Hat Linux for a college assignment, sparking his interest in customizing and modifying software. - He was drawn to the collaborative, open‑source community that let anyone contribute ideas and improvements, giving users a sense of empowerment rather than powerlessness. - Concerned that open‑source tools often lack user‑friendly interfaces, Mo pursued a dual degree in computer science and electronic media followed by a master’s in human‑computer interaction to make free software more accessible. - After completing his graduate program, he joined Red Hat to learn how the company operates and to help bridge the gap between powerful open‑source technology and intuitive user experiences. - His ongoing motivation is the vibrant community culture at Red Hat, where shared knowledge and collective problem‑solving create a supportive environment for developers and users alike. ## Sections - [00:00:00](https://www.youtube.com/watch?v=SkXgG6ksKTA&t=0s) **From Queens to Red Hat** - Mo recounts his New York upbringing and how his brother’s purchase of Red Hat Linux sparked his passion for customizing code and joining the open‑source community. ## Full Transcript
Mo thank you for joining me today thank
you so much for having you have um just
about the most Irish name ever I do very
proud you you weren't born in Ireland no
my grandparents oh your grandparents oh
I see where did you grow up New York
Queens oh you're oh see so tell me a
little bit about how how you got to Red
Hat what was your path when I was in
high school it was a chatty girl teenage
girl on the phone we had one phone line
my old older brother was studying at the
local State College computer science and
he had to tell net in to compile his
homework one phone line and I'm on it
all the time he got very frustrated and
he needed a compiler to do his homework
so he bought Red Hat Linux from Comp USA
brought it home and that was on the
family computer so I learned Linux and I
started playing around with it I really
liked it because you could customize
everything like the entire user
interface you could actually modify the
code of the programs you were using to
do what you wanted and for me it was
really cool cuz especially when you're a
kid and like people tell you this is the
way things are and you just have to deal
with it it's nice to be like I'm going
to make things the way I want modify the
code you playing yeah it was amazing and
it was just such a time and like before
it was cool I was doing it and what I
saw in that as sort of the potential
like number one of like a community of
people working together and like the
internet existed it was slow it involved
modems but there were people that you
could talk to who would give you tips
and you share information and this
collaborative building something
together is really something special
right I could file a complaint to
whatever large software company made
whatever software I was into or I could
go to an open source software community
and be like hey guys I think we should
do this and like yeah okay I'll help
I'll pitch in so you don't feel
powerless you feel like you can have an
impact and that was really exciting to
me however open source software has a
reputation for not having the best user
interface not the best user experience
so so I ended up studying computer
science and electronic media dual major
and then I did human computer
interaction as my masters and my thought
was wouldn't it be nice if this free
software accessible to anybody if it was
easier to use so more people could use
it and take advantage of it and so long
story short I uh ended up going to Red
Hat saying hey I want to learn how you
guys work let me Ed in your team draft
out of my graduate program and I'm like
I want to do this for a living this is
cooler so I thought this is the way to
go and I've been there ever since they
haven't been able to get rid of
me to backtrack just a little bit you
were talking about the sense of
community that surrounds this way of
thinking about software talk a little
bit more about what that Community is
like the benefits of that Community why
it appeals to you sure well and you know
part of the reason I actually ended up
going to The Graduate School track
suddenly you're a peer of your
professors and you're working side by
side with them mhm at some point they
retire and you're the Next Generation so
it's sharing information building on the
work of others in sort of this cycle
that extends past human lifespan and in
the same way like the open source model
is very similar but you're actually
you're building something and it's
something in me I'm just really
attracted like I don't like talking
about stuff I like doing stuff with open
source software the software doesn't
cost anything the code is out there
generally uses Open Standards for the
file formats I can open up files that I
created in open source tools as a high
school student today mhm cuz they were
using open format and that software
still exists I can still compile the
code and it's an active Community
project like these things can Outlast
any single company in the same way that
the academic Community has been going on
for so many years and hopefully will
continue moving on so was sort of like
not just the community around it but
just the knowledge sharing and also
bringing up the Next Generation as well
like all of that stuff really appealed
to me and also at the center of it the
fact that we could democratize it by
following this Open Source process and
feel like we have some control we're not
at the mercy of some faceless
Corporation making changes and we have
no impact like that really appealed to
me too yeah for those of us who are not
software efficient AOS take a step
backwards and give me a kind of
description of terms what's the opposite
of Open Source proprietary proprietary
is what we say so so specifically and
practically the difference would be what
between something that was open source
and something that was proprietary sure
so there's a lot of difference so with
open- Source software you get these
rights when you're given the software
you get the right to be able to share it
and depending on the different licenses
that are considered open source have
different little things that you have to
be aware of with proprietary code it's
100% copyright the company even a lot of
times when you sign your employment
contract for a software company you
write code for them you don't own it you
sign over your rights to the company so
if you leave the company the code
doesn't go with you it stays in the
ownership of that company so then when
like one company buys out another and
kills a product that code's gone it's
gone for a business why would a business
want to be have open source code as
opposed to proprietary well for the same
reasons like say you're a business
you've invested all this money into this
software platform right and you've
upskilled your employees on it and it's
a core part of business and then few
years later that company goes out of
business or something happens or even
something less drastic you really need
this feature but for the company that
makes the software it's not in their
best interest it's not worth the the
investment they're not going to do it
how do you get that feature you either
have to completely migrate to another
solution and this is something it's core
at your business that's going to be a
big deal to migrate but if it's open
source you could either hire a team of
experts you could hire software engine
Engineers who are able to go do this for
you go in the Upstream software
Community implement the feature that you
want and it'll be rolled into the next
version of that company software so even
if that company didn't want to implement
the feature if they did it open source
they would inherit that feature from the
Upstream Community is what we call it so
you have some control over the situation
if it's open source you have an
opportunity to actually affect change in
the product and you could then pick it
up or pay somebody else to pick it up or
another could form and pick it up and
keep it going so there's more
possibilities if it's open source it's
more like it's like an insurance policy
almost so Innovation from the standpoint
of the customer Innovation is a lot
easier when you're working in an open
source environment absolutely yeah so
now at Red Hat you're working with
something called instruct lab tell us a
little bit about what that is so the
thing that really excites me about
getting to work on this project is AI is
sort of the has been the scary thing for
me because it's it's one of those things
like in order to be able to pre-train a
model you have to have unobtanium gpus
you have to have Rich resources it takes
months it takes expertise there's a
small handful of companies that can
build a model from pre-train to the
something usable and it kind of feels
like those early days when I was kind of
delving in software in the same way I
think if more people could contribute to
AI models then it wouldn't be just
influenced by whichever company had the
resources to build it and there's been a
lot of emphasis on pre-training models
so taking massive terabytes data sets
throwing them through masses of gpus
over months of time spending hundreds of
millions of dollars to build a base
model but what instruct lab does is say
okay you have a base model we're going
to fine-tune it on the other end it
takes less compute resources the way
we've built instruct lab you can play
around with the technology and learn it
on an off-the-shelf laptop that you can
actually buy so in this way we're
enabling a much broader set of people to
play with AI to contribute it to modify
it and I'll tell you one story from Red
Hat sui who is our chief diversity
officer very interested in inclusive
language and open source software
doesn't have any experience with AI we
have a community model that we have an
upstream project around for people to
contribute Knowledge and Skills to the
model she's like I want to teach the
model how to use inclusive language like
replace this word with this word or this
word with this word I'm like oh that's
so cool so she paired up with Nicholas
who is a technical guy at red hat and
they built and submitted a skill to the
model that you can just tell the model
can you please take this document and
translate this language to more
inclusive language and it will do it and
they submitted it to the community they
were so proud it was like that's the
kind of thing that like you know maybe a
company would be incentivized to do that
but if you have some tooling that's open
source and something that anybody could
access then those communities could
actually get together and build that
knowledge into AI models just so I
understand what you guys have is the
structure for an AI system and in other
cases individual companies own and train
their own AI systems it takes enormous
amount of resources they Hoover up all
kinds of information train it according
to their own hidden set of rules and
then a customer might
use that for some price what you're
saying is in the same way that we
democratized the writing of software
before let's democratize the training of
an AI system so anyone can contribute
here and teach the model the things that
they're interested in teaching the model
I'm guessing you correct me on the one
hand this model at least in the
beginning is going to have a lot fewer
resources available to it but on the
other hand it's going to have a much
more diverse set of inputs that's right
and the other thing is that IBM
basically as part of this project has
something called the granite model
family and they've donated some Granite
models so these are the ones that take
the months and terabytes of data and all
the gpus to train so IBM has created one
of those and they have listed out and
link to the data sets that they used and
they talk about the relative proportions
they used when pre-training so it's not
just a black box you know where the data
came from which is a pretty open
position to take that is what we
recommend as the base so you use the
instruct Lab Tuning you take this Bas
Granite model that IBM has provided and
you use the instruct lab tooling that
Red Hat Works on and you use that to
fine-tune the model to make it whatever
you want what I want to go back for a to
the partnership between IBM and red hat
here with them providing the granite
model to your instruct lab is this the
first time red hat and IBM have
collaborated like this I think it's
something that's been going on like
another a product within the red hat
family would be open shift AI where they
collaborate a lot with IBM research team
like BLM is one of the components of
that product that there's a nice kind of
exchange and collaboration between the
two companies yeah how large is the
potential community of people who might
contribute to instruct lab it it could
be thousands of people I mean we'll see
it's early days this is early technology
that was invented at IBM research that
they partnered with us at Red to kind of
build the software around it there's
still more to go like right now we have
a team in the community that's actually
trying to build a web interface to make
it easier for anybody to contribute so
we have a lot of those sort of user
experience for the contributor to the
model stuff to work out that we're still
actively building on but like my vision
for it even is I like going back to that
academic model of learning from what
others and building upon it over time it
would be very good for us to sort of go
out and try to collaborate with
academics of all Fields like hey you
know the model doesn't know about your
field would you like to put something
into the model about your field so it
knows about it or even you know talk to
the model it got it wrong let's correct
it can we lean on your expertise to
correct it and make sure it gets it
right and sort of use that Community
model as a way for everybody to
collaborate because before instruct lab
my understanding is if you wanted to
like take a model that's open source
licensed and with it you could do that
you could take a model kind of off the
shelf from hugging face and fine-tune it
yourself but it's a bit of a dead end
because you made your contributions but
there's no way for other people to
collaborate with you so the way that
we've built this is based on how the
technology Works everybody can
contribute to it this is something that
you can keep growing and growing and
growing over time yeah yeah what's the
level of expertise necessary to be a
contributor you don't need to be a data
scientist and you don't need to have
exotic Hardware where honestly if you
don't even have laptop Hardware that
meets the spec for doing instruct lab's
laptop version you can submit it to the
community and then we'll actually build
it for you we have Bots and stuff that
do that and we're hoping over time to
make that more accessible first by
having a user interface and then maybe
later on having a web service yeah so
give me an example of how a business
might make use of instruct lab one of
the things that businesses are doing
with AI right now is using hosted API
services are quite expensive
but they're finding value but it's hard
given the amount of money they're
spending and one of the things that's a
little scary about it too is like you
have very sensitive internal documents
and you have employees maybe not
understanding what they're actually
doing CU you know how would you if
you're not technical enough when you're
asking said public web service AI model
information about you're copy pasting
internal company documents it's going
across the internet into another
company's hands and that company
probably shouldn't have access to that
so what both redhead and IBM in the
space are looking at like the instruct
lab model it's very modest it's 7
billion parameter model very small it's
very cheap to serve inference on a 7
billion parameter model it's competing
with trillion parameter models that are
hosted you take this small model that is
cheap to run inference on you train it
with your own company's proprietary data
inside the walls of your company on your
own Hardware you can do all sorts of
actual data analysis on your most
sensitive data and have the confidence
that it's not left the premises in that
use case you're not actually training
the model for everyone you're just
taking it and doing some private stuff
on it exactly which doesn't leave the
building but that's separate from an
interaction where you're doing something
that contributes overall right and
that's that's something maybe that I I
should be more clear about is there's
sort of two tracks here and this is very
red hat classic you have your Upstream
Community track and you have your
business product track so the Upstream
Community track is just enabling anybody
to contribute to a model in a
collaborative way and play with it the
downstream product business oriented
track is now take that Tech that we've
honed and developed in the open
community and apply it to your business
Knowledge and Skills Let's do an
imaginary case study sure I'm a law firm
I'm an entertainment law I have 100
clients who are big stars they all have
incredibly complicated contracts I feed
a thousand of my company's contracts
from the last 10 years into the model
and then every time I have a new
contract I ask the model am I missing
something can you go back and look
through all our own contracts and show
me a contract that is missing key
components or exposes us to some
liability there in that case the the
model would know my Law Firm contracts
really really well it's as if they've
been working at my Law Firm they're not
distracted by other people's particular
Styles or or a bunch of contracts you
know from the utility industry or the
they know entertainment law contracts
exactly yeah and you can train it in
your own image your style of doing
things it's something that your company
can produce that is uniquely helpful to
you no third party could do that because
no third party understands how you do
business business and understands your
history and your documents so it's sort
of a way of getting value out of the
stuff you already have sitting in a file
cabinet somewhere it's it's very cool
yeah give me any sort of a a real world
case study where you think the business
use case would be really powerful what's
a business that really could see an
advantage to using uh instruct lab in
this way the the demo that I've given a
couple times at different events used an
imaginary insurance company so you say
you have this company you have to
recommend repairs for various types of
claims you've been doing this for years
you know if you know the the
windshield's broken and you gotten this
type of accident and it's this model car
these are the kinds of things you want
to look at so you could talk to any
insurance agent in the field and be like
oh you know it's a it's a Tesla you
might want to look at the battery or
something like they'll have some latent
knowledge just so you can take that and
train it into them model honestly I
think these kind of new technologies are
better when they're less visible so say
you have the claims agents in the field
and they have this tool and they're kind
of entering the claim data they're
they're on the scene at the car and it
might say oh look I see this is a Ford
Fiesta these are things you want to look
at for this type of accident as you're
entering the data it could be going
through the knowledge you had loaded
into the model and be making these
suggestions based on your company's
background and hey you know let's not
make the same mistake twice let's make
new mistakes and let's learn from the
stuff we already did yeah so that's one
example but there's so many different
Industries and ways that this could help
and it could make those agents in the
field more efficient have you had anyone
talk to you about using instruct lab in
a way that surprised
you I
mean some people have done funky things
but sort of playing with the skills
stuff that's where I see a lot of
creativity the difference between
Knowledge and Skills is that knowledge
is pretty pretty understandable right
like oh historical insurance claims or
you know legal contracts skills are a
little different so whenever somebody
submits a skill sometimes it tends to be
really creative because it's not
something that's super intuitive
somebody submitted a skill I don't know
how well it worked but it was like
making asy art like draw me a I don't
know draw me a dog and it do like an
aski art dog I mean it's stuff that you
can do programmatically one that was
actually very very helpful was you know
take this table of data and convert to
this format like oh that's nice that
actually saves me time how far away are
we from the day when I Malcolm glob well
technology ignoramus can go home and
easily interact with instruct lab maybe
a few months few months I thought you
going to say a few years no I think it'
be a few months wow I hope it's power
open source Innovation yeah oh that's
really interesting yeah I I'm always
taken by surprise I'm still thinking in
20th century terms about how long things
take and you're in the 22nd century as
far as I can tell honestly the instruct
Lab Core invention was invented in a
hotel room at an AI conference in
December with an amazing group of IBM
research guys December of 2023 wait back
up you have to tell the story this group
of guys we've been working with they
they were at this conference together
and it's a really funny story because
you know it's hard to get access to gpus
and like even you know you're at IBM and
it's hard to get access everybody wants
access they did it over Christmas break
because nobody was using the cluster at
the time and they ran all of these
experiments and I'm like whoa this is
really cool and um wa and their idea was
we can do a strip down AI
model and was the idea and even back
then combine it with granite what was
the core the original idea the original
idea it's sort of multi there's like
multiple aspects to it so like one of
the aspects actually came on later but
it starts at the beginning of the
workflow is you're using a taxonomy M to
organize how you're fine-tuning the
model so the old approach they call it
the blender approach you just take a
bunch of data of roughly the type of
data that you'd like and you kind of
throw it in and then see what comes out
don't like it okay throw in more try
again see what comes out they had used
this taxonomy technique so you actually
build like a taxonomy of like categories
and subfolders of like this is the
Knowledge and Skills that we want to
train into the model and that way you're
sort of systematic about what you're
adding and you can also identify gaps
pretty easily oh I don't have a category
for that let me add that so that's like
one of the the parts of the invention
here Point number one is Let's Be
intentional and deliberate in how we
build and chain this thing yeah and then
the next component would be okay so it's
actually quite expensive part of the
expense of like tuning models and just
training models in general is coming up
with the data and what they wanted to do
is have a technique where you could have
just a little bit of data and expand it
with something they're calling synthetic
data generation and this is where it's
sort of like you have this student and
teacher workflow so you have your
taxonomy the taxonomy has sort of the
knowledge like a business's knowledge
documents their insurance claims and it
has these quizzes that you write and
that's to teach the model so I'm writing
a quiz based just like you do in school
you read the chapter on the American
Revolution and then you answer a 10
question quiz or you're giving the model
quiz you need at least five questions
and answers and the answers need to be
taken from the context of the document
and then you run it through a process
called synthetic data generation and it
looks at the document so it'll look at
the history chapter it'll look at the
questions and answers and then it'll
look to that original document and come
up with more questions and answers based
on the format of the questions and
answers you made so you can take take
five questions of answers amplify them
into 100 questions and answers 200
questions and answers and it's a second
model that is making the questions and
answers so it's synthetic data
generation using an AI model to make the
questions we use an open source model to
do that so that's the second part and
then the third part is we have a
multiphase tuning technique to actually
take the synthetic data and then
basically bake it into the model so sort
of that's the approach a general
philosophy the approach is using Granite
because we know where the data came from
another approach is the fact that we're
using small models that are cheap to run
inference on they're small enough that
you can tune them on laptop Hardware you
don't need all the fancy expensive GPU
Mania you're good so sort of like a
whole system it's like not any one
component but it's sort of the approach
they took was somewhat novel and they
were very excited when they saw the
experimental results there was a meeting
between red hat and IBM it was actually
an IBM research meeting that red Hatter
from invited to and I think the the red
Hatters invol sort of saw the potential
whoo we can make models open source
finally rather than them just being
these endless dead Forks we can make it
so people could contribute back and
collaborate around it so that's when Red
Hat became interested in it and we sort
of worked
together and the research Engineers from
IBM research who came up with the
technique and then my team the software
Engineers who know how to take research
code and productize it into actually
runnable supportable software kind of
got together we've been hanging out in
the Boston office at red hat and
building it out April 18th was when we
went open source and we made all of our
repositories with all of the code public
and right now we're working towards a
product release or supported product how
long did it take you to be convinced of
the value of this idea I mean so people
get together in this hotel room they're
running these experiments over Christmas
are you aware of the experiments is they
running them when did they no I didn't
find out till February you find out
February so they come to you in February
and they say mo can you recreate that
conversation
well our CEO Matt Hicks and then Jeremy
eer who's one of our distinguished
engineers and Steve watt who's a VP were
present I think at that meeting so they
kind of brought it back to us and said
listen we've invited these IBM research
folks to come visit in in Boston you
know work with them like see does this
have any Merit could we build something
from it and so they gave us some
presentations we were very excited when
they came to us it only had support for
Mac laptops of course you know Red Hat
we're Linux people so we're like all
right we got to fix that so A bunch of
the junior Engineers around the office
kind of came in they're like okay we're
going to build Linux support and they
had it within like a couple days it was
crazy cuz this was just meant to be hey
guys you know what these are invited
guests visiting our office see what
happens we end up doing like weeks of
hack fests and late night pizzas in the
conference room and like playing around
with it and learning and it was it was
very fun very cool do anyone else doing
anything like this is not my
understanding that anybody else is doing
it yet maybe others will try a lot of
the focus has been on that pre-training
phase mhm but for us again that
fine-tuning it's more accessible cuz you
don't need all the Exotic Hardware it
doesn't take months you can do it on a
laptop you can do a smoke test version
of it less than an hour what does the
word smoke test me smoke test means
you're not doing a full fine tuning on
the model it's a different tuning
process it's like kind of lower quality
so it'll run on lower grade Hardware so
you can kind of see hm did it move the
model or not but it's not going to give
you like the full picture you need
higher-end Hardware to actually do the
full thing so that's what the product
will enable you to do once it's launched
is you're going to need the gpus but
when you have them we'll help you make
the best usage of them yeah yeah and
there's a little detail I want to go
back to Sure in order to run the tests
on this idea way back when they
needed time on the gpus so this is this
would be the in-house IBM and they were
quiet at Christmas so how much time
would you need on the gpus to kind of
get proof of concept well what happens
is and it's it's it's sort of like a lot
of trial and error right and there's a
lot about this stuff that like you come
up with a hypothesis you test it out did
it work or not okay it's just like you
know in the lab with you know buns and
burners and beers and whatever so it it
really depends but it it can be hours it
can be days it really depends on what
they're trying to do and then sometimes
they can cut the time down you know with
the number of gpus you have so like I
have a cluster of 8 gpus okay it might
take a day but then if I can get 32 I
can pipeline it and make it go faster
and get it down to a few hours so it
really depends you know but it's like
everybody's home for the holidays it's a
lovely playground to to get that stuff
going fast yeah let's jump forward one
year tell me the status of this project
tell me who's using it tell me how big
is it give me
your optimistic but
plausible prediction about what instruct
lab looks like a year from now a year
from now I would like to see kind of a
Vibrant Community
around not just building knowledge
skills into a model but coming up with
better techniques and Innovation around
how we do it so I'd like to see like the
contributor experience as we grow more
and more contributors to be refined so
like a year from now Malcolm Gladwell
could come impart some of his wisdom
into the model and it wouldn't be
difficult it wouldn't be a big lift I
would love to see the user interface
tooling for doing that to be more
sophisticated I would love to see more
people taking this and even using it
maybe you're not sharing it with the
community but you're using it for some
private usage like I I'll give you an
example I'm in contact with a fellow who
is doing AI research and he's working
with doctors there are GPS in an area of
Canada where there's not enough GPS for
the number of patients so you know
anything you can do to save doctors time
to get to the next patient like one of
the things that he has been doing
experiments with is can we use an
open-source licensed model that the
doctor can run on their laptop so they
don't have to worry about all of the
different privacy rules like it's
private it's on the laptop right there
take his live transcription of his
conversation with the patient and then
convert it automatically to a soap
format that can be entered in the
database typically this will take a
doctor 15 to 20 minutes of
paperwork why not save him the paperwork
at least have the model take a stab and
does the model then hold on to that
information and he can inter he
interacts with the model again when well
that's the thing not with instruct lab
maybe that could be a future development
it doesn't once you're doing inference
it's not ingesting that what you're
saying to it back in it's only the fine
tuning phase so the idea would be the
doctor could maybe load in past patient
data as knowledge and then when he's
trying to diagnose maybe you know what
I'm saying like but the the main idea is
somebody might have some private usage I
would love to see more usage of this
tool to enable people who otherwise
never would have had access to this type
of Technology who never like you know a
small country Je GP doctors it doesn't
have gpus they're not going to hire some
company to custom build them a model but
maybe on the weekend if he's a techie
guy he could play with interesting mod I
mean the more you talk the more I'm
realizing that the Simplicity of this
model is the killer app here once you
know you can run it on a laptop you have
democratized use in a way that's
inconceivable with some of these other
much more complex but that's interesting
because one would have thought
intuitively that at the beginning that
the winner is going to be the one with
the biggest most complex version and
you're saying actually no there's a
whole series of uses where being lean
and focused focused is actually you know
it enables a whole class of uses maybe
another way of saying this is who
wouldn't be a potential instruct Lab
customer we don't know yet it's it's so
new like we haven't really had enough
people experimenting and playing with it
and out all the things yet but that's
that's the thing that's so exciting
about it is like I can't wait to see
what people do is this the most exciting
thing you've worked done in your career
I think so I think so well we are
reaching the end of our time but before
we finish we're going to do a little
speed round sure all right complete the
following sentence in five years AI will
be
boring it will be integrated it'll just
work and there will be no now with AI
thing it'll noral it'll be what's the
number one thing that people
misunderstand about AI it's just Matrix
algebra it's just numbers it's not
sensient it's not coming to take us over
it's just numbers you're on this side of
you're on the uh Team Humanity yeah
you're on team un good what advice would
you give yourself 10 years ago to better
prepare for today Learn Python for real
it's a programming language that is
extensively used in the community I've
always in it but I wish I had taken it
more seriously yeah did you say you had
a daughter I have three daughters you
have three daughters I have two you're
if you got three you're you're you're on
your
own are you making them study
python I am actually trying to do that
the we're using a microbit
microcontroller tool to do like a custom
video game controller they prefer
scratch because it's a visual
programming language but it has a python
interface too and I'm like pushing them
towards python good um
chat box and image generators are the
biggest things to Consumer AI right now
what do you think is the next big
business
application private models small models
fine-tuned on your company's data for
you to use exclusively are you using AI
in your own personal life these days
honestly I think a lot of us are using
it and we don't even realize it yeah I
mean I'm a ficio of foreign languages
there's translation programs that are
built using machine learning learning
underneath one of the things I've been
dabbling with lately is using Tex
summarizations because I tend to be very
loquacious in my note taking and that is
not so useful for other people who would
just like a paragraph so that's
something I've been experimenting with
myself just to help my everyday work
yeah we hear many definitions of open
related to technology what's your
definition of open and how does it help
you innovate my definition of open is
basically sharing and being vulnerable
like not just sharing in a have a cookie
way but in a you know what I don't
actually know how this works could you
help me and being open to being wrong
being open to somebody helping you and
making that collaboration work so it's
not just about like the artifact you're
opening it's your approach like how you
do things being open yeah yeah Mo I
think that wraps us up how can listeners
follow your work and learn more about
granite and instruct lab sure you can
visit our project web page at instruct
lab . or you can visit our GitHub at
github.com instruct laab we have lots of
instructions on how to get involved in
instruct lab wonderful thank you so much
thank you malcome