Llama 3.2 Sparks Open‑Source Revolution
Key Points
- The panel debated whether an open‑source AI model will surpass all proprietary offerings by 2025, with most guests confidently predicting a “yes.”
- A major highlight was the launch of LLaMA 3.2, Meta’s newest open‑source model family that spans from 1 billion‑parameter lightweight versions up to much larger variants.
- LLaMA 3.2 introduces three key advances: ultra‑light models tailored for IoT and edge use cases, integrated multimodal vision capabilities for tasks like image captioning, and expanded support for diverse deployment scenarios.
- Throughout the discussion, the hosts emphasized the tension between AI’s growing computational demands and sustainability concerns, underscoring the importance of efficient, open‑source solutions.
Full Transcript
# Llama 3.2 Sparks Open‑Source Revolution **Source:** [https://www.youtube.com/watch?v=FnO6TD9LtPY](https://www.youtube.com/watch?v=FnO6TD9LtPY) **Duration:** 00:33:50 ## Summary - The panel debated whether an open‑source AI model will surpass all proprietary offerings by 2025, with most guests confidently predicting a “yes.” - A major highlight was the launch of LLaMA 3.2, Meta’s newest open‑source model family that spans from 1 billion‑parameter lightweight versions up to much larger variants. - LLaMA 3.2 introduces three key advances: ultra‑light models tailored for IoT and edge use cases, integrated multimodal vision capabilities for tasks like image captioning, and expanded support for diverse deployment scenarios. - Throughout the discussion, the hosts emphasized the tension between AI’s growing computational demands and sustainability concerns, underscoring the importance of efficient, open‑source solutions. ## Sections - [00:00:00](https://www.youtube.com/watch?v=FnO6TD9LtPY&t=0s) **Open‑Source AI Supremacy Forecast 2025** - A panel debates whether an open‑source model will outclass all proprietary AI systems by 2025, weighing predictions, sustainability, and energy trade‑offs. ## Full Transcript
what comes next in open source if you
just combine this recipe and map it to
other models I'm expecting a lot of very
powerful models because ai's prediction
it's just pretty limited right I guess I
might take a bit of issue where AI is
fundamentally about prediction why
exactly are people so excited about the
use of AI in sustainable development so
you can see how people are are trying to
Wrangle how do I balance the computer
that's needed versus how do you how do
you look at the energy consumption all
that and more on today's episode of
mixture of
experts I'm Tim Hong and I'm exhausted
it's been another crazy week of news in
artificial intelligence but we are
joined today as we are every Friday by a
worldclass panel of people to help us
all sort it out Mario m is director of
product management at Watson X AI sharne
is senior partner Consulting on AI for
US Canada and Latin America and Skyler
Speakman is a senior research
[Music]
scientist so the way we're going to
begin is what we've been doing for the
last few episodes I think it's just a
fun way to get started is to ask each of
you a simple round the horn question for
all the listeners uh the guests have not
been prepped as to what this question
will be so you'll be hearing their
unvarnished instinctual response to a
really difficult question so here's the
question in 2025 a near a few months
from now will there be an open- Source
model that is absolutely better than any
proprietary model on the market show bit
yes or no it'll get
close okay
Skyler I'm sorry what no uh yes there
will
be great and Mariam what do you think
and big yes okay whoa all right nice um
very exciting well that's actually the
lead in for our first segment today one
of the big announcements of course is
the release of llama 3.2
um if you've been following the news or
been living under a rock llama is the uh
sort of best-in-class Open Source model
uh that meta has been really helping to
kind of um advance in the marketplace um
and their release uh just earlier this
week featured a large range of different
models small ones big ones um and Mariam
I understand you were involved actually
in the release um do you want to tell us
a little bit about kind of your
experiences and how that was yes it's
just so exciting to be part of that
market moment on the first day when the
models are released to the market it's
available on the
platform excitement just just it's just
amazing yeah yeah I think from the
outside one thing I think itd be helpful
for our listeners to learn a little bit
more about is what's different with 3.2
release um you know is it just more open
source uh what should we be paying
attention to well there are really three
things that they released U with 3.2 the
first one is lightweight unlocking all
the iot and age use cases with the
release of llama um three billion and 1
billion the second thing was the multi-
model Vision support it's Imaging TT out
you can think of uh unlocking use cases
like image captioning chart
interpretation uh visual Q&A on the
images and the beauty of that is the way
that they did it was they separated the
image encoder from the large language
encoder and trained that adopter in a
way that now the model is not changed
comparing to the 3.1 so it can be used
as a dropping replacement for the
3.1 llama 11 billion and uh the um 70
billion variants but the image encoder
that is added to that now is going to
enable the model to process image in and
input out so that's the second thing and
the third thing that released they
released on the model side is the Llama
guard for the vision like the safety of
these multimodal models matters and they
release the Lama guard that is also
available in our platform for the
customers yeah that's awesome so there's
a lot to go through here um I think
maybe to pick up on that first theme uh
show bit I know you know the the drum
you always beat when you come on mixture
of experts is the models are going to
get smaller and it's a good thing um do
you want to talk a little bit about how
this matters for people who are uh
implementing this kind of stuff in the
Enterprise yes so a lot of my clients we
are deploying uh these small language
models on device and quite a few times
it's just because they don't have good
internet access in the factory floor or
people who are running around in the
field things of that nature right so we
have to do a lot of that computation on
device especially if you're looking at
our federal clients or manufacturing and
so on so forth right in those cases for
the last few months I've been super
impressed by the momentum we have had in
this AI space going towards much smaller
more efficient models so in the 1
billion to 2 and a half three billion
parameter space we've seen a influx of a
lot of models so I have been running uh
Google's Gemma Apple's open Elm we've
had Microsoft's 53.5 there' have been
some amazing models have delivered quite
a bit of value U we have from from meta
now the one billion parameter model I
was able to download that just before I
took a flight so I was able to
experiment for the next three hours with
these small models and by the way I was
looking at the meta Connect using our
the Oculus glosses it was a completely
experience being there life so I got I
got a chance to go experiment with these
models there are certain things that we
do for our clients where we add another
layer of some fine-tuning to these
models and the fact that they are small
and I can fine-tune them because they're
open I'm able to deliver much higher
accuracy with a much much smaller
footprint I think that's where you get
gold the return on investment you get
from these small models that you can
then fine tune and then run on device
that opens up a whole lot of use cases
for our clients if you've not been able
to do if you're going and calling an API
call back and forth yeah definitely and
Skyler I guess this kind of response
puts maybe your response to the round
the horn question into context you know
I think I was like are we going to have
an open source model that's better than
the best model in the world I guess kind
of that's not what you think is exciting
about this release right I feel like
you're you're like chomping at the bit
to talk about how great are if if they
had come out with a 500 billion
parameter model that would have been
yeah for me but if they're emphasizing
the three billion and 1 billion
parameter space that gets me so excited
because it's away from the bigger is
better idea and that bigger is better
idea has crowded out other really cool
research problems that probably should
have been worked on while people were
scaling larger and larger and larger so
to see a major player like meta come out
and make some noise about a three
billion 1 billion parameter model I
think that's just some really
outstanding work and in the larger
context it also really shifts
decision makers to not be gated behind
the ones that have access to running a
400 billion parameter model so I I think
that type of that kind of power Dynamic
if if open source is continually getting
these smaller scales I I think that's
just a really good direction so uh yeah
kudos to that about llama coming out and
saying one billion in three parameter
space has is showing uh skills and and
again being able to download right
before you said you hopped on a plane I
mean that type of thing um that's a
really great direction to see these
these types of foundation models going
so there are a couple other things in
this in the space as well the 128k
window the context window that was
pretty surprising to me for such a small
siiz model why is it surprising yeah I
think some folks might not actually have
a familiarity there it's worth I think
for them to hear that subtlety yeah yeah
so the fact is you can put more context
into that into that prom that you're
asking right it's
128,000 tokens I can pass in this
context so if I'm looking at a whole
email thread chain on device I can pass
that in so that kind of a response or or
eventually we'll start to see more
models that can handle images and stuff
too that are this small size currently
the Pix Model 12 billion parameters or
meta 11 billion those are the ones that
are doing images but I'm very hopeful
that soon we'll see more image
capabilities come down to this two three
billion parameter models as well so
doing that on device when you're walking
around taking a picture of uh equipment
and saying what's wrong with this or
what's the meter reading things that
nature I'm I'm super excited as as the
capabilities increase there are a few
things that are lack that uh I would
like to see come out in the future
things like function calling being able
to do like being able to create a plan
and have more agentic flows between
these smaller models I'm very excited
about the future iterations of these
models as well maram when you compare we
have been working on granite models for
a while and we've always has been
focused on small models can you give you
a perspective on the small model size
what are you seeing has a good size like
7 billion to 2 billion what where do you
see the great threshold of performance
and size well it depends on the use case
right if you have an iot or Ed use case
the smaller the better but also the
smaller the better in a case that like
it has impact on the latency is faster
it has impact on the energy consumption
and carbon foodprint generation and it
has impact on cost so if we can get the
performance that we need for from a
smaller model that's that's well suited
for that use case but but the Skyler to
your point what excites me about this
release and the lightweight is the way
that they achieved that lightweight
models like if you look into the paper
of how they did that they grabbed the
Llama 8B and they structurally pruned it
so it's like cutting cutting the network
making it smaller but then they use the
very large general purpose models the
405b that they had as a teacher model
for distill
to to bridge that Gap if you just
combine this
recipe and map it to other models I'm
expecting a lot of very powerful models
coming to the market moving forward just
with a combination of it distillation
and pruning yeah for sure and I think
one of the most interesting things is as
it gets sort of cheaper and cheaper and
more available I think we'll also see
like lots of use cases right like so far
we've been gated by how much investment
you need to put into these models mod
and how expensive they are to run but I
think it's almost like as it becomes
more accessible we'll also just see like
well why not just plug a model in right
like it'll end up being something that
you can apply for all sorts of different
applications that you know we would have
thought it been like ridiculous to do a
few years ago because it would have been
too expensive to even think of doing hey
Mariam just on on the latency part I was
stunned I'm I'm in the flight I have a
one bilon parameter model running it's
giving me 2,000 tokens a second response
that's like 1500 words is generating per
second like that's the I want when I'm
looking at a model on my phone
responding like I I just I became a
Believer when I saw that speed of the
response the lency yeah the vision of
view like on the plane with the goggles
using a model I just like your your seat
neighbor being like who's thisy playing
with LM exactly I'm waiting for the new
Airline documentation that come out that
says please do not run llms on devices
while the plane is in Flight you know
like um so maram I guess before we move
on to our next topic what comes next do
you think like are we going to see more
releases of this kind um is this going
to be the big release for a while like
what should we expect I'm expecting to
see a lot of movement in open source and
open Community listen the future of AI
is open it gives really this openness
drives Innovation and it gives you three
things one making the technology
accessible to a wider audience and when
you open it up to a wider audience it
gives you a chance to stress test your
technology right so we can advance
safety of these models together with the
power of community it gives you an
acceleration on Innovation and
contribution back to building better
models for different use cases so a
combination of accessibility safety
enhancement and acceleration in
Innovation is what I'm expecting to see
in the open community and because of
that we are going to see a lot more
powerful smaller models emerging in the
next six months
[Music]
two researchers Arvin Nan and his
collaborator SAS kapor came out with a
book uh which was called AI snake oil um
and it's basically the book adaptation
of sort of a wildly successful substack
they've been running for a while uh
where they essentially kind of point out
all the places where AI is being
oversold overhyped or being deployed in
ways that are um you know not
necessarily like the best use of the
technology um and what's so fun is Arvin
you know took to the internet to
basically say we're so confident of our
arguments here that we want to put a
bounty out if you think we're wrong on
anything that we're arguing in this book
um tell us right and we can we can put a
bet on it right in two to five years and
there are sort of argument is that like
the kinds of critiques that they're
pointing out about AI systems are things
that don't have to do with like
technological capabilities and have to
do more with like what can we actually
predict in the world so one of the
things they say is you know AI really
can't predict individual life outcomes
or you know the success of cultural
products like books and movies or things
like pandemics right they're kind of
arguing that like prediction can only do
so go so far and AI is ultimately a
prediction machine and so there's
actually like kind of just so far this
technology can go I think I just wanted
to kind of first start there is like I'm
curious if that group sort of buys that
argument like you know do we think that
this prediction thing is just limited in
a certain way and that actually caps
kind of what AI can be used for or
should be used for um I guess Skylar
maybe I'll throw it to you if you got
any responses there I guess I might take
a bit of issue where AI is fundamentally
about
prediction um I think the gains that we
have seen recently on this idea of the
Transformer being used to do the next
token prediction in that sense yes but
because it's able to do that next token
prediction there are so many other use
cases that are not prediction focused so
it is it's this idea about yes we have
to understand what this length of what
this context of data is and underlying
it that transform model does rely on
that prediction but it is so much bigger
than just prediction so I I would really
probably take that issue that um
prediction is very difficult um but the
other Downstream tasks that you can do
after that prediction task is is really
what has probably moved this space
forward so don't get too hung up on the
prediction uh capabilities of a model
yeah I'm I'm be the Skyler on that uh if
you look into traditional ml prediction
was key and all the use cases the
majority of the use cases Enterprise use
cases that we were using traditional ml4
was a reflection of really prediction
but then when it comes to generative AI
the the the the prominent use cases
productivity unlocks that it does which
is a function of content generation code
generation it it can be prediction in a
sense as Skyler said like the next token
but that's I don't think that's the
prediction in the use case as a use case
so for that reason I I I don't 100%
agree that the prediction use case is
the primary use case that AI is designed
to deliver yeah that's actually very
interesting I hadn't really thought
about it like that um this has come up
in some of the episodes we've done
before but you know this is one of the
debates I find most interesting is oh
well at some point machine learning kind
of diverge from computer science because
the way you program a computer is quite
different from the way that you you know
test evaluate and F tuna model you're
almost saying that actually there's even
another distinction could be made which
is basically this sort of like
traditional machine learning if you will
right we almost kind of diverge a little
bit from like the kinds of concerns that
we have in generative AI or whatever you
want to call it but like this kind of
current generation is almost so
different in kind that there's almost
like a different set of problems I don't
know if that's kind of what you both are
chasing after I do think there there is
a Divergence away from classical machine
learning you know uh take all of your
decision trees your regressions all
those pH and then generative AI those
those have diverged and I'm trying to
trying to keep up with it you know
that's my my previous background was in
the classical uh machine learning space
and then man we're we're in for a wild
ride on generative AI so uh Tim being a
podcast let me just quickly recap uh the
book I had uh I had the pleasure of
listening to the audio book on the
flight while I was hacking oh you did
okay you did the homework I was in a
very meta phase because I'm trying to
hack something while I'm listening to
this book on
AI there the two authors are brilliant
there are two of The 100 top influential
people in AI according Time Magazine U
there are five points they make in the
book the first one is around making
they're saying that AI predicts but
doesn't truly understand the context uh
there's the second point is around there
are AI will reinforce our biases in
areas like policy hiring things that
nature uh third one is around you have
got to be spe skeptical about anything
that's blackbox AI solution the point
that Mariam had just made about openness
and that's the future Direction uh then
you had there should be stricter
regulations and accountability
especially when an AI is making an
outcome that could have an adverse
impact elsewhere and uh ethics and
ethics in AI has to be focused on Beyond
just the technical capabilities that we
are making right so none of these are
ground baking statements that uh that
we've not heard before but the very
first one I think that's where Skyler
started was AI is making predictions and
in a lot of cases we expect a intern or
a junior person to make a prediction
look at a pattern and raise their hand
when they see something that's not
working my wife is a physician she spent
14 years in medicine becoming a doctor
right she does critical care lungs and
sleep medicine she has a set of medical
assistants Mas or nurse practitioners
who are helping patients as well she
expects them to raise their their hand
when they see a pattern break here's the
the stats that they've had from all
their tests a patient comes to them and
say hey something looks different here
so all she's asking is recognize the
pattern and call me as an expert I think
that's where we should be with ai ai is
augmenting us we should be very precise
in saying pattern recognition is a good
thing I want AI to do patterns and I
think there's too much of a of a gap
between pattern recognition and getting
to the root cause analysis of being what
caused this that causal modeling
requires years of experience and I think
that's the relationship I would like to
have with our AI be able to find
patterns and raise your hand come to me
for expert advice so I think we're
heading in a good direction the name of
the book is very catchy but I think the
points that they're making are pretty
grounded in what we see in reality today
yeah for sure and I think I think to
pick up on that point I agree I
mean I think that's kind of the dream of
how this technology should be deployed
you know I think part of their worry is
that they feel like the the Market's not
going to provide that right that there
will be a tendency to be like yeah let's
just implement the AI and it will do
everything for us um and I guess maybe a
question i' POS back to the group is
like how do we do a good job fighting
that right because I think sh I want to
live in the world that you're describing
um but I think a lot of people who are
particularly getting used to the
technology or new to the technology
almost have a tendency to kind of apply
it for that causal stuff which is
actually where we kind of want to
preserve the the human role um and so
I'm curious like in people's
conversations with you know friends and
family and others like are there things
that they've done to kind of like you
know help to set level set with the
technology properly I think an example
that has come up with this in our
conversation recently my parents were
both teachers uh Public School teachers
and we were talking about whether AI is
going to replace teaching and uh similar
to the healthcare ideas I would really
like to see AI be very measured in
education because there's a there's a
there's got to be a human connection
there that comes through um and so to to
back off a little bit in into that that
face similar to shit's analogy with the
uh the medical situation about where we
really see these specific roles and I I
think an AI instructor would actually
would be would be terrible I don't want
that I don't wouldn't want that world
but having AI being able to assist
students and assist that interaction
between a human teacher and the students
I think that would be a really cool
example of this where we'd want to pull
back a little bit and not go full
automation uh and and education probably
in health as well I will push back a bit
sker on the whole education piece I
think if you follow Salman Khan doing
Khan Academy Khan
Migo I think the impact he's having
surgically with AI he's figured out a
good blend between teachers students and
where AI becomes a co-pilot for them
right so I think to your point of
creating the human connection 100% my
mom was was teacher as well growing up
and unfortunately she was also the
principal of my school so that did not
go well with me but wait while you were
at when I was at the school so oh my go
unpunished but the fact that you can
understand the nuances today a teacher
is addressing 60 kids in a room and she
has to go talk at the at the same level
for each one of them so you can't adapt
the training to people who have who have
different come from different language
backgrounds as an example right or there
are certain sections in the book that
some people will take longer to
understand some will take short of time
to understand right so I think adapting
uh the teaching curriculum to that
student AI can do a great job you can
take people from MIT great phcs
professors and you can take that course
work and translate that in Canada for
some person in a village in India right
I think that I think a can play a very
positive role and back to what Tim was
saying we need your parents Skyler to
tell us where AI should be augmenting
like taking the same lesson and creating
multiple flash cards and different
adapting that lesson and things of that
nature and there are lots of things that
you can do with AI in that space of
teaching too right so next week my
parents will be on the podcast and uh
we'll they'll uh we should definitely do
a parents episode where it's just
everybody's parents but none of the
usual guest that would be so much fun
from this I've learned I need to joke I
need to check back in with KH Academy I
think the last time I was there they
were YouTube videos so I think maybe
that space is really expanded I need to
go check back into that yeah for sure
it's cool yeah they're doing a lot of
interesting
[Music]
experiments I want to make sure we get
time for the last topic which is a
really broad one um but I think it
connects a bunch of stories that have
kind of played out over the last few
weeks uh and isn't really anything that
we've covered in too much detail on
mixture of experts in the past and the
topic specifically is the relationship
between general of AI and sustainability
um this week was the UN General Assembly
and it was very interesting to me that
the US state department said we're going
to bring a bunch of people together all
the CEOs of all these companies to talk
about how AI is going to be used for the
sustainable development goals um and
then similarly you know um IBM just
released a paper fairly recently talking
about some collaborations they've been
doing with NASA specifically around
predicting sort of climate and building
climate models that are available um and
I guess sh I want to turn to you because
my understanding is actually you gave a
talk or we're on a panel recently
specifically on this topic I'm wondering
if you can give our listeners sort of a
sense of like how this sort of
connection is evolving like using this
technology for these types of really
really big problems where you know I
think uh as someone who hasn't really
been as deep in the space I'm kind of
like how does chat GPT help save the
world uh I I'm not I know that's not the
case but if you can give us a little bit
more color on like how are people using
this Tech in space absolutely and Tim um
IBM does a lot of work in the space we
have our own commitment to being carbon
uh neutral by 2030 and we're doing a
great job against that already uh this
week I I spent a lot of time in New York
with a lot of global leaders and like
celebrities in the space and got very
humbled by the kind of problems that
everybody's dealing with so the the
entire conversation is focused around AI
can help solve some sustainability U
goals for us and we need that compute
power to be able to solve these gnarly
problems right so making predictions on
what happens to to climate all over the
world at a very granular level how do
you forecast what what events May happen
and things that nature there's lot that
happens in that space how do optimize
the cost envelope of running businesses
things that nature on the flip side you
have a cost a climate and environmental
cost that comes with running these
models right to just give you a few data
points if you ask chat GPD or massive
model like that a question to go create
something right it consumes a 500 mL
bottle of water to answer that question
right that's just the water consumption
that goes behind these things just cool
down centers and whatnot the data
centers Bloomberg came up with the study
all the data centers together uh would
be the 17th largest country in energy
consumption countries like Italy or um
use more use less energy than the data
centers do today in countries like
Ireland Where they' Have Become a center
where all these International Tech firms
have all their data centers as well the
data centers in in Ireland use 12% of
the national energy consumption it's
more than all the households combined
right so you're starting to get to these
numbers where if you look at any of
these graphs of the energy consumption
and then you see where we are today you
get to a stage where companies like
Microsoft are now partnering with
nuclear reactors that things that would
had melted down we're now trying to
resurrect them so that they can power it
was a Three Mile Island right which
famously had some trouble you know a
little while back so so you can see how
people are are trying to Wrangle how do
I balance the compute that's needed
versus how do you how do you look at the
energy consumption so my talk was
about we have to be computationally
responsible that was the title of the
talk and we were talking about how do
you figure out the right balance from
the chip level all the way up to how do
you end up using the models and uh and I
was suggesting that like how you have
cars that come with MP MPG miles per
gallon sticker that one number somebody
can look at and say yes this is what I'm
doing when you're booking a flight I
know the carbon emissions so I think as
part of that we need to be very
conscious about if I'm using chat GPD as
a calculator to add two numbers versus
using the actual calculator there's a
huge Delta between what and we'll get
the answer wrong exactly right yeah I
think there are some really good use
cases of where AI has been helping
augment we do a lot of work with with uh
with forestation we look at how how how
land use has increased we are predicting
catastrophic events with with
governments all across the world we're
trying to to help them with wild
wildfires and stuff like that so I'm
overall very impressed with how IBM has
taken a position on sustainability using
AI for good and we are super focused on
smaller models energy efficient all the
way down to how do we optimize our
compute and this is also part of our
whole AI alliance with and all the other
companies where we are collectively
trying to reduce the threshold required
to go Implement AI across the world
especially in Africa in parts of Europe
and Asia and things of that nature as
well show but I I like that bottle of
water analogy um there was a paper came
out from signal and hugging face just
this last week and it was on
sustainability and um the energy that's
being used here and one of the units of
analysis they used is how many cell
phone
charges this thing the aquari would use
and highest was image generation and
we're approaching a query to an image
generating model is getting close to a
cell phone's overnight charge and I just
I just really liked that kind of unit of
analysis because it brings it home so
much more about okay I put in that query
for an image generation and now I have
to think about that's the power of a
cell phone for you a day or two uh so I
think it's really cool to try to maybe
think about more creative metrics that
we can present this to the world about
just how power hungry or water thirsty
these these models are otherwise I see
Millow mowatt hours I'm not I'm not an
electrical engineer uh and it I don't
really appreciate it but you tell me how
many you know bottles of water it is or
how many um cell phone charges and and
it clicks so uh yeah yeah that's
interesting would you want it to be like
metered so like as you're you know
you're using Claude or something and
it's like here's how much power you've
you know used yeah yeah um that would be
that would be really useful Mar we've
done a lot of work with granite models
with three and we open sourced them do
you want to share with the audience what
we're doing with our Granite models with
granite we are focusing on the smaller
model um for the exact same reason that
you mentioned like let me let me just
share some data points if you look into
a five hosting a 500 billion large
language model on A1 100s roughly you
need 16 A1 100s for that hosting if you
look into a 20 billion models parameter
model just one single A1 100 so the API
call that you send to a 20 billion model
versus a 500 billion model is
16x more energy efficient just because
it's 16 times less GPU just ignoring all
the cost and latency and all the other
concerns just for
sustainability because of this what we
see in the market emerging is looking
into the smallest model that makes sense
and customize that on their proprietary
data that's the data about their users
that's the domain specific data to
create something differentiated that
delivers the performance that they need
on a Target use case for a fraction of
the cost and by cost I mean cost in
terms of energy carbon footprint and
everything together that's the guiding
principles for granite like we've been
focusing on a smaller Enterprise ready
models that are rooted in value and
Trust and allow our company the
companies to use their own data on
granite to make the custom model if you
look into our Granite custom uh the open
source models they are released under a
Apache Apache 2.0 license what it gives
Enterprises is the freedom and
flexibility to customize those models
for their own commercial purposes with
no
restriction which is really the power of
granite I love that and Mariam U the
this week we also released our prit Next
Generation models for granite right and
just to share with the audience we as
IPM have been partnering with NASA and
the problem we're trying to solve
generally we have uh these machine
learning models that make predictions on
forecasting weather patterns and things
of that nature right this is the first
time it has ever been done where we have
created a foundation model where a pixel
where square inch or of the of the earth
we're using those as tokens we're trying
to predict what will happen next right
in soad using text so we have built this
Foundation model that combines weather
data and climate data together in one
model so in that model can then be
adapted for various use cases in the
current state we have things like if you
want to do forecasting in Florida for
for rainfall there'll be completely
different model if you're trying to do
deforestation somewhere else it'll be
completely different model so the first
time we have combined a model that can
be easily adapted this like the
foundation models that we've built and
as mic drop open source is completely to
the community so now you can go and take
the these PR models from hugging face
deploy them for the same model mult
multiple things the next iteration where
I think we will hopefully go this is
starting to do what multimodal models
did you used to have one model that
detex one model that did image and then
just like meta 3.2 billion 3.2 now we've
combining the two together so the same
model can do both of them I'm hoping
that we'll come to that point with
Foundation models for with weather and
climate we can then start to connect
what's happening in two different places
the climate patterns are changing the
forestation is changing it'll be able to
think through and combine those two so
we've made the first step towards a new
future where Foundation models will be
able to combine all of this data
together and the same model can answer
all of these questions exactly I got
super excited about this the these
models and also think about it 40 Years
of NASA satellite images are at our
fingerprint now with this models to use
it for weather forecast
um climate prediction seasonal
prediction and use that to inform
decisions for planning
mitigations um for climate Andes that's
exciting that's super exciting it's a
great note to end on just because I
think like both it's a model that's open
source listeners you can go and download
and play with it if you want it um and
uh and I think it's a great application
I think show I was talking about earlier
like I think it's so useful to get
Beyond simply like oh how does a chatbot
save or gain sustainability there so all
these other aspects in that I think
people don't think about when this this
topic tends to come
up um well great everybody so that's all
the time we have uh for today uh thanks
for joining us uh if you enjoyed what
you heard you can get us on Apple
podcasts uh Spotify and podcast
platforms everywhere uh show bit Skyler
Mariam thanks for joining us and we hope
to have you on uh sometime in the future