djy the biggest concern I hear from the
audience is number one it's too late for
me to get into AI people say that or
there's no opportunities or it's going
to take all my jobs away the last one is
absolutely not true so let's start there
and I think the way to think about that
is people who use AI will take the jobs
away from people who don't use AI so
going back to your first uh question I
think you should jump in not just you
and I but everyone yeah uh it's not too
late because the space is so new that
even finding experts in this area is
very difficult because there are very
few of
them hey everyone it's David bomble
coming to you from Cisco live back with
the amazing vjy vjy great to have you
here thank you for having me David so
you work in I I want to say mad
scientist because I don't want to insult
you but it sounds like you guys are
really doing some really interesting
things so tell us about you know the
organization and some of the great
things coming out of it sure so I'll
take M scientist is a good one but we
actually deliver things that are good
for Humanity so we're not the crazy
James Bond villain out here but out
shift it has a pretty clear Mission the
mission of the group is to take Cisco
into new markets so go into new problem
spaces that Cisco hasn't been in and
build a meaningful business in those
markets so we are completely end to end
in the sense of think of us like as an
inter
y combinator so we've got product
engineering all the way to customer
success and sales and marketing all
fully contained because when you're
dealing with new personas user or buyer
personas you need to have empathy with
those personas and that's a tough nut to
crack I mean it's I always feel it's
easier to build the product the goto
Market is where you succeed or fail and
that's uh that's a tough problem I mean
that's a problem with with a lot of you
know inventors and people like that
right they create something really
interesting but how do you actually make
it useful for a wide audience that's
right and I think that's where the mad
scientist analogy probably breaks down
because I think it's a mad scientist and
the Mad salesperson and the Mad
marketing person all combined where do
you see it going where do you see the
future going because I mean you working
on all this cool technology like give us
some Vision about where things are going
sure I mean I think the way I'd like to
describe it is like so Tech first of all
Tech changes happen in Step functions
this is one of those step functions and
there have been a few in the past I mean
the internet mobile phone especially
with uh touch interfaces and the app
store uh but to us this is as big if not
bigger and you will see the
ramifications of this over time so I'll
give you an example that was probably
more related to our audience which is in
the it space uh Cloud so that was a step
function because nobody is very few are
building and deploying from the ground
up I mean if you were to stand up a web
server today yeah you're not going to
buy a spark system and build everything
from scratch but what happened when
Cloud rolled around for the first time
you did what was called lift and shift
so you knew things work like this on
Prem and you just took that blueprint
and just moved it to the cloud you did
not do Cloud native but now everything
is cloud native you you're doing
microservices you're doing containers
and serverless and you're actually using
native services from AWS and Google and
Microsoft and so on is the same thing
that's going to happen here so I think
right now what people are doing is I
you're seeing this thing called
assistants and agents and you'll have
these things everywhere and they'll be
augmenting your workflow but to me this
is the lift and shift face because
you're not stepping back and saying what
should the user experience look like if
I started from scratch yeah because
right now one of the things that you see
is prompt fatigue yeah I was going to a
you about that yeah I mean I think if
you so you if you think about Siri or
Alexa or Google assistant how many times
do you have to ask the same question
exactly yeah to get a song play right
and uh think about Bard or GPT and I
want to ask like explain Quantum to me
you need to ask that question like 10
times over to get the right answer and
the problem is you might not be able to
trust the answer you get right because
it could be making it up it's making it
up you need to verify it you need to ask
it 10 times over to figure out the
essence of what you need yeah uh so
sometimes like especially if you're
dealing with a SAS product sometimes
it's just easier to point and click and
we've been used to that so now with the
lift and shift notion that's happening
right now is you've got a pro point and
click interface and you've got an
assistant on the side and those worlds
are like ships in the night they don't
communicate and so what we doing is
we've just shipped a context aware
assistant for one of our products
panoptica which is a security product
okay and the idea behind that is how
about we blend the positives of both of
these worlds together so I might be
looking at an attack path that an
attacker might take to compromise my
application in this case and it shows
that your AWS S3 buckets are configured
now I might have an assistant sitting on
the side and I might be asking it 10
prompts and 10 questions and racking up
my costs yes yeah people forget that
right the token people forget that
exactly and so and then you might get
some response which is not even relevant
or it's like a little bit vague and
general purpose so what we've done is we
said we we've introduced something
called a context aware assistant which
is almost like a Google Lens I don't
know if you've used that feature uh in
Google where you can take your camera
and point it to anything and it tells
you what that is so we have a Google
Lens like feature on a UI okay so you
can take a lens like icon and basically
point it to anything in the UI so you
could point it to this S3 bucket which
is blinking and as soon as you point it
the assistant appears next to it and
every prompt that you type in is
actually now context aware to that place
oh that's correct you pointed it so you
don't have to go around trying to figure
out what's going on so everything that
you ask is actually about that S3 bucket
in your environment so it's very
specific to your environment as well
well it's not just one object you can
drag and drop you can select multiple
objects multiple tables so it's a
complete uh multimodal as they say is a
multimodal experience uh between text
and images in the UI but to us it's like
that's huge nobody's done that is in
industry first but that's also like step
two to us it's like if we were given a
blank slate yeah what would you do what
would that look like and I'm not ready
to answer that yet but it's a teaser for
the next time around oh brilliant okay
so we can expect something yes I think
uh just the information delus that all
of us are facing and the way all of
these uh user interfaces have been
designed they don't blend together
nicely yeah I think the problem is when
people think about AI they just think
it's cat GPT for a lot of people right
but AI isn't just that yeah actually the
thing that we are seeing is again again
going back to the step function analogy
um the step function happened November
22 because you took something that that
was that existed for a while uh it's
been a linear progression but then you
made it consumable by everybody so you
democratized it de to some degree and so
now everybody is can can talk to CH gbt
and barred now and so on so 23 was low
hanging fruit people are touching it
feeling it smelling it it's like trying
to figure out what's going on yeah uh
now you're getting a good sense of
what's happening so 24 is going to be
about serious deployments okay and so we
are going to see security assistance and
networking assistance and we're going to
see real critical business uh critical
use cases pop up with these assistants
and and generative AI uh backed systems
so because of that there's another thing
happening so the entire model space is
evolving so you are going to have a GPD
5 or you're going to have a Gemini next
version for your everyday needs so these
are general purpose Foundation models
but there is a rise of specific
Foundation models for specific use cases
so you will see security Foundation
models and networking or maybe
infrastructure Foundation models as an
example so that's one thing that's going
to happen and these things will be
smaller more efficient and more accurate
the other thing that's going to happen
is open source is coming in in a big way
and so a bunch of these models are
actually already open source so lar 2 is
open source Mistral has a has a pretty
decent open source model all of these
open source models will get picked up by
Savvy individuals and get customized to
their use case so like we are thinking
you should have models running in a
switch running in a Miri camera oh so AI
directly inside yeah directly inside
because if you think about all of these
models they're becoming multimodal so
it's text to speech speech to visual uh
text to visual so a camera or a switch
can benefit from these highly efficient
accurate smaller Foundation models so
not calling them large language models
but smaller Foundation models that are
highly distributed in nature and that's
going to happen quite a bit so
and in fact one of the things that we
are seeing is also specific use cases
within a singular app pick one app and
I'll pick panoptica which is something
that we ship it's a cloud application
security product for different features
within that same app I have multiple
models so one app can have like 10 20 30
models sitting inside the app so how do
you deal with the complexity of all of
this so we are we are actually beyond
the hype cycle to some degree and we're
in the space of making this well
abstracted uh bringing in velocity
bringing in the trust safety security
and scale aspects to the foundation Mar
model family and that's where I think 24
is going to be so tell us about this new
what's the right name for it this new
product that's come out yeah so it's
actually we just released a product
called moic and the idea is uh if you
think about generative AI That's a buzz
everywhere and it allows you to do some
interesting things I mean generative AI
could make all of us speak like like uh
Joe Biden or Donald Trump or whoever
which is good and bad which is good and
bad and uh but it allows we feel that it
is a step function when it comes to
human productivity and human creativity
both and it's going to change the way
all of us do business you talking about
aot exactly and so when you think about
the potential of generative AI there is
this urge to build stuff with it yeah
and it's one thing to do build your apps
or plan your wedding or go on a trip and
use B and chat GPT it's a different
thing to configure your network or
remediate your security uh problems I'm
hallucinating right yeah hallucinations
toxicity bias security problems Which
models are the right things for the
problem because this space is evolving
so rapidly so there's a whole host of
problems and so what we decided was
build a product mic which allows you to
safely and rapidly create as well as
deploy generative AI applications and
for us applications are basically motifs
that's where the name comes from so you
might think of a motif in music or you
might think of a motif in a computer
vision or video or in our case it's
primarily text and code but these are
motifs that you start with and the
product allows you to create these
motives rapidly across a variety of
providers so whether you're using GPT or
Bard or anthropic or coher or you're
using something open source that might
be in a cloud provider or on Prem it
doesn't matter so you have multiple
providers we abstract things out for you
and then it allows you to figure out
security trust and safety and Roi
because these things are not cheap so
are you getting the rois that you
planned for so the product actually is
pretty comprehensive and allows you to
go on that Journey so this is a product
that I could buy and then create an AI
for my own organization is is that the
idea or is it more than that so think of
it as uh think of it as a SAS
application okay that is a wrapper
around your Enterprise so we're calling
it a Enterprise gen Hub so it's a hub
that you go to and the other
complication that we're seeing in
organizations today is the creation of
gen apps as well as the deployment of
these apps is actually a multi-p Persona
multi-team organization problem because
you have the builders who are trying to
build something out of generative yet
you have the GM who's like I'm paying
for this stuff yeah am I getting the ROI
that I need am I getting the insights so
you have the GM and then you have a
whole set of approvers so you have
approvers like cios security officers
you have legal privacy and Trust because
all of these issues crop up I mean we we
are a big believer in responsible Ai and
trustworthy Ai and so everything that we
would like to roll out and our customers
would like to consume or roll out uh
should be based on trustworthy and
responsibility principles so how do you
enable that so all of these personas
need to come together and that's why
we're calling this a geni hub for the
Enterprise because this is a singular
place where all of these personas come
together the other thing that happens is
and one of the things you can realize
planning a birthday party is one thing
yeah that's that's easy you might plan
your trip to London yeah but let's say
you want to build a finance bot yeah for
your organization let's say you want it
to be credible enough that is actually
sitting in the boardroom with you and is
actually answering questions for the CFO
that's where we all should be headed
towards but that's a tough problem it's
tough because of whole slew of things
that I just talked about but also the
assistant does not have context around
your organization so what are my
financial reports for this quat that's
highly sensitive highly contextual
highly there's a recency element to it
right and so how do you like sort of
capture some of those knowledge sources
and data sources to then customize your
assistant so that it can be useful as a
finance assistant for you in the
boardroom and so bringing all of these
data sources your policies in the
organizations your compliance needs and
your identities all together along with
these personas that I talked about and
then enabling velocity enabling trust
enabling safety security and Roi kpis is
what the product does is that something
to do with a rag or is that something
different so rag is one of the
techniques we use so rag stands for
retrieval augmented Generation Y and the
idea behind rag is every Foundation
model out there is actually a general
purpose model and so it's it's actually
learned on the entirety of human public
knowledge but does not know the ins and
outs of an organization and the
knowledge sources and the documents that
exist within your organization so rag is
a technique where you can take a
foundation model and you can customize
it by feeding its sources that are
particular to you and in fact it gets a
little bit more Nuance than that because
like we were talking about the finance
assistant yeah you would not want
engineer to see exactly anything from
that assistant you would not want HR and
vice versa you would not want Finance to
look at HR documents and so on right so
it's not just within the Enterprise but
it's within the teams and that's where
identities across both humans and
machines and documents all come into
play when you think about customizing
these assist for Enterprise wide use
cases so looking at sort of the the
world that we're in cyber security
networking Cisco very strong in those
areas what do you seeing Cisco sort of
viewpoint of AI yeah so I think uh so
Cisco has been working with AI models
for a long time even before yeah people
forget people forget I mean I think if
you think about WebEx uh one of the most
fascinating examples that we have is
noise cancellation and that's a pretty
impressive AI model is not a generative
AI model but it's a pretty impressive AI
model that actually filters out
everything but your voice and makes it
really effective so we've been working
with AI models for a long long long time
uh but the way we think about AI within
Cisco is in two big buckets one is
productivity and the other is product so
productivity is especially after
generative AI what are the tools and
software that we can roll out within the
organization to make all of us more
productive yeah and so we had an ey talk
uh yesterday uh with we brought in
Fletcher who's our CIO and he's been
rolling out a whole slew of tools within
Cisco so for example GitHub co-pilot for
uh for engineers to develop code uh we
are also rolling out our own version of
a chat GPT like interface inside which
is customized with our data sources and
makes it more effective for Cisco
employees there are there are
applications in HR and in legal and in
marketing that is being rolled out
because all of these make all of us more
productive more creative and I think
that is something that all of us should
benefit from so that's the productivity
side and then there's the product side
and within product also I'd like to
distinguish in between two buckets one
is AI for improving product and the
other one is Building Product to improve
AI okay yeah yeah and so AI for
improving product is using these models
in our networking and collaboration and
security and absorbability products to
make these products better an assistant
being one great example Auto remediation
or uh figuring out anomaly detection and
so on and so forth are all examples of
using AI within the product product for
AI is actually a tougher nut to crack so
this is what do you build to push the
bar forward on on on AI itself and so
one of the products that we just
announced and we briefly touched upon
Motif is actually in that bucket the
other product that we've talked about
for a while now is silicon 1 so if you
build out clusters from the ground up
for training your models of inferencing
as well our silicon 1 based products are
actually the def facto fabric enabler
for most of the Clusters that you might
be using so if you think about any
hyperscaler and what they use for their
AI clusters you can bet your life that
they'll be using silicon one underneath
oh that's interesting so that that's uh
cuz you always you always think it's
something else you always think it's
something else but is a silicon one and
so that's at the lowest end and then as
you go up the stack moic to us sits at
the highest level of the stack and so as
you again roll out these AI first
applications how can you compress that
time to roll out from months which is
what where it is today to days or even
hours and then when you roll that out
like we discussed how do you figure out
Roi and your kpis and actually get
deeper into insights not just from the
sense of how much am I spending with a
chat GPT or a GPT or a Bard but also go
inside the prompts that you're sending
out the responses that you're getting
back and figure out how effective that
prompt response session has been so
almost like a deep packet inspection
version yeah of of uh of geni problem
space and we are calling this The Prompt
Processing Unit so it's like an npu but
it's for the prompt or a dpu and it's
for the prompt so that's the other thing
that we are innovating around within
Motif and then at Cisco we've been a big
believer in respons AI for a really
really long time so we've had human
rights and privacy as it pertains to
data all the way from 2012 yeah because
it's a worry right all my data is out
there getting mind especially in Europe
and with the EU data Act and the AI act
about to be signed in a couple of months
couple weeks I guess uh it's a big worry
and so what do we do about that so we
are enabling moic with trust and safety
practices and Frameworks so you can
deploy your gen first applications in a
way that is that minimizes cannot
probably remove completely uh but that
minimizes toxicity bias hallucinations
and false content and so on so that's
the third pillar and finally a security
and that's a big one for for our
audience and if you think about two
years out you ask me what do you think
is going to happen with AI two years
from now I feel we'll stop talking about
AI because it'll be embedded into every
product that you see so it's just a
matter of Life fact of life and so
electricity it's electricity and so if
you think about that world is just
another component in your application
and in that kind of a world how do you
ensure that these models which are now
spitting out recommendations or spitting
out content how do you make sure that
they are not compromised and if you're
compromised and that compromise can
happen anywhere in that pipeline it can
happen when you are asking the model for
something so prompt injection all the
way to when you're training or fine
tuning that model as well so we just
rolled out gen protection in the
panoptica product which actually starts
getting into this sort of llm protection
starting from the inference side first
but then slowly moving towards uh fine
tuning and and and trading as well D Jo
the biggest concern I hear from the
audience is number one it's too late for
me to get into AI people say that or
there's no opportunities or it's going
to take all my jobs away the last one is
absolutely not true so let's start there
and I think the way to think about that
is people who use AI will take the jobs
away from people who don't use AI yeah
so going back to your first uh question
I think you should jump in not just you
and I but everyone yeah uh it's not too
late because the space is so new that
even finding experts in this area is
very difficult because there are very
few of them this SPAC is was invented
pretty much in 2017 by a core team that
is a handful of people or a little bit
more than that so it's not too late
everybody's figuring it out including us
and so jump in right now that's the
biggest mistake you can do is not jump
in jump in right now there are lots of
free courses available go and read up a
lot of material even at out shift.com go
and figure that out use the tools there
are free versions out there get familiar
with with them and then use them in your
day-to-day and if you become an expert
which you will if you spend the time
your jobs are safe I mean this is this
is an assistant an augmentation of what
you do day to-day it's not a replacement
of what you do day today that's never
going to happen the way I would describe
it is like uh yes if you were a machine
language programmer and you were
stubborn enough to say I'm not going to
Learn Python or any high level
programming language you're doing
yourself a disservice you have to learn
a high level programming language to me
this is the next level of abstraction so
get your AI knowledge base up
Incorporated in your day-to-day and then
learn to use it as a tool like you would
use anything else do I need to get a PhD
though if you were building Foundation
models maybe but 99.99% of the world
doesn't need to do that just the way
99.99% of the world does not need to
think about how containers I implemented
you just use the services from a cloud
provider or an non-print provider and
build your application so concentrate on
the use cases concentrate on your
business outcomes take the services that
exist from providers like us and just
run fast DJ I want to thank you for
sharing going back to how we started I
wouldn't call you a mad scientist but
you know it's fantastic to hear about
what's happening in the labs and then
how it's changing all of our lives but
also giving us opportunity because
that's the worry that people have right
thanks so much for you know giving us
hope and saying that now's the right
time is there a place that people can
follow you perhaps connect is there a
website that people can go to I'll put
the links below for everyone who's
watching but where's a good place to you
know connect yeah we are actually
everywhere so you can go to out shift by
Cisco on LinkedIn uh you can go to out
shift.com and the product announcements
that we just made around Motif and the
Gen protection it's motif. so go and get
in touch with us and we are actually a
very learning forward organization as
well so you can actually go there and
learn a lot more about Cloud native and
security and Ai and so just go there and
visit and learn djy thanks so much I
really appreciate it great to see you
again man y
absolutely