ð VivaTech 2024 : Keynote - More than a Model: The Gen AI Essentials for Business Innovation
Summary
TLDRãã®ãããªã¹ã¯ãªããã§ã¯ãçæçAIïŒgenerative AIïŒã®æªæ¥ãšãã®å¿çšã«ã€ããŠèªãããŠããŸããã¹ããŒã«ãŒã¯Amazonã§ã®ã€ã³ã¿ãŒã³ã·ãããéããŠã¯ã©ãŠãã³ã³ãã¥ãŒãã£ã³ã°ã®åºç€ãç¯ãããã®åŸã®20幎éã§æè¡æ¥çã§æãé©æ°çãªæè¡ã«é¢äžããŠãããšèªããŸããçæçAIã¯å»çãããŒã±ãã£ã³ã°ãã²ãŒã ãªã©æ§ã ãªæ¥çã§é©æ°ããããããã«ã¹ã¿ãã€ãºãããèšèªã¢ãã«ãã¿ã¹ã¯èªååãã»ãã¥ãªãã£ãšã¬ããã³ã¹ãåãããšã³ã¿ãŒãã©ã€ãºã¢ããªã±ãŒã·ã§ã³ã«çµã¿èŸŒãŸããŠããŸããAmazon BedrockãšAmazon Qã¯éçºè ãšããžãã¹ãŠãŒã¶ãŒãçæçAIã掻çšããæ¥åã®å¹çåãšæ°ããã¢ããªã±ãŒã·ã§ã³ã®éçºãä¿é²ããããŒã«ãšããŠçŽ¹ä»ãããŠããŸãã
Takeaways
- ð§âðŒ ã¹ããŒã«ãŒã¯ããžã§ãã©ãã£ãAIã掻çšããŠæ°ãã補åããµãŒãã¹ãåµé ããããšã«æ ç±ãçãããŠãããã«ããŒã§ããã倢ãè¿œã人ã ãšå ±éããŠããŸãã
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- ð ããŒã¿ã¯ããžãã¹ã顧客ãç解ããããã®éèŠãªå·®å¥åèŠå ã§ãããRAGïŒRetrieve Augmented GenerationïŒããã¡ã€ã³ãã¥ãŒãã³ã°ãç¶ç¶çããªãã¬ãŒãã³ã°ãéããŠã¢ãã«ã«ããŒã¿ã掻çšã§ããŸãã
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Q & A
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mral AIã®å ±ååµèšè ã§ããTimothy Lacroixãšã®ã€ã³ã¿ãã¥ãŒãè¡ãããŠãããmral AIã®ããžã§ã³ãšå°æ¥æ§ã«ã€ããŠèªãããŠãããmral AIã¯ç¬èªã®ã¢ãããŒãã§AIåéã«åå ¥ããå¹çæ§ãšå€èšèªã®ãµããŒãã«éç¹ã眮ããŠããã圌ãã®ã¢ãã«ã¯ã³ã¹ãå¹çã«åªããŠãããç¹ã«ãšãŒãããã®èšèªã«ãããŠã¯ããã©ãŒãã³ã¹ãåªããŠãããšåŒ·èª¿ãmral AIã¯ä»åŸãå€æ§ãªã¢ãã«ã®éçºãšã«ã¹ã¿ãã€ãºã®ç ©éãã軜æžããããå€ãã®ãŠãŒã¶ãŒãAIã¢ããªã±ãŒã·ã§ã³ãæ§ç¯ã§ããããã«ããããšãç®æããŠãããšèªãããŠããã
Mindmap
Keywords
ð¡çæçAIïŒGenerative AIïŒ
ð¡ã¯ã©ãŠãã³ã³ãã¥ãŒãã£ã³ã°
ð¡ãã£ãŒãã©ãŒãã³ã°ïŒDeep LearningïŒ
ð¡ã«ã¹ã¿ãã€ãºïŒCustomizationïŒ
ð¡ã¿ã¹ã¯èªååïŒTask AutomationïŒ
ð¡ã»ãã¥ãªãã£ãšã¬ããã³ã¹ïŒSecurity and GovernanceïŒ
ð¡ã¢ãã«ã®éžæïŒModel SelectionïŒ
ð¡ã¹ã±ãŒã©ããªãã£ïŒScalabilityïŒ
ð¡ã¢ããŸã³ããããã¯ïŒAmazon BedrockïŒ
ð¡ã¢ããŸã³QïŒAmazon QïŒ
Highlights
Generative AI is at the center of boardroom discussions and industries are reinventing themselves with it.
Generative AI can automate end-to-end advertising campaigns and provide hyper-personalized customer experiences in marketing.
In healthcare, generative AI supports clinicians by summarizing clinical notes and suggesting imaging modalities to increase diagnostic accuracy.
Generative AI in gaming creates individualized experiences with infinite variations based on player actions.
Common themes and use cases for generative AI include enhancing customer experiences, boosting employee productivity, and optimizing business operations.
Large language models (LLMs) have deep reasoning capabilities and can fundamentally change how we innovate and build new products.
Foundational models for generative AI are evolving rapidly, with new models being released frequently.
Customers will need flexibility to use a combination of models for different use cases in generative AI.
Model customization is key for businesses to differentiate their generative AI applications using their own data.
Techniques like retrieve augmented generation (RAG), fine-tuning, and continued pre-training can augment data with models.
Strong data foundations are crucial for companies that move fastest with generative AI.
Task automation with generative AI can save time in creating autonomous agents for everyday business tasks.
Security and governance are important when deploying generative AI applications to manage risks like hallucination.
Amazon Bedrock is a fully managed service that simplifies building and scaling generative AI applications.
Amazon Q is a gen-powered assistant designed for security and privacy, enhancing productivity across various roles.
Amazon Q Apps allows every employee to create apps on enterprise data securely and quickly.
Mol AI focuses on efficiency and accuracy for a given use case, including support for European languages.
Mol AI is working on multimodal models and aims to simplify the customization of models for a wider audience.
Transcripts
good afternoon everyone really excited
to be here talking about all things
about generative AI just a little bit
about
me first is I'm a builder and a dreamer
at heart just like many of you have been
super excited in being able to build
with generative Ai and if you look at my
own history I started as an intern in
Amazon more than um 18 20 years ago and
my internship project was to build um
what turned out to be one of the first
uh cloud computing
servers
actually
and over the past two decades I had the
opportunity to be part of some of the
most
Artful technology in the tech
industry sorry can we go back a slide I
think it's uh
yeah
and the beginning of cloud industry was
actually pathbreaking in a big way for
the first time in the history we had
compute storage became a programmable
resource then Big Data Systems like
database and analytics became
programmable and then we had machine
learning which started from like
traditional rule-based systems to uh
linear machine learning to gradient
boosting trees to deep learning they all
became programmable and then we entered
into the era of generative AI where
large language models took place and
with the era of generative AI disc
Discovery now we are entering a space
where AI is now at the center of every
boardroom discussion and every industry
is Reinventing it self with generative
AI to begin with in marketing industry
gen can automate endtoend advertising
campaigns and enable remarkably hyper
personalized customer
experiences in the healthcare industry
it can support clinicians by listening
to Patient conversations summarize
clinical notes and even suggesting
Imaging modalities to increase the
diagnostic accuracy
and then in gaming industry it can
create
individualized experience with infinite
variation of characters missions and
interactions based on the individual
player
actions and across all these industries
we are seeing a few common themes and
use cases emerge that simply wouldn't be
possible without generative AI from
significantly enhanced customer
experiences through highly intelligent
generative AI Assistance or personal
personal virtual
assistance to boost in employee
productivity but things like
conversational search text summarization
or being able to generate code and
coding
assistance and to finally highly
optimize business operations through
intelligent document processing or being
able to do Predictive
Analytics just a decade ago these
projects and ideas were just stuff of
dreams and now they are part of what I
call as a new gen powered reality a
reality that often feels like magic
because these llms have deep reasoning
capabilities and we can add them to our
Enterprise
applications and this technology will
fundamentally change the way we innovate
and build new products and if you look
at the presentation what you're seeing
right here on the vi Tech stage all
these images have been generated by
gen now with with all of these
possibilities many organizations are
asking themselves how do we actually get
started how do I innovate with
generative AI so before we get into it
let's look at uh what is a key look at
the technology that is powering every
gen application
ation the foundational models I'm sure
that many of you are already
experimenting and playing with many of
these foundational models these are
large language models or other
foundational models it seems like every
day we are learning about it another new
model that got released and new powerful
models from companies like anthropic
meta mrol Ai and many many more just
last month even amaz we introduced new
additions to Amazon's own foundational
model called Titan the pace of
innovation we are seeing in the space as
like
unprecedented at AWS we were the first
one to recognize no one model will rule
them all these Foundation models will
continue to evolve at an amazing speed
and customers will need the flexibility
to use a combination of models for
different use cases imagine if you are a
retailer you might want to use
anthropics CLA model to generate a
product description for a new shoe
launch but you may also want to use
stability ai's image generation stable
diffusion model to present a unique
background for your product
image now to enable more developers not
just machine learning scientists to take
advantage of gen it should be easy to
access and evaluate the AI models for
each of your use
case we believe that in the very near
future every developer need to build gen
applications because gen will be the
fundamental part of every experience we
create in the digital world so how can
we make this future a new reality what
would it take to create a seamless
endtoend customer customer experience
that is powered by generative
Ai and I talked about model choice but
in addition to model choice you need lot
more capabilities to build H
applications first you need the ability
to customize these models with your data
next you need the ability to be able to
automate various tasks in your
organization with Ani and finally you
also want the a to Leverage The built-in
security and governance controls to
mitigate any potential risk and finally
you need the ability to scale the
innovation without having to manage the
infrastructure to run these Large Scale
Models now let's double click on some of
these today starting with model
customization now when it comes to
generative AI that know your business
and your customers your data is your
differentiator data is the difference
between a generic gen model powered
application and an application that
knows your business and your customer
now you might be wondering what is the
best way to actually augment data with
your models there are a few different
ways and to how you can use your data to
maximize their
value the easiest place to start is with
a technique called retrieve augmented
generation or rag with rag the
developers need to retrieve their own
company data to add context or
information to their proms using which
they ultimately create better responses
to their customer questions or
requests now you don't need to just stop
at Rack to further improve the accuracy
of your model outputs you can also use
and update the outof the Box found ation
models to create a fine-tune model with
the process of fine-tuning you're taking
a small set of labeled data examples
that is very specific to your company
data to train the model with your
corporate lingo and business
policies and then to take it one step
further you can change the underlying
coree parameters of the model through a
process called continued pre-training
continued pre- training means you pick
up from where the model provider left
off and you train with unsupervised
training with actually totally
non-annotated data this means
essentially you are extending the model
to be an expert in your company's
knowledge base just like how you are
trained a custom model all together now
I talked about all these three
techniques not only do you need tools to
support all these three form of
customization but you also need to
organize your
data what we have seen is that the
companies that move fastest with Gen are
the companies that have the strong data
foundations these are the companies that
typically store various types of data
including their Vector data that can be
used for customizing models and they
have them integrated and across all the
use cases they also use all the
different variety of database tools Al
together with a well governed manner as
well now let's talk about also task
automation with generative
AI one of the most common time consuming
projects for AI developers is creating
autonomous agents that help perform
everyday task for your business and for
your customers for example let's say a
customer wants to exchange black shoes
for brown shoes that they purchase
through an online retailer they use the
sites customer service chatbot interface
to communicate their requests and
confirm that their order returns were
accepted and a new order was placed this
process seems incredibly simple right
but to program such a thing there is
actually a lot of manual programming
that is involved in creating even a gen
power chat
interface first to make this happen
developers need to go through series of
set of steps and all of these are time
consuming like you need to define the
instructions and orchestrate the set of
workflows that you need to go through
you need to write custom code and
finally you need to manage
infrastructure to run these agents all
of this process can take weeks and
require high level of machine learning
Lear expertise being able to tweak the
prompts
appropriately and that's what slows down
the ability to leverage gen to do
Automation and finally let's look at
security and
governance a common challenge when it
comes to deploying a gen application is
hallucination these large language
models have hyper parameters that can
help manage them but you can also use
safeguards like system prompts to site
the sources for the model outputs so
that you can actually get control of
hallucinations there are also new
controls for data privacy for Enterprise
grade geni your data should never be
exposed to the core foundational models
to train their base Foundation model
sending your company data to a third
party is a large risk for any
organization
and for higher risk applications quality
control is an absolute must AI can help
us make predictions but not decisions
which is why human judgment is
absolutely Paramount when it comes to
inference implementing all these best
practices in responsible manner is
Central to building trust with your
customers to build AI responsibly you
will also need need to consider how you
are going to monitor AI system Behavior
prevent AI ABS abuse and Implement
educational programs to rescale your
Workforce at AWS we are investing
heavily in responsible innovations that
enable guardrails for these models
evaluation support and watermarking and
many more now that we have covered a lot
about what does it take to build gen
applications let me start explaining by
how Amazon is meeting these needs for
developers in one easy to use place that
is Amazon
Bedrock Bedrock is a fully managed
service that makes it easy to build and
scale your gen applications and we
recently announced that it is available
in our Paris
region tens of thousands of customers
are already using Bedrock as the core
foundation for the Gen strategy because
it gives them access to the broadest
selection of foundational models from
leading AI companies and with the
addition of a new feature in Bedrock
called the custom model import companies
that are building their own foundational
models can import onto bedrock and
leverage the rest of
capability we know that model choice is
important but what about all the other
things that I talked about
in fact what we have learned is that
majority of Amazon Bedrock customers use
more than one model and this includes
customers like Al liquid which leverages
a variety of models on Bedrock to
quickly prototype digital
experiences now Beyond actually picking
the right model there are a bunch of
other things that you need to streamline
to build J applications faster
and with tools like Bedrock we are able
to do things like knowledge bases for
Bedrock or agents for Bedrock to do
agent Automation and builtin Enterprise
great security and privacy and with
support for various regulatory standards
and
gdpr now by enabling every developer to
build with Gen really easy every
business will be able to move faster and
innovate faster with
Gen but if you want to harness the power
of gen for everybody you also need to
make it easy for every employee not just
developers to leverage it for their
daily
task one way we can accomplish this is
by leveraging gen assistance that
integrate your Enterprise data with an
AI application enabling you to quickly
and easily take advantage of gen for
accelerating employee productivity these
assistants can streamline various set of
tasks just even today right from helping
a software developer to a data analyst
to data scientist we believe these
assistance will provide enormous
impact in fact the first set of area
that is going to get really
revolutionalized is automating
repetitive development Vel ER task these
assistance can remove the heavy lifting
associated with software development
like coding writing tests app upgrades
and security scanning they will also
help employees access their information
faster and get insights and take actions
based on their insights and these
assistants also have the potential to
help everyone build their own gen
application at AWS we have invested in
accelerating the productivity in each of
these areas with our own gen powered
assistant called Amazon
q q is the most capable gen assistant
available today and we built it with
security and privacy in mind right from
the get-go with Q you can get your job
done faster whether you are in it or in
finance let me share a few examples on
how you can put Q into
action with q for developers we are
helping developers become more efficient
across the entire app development cycle
right from planning to coding to testing
to create data engineering pipelines Q
even comes with built-in agent
capabilities to autonomously perform a
wide variety of tasks everything from
implementing features to performing
software
upgrades for example let's say you want
to ask Q to add a new checkout feature
to your e-commerce app it will analyze
your existing code base and map out the
implementation and execute the required
code changes and test in just
minutes I've been inspired by all of the
positive feedback we are receiving for Q
to date and it is the number one coding
assistant in thew bench
today but accelerating productivity
shouldn't just stop at
developers you want it to take it to
every employee in an organization and
that's what Q for business does it
connects to all your company data across
more than 40 plus Enterprise systems and
it provides summaries insights and you
can do analytics all in a secure
fashion but we wanted to take you one
step further and we asked ourselves how
do we Empower every business user to
build their own application so now let's
take a quick look at how we are bringing
this Vision to
life Amazon Q apps enables every
employee to securely create apps upon
Enterprise data in seconds like maryan
HR who needs to create an onboarding
plan for a new hire Amazon Q apps
identifies that this could be turned
into a useful app Mary likes the idea
and goes ahead to create the app with
just a single click and Nikki in sales
who is browsing the Amazon Q apps
library for an app to help her pitch the
company's wide range of products to her
customers she discovers an app created
by her coworker the app generates a
custom sales script tailored to her
request this is teamwork without
boundaries because with Amazon Q apps
everyone can
[Music]
build so there it is Amazon Q and Q apps
will radically change how our customers
and our employees can do their work
every day and now with this new era of
gen Discovery there has never been a
better time to be a
builder I believe that what you are
inventing today and tomorrow can lead to
a profound impact on the world changing
Industries and changing everyone's lives
so while you are here as you learn about
the tools and partners to invent your
next application and
experience please ask yourself what
magic will you build with
ji and now I'd like to invite another
Builder to the stage who is building a
magic of his own with latest
foundational models please welcome
Timothy lacroy co-founder of mral AI
[Applause]
[Music]
[Applause]
[Music]
so
Tim I know mol AI has been extremely
popular so let's talk about what
inspired mrol AI to start a new player
in the AI space in France rather than
let's say in the United States um so I
think there are two parts this question
why did we want to uh create a new
player I think we just wanted to do our
own thing uh we had been working at
large tech companies for a while and
wanted to to get the speed and control
over what we did French really never was
a question for us we're French uh all of
our life is there and there is um a lot
of talent there so it wasn't really a
question and I think it doesn't prevent
us from being a global company we have
offices in London and San Francisco so
okay all right let's talk about what is
the vision of meal Ai and uh how do and
your vision for the future fut of
artificial intelligence I mean I hadn't
seen your slides before but it's pretty
much the same as yours uh okay glad we
are alive I think what I see in the near
future is um a lot more integration uh
needs to be built so all of this uh
agent function calling Behavior will
develop rapidly on the application layer
and on our end we will build uh all of
the tools that are required to uh
actually act uh on these functions that
are available um this will enable a wide
variety of tasks and with this variety
comes the need for um models that
optimized for the complexity of these
tasks um so yeah more gentic behaviors
and more variety of models so when you
talk about variety of models you're
talking about like different sizes of
models or different vertical Industries
how do you think about that uh both so
anytime you verticalized uh or even fine
tuna model what you unlock is is better
capabilities for a smaller size smaller
size can mean either reduced cost or
reduced latency so better user
experience uh verticalization when it's
done for example by us is a bit more
generic but for the practitioner or
model builder uh it can enable some
applications that are maybe easy to
build with the larger models but
wouldn't have the right um latencies or
would be too expensive to put to product
yeah so that's where you're talking
about like different smaller sizes and
being able to customize it with the kind
of techniques we talked about like fine
tuning and Rag and so forth now being in
the middle of all these Innovation and
building your own foundational models
what are you seeing as like major Trends
in artificial intelligence are we going
to keep scaling uh forever this is going
to be all about computer and data
forever or are you going to see a
different breakthrough what are what do
you see in the coming months and coming
years in your opinion I mean people will
always try to scale um I personally tend
to be doubtful of infinite scaling I
don't think these things hold for a
while one limit uh that we run into
quite quickly is the amount of data um
Humanity has only been writing stuff for
so long uh once we've read all of it uh
there is going to be a limit um so some
breakthroughs will be needed but I don't
think we've seen the end of scaling yet
um so that's one uh Direction the the
other is um getting better at the lower
end of the scale I don't think we've
done quite enough work on getting small
models to do what they need um this is
because we don't really understand the
scope of what these models need uh if
you were to tell me okay I want a model
in Alexa what should it do I don't think
this has been built yet and this will
enable lots of research on smaller
models as well yeah and I know we
actually expanded our part parnership
and mrol AI is now available as part of
uh Bedrock how do you envision actually
our customers I mean millions of
customers that are innovating on AWS
today will benefit from mol what kind of
things are possible uh so I think
something that we've seen when building
M roll was that Enterprise customers
really want to uh build AI applications
in their cloud of choice they don't want
to uh move out of it so for customers
it's already uh an ability to get more
choice on uh their current platform I
think one benefit of using mistal models
is the care that we put into uh always
being on the efficiency FR Frontier all
of our models we strive to make them as
uh good as possible for their cost um
and especially in um European languages
with which we started so French uh I
mean English is wider than Europe uh
German uh Spanish Italian uh these
languages are really first class
citizens for us and so um performance
might be even better when using misi
models on this yeah I like that you're
focusing a lot on the efficiency and uh
uh add accuracy for a given use case and
also on languages that are not just
always English based uh it makes gen
more accessible one final teaser
question um what should customers expect
from your future of mral AI in the
coming months any spoilers um I mean I
don't know if it's much of a spoiler I
think we're uh we're working on
multimodal models uh so this will happen
uh I think reasonably soon uh we're
working on some verticalization on code
as well um it's also the easiest for us
because that's what we use every day uh
but more generally I think our goal in
the near future is to help with the
hassle of customizing models which I
think today is still very complex and
and we want to enable this for pretty
much anyone um to do ad scale and up to
prodct that's awesome hey thanks again
for actually coming here and uh joining
me today in this discussion and uh
thanks again for everyone in the
audience for taking good time
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