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please welcome Andrew
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[Applause]
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in thank you it's such a good time to be
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a builder I'm excited to be back here at
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snowfake
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build what i' like to do today is share
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you where I think are some of ai's
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biggest
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opportunities you may have heard me say
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that I think AI is the new electricity
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that's because a has a general purpose
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technology like electricity if I ask you
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what is electricity good for it's always
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hard to answer because it's good for so
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many different things and new AI
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technology is creating a huge set of
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opportunities for us to build new
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applications that weren't possible
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before people often ask me hey Andrew
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where are the biggest AI opportunities
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this is what I think of as the AI stack
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at the lowest level is the
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semiconductors and then on top of that
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lot of the cloud infr to including of
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Course Snowflake and then on top of that
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are many of the foundation model
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trainers and models and it turns out
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that a lot of the media hype and
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excitement and social media Buzz has
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been on these layers of the stack kind
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of the new technology layers when if
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there's a new technology like generative
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AI L the buzz is on these technology
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layers and there's nothing wrong with
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that but I think that almost by
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definition there's another layer of the
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stack that has to work out even better
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and that's the applic apption layer
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because we need the applications to
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generate even more value and even more
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Revenue so that you know to really
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afford to pay the technology providers
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below so I spend a lot of my time
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thinking about AI applications and I
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think that's where lot of the best
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opportunities will be to build new
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things one of the trends that has been
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growing for the last couple years in no
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small pop because of generative AI is
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fast and faster machine learning model
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development um and in particular
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generative AI is letting us build things
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faster than ever before take the problem
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of say building a sentiment cost vario
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taking text and deciding is this a
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positive or negative sentiment for
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reputation monitoring say typical
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workflow using supervised learning might
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be that will take a month to get some
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label data and then you know train AI
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model that might take a few months and
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then find a cloud service or something
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to deploy on that'll take another few
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months and so for a long time very
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valuable AI systems might take good AI
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teams six to 12 months to build right
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and there's nothing wrong with that I
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think many people create very valuable
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AI systems this way but with generative
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AI there's certain cles of applications
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where you can write a prompt in days and
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then deploy it in you know again maybe
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days and what this means is there are a
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lot of applications that used to take me
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and used to take very good AI teams
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months to build that today you can build
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in maybe 10 days or so and this opens up
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the opportunity to experiment with build
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new prototypes and and ship new AI
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products that's certainly the
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prototyping aspect of it and these are
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some of the consequences of this trend
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which is fast experimentation is
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becoming a more promising path to
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invention previously if it took six
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months to build something then you know
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we better study it make sure there user
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demand have product managers we look at
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it document it and and then spend all
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that effort to build in it hopefully it
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turns out to be
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worthwhile but now for fast moving AI
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teams I see a design pattern where you
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can say you know what it take us a
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weekend to throw together prototype
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let's build 20 prototypes and see what
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SS and if 18 of them don't work out
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we'll just stitch them and stick with
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what works so fast iteration and fast
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experimentation is becoming a new path
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to inventing new user
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experiences um one of interesting
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implication is that evaluations or evals
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for short are becoming a bigger
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bottleneck for how we build things so it
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turns out back in supervised learning
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world if you're collecting 10,000 data
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points anyway to trade a model then you
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know if you needed to collect an extra
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1,000 data points for testing it was
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fine whereas extra 10% increase in cost
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but for a lot of large language Mel
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based apps if there's no need to have
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any trading data if you made me slow
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down to collect a thousand test examples
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boy that seems like a huge bottleneck
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and so the new Dev velopment workflow
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often feels as if we're building and
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collecting data more in parallel rather
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than sequentially um in which we build a
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prototype and then as it becomes import
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more important and as robustness and
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reliability becomes more important then
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we gradually build up that test St here
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in parallel but I see exciting
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Innovations to be had still in how we
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build evals um and then what I'm seeing
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as well is the prototyping of machine
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learning has become much faster but
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building a software application has lots
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of steps does the product work you know
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the design work does the software
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integration work a lot of Plumbing work
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um then after deployment Dev Ops and L
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Ops so some of those other pieces are
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becoming faster but they haven't become
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faster at the same rate that the machine
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learning modeling pot has become faster
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so you take a process and one piece of
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it becomes much faster um what I'm
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seeing is prototyping is not really
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really fast but sometimes you take a
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prototype into robust reliable
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production with guard rails and so on
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those other steps still take some time
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but the interesting Dynamic I'm seeing
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is the fact that the machine learning p
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is so fast is putting a lot of pressure
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on organizations to speed up all of
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those other parts as well so that's been
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exciting progress for our few and in
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terms of how machine learning
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development um is speeding things up I
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think the Mantra moved fast and break
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things got a bad rep because you know it
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broke things um I think some people
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interpret this to mean we shouldn't move
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fast but I disagree with that I think
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the better mindra is move fast and be
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responsible I'm seeing a lot of teams
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able to prototype quickly evaluate and
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test robustly so without shipping
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anything out to The Wider world that
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could you know cause damage or cause um
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meaningful harm I'm finding smart teams
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able to build really quickly and move
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really fast but also do this in a very
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responsible way and I find this
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exhilarating that you can build things
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and ship things and responsible way much
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faster than ever
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before now there's a lot going on in Ai
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and of all the things going on AI um in
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terms of technical Trend the one Trend
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I'm most excited about is agentic AI
00:06:44
workflows and so if you to ask what's
00:06:46
the one most important AI technology to
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pay attention to I would say is agentic
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AI um I think when I started saying this
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you know near the beginning of this year
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it was a bit of a controversial
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statement but now the word AI agents has
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is become so widely used uh by by
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Technical and non-technical people is
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become you know little bit of a hype
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term uh but so let me just share with
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you how I view AI agents and why I think
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they're important approaching just from
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a technical
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perspective the way that most of us use
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large language models today is with what
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something is called zero shot prompting
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and that roughly means we would ask it
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to uh give it a prompt write an essay or
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write an output for us and it's a bit
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like if we're going to a person or in
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this case going to an AI and asking it
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to type out an essay for us by going
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from the first word writing from the
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first word to the last word all in one
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go without ever using backspac just
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right from start to finish like that and
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it turns out people you know we don't do
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our best writing this way uh but despite
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the difficulty of being forced to write
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this way a Lish models do you know not
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bad pretty
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well here's what an agentic workflow
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it's like uh to gener an essay we ask an
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AI to First write an essay outline and
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ask you do you need to do some web
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research if so let's download some web
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pages and put into the context of the
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large H model then let's write the first
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draft and then let's read the first
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draft and critique it and revise the
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draft and so on and this workflow looks
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more like um doing some thinking or some
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research and then some revision and then
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going back to do more thinking and more
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research and by going round this Loop
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over and over um it takes longer but
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this results in a much better work
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output so in some teams I work with we
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apply this agentic workflow to
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processing complex tricky legal
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documents or to um do Health Care
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diagnosis Assistance or to do very
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complex compliance with government
00:08:45
paperwork so many times I'm seeing this
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drive much better results than was ever
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possible and one thing I'm want to focus
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on in this presentation I'll talk about
00:08:53
later is devise of visual AI where
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agentic repal are letting us process
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image and video data
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but to get back to that later um it
00:09:03
turns out that there are benchmarks that
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show seem to show a gentic workflows
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deliver much better results um this is
00:09:10
the human eval Benchmark which is a
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benchmark for open AI that measures
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learning out lar rage model's ability to
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solve coding puzzles like this one and
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um my team collected some data turns out
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that um on this Benchmark I think it was
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POS K Benchmark POS K metric GB 3.5 got
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48% right on this coding Benchmark gb4
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huge Improvement you know
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67% but the improvement from GB 3.5 to
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gbd4 is dwarf by the improvement from
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gbt 3.5 to GB 3.5 using an agentic
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workflow um which gets over up to about
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95% and gbd4 with an agentic workflow
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also does much better um and so it turns
00:09:58
out that in the way Builders built
00:10:00
agentic reasoning or agentic workflows
00:10:03
in their applications there are I want
00:10:05
to say four major design patterns which
00:10:07
are reflection two use planning and
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multi-agent collaboration and to
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demystify agentic workflows a little bit
00:10:14
let me quickly step through what these
00:10:16
workflows mean um and I find that
00:10:19
agentic workflows sometimes seem a
00:10:21
little bit mysterious until you actually
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read through the code for one or two of
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these go oh that's it you know that's
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really cool but oh that's all it takes
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but let me just step through
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um to for for concreteness what
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reflection with ls looks like so I might
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start off uh prompting an L there a
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coder agent l so maybe an assistant
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message to your roles to be a coder and
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write code um so you can tell you know
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please write code for certain tasks and
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the L May generate codes and then it
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turns out that you can construct a
00:10:52
prompt that takes the code that was just
00:10:54
generated and copy paste the code back
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into the prompt and ask it you know he
00:10:59
some code intended for a Tas examine
00:11:01
this code and critique it right and it
00:11:04
turns out you prompt the same Elum this
00:11:05
way it may sometimes um find some
00:11:09
problems with it or make some useful
00:11:12
suggestions out proofy code then you
00:11:14
prompt the same LM with the feedback and
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ask you to improve the code and become
00:11:19
with with a new version and uh maybe
00:11:21
foreshadowing two use you can have the
00:11:23
LM run some unit tests and give the
00:11:25
feedback of the unit test back to the LM
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then that can be additional feedback to
00:11:29
help it iterate further to further
00:11:31
improve the code and it turns out that
00:11:33
this type of reflection workflow is not
00:11:35
magic doesn't solve all problems um but
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it will often take the Baseline level
00:11:39
performance and lift it uh to to better
00:11:43
level performance and it turns out also
00:11:46
with this type of workflow where we're
00:11:47
think of prompting an LM to critique his
00:11:49
own output use it own criticism to
00:11:51
improve it this may be also foreshadows
00:11:54
multi-agent planning or multi-agent
00:11:56
workflows where you can prompt one
00:11:58
prompt an LM to sometimes play the role
00:12:00
of a coder and sometimes prom on to play
00:12:03
the role of a CR of a Critic um to
00:12:06
review the code so such the same
00:12:08
conversation but we can prompt the LM
00:12:10
you know differently to tell sometimes
00:12:13
work on the code sometimes try to make
00:12:15
helpful suggestions and this same
00:12:17
results in improved performance so this
00:12:19
is a reflection design pattern um and
00:12:24
second major design pattern is to use uh
00:12:27
in which a lar language model can be
00:12:29
prompted to generate a request for an
00:12:31
API call to have it decide when it needs
00:12:34
to uh search the web or execute code or
00:12:37
take a the task like um issue a customer
00:12:39
refund or send an email or pull up a
00:12:41
calendar entry so to use is a major
00:12:43
design pattern that is letting large
00:12:45
language models make function calls and
00:12:47
I think this is expanding what we can do
00:12:49
with these agentic workflows um real
00:12:52
quick here's a planning or reasoning
00:12:55
design pattern in which if you were to
00:12:57
give a fairly complex request you know
00:12:58
generate image or where girls reading a
00:13:01
book and so on then an LM this example
00:13:04
adapted from the hugging GTP paper an LM
00:13:06
can look at the picture and decide to
00:13:09
first use a um open pose model to detect
00:13:12
the pose and then after that gener
00:13:14
picture of a girl um after that you'll
00:13:17
describe the image and after that use
00:13:19
sex the spe or TTS to generate the audio
00:13:21
but so in planning you an L look at a
00:13:24
complex request and pick a sequence of
00:13:27
actions execute in order to deliver on a
00:13:30
complex task um and lastly multi Asian
00:13:33
collaboration is that design pattern
00:13:35
alluded to where instead of prompting an
00:13:37
LM to just do one thing you prompt the
00:13:40
LM to play different roles at different
00:13:42
points in time so the different agents
00:13:44
simulate agents interact with each other
00:13:46
and come together to solve a task and I
00:13:49
know that some people may may wonder you
00:13:52
know if you're using one why do you need
00:13:54
to make this one play the role with
00:13:57
multip multiple agents um many teams
00:13:59
have demonstrated significant improved
00:14:02
performance for a variety of tasks using
00:14:04
this design pattern and it turns out
00:14:07
that if you have an LM sometimes
00:14:08
specialize on different tasks maybe one
00:14:10
at a time have it interact many teams
00:14:13
seem to really get much better results
00:14:14
using this I feel like maybe um there's
00:14:18
an analogy to if you're running jobs on
00:14:20
a processor on a CPU you why do we need
00:14:23
multiple processes it's all the same
00:14:25
process there you know at the end of the
00:14:27
day but we found that having multiple FS
00:14:29
of processes is a useful extraction for
00:14:31
developers to take a task and break it
00:14:33
down to subtask and I think multi-agent
00:14:35
collaboration is a bit like that too if
00:14:37
you were big task then if you think of
00:14:39
hiring a bunch of agents to do different
00:14:41
pieces of task then interact sometimes
00:14:43
that helps the developer um build
00:14:46
complex systems to deliver a good
00:14:48
result so I think with these four major
00:14:52
agentic design patterns agentic
00:14:54
reasoning workflow design patterns um it
00:14:57
gives us a huge space to play with to
00:14:59
build Rich agents to do things that
00:15:01
frankly were just not possible you know
00:15:04
even a year ago um and I want to one
00:15:08
aspect of this I'm particularly excited
00:15:10
about is the rise of not not just large
00:15:13
language model B agents but large
00:15:15
multimodal based a large multimodal
00:15:17
model based agents so um give an image
00:15:21
like this if you were wanted to uh use a
00:15:25
lmm large multimodal model you could
00:15:27
actually do zero shot PR and that's a
00:15:29
bit like telling it you know take a
00:15:31
glance at the image and just tell me the
00:15:33
output and for simple image thoughts
00:15:36
that's okay you can actually have it you
00:15:38
know look at the image and uh right give
00:15:40
you the numbers of the runners or
00:15:42
something but it turns out just as with
00:15:44
large language modelbased agents SL
00:15:46
multi modelbased model based agents can
00:15:48
do better with an itative workflow where
00:15:51
you can approach this problem step by
00:15:53
step so detect the faces detect the
00:15:55
numbers put it together and so with this
00:15:58
more irrit workflow uh you can actually
00:16:00
get an agent to do some planning testing
00:16:03
right code plan test right code and come
00:16:06
up with a most complex plan as
00:16:08
articulated expressing code to deliver
00:16:11
on more complex thoughts so what I like
00:16:14
to do is um show you a demo of some work
00:16:17
that uh Dan Malone and I and the H AI
00:16:20
team has been working on on building
00:16:22
agentic workflows for visual AI
00:16:27
tasks so if we switch to my
00:16:31
laptop
00:16:32
um let me have an image here of a uh
00:16:38
soccer game or football game and um I'm
00:16:41
going to say let's see counts the
00:16:43
players in the vi oh and just so fun if
00:16:47
you're not how to prompt it after
00:16:49
uploading an image This little light
00:16:50
bulb here you know gives some suggested
00:16:53
prompts you may ask for this uh but let
00:16:55
me run this so count players on the
00:16:57
field right and what this kicks off is a
00:17:00
process that actually runs for a couple
00:17:02
minutes um to Think Through how to write
00:17:04
code uh in order to come up a plan to
00:17:07
give an accurate result for uh counting
00:17:10
the number of players in the few this is
00:17:11
actually a little bit complex because
00:17:12
you don't want the players in the
00:17:13
background just be in the few I already
00:17:15
ran this earlier so we just jumped to
00:17:18
the result um but it says the Cod has
00:17:22
selected seven players on the field and
00:17:26
I think that should right 1 2 3 4 5 six
00:17:28
seven
00:17:30
um and if I were to zoom in to the model
00:17:33
output Now 1 2 3 4 five six seven I
00:17:37
think that's actually right and the part
00:17:39
of the output of this is that um it has
00:17:45
also generated code uh that you can run
00:17:48
over and over um actually generated
00:17:51
python code uh
00:17:54
that if you want you can run over and
00:17:56
over on the large collection of images
00:17:59
es and I think this is exciting because
00:18:01
there are a lot of companies um and
00:18:04
teams that actually have a lot of visual
00:18:06
AI data have a lot of images um have a
00:18:09
lot of videos kind of stored somewhere
00:18:12
and until now it's been really difficult
00:18:15
to get value out of this data so for a
00:18:18
lot of the you know small teams or large
00:18:20
businesses with a lot of visual data
00:18:23
visual AI capabilities like the vision
00:18:25
agent lets you take all this data
00:18:27
previously shove somewhere in BL storage
00:18:29
and and you know get real value out of
00:18:31
this I think this is a big
00:18:32
transformation for AI um here's another
00:18:35
example you know this says um given a
00:18:38
video split this another soccer game or
00:18:42
football
00:18:43
game so given video split the video
00:18:46
clips of 5 Seconds find the clip where
00:18:48
go is being scored display a frame so
00:18:50
output so Rand is already because takes
00:18:52
a little the time to run then this will
00:18:54
generate code evaluate code for a while
00:18:56
and this is the output and it says true
00:19:00
1015 so it think those a go St you know
00:19:04
around here around between
00:19:06
the right and there you go that's the go
00:19:10
and also as instructed you know
00:19:13
extracted some of the frames associated
00:19:15
with this so really useful for
00:19:17
processing um video data and maybe
00:19:21
here's one last example uh of of of the
00:19:23
vision agent which is um you can also
00:19:25
ask it FR program to split the input
00:19:27
video into small video chunks every 6
00:19:29
seconds describe each chunk andore the
00:19:32
information at Panda's data frame along
00:19:33
with clip name s and end time return the
00:19:35
Panda's data frame so this is a way to
00:19:38
look at video data that you may have and
00:19:41
generate metadata for this uh that you
00:19:44
can then store you know in snow fake or
00:19:46
somewhere uh to then build other
00:19:48
applications on top of but just to show
00:19:50
you the output of this um so you know
00:19:54
clip name start time end time and then
00:19:57
there actually written code um here
00:20:00
right wrot code that you can then run
00:20:02
elsewhere if you want uh let me put in a
00:20:03
stream the tab or something that you can
00:20:06
then use to then write a lot of you know
00:20:10
text descriptions for this um and using
00:20:15
this capability of the vision agent to
00:20:17
help write code my team at Landing AI
00:20:21
actually built this little demo app that
00:20:24
um uses code from the vision agent so
00:20:26
instead of us sing the write code have
00:20:28
the Vision agent write the code to build
00:20:30
this metadata and then um indexes a
00:20:34
bunch of videos so let's see I say
00:20:36
browsing so skar airborne right I
00:20:39
actually ran this earlier hope it works
00:20:42
so what this demo shows is um we already
00:20:45
ran the code to take the video split in
00:20:47
chunks store the metadata and then when
00:20:50
I do a search for skier Airborne you
00:20:52
know it shows the clips uh that have
00:20:55
high
00:20:57
similarity right right oh marked here
00:20:59
with the green has high similarity well
00:21:02
this is getting my heart rate out seeing
00:21:03
do that oh here's another one whoa all
00:21:08
right all right and and the green parts
00:21:11
of the timeline show where the skier is
00:21:13
Airborne let's see gray wolf at night I
00:21:18
actually find it pretty fun yeah when
00:21:20
when you have a collection of video to
00:21:22
index it and then just browse through
00:21:24
right here's a gray wolf at night and
00:21:26
this timeline in green shows what a gr
00:21:29
wolf and Knight is and if I actually
00:21:30
jump to different part of the video
00:21:33
there's a bunch of other stuff as well
00:21:35
right there that's not a g wolf at night
00:21:37
so I that's pretty cool
00:21:40
um let's see just one last example so
00:21:47
um yeah if I actually been on the road a
00:21:50
lot uh but if sear if your luggage this
00:21:53
black luggage right
00:21:56
um there this but it turns out turns out
00:21:59
there actually a lot of black Luggage So
00:22:00
if you want your luggage let's say black
00:22:02
luggage with
00:22:04
rainbow strap this there a lot of black
00:22:08
luggage out
00:22:09
there
00:22:11
then you know there right black luggage
00:22:14
with rainbow strap so a lot of fun
00:22:16
things to do um and I think the nice
00:22:18
thing about this is uh the work needed
00:22:22
to build applications like this is lower
00:22:25
than ever before so let's go back to the
00:22:27
slides
00:22:30
um
00:22:33
and in terms of AI opportunities I spoke
00:22:37
a bit about agentic workflows and um how
00:22:42
that is changing the AI stack is as
00:22:44
follows it turns out that in addition to
00:22:48
this stack I show there's actually a new
00:22:51
emerging um agentic orchestration layer
00:22:54
and there little orchestration layer
00:22:56
like L chain that been around for a
00:22:58
while that are also becoming
00:22:59
increasingly agentic through langra for
00:23:02
example and this new agentic
00:23:04
orchestration layer is also making
00:23:06
easier for developers to build
00:23:08
applications on top uh and I hope that
00:23:10
Landing ai's Vision agent is another
00:23:13
contribution to this to makes it easier
00:23:15
for you to build visual AI applications
00:23:17
to process all this image and video data
00:23:21
that possibly you had but that was
00:23:22
really hard to get value all of um until
00:23:25
until more recently so but fire when I
00:23:28
you what to think are maybe four of the
00:23:30
most important AI Trends there's a lot
00:23:32
going on on AI is impossible to
00:23:34
summarize everything in one slide if you
00:23:36
had to make me pick what's the one most
00:23:38
important Trend I would say is a gentic
00:23:40
AI but here are four of things I think
00:23:42
are worth paying attention to first um
00:23:45
turns out agentic workflows need to read
00:23:47
a lot of text or images and generate a
00:23:49
lot of text so we say that generates a
00:23:51
lot of tokens and their exciting efforts
00:23:54
to speed up token generation including
00:23:56
semiconductor work by Sova Service drop
00:23:59
and others a lot of software and other
00:24:01
types of Hardware work as well this will
00:24:02
make a gentic workflows work much better
00:24:05
second Trend I'm about excited about
00:24:07
today's large language models has
00:24:09
started off being optimized to answer
00:24:11
human questions and human generated
00:24:14
instructions things like you know why
00:24:16
did Shakespeare write mcbath or explain
00:24:18
why Shakespeare wrote Mac beath these
00:24:19
are the types of questions that L
00:24:21
langage models are often as answer on
00:24:23
the internet but agentic workflows call
00:24:25
for other operations like to use so the
00:24:28
fact that large language models are
00:24:30
often now tuned explicitly to support
00:24:32
tool use or just a couple weeks ago um
00:24:35
anthropic release a model that can
00:24:37
support computer use I think these
00:24:39
exciting developments are create a lot
00:24:41
of lift rate create a much higher
00:24:43
ceiling for what we can now get atic
00:24:45
workloads to do with L langage models
00:24:48
that tune not just to answer human
00:24:50
queries but to tune EXA explicitly to
00:24:53
fit into these erative agentic workflows
00:24:57
um third
00:24:58
data engineering's importance is rising
00:25:01
particularly with unstructured data it
00:25:03
turns out that a lot of the value of
00:25:05
machine learning was a Structure data
00:25:07
kind of tables of numbers but with geni
00:25:10
we're much better than ever before at
00:25:12
processing text and images and video and
00:25:14
maybe audio and so the importance of
00:25:17
data engineering is increasing in terms
00:25:19
of how to manage your unstructured data
00:25:21
and the metad DAT for that and
00:25:22
deployment to get the unstructured data
00:25:24
where it needs to go to create value so
00:25:26
that that would be a major effort for a
00:25:28
lot of large businesses and then lastly
00:25:31
um I think we've all seen that the text
00:25:32
processing revolution has already
00:25:34
arrived the image processing Revolution
00:25:36
is in a slightly early phase but it is
00:25:38
coming and as it comes many people many
00:25:40
businesses um will be able to get a lot
00:25:42
more value out of the visual data than
00:25:45
was possible ever before and I'm excited
00:25:48
because I think that will significantly
00:25:49
increase the space of applications we
00:25:51
can build as well so just wrap up this
00:25:56
is a great time to be a builder uh gen
00:25:59
is learning us experiment faster than
00:26:01
ever a gentic AI is expanding the set of
00:26:03
things that now possible and there just
00:26:05
so many new applications that we can now
00:26:08
build in visual AI or not in visual AI
00:26:11
that just weren't possible ever before
00:26:13
if you're interested in checking out the
00:26:15
uh visual AI demos that I ran uh please
00:26:19
go to va. landing.ai the exact demos
00:26:21
that I ran you better try out yourself
00:26:24
online and get the code and uh run code
00:26:26
yourself in your own applications so
00:26:28
with that let me say thank you all very
00:26:31
much and please also join me in
00:26:32
welcoming Elsa back onto the stage thank
00:26:34
you
