Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated:
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So thank
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you very much for
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joining us today.
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We are very excited
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to speak
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a little bit
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about perplexity.
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Let me start
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about
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the beginnings,
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because I
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think it's
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very interesting.
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What inspired
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you to
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to create,
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perplexity
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as a search engine?
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I've been
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pretty motivated
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and obsessed
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about search.
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In fact, like, it's
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a combination
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of many,
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like, experiences
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that came together.
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My childhood
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has always
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been about like,
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was the emphasis,
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like,
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be a better
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knowledgeable.
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And when I went
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to my PhD
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program, Berkeley,
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I understanding of
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like the import
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of deciding things.
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And then,
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I got pretty
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I was in Berkeley,
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so Berkeley
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is pretty close
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to Silicon Valley,
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so you know
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and then I thought,
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okay, maybe
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I already finished
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my undergrad.
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So it's a long end.
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And I used to
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just be like
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an intern
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sleeping in
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the office
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all the time.
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And you
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read all
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these books,
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and I was wearing
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inspired by their
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PhD background,
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translating
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to a company.
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So I kind of wanted
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to do
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something
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like that.
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And it was
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also interesting
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how like,
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citations
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influence
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Larry Page
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to build a Google.
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Because the idea
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of page rank
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was based on
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academic citation.
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So given
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all these life
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experiences, my,
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in the
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in the mid 2022,
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when I felt like
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I was transitioning
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from something
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that was
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very research
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project oriented
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to like
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actually being,
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consumer facing
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applications
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with like, projects
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like GitHub
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Copilot really
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breaking out
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and like,
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getting a lot
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of adoption.
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I felt like
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I could go
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and realize
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my dream.
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And, that's
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when, like,
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you know,
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I got to realize
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my dream of
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starting company
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and working
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on things
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that truly
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are exciting to me.
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My investors said,
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hey, like,
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this is too far out
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and future
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specific thing
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you build up
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from there
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and all connects.
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It's very hard
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to search on.
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You can't go
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and find
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any specific post.
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You can go and find
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which was
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having like by
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which people
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you can go
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and find posts
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of some person
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like by
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another person.
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You can go
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and find like
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who follows
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this person
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that doesn't follow
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the other person
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like these kind
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of searches.
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So I wanted to
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build something
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like that
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where
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you could not do it
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without language
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models like you.
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Kig any query,
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you would have
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to convert that
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into some kind of,
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structured query
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language SQL.
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It's called SQL
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running
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against the
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database of
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all tweets
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organized
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in
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relational databases
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and then
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pull it together.
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But because
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language models
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began to work
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like coding
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language models,
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we built this
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amazing demo.
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It was working
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very well that like
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not only I use that
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or any investor
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I potential
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investors
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showed it to,
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they all started
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using it
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because
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something like
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that never existed.
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So I think that's
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what gave me
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the confidence
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that like
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we could go
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and do this
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for many other
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domains on
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the internet,
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not just Twitter,
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LinkedIn, GitHub,
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a lot of
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these websites,
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but then
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it would be
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a very
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difficult thing
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to scale this up
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if we did a domain
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by domain
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with domain,
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because the
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internet has so
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many domains.
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So we
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found a more
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general solution
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that just
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extracted links
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and then let the
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AI model
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do the reasoning.
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You, when you're
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trying to create it
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like it
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starts from a
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very simple idea
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that's very
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ingrained
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in nature.
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You scope it down
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scope of now
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and then, you solve
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some of IT
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problems,
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and then when
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and then you
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try to scale up
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and you
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realize like,
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oh, well,
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there's a much
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simpler solution.
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And then
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that ends up
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becoming a problem.
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What do you think
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are the
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biggest milestones
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for perplexity?
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This last year?
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So at the beginning
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of last year,
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a bunch of links
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given to you
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for like
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2 or 3 word
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queries on Google.
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So, at the
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time of
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Google's IPO
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in 2004,
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Google served
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like 100 to
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200 million
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daily queries.
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So, obviously
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the internet
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was much smaller
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at the time,
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which is actions.
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So that's
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the big focus area
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for us this year.
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And I think
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continuing
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to get better
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at answering
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even harder
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questions,
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ambiguous questions
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where sources on
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the web
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are not sufficient
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answer
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when I
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to go and
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elicit input
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from some experts
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on the field
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or proprietary data
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sets models,
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chaining all
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these actions
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together
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to accomplish that.
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Basically more
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agent work
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is, is kind of like
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how we,
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we plan to adapt
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to all the fast
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changes in AI.
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How do you think
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perplexity
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nowadays?
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It's, changing
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the way
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that the
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people use AI.
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Yeah.
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So yesterday
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we released
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this thing called,
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the Assistant.
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It works on
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Android phones.
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Okay.
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So the way
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it works is,
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like all Android
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phones have,
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like, a way
323
00:05:02,844 --> 00:05:03,428
to activate
324
00:05:03,428 --> 00:05:04,095
the system.
325
00:05:04,095 --> 00:05:04,929
And, I'm
326
00:05:04,929 --> 00:05:05,346
showing this
327
00:05:05,346 --> 00:05:06,180
demo on a pixel
328
00:05:06,180 --> 00:05:07,140
when you can
329
00:05:07,140 --> 00:05:07,390
use the
330
00:05:07,390 --> 00:05:08,433
power button.
331
00:05:08,433 --> 00:05:08,850
So you just
332
00:05:08,850 --> 00:05:09,475
click that
333
00:05:10,810 --> 00:05:11,352
and you say,
334
00:05:11,352 --> 00:05:12,562
can you play,
335
00:05:12,562 --> 00:05:13,771
video of,
336
00:05:13,771 --> 00:05:14,230
Donald
337
00:05:14,230 --> 00:05:14,897
Trump singing
338
00:05:14,897 --> 00:05:16,774
Baby Shark,
339
00:05:16,774 --> 00:05:19,986
playing Donald.
340
00:05:20,987 --> 00:05:23,531
Really useful.
341
00:05:23,531 --> 00:05:24,073
Or you can,
342
00:05:24,073 --> 00:05:24,324
you know,
343
00:05:24,324 --> 00:05:25,575
like loud sings.
344
00:05:25,575 --> 00:05:26,075
Can you play
345
00:05:26,075 --> 00:05:26,826
Despacito
346
00:05:26,826 --> 00:05:30,038
on Spotify?
347
00:05:30,455 --> 00:05:30,913
Okay.
348
00:05:30,913 --> 00:05:31,205
Playing
349
00:05:31,205 --> 00:05:32,874
Despacito by Luis
350
00:05:32,874 --> 00:05:34,334
Si and Daddy
351
00:05:34,334 --> 00:05:35,501
on Spotify.
352
00:05:35,501 --> 00:05:35,918
Like for a
353
00:05:35,918 --> 00:05:36,711
searching the web,
354
00:05:36,711 --> 00:05:37,337
then finding
355
00:05:37,337 --> 00:05:38,171
some detail, then
356
00:05:38,171 --> 00:05:38,629
going
357
00:05:38,629 --> 00:05:40,548
your app, then
358
00:05:40,548 --> 00:05:41,215
setting some
359
00:05:41,215 --> 00:05:41,841
action on
360
00:05:41,841 --> 00:05:43,301
on top of it.
361
00:05:43,301 --> 00:05:43,801
Or like you're
362
00:05:43,801 --> 00:05:44,552
doing research on
363
00:05:44,552 --> 00:05:45,136
like when
364
00:05:45,136 --> 00:05:45,928
is the next.
365
00:05:45,928 --> 00:05:47,555
Oh classical match.
366
00:05:47,555 --> 00:05:47,930
I like
367
00:05:47,930 --> 00:05:48,348
can you make a
368
00:05:48,348 --> 00:05:49,474
ticket for me
369
00:05:49,474 --> 00:05:49,724
or like
370
00:05:49,724 --> 00:05:50,016
can you go
371
00:05:50,016 --> 00:05:50,558
and find the
372
00:05:50,558 --> 00:05:51,559
tickets available.
373
00:05:51,559 --> 00:05:52,393
What is the price.
374
00:05:52,393 --> 00:05:53,353
Like the.
375
00:05:53,353 --> 00:05:54,145
I think this is
376
00:05:54,145 --> 00:05:54,937
very like.
377
00:05:54,937 --> 00:05:55,813
Your regular task
378
00:05:55,813 --> 00:05:56,064
that the.
379
00:05:56,064 --> 00:05:56,564
People do
380
00:05:56,564 --> 00:05:57,565
executive around
381
00:05:57,565 --> 00:05:58,900
periodically.
382
00:05:58,900 --> 00:05:59,108
Right.
383
00:05:59,108 --> 00:05:59,525
Oh like
384
00:05:59,525 --> 00:06:00,276
every morning
385
00:06:00,276 --> 00:06:01,069
give me an alert
386
00:06:01,069 --> 00:06:02,487
of like
387
00:06:02,487 --> 00:06:03,946
the stock market
388
00:06:03,946 --> 00:06:04,781
or whenever
389
00:06:04,781 --> 00:06:05,907
Nvidia stock
390
00:06:05,907 --> 00:06:06,908
goes or like let's
391
00:06:06,908 --> 00:06:07,658
say Bitcoin
392
00:06:07,658 --> 00:06:08,368
goes about
393
00:06:09,577 --> 00:06:11,079
5% on a day
394
00:06:11,079 --> 00:06:12,246
automatically,
395
00:06:12,246 --> 00:06:14,749
you know,
396
00:06:14,749 --> 00:06:15,792
if you find
397
00:06:15,792 --> 00:06:16,334
the velocity
398
00:06:16,334 --> 00:06:17,168
of increase
399
00:06:17,168 --> 00:06:19,212
is pretty high,
400
00:06:19,212 --> 00:06:20,296
buy some Bitcoin
401
00:06:20,296 --> 00:06:21,047
on my behalf
402
00:06:21,047 --> 00:06:21,756
or like anytime
403
00:06:21,756 --> 00:06:22,673
Bitcoin goes below
404
00:06:22,673 --> 00:06:22,965
a certain
405
00:06:22,965 --> 00:06:23,341
threshold,
406
00:06:23,341 --> 00:06:23,758
if you really
407
00:06:23,758 --> 00:06:24,008
believe
408
00:06:24,008 --> 00:06:25,093
in the future
409
00:06:25,093 --> 00:06:25,676
just by like
410
00:06:25,676 --> 00:06:26,260
thousand dollars
411
00:06:26,260 --> 00:06:26,677
with Bitcoin.
412
00:06:26,677 --> 00:06:27,553
For me,
413
00:06:27,553 --> 00:06:29,639
I think the money
414
00:06:29,639 --> 00:06:30,765
like like
415
00:06:30,765 --> 00:06:31,891
you know existing.
416
00:06:31,891 --> 00:06:32,558
So the users
417
00:06:32,558 --> 00:06:34,477
the big tech has.
418
00:06:34,477 --> 00:06:35,895
So
419
00:06:35,895 --> 00:06:37,355
what new things
420
00:06:37,355 --> 00:06:38,189
can we expect
421
00:06:38,189 --> 00:06:39,524
for perplexity
422
00:06:39,524 --> 00:06:40,566
soon in the next
423
00:06:40,566 --> 00:06:41,275
the months.
424
00:06:41,275 --> 00:06:41,526
Yeah.
425
00:06:41,526 --> 00:06:42,068
So we,
426
00:06:42,068 --> 00:06:43,319
we want to make
427
00:06:43,319 --> 00:06:44,028
all those agents
428
00:06:44,028 --> 00:06:45,822
working better,
429
00:06:45,822 --> 00:06:47,115
give a read, access
430
00:06:47,115 --> 00:06:48,074
to your emails
431
00:06:48,074 --> 00:06:48,491
and, like,
432
00:06:48,491 --> 00:06:49,242
calendar and,
433
00:06:49,242 --> 00:06:49,992
like, remind
434
00:06:49,992 --> 00:06:52,537
you about things,
435
00:06:52,537 --> 00:06:53,496
cancel meetings
436
00:06:53,496 --> 00:06:54,038
that are
437
00:06:54,038 --> 00:06:54,705
not productive
438
00:06:54,705 --> 00:06:57,041
for you.
439
00:06:57,041 --> 00:06:57,792
Like, like, book
440
00:06:57,792 --> 00:06:58,209
flights on
441
00:06:58,209 --> 00:06:58,876
your behalf
442
00:06:58,876 --> 00:07:00,420
and all stuff like,
443
00:07:00,420 --> 00:07:01,045
get truly
444
00:07:01,045 --> 00:07:01,712
personalized,
445
00:07:01,712 --> 00:07:02,505
like, understand,
446
00:07:02,505 --> 00:07:02,880
like what
447
00:07:02,880 --> 00:07:03,548
you really want
448
00:07:03,548 --> 00:07:04,215
and do stuff
449
00:07:04,215 --> 00:07:04,882
on your behalf.
450
00:07:04,882 --> 00:07:05,341
I think that's
451
00:07:05,341 --> 00:07:05,967
that's very one.
452
00:07:05,967 --> 00:07:07,593
Good. Okay. So
453
00:07:08,678 --> 00:07:09,929
let me put in, in
454
00:07:09,929 --> 00:07:12,098
other contexts,
455
00:07:12,098 --> 00:07:13,224
let's think about
456
00:07:13,224 --> 00:07:14,475
five years. Yeah.
457
00:07:14,475 --> 00:07:15,268
So I think it's,
458
00:07:15,268 --> 00:07:15,518
it's a
459
00:07:15,518 --> 00:07:18,729
lot in AI but.
460
00:07:19,480 --> 00:07:20,148
What do you think
461
00:07:20,148 --> 00:07:21,524
perplexity could be
462
00:07:21,524 --> 00:07:22,608
in five years?
463
00:07:22,608 --> 00:07:23,484
I think it can be
464
00:07:23,484 --> 00:07:24,277
one of the world's
465
00:07:24,277 --> 00:07:27,321
best, assistants.
466
00:07:27,321 --> 00:07:29,115
Like giving
467
00:07:29,115 --> 00:07:29,699
the life
468
00:07:29,699 --> 00:07:30,908
of a billionaire,
469
00:07:30,908 --> 00:07:34,078
the average person.
470
00:07:34,787 --> 00:07:35,371
You know,
471
00:07:35,371 --> 00:07:35,705
let me
472
00:07:35,705 --> 00:07:36,122
give you, like,
473
00:07:36,122 --> 00:07:36,914
some examples,
474
00:07:36,914 --> 00:07:37,206
right?
475
00:07:37,206 --> 00:07:38,040
Like,
476
00:07:38,040 --> 00:07:38,875
once upon a time,
477
00:07:38,875 --> 00:07:40,001
like vacations
478
00:07:40,001 --> 00:07:40,960
was a luxury.
479
00:07:40,960 --> 00:07:42,712
Okay. Right.
480
00:07:42,712 --> 00:07:44,255
In fact, like,
481
00:07:44,255 --> 00:07:45,465
only really
482
00:07:45,465 --> 00:07:45,965
rich people
483
00:07:45,965 --> 00:07:46,632
could afford
484
00:07:46,632 --> 00:07:47,175
a vacation
485
00:07:47,175 --> 00:07:47,675
to, like,
486
00:07:47,675 --> 00:07:48,301
even places
487
00:07:48,301 --> 00:07:48,926
like Hawaii
488
00:07:48,926 --> 00:07:50,803
in the US and,
489
00:07:50,803 --> 00:07:51,345
because it
490
00:07:51,345 --> 00:07:51,762
just cost
491
00:07:51,762 --> 00:07:52,221
a lot of money
492
00:07:52,221 --> 00:07:52,722
to go there.
493
00:07:52,722 --> 00:07:53,723
Earlier
494
00:07:53,723 --> 00:07:54,432
flights was
495
00:07:54,432 --> 00:07:55,391
full time,
496
00:07:55,391 --> 00:07:55,933
but with the
497
00:07:55,933 --> 00:07:56,684
indulgence of
498
00:07:56,684 --> 00:07:59,228
an Einstein.
499
00:07:59,228 --> 00:07:59,770
Taking a look
500
00:07:59,770 --> 00:08:00,646
at your life,
501
00:08:00,646 --> 00:08:01,606
your problems,
502
00:08:02,565 --> 00:08:03,816
and your
503
00:08:03,816 --> 00:08:04,984
constraints
504
00:08:04,984 --> 00:08:05,526
and trying to
505
00:08:05,526 --> 00:08:06,068
make it better.
26473
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