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I<00:00:20.540><c> guess</c><00:00:21.540><c> it</c><00:00:21.660><c> seems</c><00:00:21.810><c> like</c><00:00:22.020><c> we</c><00:00:22.140><c> keep</c><00:00:22.490><c> redefining</c>

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I guess it seems like we keep redefining
 

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I guess it seems like we keep redefining
AI<00:00:23.730><c> to</c><00:00:23.880><c> be</c><00:00:24.150><c> things</c><00:00:24.360><c> that</c><00:00:24.450><c> computers</c><00:00:24.779><c> can't</c><00:00:25.200><c> do</c>

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AI to be things that computers can't do
 

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AI to be things that computers can't do
that<00:00:26.220><c> seems</c><00:00:26.460><c> like</c><00:00:26.610><c> the</c><00:00:26.730><c> general</c><00:00:28.250><c> definition</c>

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that seems like the general definition
 

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that seems like the general definition
of<00:00:29.640><c> AI</c><00:00:29.790><c> guess</c><00:00:30.180><c> to</c><00:00:30.480><c> me</c><00:00:31.489><c> artificial</c>

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of AI guess to me artificial
 

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of AI guess to me artificial
intelligence<00:00:32.970><c> is</c><00:00:33.120><c> about</c><00:00:33.450><c> making</c><00:00:34.280><c> computers</c>

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intelligence is about making computers
 

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intelligence is about making computers
think<00:00:36.360><c> in</c><00:00:36.540><c> ways</c><00:00:36.720><c> that</c><00:00:37.079><c> humans</c><00:00:37.590><c> can</c><00:00:37.739><c> think</c>

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think in ways that humans can think
 

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think in ways that humans can think
making<00:00:38.160><c> them</c><00:00:38.340><c> as</c><00:00:38.460><c> smart</c><00:00:38.820><c> as</c><00:00:39.060><c> as</c><00:00:39.420><c> humans</c><00:00:40.020><c> are</c>

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making them as smart as as humans are
 

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making them as smart as as humans are
but<00:00:40.950><c> specific</c><00:00:41.460><c> to</c><00:00:41.550><c> our</c><00:00:41.670><c> work</c><00:00:41.880><c> it's</c><00:00:42.270><c> less</c>

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but specific to our work it's less
 

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but specific to our work it's less
around<00:00:43.430><c> making</c><00:00:44.430><c> machines</c><00:00:44.850><c> think</c><00:00:45.090><c> like</c><00:00:45.239><c> humans</c>

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around making machines think like humans
 

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around making machines think like humans
and<00:00:45.840><c> more</c><00:00:45.989><c> around</c><00:00:46.200><c> helping</c><00:00:47.070><c> computers</c><00:00:47.640><c> take</c>

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and more around helping computers take
 

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and more around helping computers take
care<00:00:48.720><c> of</c><00:00:48.930><c> business</c><00:00:49.710><c> processes</c><00:00:50.130><c> that</c><00:00:50.250><c> are</c>

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care of business processes that are
 

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care of business processes that are
important<00:00:50.670><c> to</c><00:00:50.730><c> our</c><00:00:50.760><c> customers</c><00:00:50.940><c> so</c><00:00:52.160><c> simple</c>

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important to our customers so simple
 

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important to our customers so simple
things<00:00:53.280><c> like</c><00:00:53.520><c> back</c><00:00:54.180><c> office</c><00:00:54.540><c> processing</c><00:00:55.230><c> or</c>

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things like back office processing or
 

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things like back office processing or
identifying<00:00:56.550><c> what's</c><00:00:56.760><c> in</c><00:00:56.910><c> an</c><00:00:57.000><c> image</c><00:00:57.270><c> that's</c>

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identifying what's in an image that's
 

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identifying what's in an image that's
the<00:00:57.750><c> kind</c><00:00:57.930><c> of</c><00:00:57.990><c> AI</c><00:00:58.140><c> that</c><00:00:58.440><c> crowd</c><00:00:58.650><c> Florida's</c><00:01:03.830><c> well</c>

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the kind of AI that crowd Florida's well
 

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the kind of AI that crowd Florida's well
I<00:01:04.890><c> think</c><00:01:04.979><c> I</c><00:01:05.250><c> think</c><00:01:06.650><c> AI</c><00:01:07.650><c> is</c><00:01:09.500><c> amazing</c><00:01:10.500><c> at</c><00:01:10.770><c> doing</c>

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I think I think AI is amazing at doing
 

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I think I think AI is amazing at doing
things<00:01:11.970><c> where</c><00:01:12.329><c> it</c><00:01:12.689><c> can</c><00:01:12.869><c> find</c><00:01:13.110><c> lots</c><00:01:13.350><c> and</c><00:01:13.590><c> lots</c>

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things where it can find lots and lots
 

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things where it can find lots and lots
of<00:01:13.829><c> training</c><00:01:14.189><c> data</c><00:01:14.340><c> so</c><00:01:14.909><c> in</c><00:01:15.360><c> cases</c><00:01:15.750><c> where</c><00:01:17.240><c> it's</c>

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of training data so in cases where it's
 

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of training data so in cases where it's
maybe<00:01:18.390><c> backing</c><00:01:18.750><c> up</c><00:01:18.840><c> so</c><00:01:19.170><c> AI</c><00:01:19.500><c> is</c><00:01:19.679><c> is</c><00:01:20.189><c> fantastic</c>

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maybe backing up so AI is is fantastic
 

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maybe backing up so AI is is fantastic
when<00:01:21.780><c> it</c><00:01:22.409><c> can</c><00:01:22.649><c> look</c><00:01:22.859><c> at</c><00:01:23.009><c> lots</c><00:01:23.310><c> and</c><00:01:23.609><c> lots</c><00:01:23.850><c> of</c>

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when it can look at lots and lots of
 

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when it can look at lots and lots of
examples<00:01:25.049><c> because</c><00:01:25.560><c> it's</c><00:01:25.890><c> nowhere</c><00:01:26.100><c> near</c><00:01:26.249><c> as</c>

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examples because it's nowhere near as
 

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examples because it's nowhere near as
smart<00:01:26.640><c> as</c><00:01:27.090><c> a</c><00:01:27.149><c> human</c><00:01:27.329><c> but</c><00:01:27.780><c> it</c><00:01:28.259><c> can</c><00:01:28.560><c> have</c><00:01:29.520><c> way</c>

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smart as a human but it can have way
 

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smart as a human but it can have way
more<00:01:29.819><c> patience</c><00:01:30.479><c> than</c><00:01:30.509><c> a</c><00:01:30.630><c> human</c><00:01:30.869><c> can</c><00:01:30.899><c> write</c><00:01:31.229><c> so</c>

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more patience than a human can write so
 

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more patience than a human can write so
it<00:01:31.469><c> can</c><00:01:31.590><c> look</c><00:01:31.770><c> at</c><00:01:31.889><c> so</c><00:01:32.159><c> many</c><00:01:32.340><c> more</c><00:01:32.609><c> things</c><00:01:33.359><c> in</c>

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it can look at so many more things in
 

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it can look at so many more things in
human<00:01:33.869><c> can</c><00:01:34.049><c> so</c><00:01:34.289><c> cases</c><00:01:34.979><c> where</c><00:01:35.130><c> there's</c><00:01:35.280><c> lots</c><00:01:35.399><c> of</c>

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human can so cases where there's lots of
 

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human can so cases where there's lots of
training<00:01:35.789><c> data</c><00:01:35.909><c> it's</c><00:01:36.090><c> gonna</c><00:01:36.210><c> be</c><00:01:36.420><c> really</c>

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training data it's gonna be really
 

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training data it's gonna be really
successful<00:01:36.899><c> like</c><00:01:37.350><c> for</c><00:01:37.530><c> example</c><00:01:38.210><c> Facebook's</c>

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successful like for example Facebook's
 

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successful like for example Facebook's
face<00:01:39.450><c> recognition</c><00:01:39.659><c> is</c><00:01:40.049><c> fantastic</c><00:01:40.679><c> right</c>

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face recognition is fantastic right
 

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face recognition is fantastic right
because<00:01:41.130><c> you</c><00:01:41.460><c> know</c><00:01:41.549><c> they</c><00:01:41.670><c> get</c><00:01:41.789><c> billions</c><00:01:42.119><c> of</c>

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because you know they get billions of
 

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because you know they get billions of
selfies<00:01:42.659><c> uploaded</c><00:01:43.020><c> every</c><00:01:43.200><c> day</c><00:01:43.350><c> right</c><00:01:43.799><c> or</c>

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selfies uploaded every day right or
 

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selfies uploaded every day right or
Google<00:01:44.459><c> Translate</c><00:01:44.999><c> has</c><00:01:45.209><c> is</c><00:01:45.479><c> it's</c><00:01:45.840><c> quite</c><00:01:46.170><c> quite</c>

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Google Translate has is it's quite quite
 

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Google Translate has is it's quite quite
good<00:01:46.590><c> not</c><00:01:47.159><c> because</c><00:01:47.459><c> the</c><00:01:47.700><c> AI</c><00:01:47.909><c> deeply</c>

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good not because the AI deeply
 

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good not because the AI deeply
understands<00:01:49.020><c> the</c><00:01:49.139><c> text</c><00:01:49.530><c> but</c><00:01:49.709><c> because</c><00:01:50.069><c> Google</c>

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understands the text but because Google
 

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understands the text but because Google
has<00:01:50.850><c> so</c><00:01:51.270><c> many</c><00:01:51.709><c> examples</c><00:01:52.709><c> of</c><00:01:53.039><c> documents</c><00:01:53.639><c> in</c>

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has so many examples of documents in
 

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has so many examples of documents in
language<00:01:54.600><c> 1</c><00:01:54.749><c> and</c><00:01:54.869><c> language</c><00:01:55.079><c> to</c><00:01:55.289><c> that</c><00:01:55.979><c> that</c>

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language 1 and language to that that
 

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language 1 and language to that that
they're<00:01:56.939><c> able</c><00:01:57.060><c> to</c><00:01:57.240><c> see</c><00:01:57.569><c> so</c><00:01:57.869><c> many</c><00:01:57.990><c> edge</c><00:01:58.380><c> cases</c>

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they're able to see so many edge cases
 

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they're able to see so many edge cases
and<00:01:59.189><c> situations</c><00:01:59.909><c> to</c><00:01:59.969><c> make</c><00:02:00.029><c> the</c><00:02:00.179><c> translation</c>

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and situations to make the translation
 

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and situations to make the translation
work<00:02:00.749><c> well</c><00:02:00.959><c> I</c><00:02:01.770><c> think</c><00:02:01.979><c> where</c><00:02:02.159><c> it's</c><00:02:02.249><c> trouble</c>

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work well I think where it's trouble
 

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work well I think where it's trouble
struggles<00:02:03.090><c> is</c><00:02:03.179><c> cases</c><00:02:03.539><c> where</c><00:02:03.779><c> it's</c><00:02:04.229><c> really</c>

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struggles is cases where it's really
 

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struggles is cases where it's really
hard<00:02:04.590><c> to</c><00:02:04.679><c> get</c><00:02:04.770><c> training</c><00:02:05.069><c> data</c><00:02:05.249><c> right</c><00:02:05.459><c> so</c><00:02:05.700><c> it's</c>

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hard to get training data right so it's
 

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hard to get training data right so it's
it's<00:02:06.509><c> phenomenally</c><00:02:07.499><c> hard</c><00:02:07.920><c> for</c><00:02:08.340><c> robots</c><00:02:08.670><c> to</c>

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it's phenomenally hard for robots to
 

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it's phenomenally hard for robots to
grab<00:02:09.030><c> things</c><00:02:09.330><c> right</c><00:02:09.810><c> I</c><00:02:09.840><c> mean</c><00:02:09.990><c> amazing</c><00:02:10.470><c> how</c>

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grab things right I mean amazing how
 

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grab things right I mean amazing how
what<00:02:11.069><c> a</c><00:02:11.100><c> challenge</c><00:02:11.430><c> that</c><00:02:11.550><c> is</c><00:02:11.580><c> and</c><00:02:11.910><c> I</c><00:02:12.150><c> think</c>

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what a challenge that is and I think
 

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what a challenge that is and I think
that's<00:02:12.540><c> because</c><00:02:12.870><c> there's</c><00:02:13.020><c> not</c><00:02:13.200><c> a</c><00:02:13.230><c> lot</c><00:02:13.560><c> of</c><00:02:13.590><c> easy</c>

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that's because there's not a lot of easy
 

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that's because there's not a lot of easy
ways<00:02:14.250><c> that</c><00:02:14.459><c> we</c><00:02:14.580><c> know</c><00:02:14.700><c> now</c><00:02:14.910><c> to</c><00:02:14.940><c> generate</c>

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ways that we know now to generate
 

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ways that we know now to generate
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training data around grasping
 

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training data around grasping
[Music]

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sure<00:02:23.040><c> I</c><00:02:23.069><c> mean</c><00:02:23.250><c> so</c><00:02:23.370><c> so</c><00:02:23.849><c> my</c><00:02:24.060><c> company</c><00:02:24.299><c> CrowdFlower</c>

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sure I mean so so my company CrowdFlower
 

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sure I mean so so my company CrowdFlower
is<00:02:25.019><c> key</c><00:02:25.290><c> focus</c><00:02:25.650><c> is</c><00:02:25.799><c> something</c><00:02:26.069><c> I've</c><00:02:26.159><c> always</c>

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is key focus is something I've always
 

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is key focus is something I've always
cared<00:02:26.609><c> a</c><00:02:26.700><c> ton</c><00:02:26.879><c> about</c><00:02:27.150><c> which</c><00:02:27.420><c> is</c><00:02:27.599><c> making</c><00:02:28.500><c> AI</c>

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cared a ton about which is making AI
 

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cared a ton about which is making AI
actually<00:02:29.219><c> useful</c><00:02:29.549><c> for</c><00:02:29.760><c> for</c><00:02:30.390><c> real</c><00:02:30.540><c> companies</c>

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actually useful for for real companies
 

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actually useful for for real companies
in<00:02:31.019><c> the</c><00:02:31.079><c> real</c><00:02:31.260><c> world</c><00:02:31.469><c> and</c><00:02:31.650><c> I</c><00:02:32.099><c> think</c><00:02:32.189><c> when</c><00:02:32.489><c> you</c>

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in the real world and I think when you
 

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in the real world and I think when you
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look at when you look at the opportunity
 

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look at when you look at the opportunity
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of AI I think every company can benefit
 

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of AI I think every company can benefit
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from it there's there's so many places
 

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from it there's there's so many places
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where we have humans doing things that
 

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where we have humans doing things that
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you know almost like a simple pattern
 

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you know almost like a simple pattern
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matching algorithm would actually make
 

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matching algorithm would actually make
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things much more effective right and so
 

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things much more effective right and so
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you know if you look at why companies
 

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you know if you look at why companies
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struggle with it I actually think the
 

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struggle with it I actually think the
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algorithm is typically the least of
 

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algorithm is typically the least of
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people's problems right like everybody
 

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people's problems right like everybody
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kind of focuses on like oh is it you
 

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kind of focuses on like oh is it you
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know deep learning or SVM's or logistic
 

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know deep learning or SVM's or logistic
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regression and how do you do it but you
 

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regression and how do you do it but you
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know I think that the part that I'm
 

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know I think that the part that I'm
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really interested in because I think
 

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really interested in because I think
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it's the most difficult pieces you know
 

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it's the most difficult pieces you know
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how do you actually train these
 

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how do you actually train these
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algorithms how do you do feature
 

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algorithms how do you do feature
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selection and how do you actually deploy
 

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selection and how do you actually deploy
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them in the real world so that they
 

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them in the real world so that they
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actually get used
 

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actually get used
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well<00:03:20.100><c> one</c><00:03:20.220><c> thing</c><00:03:20.340><c> I</c><00:03:20.400><c> really</c><00:03:20.610><c> admire</c><00:03:20.880><c> about</c><00:03:20.910><c> the</c>

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well one thing I really admire about the
 

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well one thing I really admire about the
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the the by the bay conference is the
 

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the the by the bay conference is the
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authenticity of people that it brings
 

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authenticity of people that it brings
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out so it seems like that it's really by
 

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out so it seems like that it's really by
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the bay does an excellent job of getting
 

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the bay does an excellent job of getting
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getting speakers that AI professionals
 

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getting speakers that AI professionals
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actually really expect it really respect
 

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actually really expect it really respect
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right so you know I think a lot of
 

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right so you know I think a lot of
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conferences they kind of just look for
 

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conferences they kind of just look for
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the most famous people to a general
 

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the most famous people to a general
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audience but you can tell that AI by the
 

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audience but you can tell that AI by the
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bay is is made by people that really
 

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bay is is made by people that really
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understand artificial intelligence and
 

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understand artificial intelligence and
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are deep inside it and so when you look
 

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are deep inside it and so when you look
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at the speaker list it's people that I'm
 

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at the speaker list it's people that I'm
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personally super excited to hear talk
 

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personally super excited to hear talk
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