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my<00:00:13.230><c> name</c><00:00:13.379><c> is</c><00:00:13.500><c> Gabor</c><00:00:13.799><c> Mele</c><00:00:14.129><c> I</c><00:00:14.219><c> work</c><00:00:14.430><c> at</c><00:00:14.580><c> Sony</c>

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my name is Gabor Mele I work at Sony
 

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my name is Gabor Mele I work at Sony
Playstation<00:00:15.690><c> I'm</c><00:00:16.049><c> a</c><00:00:16.110><c> senior</c><00:00:16.530><c> director</c><00:00:16.710><c> of</c>

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Playstation I'm a senior director of
 

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Playstation I'm a senior director of
engineering<00:00:17.550><c> focus</c><00:00:18.390><c> on</c><00:00:18.539><c> ML</c><00:00:18.960><c> and</c><00:00:19.230><c> AI</c>

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engineering focus on ML and AI
 

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engineering focus on ML and AI
applications

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now<00:00:29.230><c> this</c><00:00:29.410><c> is</c><00:00:29.530><c> my</c><00:00:29.710><c> my</c><00:00:30.010><c> second</c><00:00:30.370><c> time</c><00:00:30.520><c> it</c><00:00:31.270><c> the</c>

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now this is my my second time it the
 

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now this is my my second time it the
previous<00:00:32.110><c> time</c><00:00:32.230><c> was</c><00:00:32.349><c> about</c><00:00:32.470><c> two</c><00:00:32.739><c> years</c><00:00:33.250><c> ago</c>

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previous time was about two years ago
 

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previous time was about two years ago
and<00:00:33.730><c> it</c><00:00:33.820><c> was</c><00:00:33.910><c> a</c><00:00:33.970><c> different</c><00:00:34.360><c> organization</c><00:00:34.600><c> I</c>

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and it was a different organization I
 

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and it was a different organization I
was<00:00:35.080><c> focusing</c><00:00:35.590><c> on</c><00:00:35.710><c> natural</c><00:00:36.100><c> language</c>

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was focusing on natural language
 

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was focusing on natural language
processing<00:00:36.450><c> related</c><00:00:37.450><c> but</c><00:00:37.660><c> different</c><00:00:37.900><c> than</c>

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processing related but different than
 

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processing related but different than
what<00:00:38.290><c> I'm</c><00:00:38.410><c> doing</c><00:00:38.710><c> now</c><00:00:48.150><c> favorite</c><00:00:49.150><c> and</c>

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what I'm doing now favorite and
 

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what I'm doing now favorite and
important<00:00:49.960><c> part</c><00:00:50.170><c> about</c><00:00:50.379><c> my</c><00:00:50.590><c> talk</c><00:00:50.800><c> was</c>

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important part about my talk was
 

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important part about my talk was
characterizing<00:00:51.879><c> what</c><00:00:52.059><c> an</c><00:00:52.180><c> ml</c><00:00:52.600><c> engineer</c><00:00:53.170><c> does</c>

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characterizing what an ml engineer does
 

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characterizing what an ml engineer does
we've<00:00:53.890><c> been</c><00:00:54.129><c> trying</c><00:00:54.640><c> to</c><00:00:54.790><c> make</c><00:00:55.269><c> that</c><00:00:55.480><c> really</c>

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we've been trying to make that really
 

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we've been trying to make that really
clear<00:00:56.199><c> so</c><00:00:56.739><c> that</c><00:00:56.769><c> our</c><00:00:57.010><c> software</c><00:00:57.580><c> engineers</c><00:00:57.820><c> can</c>

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clear so that our software engineers can
 

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clear so that our software engineers can
know<00:00:58.510><c> where</c><00:00:58.690><c> they</c><00:00:58.809><c> can</c><00:00:58.989><c> fit</c><00:00:59.199><c> into</c><00:00:59.410><c> that</c>

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know where they can fit into that
 

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know where they can fit into that
pattern<00:01:00.129><c> and</c><00:01:00.489><c> maybe</c><00:01:00.940><c> even</c><00:01:01.089><c> level</c><00:01:01.570><c> up</c><00:01:01.720><c> some</c><00:01:01.930><c> of</c>

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pattern and maybe even level up some of
 

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pattern and maybe even level up some of
them<00:01:02.229><c> to</c><00:01:02.559><c> be</c><00:01:02.589><c> software</c><00:01:03.580><c> engineers</c>

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them to be software engineers
 

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them to be software engineers
transitioning<00:01:04.570><c> to</c><00:01:04.659><c> ml</c><00:01:04.989><c> engineers</c><00:01:13.770><c> so</c><00:01:14.770><c> Sony</c>

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transitioning to ml engineers so Sony
 

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transitioning to ml engineers so Sony
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PlayStation's is a large organization we
 

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PlayStation's is a large organization we
have<00:01:17.350><c> many</c><00:01:17.560><c> different</c><00:01:17.650><c> offices</c><00:01:18.549><c> and</c><00:01:18.729><c> they'll</c>

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have many different offices and they'll
 

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have many different offices and they'll
have<00:01:19.479><c> different</c><00:01:19.690><c> ambitions</c><00:01:20.320><c> to</c><00:01:20.470><c> do</c><00:01:20.619><c> machine</c>

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have different ambitions to do machine
 

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have different ambitions to do machine
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learning the work we're doing within my
 

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learning the work we're doing within my
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teams is to scale out all the machine
 

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teams is to scale out all the machine
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learning applications that we have so
 

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learning applications that we have so
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that others can self-serve to the work
 

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that others can self-serve to the work
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that we're doing so if we have features
 

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that we're doing so if we have features
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of all the customer they can reuse at
 

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of all the customer they can reuse at
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what<00:01:46.429><c> a</c><00:01:46.460><c> few</c><00:01:46.850><c> of</c><00:01:46.969><c> them</c><00:01:47.119><c> there's</c><00:01:47.299><c> a</c><00:01:47.359><c> lot</c><00:01:47.600><c> of</c>

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what a few of them there's a lot of
 

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what a few of them there's a lot of
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talks on machine learning is certainly a
 

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talks on machine learning is certainly a
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hot area and an important age
 

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hot area and an important age
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leading-edge area so it's been talks
 

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leading-edge area so it's been talks
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between er Microsoft Curie I there's but
 

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between er Microsoft Curie I there's but
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yes so several of them have been super
 

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yes so several of them have been super
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relevant to what we're doing well we've
 

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relevant to what we're doing well we've
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gotten into low latency frameworks or
 

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gotten into low latency frameworks or
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now we can do very very quick
 

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now we can do very very quick
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predictions now that's a matter of like
 

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predictions now that's a matter of like
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expanding that out to apply to so many
 

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expanding that out to apply to so many
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different kinds of predictions other
 

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different kinds of predictions other
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kinds of recommendations we still
 

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kinds of recommendations we still
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haven't cracked up not so we're hoping
 

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haven't cracked up not so we're hoping
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in the next year we can do a generalized
 

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in the next year we can do a generalized
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[Music]

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[Music]
 

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[Music]
sure<00:02:38.690><c> well</c><00:02:39.690><c> I</c><00:02:39.720><c> love</c><00:02:40.080><c> the</c><00:02:40.200><c> passion</c><00:02:40.470><c> here</c><00:02:41.130><c> in</c>

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sure well I love the passion here in
 

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sure well I love the passion here in
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terms of really getting into the nuts
 

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terms of really getting into the nuts
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and bolts of how machine learning and or
 

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and bolts of how machine learning and or
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even scaled systems work at the panel
 

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even scaled systems work at the panel
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last night on server let's go really
 

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last night on server let's go really
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animated and I could see that there's a
 

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animated and I could see that there's a
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lot of depth and a lot of passion here
 

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lot of depth and a lot of passion here
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so I really welcome that to scale it I
 

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so I really welcome that to scale it I
just<00:03:00.000><c> keep</c><00:03:00.150><c> it</c><00:03:00.300><c> up</c><00:03:00.480><c> this</c><00:03:01.470><c> menu</c><00:03:01.710><c> is</c><00:03:02.040><c> really</c>

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just keep it up this menu is really
 

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just keep it up this menu is really
great<00:03:03.210><c> for</c><00:03:03.690><c> this</c><00:03:04.320><c> year</c><00:03:04.590><c> so</c><00:03:04.800><c> that's</c><00:03:05.100><c> so</c><00:03:05.370><c> far</c><00:03:05.580><c> so</c>

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great for this year so that's so far so
 

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great for this year so that's so far so
good<00:03:06.260><c> yeah</c><00:03:07.260><c> I</c><00:03:07.710><c> can't</c><00:03:07.980><c> wait</c><00:03:08.040><c> to</c><00:03:08.190><c> see</c><00:03:08.340><c> what</c>

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good yeah I can't wait to see what
 

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good yeah I can't wait to see what
happens<00:03:08.760><c> next</c>

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