This video shows a curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and code. The videos include great commentary on the future of AI by many important people in the field such as Fei-Fei Li, Luis Lamb, Gary Marcus, and more.
Enjoy the video!
Chapters:
0:00 Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff in 2021, I promise.
0:28 2020, A year in review
9:06 Where do you want AI to go?
References:
- Complete article: https://github.com/louisfb01/Best_AI_paper_2020
- GitHub repo: https://github.com/louisfb01/Best_AI_paper_2020
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Video Transcript
00:00
where do you want ai to go what what
00:02
would make you happy
00:03
what is the objective here the objective
00:05
function for those of us who are
00:06
building ai
00:08
um or trying to inform it from other
00:10
fields
00:11
what would count in success where do you
00:12
want to take it to
00:17
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04:50
but it's a trick no no it's much more
04:52
than that uh whosoever be he were they
04:54
shall have the power
04:57
whatever man it's a trick please be my
05:00
guest
05:02
come on really yeah oh this is gonna be
05:05
beautiful clint you've had a tough week
05:07
we won't hold it against you if you
05:08
can't get it up you may have seen this
05:10
before
05:10
right yeah all right
05:39
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06:24
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06:30
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06:51
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08:17
researchers and artists around the world
08:19
have been using style game 2 to do all
08:20
kinds of interesting work
08:22
at nvidia we've been pushing the
08:23
frontier of using gans to synthesize
08:25
extremely realistic images
08:28
the catch is that you need to train them
08:30
on extremely large quantities of data
08:32
so to tackle this challenge we've
08:33
invented a new approach for stylegan 2
08:35
that we call adaptive discriminator
08:37
augmentation or ada
08:38
this lets us reduce the number of
08:40
training images by a factor of 10 a
08:42
factor of 20 or more while still getting
08:44
great results
08:56
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09:06
as a scientist i want to
09:10
push the scientific knowledge and
09:12
principles of
09:13
ai further and further and the first
09:15
thing is start with
09:17
some really fundamental laws and
09:21
principles of
09:22
ai i keep saying that i still feel our
09:24
ai
09:25
era is pre-newtonian physics that we are
09:28
still studying phenomenology
09:30
and engineering but there is going to be
09:34
a moment
09:34
or a set of moments where we're starting
09:37
to understand
09:38
the principles of intelligence and that
09:41
is
09:42
the scientists in me we're in the head
09:45
of a citizen i guess
09:47
and directing stanford's human center ai
09:51
institute
09:52
i want this to be a technology that can
09:55
an idealistic way really better human
09:58
conditions
09:58
right it's so profound it's so
10:02
horizontal
10:03
it's so um it has so much human and
10:07
societal impact
10:10
it can be very very bad and can be very
10:12
very good
10:13
so there i would like to see a
10:16
framework of this technology being
10:19
developed and deployed
10:21
in the most benevolent way
10:25
what comes to mind very often so i hang
10:27
out because of the family i'm born into
10:30
with people who work at state
10:32
departments who deal with international
10:34
laws military laws etc
10:36
and there's a there's a there's a big
10:38
concern here with militarization of ai
10:40
right
10:41
danny just mentioned human decision
10:43
making humans in the loop why people are
10:45
trying to negotiate
10:46
treaties limiting should we limit should
10:49
they always be a human
10:50
in the loop when we when we build
10:52
dangerous machines
10:54
you know kill a robot as well as is
10:56
happening as we all know
10:58
in the defense world and and as will
11:01
happen so what about the growing threats
11:03
of militarization of ai which is ongoing
11:06
and what should we
11:07
uh should we do anything about it or
11:10
pessimistic can we do anything about it
11:12
or is that
11:15
genie out of the box i want the costs
11:18
and the benefits of ai to be
11:21
evenly distributed across society both
11:24
in the united states
11:26
and globally and i want the public to
11:29
trust that that's what's being done
11:31
and i think that's impossible without
11:34
changes to law
11:36
just impossible um i like one of the
11:38
sayings of michael raving
11:40
from harvard and hebrew university said
11:42
that it's great that
11:44
uh computer science has not been around
11:46
for 2000 years
11:48
and we are at a stage where very very
11:50
important results
11:51
occur in front of our eyes i also like a
11:54
saying by
11:56
alan turing in his mind paper where he
11:58
said we can only see a short distance
12:01
ahead
12:02
but we can see plenty there that needs
12:04
to be done
12:05
and as fei-fei said as scientists i want
12:08
ai to advance but since ai is having an
12:11
impact
12:12
as big as physics had in the 20th
12:15
century
12:15
engineering heading and 20th century all
12:18
the ethical issues
12:19
all the biases and all the social
12:21
implications that margaret and others
12:23
and here in francesca
12:25
have been studying they are key as
12:28
responsibility of our
12:29
ourselves as scientists and in terms of
12:32
uh
12:32
technical technically speaking what we
12:35
are trying to do
12:36
is to conciliate traditions of ai so
12:39
that we cannot see
12:41
and as you said gary before you've been
12:43
saying since the
12:44
late 90s we have to converge we have to
12:47
look for convergency
12:49
you cited the only colonial cited other
12:51
prominent scientists today
12:53
we need a way of seeing that several
12:56
techniques can contribute to this
12:57
endeavor of making ai
12:59
fairer ai less bias and ai
13:03
to make something very positive for us
13:06
as humanity we need uh as scientists to
13:09
see
13:09
our fields in a very uh human humanistic
13:13
way
13:14
so that not only the technical stuff
13:16
advances but we also have to
13:18
be guided by serious and by effective
13:20
ethical principles
13:22
laws and norms as ryan said it's hard to
13:25
do to do that at the moment we are at
13:27
the beginning of uh
13:29
an ai cambrian explosion as several
13:32
people mention here but we need to be
13:34
very aware
13:35
of the social ethical and global
13:38
implications
13:39
that ai has these days we have to be
13:41
concerned about the north south
13:44
divide about the different cultures in
13:46
order to regulate it properly
13:49
we cannot see it only from a single
13:52
cultural perspective so
13:53
that's what i want to see ai researchers
13:56
doing to be they have to be concerned
13:58
about the technical results
14:00
the outstanding results ai has been
14:02
showing but also
14:03
we have to be to care about other people
14:06
about
14:06
other people's other countries and over
14:09
and over all
14:10
for the global health of the planet
14:12
thank you very much
14:13
and merry christmas happy new year to
14:16
everyone thank you gary and vincent for
14:18
the
14:18
brilliant debate you brought today and
14:20
for the scientists
14:22
i am reminded of the old african proverb
14:24
that i'm sure you all know which is it
14:25
takes a village to raise a child
14:27
clearly it will take a village as we've
14:29
seen today
14:30
to raise an ai that is ethical robust
14:32
and trustworthy