All You Need to Know About AI But Were Afraid to Ask

have you ever seen this

nice fellow before this robot now romeo

and pepper

i’ve been working with them for 10 years

and

romeo the big one is almost my fifth

child

i discovered robotics after my master’s

in ai at the end of the 80s and i never

let them since

first i’ve been working 20 years at cea

research institute where i developed

robots for nuclear application

and others for disabled handicapped

people

then 10 years at aldebarans of

bankrobotics with these fellows

and today i’m an expert in ai at

renault for intelligent car

compared to the ai i discovered when i

was young

the ai of today is incredibly more

powerful

you can find any information you want on

the web

uh ai can defeat you defeat any chess or

go master

uh was supposed to be the the most

intelligent guy

in the world and computer vision has

made a tremendous gap

ai computers can recognize your face as

we do but

it can also detect teeny tumor much

better than any expert radiologist

at aldebaran we were trying to make our

robot

ever clever more clever and more clever

for instance we taught now to climb a

ladder

and uh pepper has been trained to to

play you know cup and bold game

and um by training so we showed it a

good gesture once and then it train

again

and again and again and finally succeed

and when

you succeed to find a good gesture it

can redo it exactly

perfectly and win at every time i’m

always completely amazed

when i see robots doing uh

wonderful tricks and i’m not the only

one

when we conducted experiments with

pepper

with elderly people they were completely

amazed uh

to see the robots doing uh a target

by themselves and the favorite moment

was when the robot goes

to charging to the charging station the

fact that the robot decides by itself

well my battery are low i have to go

charging and does it was completely

amazing for them

another thing that is really amazing to

me are the magic tricks of my son

my oldest son simon is an amateur

magician and i’m

always delighted when the cartoon i

picked up in the deck reappeared

in my in my wallet or in my pocket or in

my ear

sometimes he explained me the trick and

it’s

always off very simple

very clever requiring some dexterity of

course

and i think ai is the same kind of magic

it looks magic

but it’s not when you know what is

behind the curtain

you are amazed but a little bit

disappointed

for instance if you

change the ladder of now it will redo

exactly the same gesture and of course

it will falls down and pathetically keep

on crawling on the ground

ridiculously and and the day paper

succeeded

in in this cup and ball game we brought

it in the in the ceo’s office to show

the performance

and of course it failed actually we

we did return a paper in our lab

and with a special slope on the ground

and when we were in the ceo’s office you

know on the thick carpet

the vertical angle of the robot was not

exactly the same so the perfect gesture

there was not the perfect gesture here

and it failed of course

all these little troubles can be fixed

but what i want you to to see that the

intelligence of your machine is very

narrow

as soon as you change a little bit uh

the situation

things can go completely wrong do you

know this story

of the tesla autonomous car that went

directly

through a truck knocked over in the

middle of the highway

the vision system of tesla is one of the

best in the world

it has been trained by watching millions

of

driving hours millions of hours of

driving video

each image of it was annotated

you know by showing on this picture you

have pedestrian

a car a cycle this is a tree

this is a building this is a road

you have to show millions of bicycles to

the system for it to recognize another

bicycle and this tedious work

made by human beings is necessary to

make machine

intelligent and thanks to this

supervised learning

you are your your computer your vision

system is able to recognize 99.9

of the object you will ever cross on the

road but not 100

during the training phase it never have

seen

such a track in the middle of the road

so it was unable to recognize

this object as an obstacle you probably

thought it was a

the sky or or a billboard above the road

but not

an obstacle that should be avoided and

then it went through

today we consider that autonomous

driving will avoid

plenty of accidents that human beings

have

but it will also generate crashes that

even the worst driver

could never have ai is perfect in its

domain but as soon

as it outside of it it’s fair

it’s it’s a drama and and and the the

the worst is that

ai does not have the consciousness of

being outside of its domain

so what is going to happen now

will researchers solve all these little

problems

or will ai reach a kind of limit

in 1997

when uh deep blue defeated the the chess

world master kasparov

people say yes you know but chess it’s

simple

it will never happen with go that is

really complicated

and 20 years later it happened alphago

defeated lisa doll thanks to the huge

progress of

ai and the moore’s law you know that

makes the

the computing power twice more powerful

every two years

now we can implement solution that would

even

dare to hope in in the 80s because of

the required

computing power but today you can run

neural deep neural network on a laptop

your smartphone can defeat you at any

game you play

so it’s really difficult to guess what

will be the limitation of ai

the victory of alphago made me think

that

saying that something it is impossible

to ai

it’s impossible today researchers are

working on

unsupervised learning it means that you

don’t have to explain for each input

what is supposed to be the expected

output

in the domain of out of domain detection

we are progressing too it means that the

ai

will be able to say wow but this object

i’ve never seen before so i don’t take a

decision

yes that’s that’s the first step to our

consciousness it’s nice to know that i

don’t know

but the common sense is missing

you as a driver when you see an object

that you don’t know the name of in front

of you you know what to do you know if

you should break or turn around or or

or or or keep on but ai does not have

this implicit understanding of unknown

situations

when you think about the way you learn

to to to to make

computers learning you know how it’s

simple to learn but oh it’s complained

complex to explain

and watching children learning is a good

way to try to understand

a baby does not need to to see millions

of teddy bear to recognize another teddy

bear

but in the other way around it’s

incredible

to see how difficult it is for them to

remember that 7 times 8

is 56 when a very cheap calculator can

do it

hey daddy if a very cheap calculator can

do it why should i remember that

hey i’m talking to these people ask your

mother

excuse me um yeah so it means that um

a a community of researchers

is working on what we call developmental

robotics

they want to implement the way that

children learn and without or there is

very few supervision

only by experiment by experience the

computer will discover the world

the effect of the is motioned with the

rest of his body

and this is this is a very very complex

task it’s it requires a lot of

mathematics to do that

but it will take very very long time for

this kind of intelligence

to become a threat for humanity

because at the end of the day that is

the question that the reason why you’re

listening to me

will i overcome humanity the idea of

singularity you know singularity will be

the moment where

computers will be more clever than human

beings

as we’ve seen we’re already there

computers can defeat you at

any games a computer can store

and use more information that all humans

can do all together

alan turing you know that is one of the

father of the ai

he said ai will really exist today

you will not be able to detect if you’re

talking to a machine or

to a human being when you discuss with

uh with some chat bots today you can

have long discussion

sometimes more interesting than a

discussion that you can have with a

football fan at the stadium

some people have tried to evaluate the

computing power

of a human brain they say it’s about 20

petaflops

as you know one petaflop is one million

of billion operations per second

others say it’s a thousand petaflops

anyway

today the most powerful computers are

hundred petaflops

you’re not so we’re not so far

and what do we have do we have computers

able to create by themselves

even more intelligent computers

no so okay futurologists say that

singularity will happen in 15 20 years

from now

but what will happen in the meantime

always more computing borrower

sure self-learning machine

self-replicating machine why not

but does it mean that this will lead to

the destruction of humanity

in a way it’s a very anthropomorphic way

to see the world

think that if you are more clever than

people you will overcome them

why couldn’t we couldn’t we imagine that

we can live

peacefully side by side with very

intelligent machines

like the electricity like the internet

ai

will be more and more important in our

lives

if we try to compete in its excellence

domain

we are lost it’s like trying to run

faster than a car on the highway

it’s silly but being a pedestrian

you can climb mountains you can dance or

you can play football

of course you cannot play anymore and

you cannot run on the highway anymore

and and do you regret it and that’s my

point

why shall we regret that ai can do

for us ai is invitable at chess

okay but did we stop playing chess no

but we can have ai as a sparring partner

to prepare

the next game against our friends

it will be difficult to turn ai off

so now we have to imagine the life that

go with it

a study in 2018 showed that

60 of the jobs created in the u.s

between 1980 and 2015

had names that did not exist before you

know

because of uh internet and computers of

course

but with ai it will be the same

it’s it’s it’s funny to see that every

week you can have

new application on your smartphone i

think there are about

two million applications available on

the apple store

none of them are not all of them are

made of ai but plenty are

and and it’s incredible to see how

imaginative people are to create these

new services for us

and ai is a wonderful support for them

for their creativity

by the way it’s interesting to see that

so many people

are able to develop ai-based

applications because

ai algorithms rely on very high level

mathematics

of course you know the principle of

neural networks so you have a

data coming from the previous layers

coming here and

and each value is multiplicated by the

white a weight all together they are sum

up and then transfer

to the next neural layers

that is pretty simple what is a little

bit tricky is to compute

the multiplicative weight for each

neuron and to have the expected

output and what is a little bit

complicated

is when you try to deal with images for

instance then you need

convolutive networks be more complicated

and if you want to to deal with video

you need

convolutive and recursive neural

networks

this is really complicated and ai

researchers are working hard

to make the ai more and more efficient

thousands of

very high level scientific papers are

published every

year what i want you to understand with

this

is that even if ai is based on learning

it does not develop by itself brilliant

human beings are

improving the performance of ai

and the learning part of it is just how

to apply this generic

architecture to specific use case like

for instance

translating greek instead of hungarian

or recognizing plants

instead of birds

this make possible for almost anyone to

develop

ai based applications the idea that

researchers find as soon as a researcher

finds this this new architecture they

publish it

and you can reuse it today amongst the

best researchers in ai

come from google and and facebook they

are not really public institutional

philanthropic associations

but they publish almost everything and

then good engineer can reuse that

for its own application

it’s um actually it’s a massive change

in the way an economy can grow

finally i would like to compare the

ai to a big wave you can

consider it as a terrifying tsunami

but because ai comes from california you

also can consider it

like surfer do it’s a way to have fun

and to go fast

you have different kind of of of server

you have the researchers you have the

developers and you have the users

and as users what is really important is

you

understand what can and cannot do ai

ai cannot be human er

ai is still hard time to deal with

complex human emotions

ai cannot improvise in this complex

situation and that’s where we have a

place

to deal with people to take care of

people in complex situation

in this situation if you behave like a

robot

sooner or later you will be replaced by

a robot

but if you behave like a human you will

always

have your place thank you

在这个机器人之前你见过这个好人吗 romeo

and pepper

我已经和他们一起工作了 10 年

而大 romeo 几乎是我的第五个

孩子

在 80 年代末获得人工智能硕士学位后发现了机器人技术,我 从来没有

让他们从

一开始我在 CEA 研究所工作了 20 年,在

那里我开发了

用于核应用的机器人

和其他用于残疾残疾人的机器人,

然后在 Aldebarans of

bankrobotics 和这些研究员一起工作了 10 年

,今天我是雷诺的人工智能专家

智能汽车

与我年轻时发现的人工智能相比

今天的人工智能非常

强大

你可以在网上找到任何你想要的信息

呃人工智能可以打败你打败任何国际象棋或

围棋大师

呃应该是最强大的

世界上的聪明人和计算机视觉已经

取得了巨大的差距

人工智能计算机可以像我们一样识别你的脸,

它也可以

比 aldeb 的任何专家放射科医生更好地检测

微小的肿瘤 阿兰,我们试图让我们的

机器人

变得更聪明,更聪明,更聪明

,例如我们现在教爬梯子

,呃胡椒已经被训练

来玩你知道的杯子和大胆的游戏

,嗯,通过训练,所以我们向它展示了一个

很好的姿态 一次又一次地训练

,最终成功

,当

你成功找到一个好的手势时,它

可以完全

完美地重做它,每次都赢,

当我看到机器人做呃

奇妙的把戏时,我总是非常惊讶,我' 我不是唯一

一个

当我们对老年人进行辣椒实验时,

他们非常

惊讶

呃看到机器人自己做一个

目标,最喜欢的时刻

是当机器人

去充电到充电站时

机器人决定的事实 就其本身而言,

我的电池电量很低,我必须去

充电,这对他们来说是否完全

令人惊奇

另一件事对我来说真的很神奇

是我儿子的魔术

我的大儿子西蒙是 业余

魔术师,

当我

在甲板上捡到的卡通片重新出现

在我的钱包里或口袋里或

耳边时,我总是很高兴

有时他向我解释了诀窍,

而且

总是非常简单

非常聪明当然需要一些灵巧

我认为人工智能是同一种魔法,

它看起来很神奇,

但不是当你知道幕后是什么时,

你会感到惊讶,而是有点

失望

,例如,如果你

改变现在的梯子,它会重做

完全相同的手势和 当然

它会掉下来,可悲地继续

在地上

爬行,而日纸

在这场杯球比赛中成功了,我们把

它带到了首席执行官的办公室来

展示性能

,当然它失败了,实际上

我们确实回来了 我们实验室的一篇论文,

地面上有一个特殊的斜坡

,当我们在首席执行官的办公室时,你

知道在厚厚的地毯

上机器人的垂直角度并不

完全相同,所以完美的手势 e

这里没有完美的手势

当然失败了

所有这些小麻烦都可以解决

但是我想让你看到

你的机器的智能很

只要你改变一点

呃情况

事情可以 完全错了你

知道

这个特斯拉自动驾驶汽车的故事

直接

穿过一辆在高速公路中间被撞倒的卡车

特斯拉的视觉系统是世界上最好的视觉系统之一

它经过数百万

驾驶小时的训练 数百万小时的

驾驶视频

每张图片都有注释

你知道在这张照片上你

有行人

一辆汽车 一辆自行车 这是一棵树

这是一座建筑物 这是一条道路

你必须向系统展示数百万辆自行车

要识别另

一辆自行车,人类所做的这项繁琐的工作

对于使机器智能是必要的,

并且由于这种

监督学习,

您就是您的计算机,您的视觉

系统能够 o 识别 99.9

个您将在路上穿过的物体,

在训练阶段不是 100 个 它从未

在马路中间看到过这样的轨道,

因此它无法

将此物体识别为您可能认为的障碍物

道路上方的天空或广告牌,

但不是

应该避开的障碍物,

然后它

今天通过了

有人工智能在它的领域是完美的,但一旦它在它的

领域

之外,它是

公平的,它是一个戏剧,

最糟糕的是,

人工智能没有意识

在它的领域之外,

所以现在会发生什么

研究人员解决了所有这些小

问题,

否则人工智能会

在 1997 年达到某种极限,

当时 uh deep blue 击败了国际象棋

世界大师卡斯帕罗夫

人们说是的,你知道,但是国际象棋很

简单,

它永远不会 围棋发生的事情

真的很复杂

,20 年后它发生了 alphago

击败了 lisa doll,这要归功于 AI 的巨大

进步

和你知道的摩尔定律,这

使得计算能力每两年增加一倍,

现在我们可以实施甚至可以实现的解决方案

敢于在 80 年代寄希望于

计算能力,但今天你可以

在笔记本电脑上运行神经深度神经网络,

你的智能手机可以在你玩的任何游戏中击败你,

所以真的很难猜到

AI 胜利的限制是什么

alphago 让我觉得

说某事是不可能的

今天研究人员

正在研究

无监督学习,这意味着你

不必为每个输入

解释在 out of 域中应该是什么预期

输出 域检测

我们也在进步,这意味着

人工智能

将能够说哇,但这个对象

我以前从未见过,所以我不做决定

是的 这是我们

意识的第一步很高兴知道我

不知道

但是

当你看到一个

你不知道名字的物体在你

面前你知道要做什么时,常识会想念你作为司机 你知道

你是否应该打破或转身或或

或或或或继续

但当你思考你学习的方式时,人工智能对未知情况没有这种隐含的理解

计算机学习你知道学习是多么

简单 但是,哦,解释起来很复杂

,看着孩子学习是

尝试理解婴儿的好方法,

不需要看到数

百万泰迪熊就可以识别另一个

泰迪熊 让他们

记住 7 乘以 8

是 56 当一个非常便宜的计算器可以

做到时

嘿爸爸 如果一个非常便宜的计算器可以

做到 我为什么要记住

嘿 我在和这些人说话 问你

妈妈

对不起 嗯是的 意味着吨 hat um

aa 研究人员社区

正在研究我们所说的发展性

机器人技术,

他们希望实现儿童学习的方式,

而无需或

很少有监督,

仅通过实验通过经验

计算机将发现世界

他身体的其余部分

,这是一项非常非常复杂的

任务,它需要大量的

数学才能做到这一点,

但这种智能需要很长时间

才能成为对人类的威胁,

因为归根结底 那

就是你听我的原因

我会克服人性的问题

你知道奇点的想法将是

计算机将比人类更聪明

的时刻我们已经看到我们已经在那里

计算机可以 在

任何游戏中击败你计算机可以存储

和使用更多的信息,所有人类

都可以一起完成

艾伦图灵你知道这是

人工智能的父亲之一

他说人工智能将阅读 今天仍然存在

,当您与某些聊天机器人讨论时,您将无法检测到您是在与机器交谈还是在与人类

交谈时,您可以

进行长时间的讨论,

有时比

体育场里的足球迷

一些人试图评估

人脑的计算能力 他们说它大约是 20

petaflops 众所周知,1 petaflops 是

每秒一百万次操作

其他人说无论如何今天是 1000 petaflops

最强大的计算机是

一百千万亿次,

你不是,所以我们还没有到

现在,我们有什么

可以自己创造的

计算机甚至更智能的计算机

不那么好吧未来学家说

奇点将在 15 20 年后发生,

但会发生什么 与此同时,

总是有更多的计算借款人

确定自学习机器

自复制机器为什么不

这样做,但这是否意味着这将

导致人类

在 这是一种非常拟人化的

看待世界的方式

认为如果你比人更聪明,

你就会战胜他们

为什么我们不能想象我们可以

与非常

智能的机器和平共处,

比如电力 互联网

人工智能

将在我们的生活中变得越来越重要

如果我们试图在其卓越领域竞争

我们迷失了这就像试图

在高速公路上跑得比汽车快

这很愚蠢但作为一个行人

你可以爬山你可以跳舞或

你当然可以踢

足球,你不能再踢球了,

你不能再在高速公路上跑步了

,你后悔吗,这就是我的

观点,

为什么我们要后悔人工智能可以

为我们做点人工智能在国际象棋中是受欢迎的,

好吧,但我们停止下国际象棋了吗? 不,

但我们可以让 AI 作为陪练伙伴

准备下一场与朋友

的比赛 1980 年至 2015 年间在美国创造的工作的

名称在你知道之前并不存在,

因为当然是互联网和计算机,

但是有了人工智能,它会是一样

的,有趣的是,

每周你都可以有

新的应用程序在你的 智能手机 我

认为 Apple

Store 上有大约 200 万个应用程序

不是所有应用程序都是

由 AI 制成的,但很多都是由

AI 制成的 非常

支持他们的创造力

,有趣的是看到

这么

多人能够开发基于人工智能的

应用程序,因为

人工智能算法依赖于非常高级的

数学当然你知道神经网络的原理,

所以你有

数据 从前面的层

到这里

,每个值都乘以

白色的权重,它们

相加,然后转移

到下一个神经层

,即 非常简单,

有点棘手的是

计算每个神经元的乘法权重

并获得预期的

输出,有点

复杂的

是,例如,当你尝试处理图像时,

需要更复杂的卷积网络

,如果 你想处理视频

你需要

卷积和递归神经

网络

这真的很复杂,人工智能

研究人员正在

努力使人工智能变得越来越高效

每年都会发表数千篇非常高水平的科学论文 我希望你理解

就是即使人工智能是基于学习的,

它本身也不是发展出来的,杰出的

人类正在

提高人工智能的性能

,它的学习部分就是

如何将这种通用

架构应用于特定的用例

,例如

翻译希腊语而不是 匈牙利语

或识别植物

而不是鸟类,

这使得几乎任何人都可以

开发

基于人工智能的应用程序

一旦研究人员

发现了这个新架构,研究人员就会发现他们

发布它的想法

,你可以在今天重用它

。人工智能领域最优秀的研究人员

来自谷歌和脸书,他们

并不是真正的公共机构

慈善协会,

但他们几乎发布了所有内容和

然后优秀的工程师可以将

其重用于自己的应用程序

它实际上

是经济增长方式的巨大变化

最后我想将

人工智能与大浪进行比较,您可以

将其视为可怕的海啸,

但因为人工智能来自加利福尼亚 您

也可以将其

视为冲浪者,这是一种享受乐趣

和快速前进的方式

您拥有不同类型的服务器

您拥有研究人员您拥有

开发人员并且您拥有

用户作为用户真正重要的是

了解什么 能做和不能做 AI

AI 不能做人 er

AI 仍然很难处理

复杂的人类情绪

AI 不能在这种复杂的

情况下即兴发挥 d 这就是我们

与人打交道的地方,可以在这种情况下照顾

处于复杂情况

下的人 如果你迟早表现得像机器人一样,

你迟早会被机器人取代,

但如果你表现得像人类,你将

永远

拥有你的 地方谢谢