The birth of a word Deb Roy

[Music]

[Music]

imagine if you could record your life

everything you said everything you did

available in a perfect memory store at

your fingertips so you could go back and

find memorable moments and relive them

or sift through traces of time and

discover patterns in your own life that

previously had gone undiscovered well

that’s exactly the journey that my

family began five and a half years ago

this is my wife and collaborator rupal

and on this day at this moment we walked

into the house with their first child

our beautiful baby boy and we walked

into a house with a very special home

video recording system this moment and

thousands of other moments special for

us were captured in our home because in

every room in the house if you looked up

you’d see a camera and a microphone and

if you look down you get this bird’s-eye

view of the room here’s our living room

the baby bedroom kitchen dining room and

the rest of the house and all of these

fed into a disc array that was designed

for a continuous capture so here we are

flying through a day in our home as we

move from sunlit morning through

incandescent evening and finally lights

out for the day over the course of three

years we’ve recorded eight to ten hours

a day amassing roughly a quarter million

hours of multitrack audio and video so

you’re looking at a piece of what is by

far the largest home video collection

ever made

and what this data represents for our

family at a personal level the the

impact has already been immense and

we’re still learning its value countless

moments of unsolicited natural moments

not posed moments are captured there and

we’re starting to learn how to discover

them and find them but there’s also a

scientific reason that drove this

project which was to use this kind of

natural longitudinal data to understand

the process of how a child learns

language that child being my son and so

with many privacy provisions put in

place to protect everyone who’s recorded

in the data we made elements of the data

available to my trusted research team at

MIT so we could start teasing apart

patterns in this massive data set trying

to understand the influence of social

environments on language acquisition so

we’re looking here at one of the first

things we started to do this is my wife

and I cooking breakfast in the kitchen

and as we move through space and through

time a very everyday pattern of life in

the kitchen in order to convert this

opaque 90 thousand hours of video into

something we can start to see we use

motion analysis to pull out as we move

through space and through time what we

call space-time worms and this has

become a part of our toolkit for being

able to look and see where the

activities are in the data and with it

trace the patterns of in particular

where my son moved throughout the home

so we could focus our transcription

efforts all the speech environment

around my son all the words that he

heard for myself my wife our nanny and

over time the words he began to produce

so with that technology and that data

and the ability to with machine

assistants transcribe speech we’ve now

transcribed well over seven million

words of our home transcripts and with

that let me take you now for a first

tour into the data so you’ve all I’m

sure see

time-lapse videos where a flower will

blossom as you accelerate time I’d like

you to now experience the blossoming of

a speech form my son soon after his

first birthday would say Gaga to mean

water and over the course the next half

year he slowly learned to approximate

the proper adult form water so we’re

going to cruise through half a year in

about 40 seconds

no video here so you can focus on the

sound the acoustics of a new kind of

trajectory

[Music]

so he didn’t just learn water over the

course of the 24 months the first two

years that we really focused on this is

a map of every word he learned in

chronological order and because we have

full transcripts we’ve identified each

of the 503 words that he learned to

produce by his second birthday he was an

early talker

and so we started to analyze why why

were certain words born before others

this is one of the first results that

came out of our study a little over a

year ago that really surprised us the

way to interpret this apparently simple

graph is on the vertical is an

indication of how complex caregiver

utterances are based on the length of

utterances and the vertical axis is time

and all of the data we aligned based on

the following idea every time my son

would learn a word we would trace back

and look at all of the language he heard

that contain that word and we would plot

the relative length of the utterances

and what we found was this curious

phenomena that caregiver speech would

systematically dip to a minimum making

language as simple as possible and then

slowly ascend back up in complexity and

the amazing thing was that the that

bounce that dip lined up almost

precisely with when each word was born

word after word systematically so it

appears that all three primary

caregivers myself my wife and our nanny

were systematically and I would think

subconsciously restructuring our

language to meet him at the moment of

the birth of a word and bring him gently

into more complex language and the

implications of this there are many but

one I just want to point out is that

there must be amazing feedback loops

it’s not of course my son is learning

from his linguistic environment but the

environment is learning from him that

environment people are in these type

feedback loops and creating a kind of

scaffolding that has not been noticed

until now but that’s looking at the

speech context what about the visual

context we’re now looking at think of

this as a dollhouse cutaway of the of

our

we’ve taken those circular fisheye lens

cameras and we’ve done some optical

correction and then we can bring it into

a three dimensional life so welcome to

my home this is a moment one moment

captured across multiple cameras the

reason we did this is to create the

ultimate memory machine where you can go

back and interactively fly around and

then breathe video life into this system

what I’m going to do is give you an

accelerated view of 30 minutes again of

just life in the living room that’s me

and my son on the floor and there’s

video analytics that are tracking our

movements my son is leaving red ink I’m

leaving green ink we’re now on the couch

looking out through the window at cars

passing by and finally my son playing in

a walking toy by himself

now we freeze the action 30 minutes we

turn time into the vertical axis and we

open up for a view of these interaction

traces we’ve just left behind and we see

these amazing structures these little

knots of two colors of thread we call

social hotspots the spiral thread we

call a solo hotspot and we think that

these affect the way language is learned

what we’d like to do is start

understanding the interaction between

these patterns and the language that my

son is exposed to to see if we can

predict how the structure of when words

are heard affects when they’re learned

so in other words the relationship

between words and what they’re about in

the world so here’s how we’re

approaching this in this video again my

son is being traced out he’s leaving red

ink behind and there’s our nanny by the

door

she offers water and off go the two

worms over to the kitchen to get water

and what we’ve done is used the word

water to tag that moment that bit of

activity and now we take the power of

data and take every time my son ever

heard the word water and the context he

saw it in and we use it to penetrate

through the video and find every

activity trace that Co occurred with the

instance of water and what this data

leaves in its wake is a landscape we

call these word scapes this is the word

scape for the word water and you can see

most of the action is in the kitchen

that’s where those big Peaks are over to

the left and just for contrast we can do

this with any word we can take the word

by as a goodbye

and we’re now sumed in over the entrance

to the house and we look and we find as

you’d expect a contrast in the landscape

where the word by occurs much more in a

structured way so we’re using these

structures to start predicting the order

of language acquisition and that’s your

ongoing worth now in my lab which we’re

peering into now at MIT this is at the

Media Lab this has become my favorite

way of video graphing just about any

space three of the key people in this

project Philip the camp Ronny cubot and

Brendan Roy are pictured here Philip has

been a close collaborator and all the

visualizations you’re seeing and Michael

Fleischman was another PhD student in my

lab who worked with me on this home

video analysis and he made the following

observation that just the way that we’re

analyzing how language connects to

events which provide common ground for

language that same idea we can take out

of your home Deb and we can apply it to

the world of public media and so our

effort took an unexpected turn

think of mass media as providing common

ground and you have the recipe for

taking this idea to a whole new place

we’ve started analyzing television

content using the same principles

analyzing event structure of a TV signal

episodes of shows commercials all of the

components that make up the event

structure we’re now with satellite

dishes pulling in and analyzing a good

part of all the TV being watched in the

United States and you don’t have to now

go an instrument living rooms with

microphones to get people’s

conversations you just tuned in to

publicly available social media feeds so

we’re pulling in about 3 billion

comments a month and then the magic

happens you have the event structure the

common ground that the words are about

coming out of the television feeds

you’ve got the conversations that are

about that those topics and through

semantic analysis and this is actually

real data you’re looking at from our

data our processing each yellow line is

showing a link being made between a

comment in the wild and a piece of event

structure coming out of the television

signal and the same idea now can be

built up and we get this word scape

except now words are not assembled in my

living room instead the context the

common ground the activities are the

content on television that’s driving the

conversations and so what we’re seeing

here these skyscrapers now are

commentary that are linked to content on

television same concept but looking at

communication dynamics in a different

very different sphere so fundamentally

rather than for example measuring

content based on how many people are

watching this gives us the basic data

for looking at engagement properties of

content and just like we can look at

feedback cycles and dynamics in you know

in a family we can now open up the same

concepts and look at much larger groups

of people this is a subset of data from

our database just 50 thousand out of

several million and the social graph

that connects them through publicly

available sources and if you put them on

one plane a second plane is where the

content lives so we have the programs

and the the sporting events and the

commercials and all of the link

structures that tie them together make a

Content graph and then the important

that our dimension each of the links

that you’re seeing rendered here is an

actual connection made between something

someone said and a piece of content and

there are again now tens of millions of

these links that give us the connective

tissue of social graphs and how they

relate to content and we can now start

to probe the structure in interesting

ways so if we for example trace the path

of one piece of content that drives

someone to comment on it and then we

follower that comment goes and look at

the entire social graph that becomes

activated and then trace back to see the

relationship between that social graph

and content very interesting structure

becomes visible we call this a

co-viewing clique a virtual living room

if you will and there are fascinating

dynamics at play it’s not one-way a

piece of content an event causes someone

to talk they talk to other people that

drives TuneIn behavior back into mass

media and you have these cycles that

drive the overall behavior another

example very different another actual

person in our database and we’re finding

at least hundreds if not thousands of

these we’ve given this person a name

this is a pro amateur or pro a media

critic who has this high fan out race a

lot of people are following this person

very influential and they have a

propensity to talk about what’s on TV so

this person is a key link in connecting

mass media and social media together one

last example from this data sometimes

it’s actually the piece of content that

is special so if we go and look at this

piece of content President Obama’s State

of the Union address from just a few

weeks ago and look at what we find in in

the same data set at the same scale the

engagement properties of this piece of

content are truly remarkable a nation

exploding in conversation in real time

in response to what’s on on the

broadcast and of course through all of

these lines are flowing unstructured

language we can x-ray and get a

real-time pulse of a nation real-time

sand

of the social reactions in the different

circuits in the social graph being

activated by content so to summarize the

idea is this as our world becomes

increasingly instrumented and we have

the capabilities to collect and connect

the dots between what people are saying

in the context they’re saying and what’s

emerging is an ability to see new social

structures and dynamics that have

previously not been seen it’s like

building a microscope or telescope and

revealing new structures about our own

behavior around communication and I

think the implications here are profound

whether it’s for science

for commerce for government or perhaps

most of all for us as individuals and so

just to return to my son when I was

preparing this talk he was looking over

my shoulder and I showed him the clips I

was gonna show to you today and I asked

him for permission granted

and and then I went on to reflect isn’t

it amazing

this entire database all these

recordings I’m gonna hand up to you and

to your sister who arrived two years

later and you guys are gonna be able to

go back and re-experience moments that

you could never with your biological

memory possibly remember the way you can

now and he was quiet for a moment I

thought what am I thinking he’s he’s

five years old he’s not gonna understand

this and just as I was having that

thought he looked up at me and said so

that when I grow up I can show this to

my kids and I thought wow this is this

is powerful stuff so I want to leave you

with one last memorable moment from our

family this is our the first time our

son took more than two steps at once

captured on film and I really want you

to focus on something as I take you

through it’s a cluttered environment its

natural life my mother’s in the kitchen

cooking and of all places in the hallway

I realize he’s about to do it about to

take more than two steps and so you hear

me encouraging him realizing what’s

happening and then the magic happens

listen very carefully about three steps

in he realizes

something magic is happening and the

most amazing feedback loop of all kicks

in and he takes a breath in and he

whispers Wow and instinctively I echo I

echo back the same

so let’s fly back in time to that

memorable moment nice walking

[Music]

[Applause]

when I think about succeeding in

business I think there’s a couple of

considerations first and foremost is

teamwork today’s problems are just too

complicated to be solved as an

individual and the opportunity to work

with others collaboratively where you

can build on each other’s ideas I think

is particularly important I think a

second area that’s quite important is

the understanding of different

disciplines it is critical that one be

an expert in your major but it is

absolutely essential that you have the

ability to understand where other

disciplines the input from other

disciplines and be able to incorporate

that to make effective decisions and

then last but not least we are in a

global business community and so the

opportunity to understand cultures

different cultures around the world to

be able to incorporate some of the

learning from those cultures and to

incorporate that into your business

decisions is essential to success I have

three degrees from Cornell so I’d say

just about everything that has prepared

me came from Cornell and I am indebted

to the University for that experience

but as I think particularly about my

business school career I think the

exposure to a variety of disciplines the

wealth of resources across the

university

we’re exceptionally helpful

[音乐]

[音乐]

想象一下,如果你能把你的生活记录下来,

你所说的一切,你所做的一切都

在一个完美的记忆库中,

触手可及,这样你就可以回去

寻找难忘的时刻并重温它们,

或者筛选时间的痕迹,

发现其中的模式 你自己

以前未被发现的生活

这正是我的

家人五年半前开始的旅程

这是我的妻子和合作者

rupal 在这一天此时此刻我们

带着他们的第一个孩子走进房子

我们漂亮的男婴 我们

走进一所带有非常特别的家庭录像系统的房子,

这一刻

我们的家中捕捉到了数千个对我们来说特别的时刻,因为

在房子的每个房间里,如果你抬起头,

你会看到一个摄像头和一个麦克风,

如果你往下看,你会

看到房间的鸟瞰图 这是我们的

起居室婴儿卧室厨房餐厅和

房子的其他部分,所有这些都

输入到磁盘阵列中 这是为连续捕捉而设计的

,所以在这里,

我们在家里飞过一天,

从阳光明媚的早晨到

白炽灯的晚上,最后

在三年的

时间里熄灯,我们每天记录八到十个

小时 积累了大约 25 万

小时的多轨音频和视频,因此

您正在查看

迄今为止最大的家庭视频收藏

以及这些数据

在个人层面上对我们家庭的影响,其

影响已经是巨大的

我们仍然在学习它的价值无数

的不请自来的自然瞬间

而不是摆姿势的瞬间被捕捉到,

我们开始学习如何

发现它们,但还有一个

科学原因推动了这个

项目,即使用这种类型

自然纵向数据,以

了解孩子如何学习

语言的过程,孩子是我的儿子,因此

制定了许多隐私

条款来保护每个人 谁记录

在数据中我们将数据元素

提供给我在麻省理工学院值得信赖的研究团队,

这样我们就可以开始

在这个庞大的数据集中梳理模式,

试图了解社会

环境对语言习得的影响,所以

我们在这里看一个

我们开始做的第一件事就是

我和妻子在厨房里做早餐

,当我们穿越

时空时,厨房里非常日常的生活模式,

以便将这段

不透明的 9 万小时视频转换成

某种东西 我们可以开始看到当我们在空间和时间中移动时,我们使用

运动分析来拉出我们

称之为时空蠕虫的东西,这已经

成为我们工具包的一部分,以便

能够查看和查看

数据中活动的位置 并用它

追踪

我儿子在家里搬家的模式,

这样我们就可以集中精力转录

我儿子周围的所有语言环境他

听到的所有单词 对于我自己,我的妻子,我们的保姆,

随着时间的推移,他

开始使用这种技术和数据

以及机器

助手转录语音的能力,我们现在已经

转录了超过 700 万

字的家庭成绩单,

这让我 现在带你第一次

浏览数据,这样你就

知道

了 在他

第一个生日之后会说 Gaga 的意思是

水,在接下来的

半年里,他慢慢学会了

近似成人形式的水,所以我们

将在大约 40 秒内巡航半年,

这里没有视频,所以你可以专注 在

声音上 一种新轨迹的声学

[音乐]

所以他不只是

在前两年的 24 个月里学习水

我们真正关注的是这是

他按时间顺序学习的每个单词的地图

和 因为我们有

完整的成绩单,所以我们已经确定了他在两岁生日

时学会生产的 503 个单词中的每一个,

他是一个

早期的说话者

,所以我们开始分析

为什么某些单词先于其他单词出生,

这是第一个结果之一

一年多前从我们的研究中

得出的结果真的让我们感到惊讶

解释这个看似简单的

图表的方式是垂直的,它

表明照顾者话语的复杂程度

是基于话语的长度,

而垂直轴是时间

和所有

每次我

儿子学习一个单词时,我们根据以下想法排列的数据中,我们会追溯

并查看他听到

的所有包含该单词的语言,我们会绘制

话语的相对长度

,我们发现的是 这种奇怪的

现象,看护人的演讲会

系统地降到最低限度,使

语言尽可能简单,然后

慢慢地复杂起来,

令人惊奇的是

那个下降的反弹几乎

与每个单词的出生时间完全一致

一个单词的诞生并轻轻地将他

带入更复杂的语言

,这有很多含义,

但我只想指出的是,

必须有惊人的反馈循环

当然不是我儿子正在

从他的语言环境中学习 但是

环境正在向他学习,

环境中的人们处于这些类型的

反馈循环中,并创建了一种直到现在

还没有被注意到的脚手架,

但是那是在查看

语音

上下文我们现在正在查看的视觉上下文怎么样想想

这个 作为我们的玩具屋剖面图,

我们拍摄了那些圆形鱼眼镜头

相机,我们已经进行了一些光学

校正,然后我们可以 将它

带入 3D 生活欢迎来到

我的家 这是一个瞬间

通过多个摄像机捕捉的

原因我们这样做的原因是创建

终极记忆机器,您可以

返回并交互式地四处飞翔,

然后将视频生活注入其中 系统

我要做的是让你

再次加速观看 30 分钟

的客厅里的生活,我

和我的儿子在地板上,并且有

视频分析跟踪我们的

动作,我的儿子正在留下红墨水我' 我

留下绿色墨水我们现在坐在沙发上

,透过窗户看着经过的汽车

,最后我儿子

自己玩一个会走路的玩具

现在我们冻结动作 30 分钟我们

把时间变成垂直轴,我们

敞开心扉

我们刚刚留下的这些交互痕迹的视图 我们看到了

这些惊人的结构 这些

由两种颜色的线组成的小结 我们称之为

社交热点 我们

称之为单独热点的螺旋线 我们认为

这些 影响学习语言的方式

我们想做的是开始了解

这些模式与我

儿子接触的语言之间的相互作用,看看我们是否可以

预测听到单词时的结构如何

影响单词的学习时间

所以换句话说,

单词和它们

在世界上的含义之间的关系所以这就是我们

在这段视频中再次处理这个问题的方式我的

儿子被追踪到他留下了红色

墨水,我们的保姆在

门口

她提供水 然后把两条

虫子去厨房取水

,我们所做的是用水这个词

来标记那一刻的

活动,现在我们利用数据的力量

,每次我儿子

听到这个词 水和他

在其中看到的上下文,我们用它来

穿透视频,找到

Co 与水的实例一起发生的每一个活动痕迹,

这些数据

在其后留下的就是我们称之为景观的景观,

这就是 w ord

scape for the word water,你可以看到

大部分动作都在厨房里

,那是那些大峰

在左边的地方,为了对比,我们可以

用任何词来做这件事,我们可以把这个

词当作再见

,我们 ‘现在总和在

房子的入口处,我们看,我们发现,正如

你所期望的那样,在景观

中,“by”这个词更多地以

结构化的方式出现,所以我们正在使用这些

结构来开始预测顺序

语言习得,这就是你

现在在我的实验室中的持续价值,我们

现在在麻省理工学院正在研究这是在

媒体实验室,这已成为我最

喜欢的视频图形方式,几乎在任何

空间中,该

项目中的三个关键人物菲利普 Camp Ronny cubot 和

Brendan Roy 在这里合照 Philip

一直是密切的合作者,

你看到的所有可视化效果和 Michael

Fleischman 是我

实验室的另一位博士生,他和我一起进行了这个家庭

视频分析,他做了以下

观察 我们正在

分析语言与

事件的联系方式,这些事件为语言提供了共同点

,同样的想法我们可以

从你家 Deb 中拿出来,我们可以将它应用到

公共媒体的世界,所以我们

的努力出人意料 转而

将大众媒体视为提供

共同点,你就有了

将这个想法带到一个全新地方的秘诀

我们已经开始

使用相同的原理

分析电视内容 分析电视信号的事件结构

节目广告 剧集的所有

组件 组成

我们现在的活动结构 使用卫星

天线拉入并分析美国

正在观看的所有电视的很大一部分

,您现在不必

去带麦克风的仪器客厅

来获取人们的

对话,您只需 收听

公开可用的社交媒体订阅源,因此

我们每月会收到大约 30 亿条

评论,然后神奇的

事情发生了,您拥有活动结构、

共同点 t 这些话是关于

从电视节目中出来的,

你已经得到了

关于这些主题的对话,并通过

语义分析,这实际上是

你从我们的数据中看到的真实数据,

我们处理的每条黄线都

显示了一个链接

在野外的评论和电视信号中的一个事件

结构之间进行,

现在可以建立相同的想法

,我们得到这个词 scape

除了现在单词没有在我的

客厅里组装,而是在普通的上下文中

活动的基础

是推动对话的电视内容

,所以我们现在在这里看到的

这些摩天大楼

是与电视内容相关的评论,

同样的概念,

但从根本上

而不是为了

根据观看人数来衡量内容的示例

这为我们提供

了查看内容参与度属性的

基本数据 t 就像我们可以查看

反馈周期和动态一样,您知道

在一个家庭中,我们现在可以打开相同的

概念并查看更大

的人群 这是我们数据库中数据的一个子集

数百万中只有 5 万 以及

通过公开资源连接它们的社交图谱

,如果你把它们放在

一个平面上,第二个平面就是

内容所在的地方,所以我们有节目

、体育赛事、

广告和所有

将它们联系在一起的链接结构 制作一个

内容图,然后重要的

是我们的

维度,您在这里看到的每个链接

都是在

某人所说的内容和一段内容之间建立的实际联系

,现在又有数千万个

这样的链接给了我们

社交图的结缔组织以及它们

与内容的关系,我们现在可以开始

以有趣的方式探索结构

驱动

某人对其发表评论的意图,然后我们

关注该评论并

查看整个被激活的社交图谱

,然后回溯以查看

该社交图谱

与内容之间的关系非常有趣的结构

变得可见,我们称之为

共同

如果您愿意,可以查看集团虚拟客厅,并且有迷人的

动态在发挥作用 这不是单向的

一段内容 事件导致

某人交谈 他们与其他人交谈 将

TuneIn 行为重新带回大众

媒体,并且您有这些循环

推动整体行为 另一个

例子 非常不同 另一个真实的

人 在我们的数据库中,我们发现

至少数百个,如果不是

数千个 我们给这个人一个名字

这是一个专业的业余爱好者或专业的媒体

评论家,拥有如此高的粉丝

很多人都在关注这个

很有影响力的人,他们

倾向于谈论电视上的内容,所以

这个人是连接大众的关键环节

媒体和社交媒体一起

从这个数据中得到最后一个例子,有时

它实际上是一段特别的内容,

所以如果我们去看看这段

内容,奥巴马总统

几周前的国情咨文演讲

,看看我们

在相同的数据集中以相同的规模

发现这段

内容的

参与度真的

了不起 我们可以 X 光并获得

一个国家的实时脉搏,实时

了解社会图谱中不同回路中的社会反应

被内容激活,因此总结一下

这个想法是,随着我们的世界变得

越来越仪器化,我们

有能力收集和连接

人们

在他们所说的上下文中所说的话和正在出现的东西之间的点

是能够看到新的社会结构的能力

以前从未见过的动态这就像

建造显微镜或望远镜,并

揭示我们自己

围绕交流的行为的新结构,我

认为这里的影响是深远的,

无论是对科学

、商业、政府还是

最重要的是对我们个人而言

因此,当我准备这次演讲时,为了回到我的儿子

身边,他正在看着我的肩膀,我给他看了我

今天要给你看的剪辑,我问

他是否允许

,然后我继续思考不是’

整个数据库真是太棒了所有这些

录音我要交给你

和你两年后到达的姐姐,

你们将能够

回去重新体验

你的生物记忆可能永远无法实现的时刻

记住你现在的方式

,他安静了

一会儿 他抬头看着我说,

这样当我长大后,我可以把它展示给

我的孩子们,我想哇,这

是强大的东西,所以我想给你

留下我们家最后一个难忘的时刻,

这是我们的第一个 有一次我们的

儿子在电影中一次迈出了两步以上

,我真的希望你

在我带你穿越时专注于某件事

这是一个杂乱的环境它的

自然生活我妈妈在厨房

做饭和走廊的所有地方

我意识到他是 将要做这件事 将

采取两个以上的步骤,所以你听到

我鼓励他意识到正在发生的

事情,然后神奇的事情发生了

仔细聆听三个步骤

,他意识到

正在发生一些神奇的事情,并且

最惊人的反馈循环开始

了 他深吸一口气,他

低声说哇,我本能地回响,我

回响同样的,

所以让我们及时飞回那个

难忘的时刻,美好的散步

[音乐]

[掌声]

当我想到在商业上取得成功时

认为首先有几个考虑因素是

团队合作 今天的问题太

复杂了,无法作为个人解决,

以及与他人合作的机会,

在那里你

可以建立彼此的想法 我

认为特别重要 我认为

第二个领域是 非常重要的是

对不同

学科的理解

成为您专业的专家至关重要,但

绝对必要的是,您有

能力了解其他

学科从其他学科的输入,

并能够将其

结合以做出有效的决策和

最后但并非最不重要的一点是,我们身处

全球商业社区,因此有

机会了解

世界各地不同的文化,

以便能够

将从这些文化中学到的一些知识

融入您的商业

决策中,这对我的成功至关重要

距离康奈尔大学三度,所以我会

说几乎所有有 p 的东西 帮助

我来自康奈尔大学,我很

感谢大学的这段经历,

但当我特别想到我在

商学院的职业生涯时,我认为

接触各种学科和

整个大学的丰富资源对

我们非常有帮助