The Extension of Human Intelligence

[Music]

[Applause]

hey siri

the weather today sunny with a high of

okay can you tell me a joke

sure why did the students eat their

homework

because their teacher said it was a

piece of cake

let me try asking something more

abstract hey siri

what’s the meaning of life

all evidence shows it’s chocolate

just now i had a little conversation

with siri and i’m sure many of you have

spoken to some sort of digital assistant

whether it be siri alexa bixby you name

it

it’s so convenient that sometimes we

feel that they’re even smarter than a

real person

until we give them some creative

questions the technology behind siri and

her relatives

is artificial intelligence or a.i

today from spotify which uses ai to

give us better music recommendations to

verizon

which uses ai to improve internet

services

to instagram using ai to tackle online

bullying

artificial intelligence is all around us

in fact ai is now called the fourth

industrial revolution

following steam electricity and the

internet

but for too many people artificial

intelligence seems scary

but don’t worry today i’ll talk about my

research on ai

its core mechanism and also address what

ai holds

for the future the concept of artificial

intelligence was first seen through

depictions of robots

in films such as metropolis in the 1920s

in 1956 the term artificial intelligence

was officially coined

at the dartmouth summer research

conference by a group of scientists from

dartmouth

harvard and ibm this was a monumental

moment to nail the flag to the mast

the field of ai was launched by the bold

vision

that computers could be made to perform

intelligent tasks

but when we talk about ai we often think

of those cool robots

but actually a.i and robotics are

separate fields of study

robotics deals with machines that

interact with the physical world via

sensors

on the other hand ai tackles learning

and logical

logical perception in short while ai

acts as the brain robots act as the body

but the evolution in both areas

continues

and the line between a i and robotics is

blurring

because when combined you can get an

artificially intelligent robot

and in 2018 spacex actually flew an

artificially intelligent robot named

simon

to the international space station to

better understand how ai works

let’s look at a simple example of how

this how the computer learns

this is a short python program called

the capital cities of the world

it works when you type a country’s name

into the search box and it’ll pull up

the country’s capital

now if a response wasn’t previously

reported then it will prompt you to put

in the right answer

now some of you may be saying oh i’ve

known country capital since i was in

elementary

i don’t need ai to do that for me

however this is only the simplest

learning process here’s another example

the price of a diamond can be determined

based on seven different features

for us humans considering two or three

features can be easy

but adding on more features may hurt our

brains

for a i considering five or even ten

more features to compute

is only a matter of milliseconds from

here you can see that ai can compute at

an incredibly complex

level to understand how ai works under

the hood

we can break ai down into two main

subsets

narrow ai and machine learning narrow ai

is good at executing predetermined

actions siri

is a great application of narrow ai upon

receiving your request

siri records the sound waves from your

voice and translates them into a code

she then breaks down the code to

identify particular patterns

and keywords and when you hear her

response

it’s not a person behind the scenes but

rather just a search result

from her tremendous database and when

siri doesn’t have an answer

she simply shows you a web search that

matches your keywords

and finally she converts the results to

an audible response

but for more complicated questions we

don’t have answers and there’s no

database to search

therefore there are no predetermined

actions so we as humans learn through

experience

by trial and error and make educated

guesses via neurons

in our brain guess what a.i learns the

same way

scientists are replicating this process

using artificial neural networks

and this is the core of ai called

machine learning

and this is super cool right who

wouldn’t want to make a good educated

guess when there’s no way of telling

what’s right i certainly do

so here at school i co-founded a

forecasting club

forecasting is the versatile skill of

predicting a future event analytically

we started with fun events such as the

us open and we’ll continue with the

upcoming elections

we’re using a basic software from

microsoft and just playing around with

the data

but i have to admit with so many moving

parts it’s difficult considering all the

factors

so moving forward we’ll invite local

university professors

and ai experts to help and teach us

along the way

now here’s how machine learning actually

works there are four major components

the first is the mail carrier who

collects data for ai to learn

then we have the construction worker who

builds the model

now not fashion model but a set of

functions that calculates the outputs

this is the simulation of the human

brain that’s why these models are also

called

neural networks then the police officer

the loss function comes in to monitor

the performance

calculate the errors and redistribute

them back into the model to be

recalculated

and finally the manager who makes the

calculation process

faster via a fancy jargon called the

optimizer

and this process repeats for several

rounds until the errors

are minimized curious on how to better

use ai

this past summer i participated in the

national center of atmospheric research

artificial intelligence summer school

working with scientists from stanford

university and

noaa i learned that essentially machine

learning is fitting

inputs and outputs into an algorithm

otherwise known as

curve fitting and now let’s look at an

example

from the summer school of rain

prediction to see how this whole machine

learning process plays out

we used temperature pressure and

moisture data as inputs

to predict the chance of rain we used

past rain events to train ai and obtain

a curve

we then use the curve and sets of

functions to calculate the output

and in the meantime we used physical

functions such as mass and energy

because the calculations still need to

follow physical laws such as mass

conservation

and this process repeats for several

rounds until the curve

is fit and as you can see this machine

learning process requires ai

experts in the field today to

collaborate with other disciplines such

as

environmental science medical science

engineering financial business

etc to create models that advance each

field

and teamwork is key for future

development

while the benefits of ai are tremendous

new technology can cause concerns

like every other innovation in human

history the risks

exist consider the invention of cars and

airplanes

we all understand the risks of traveling

by these means of transportation

but they have allowed traveling to

become so much easier

and even impacted areas outside of basic

transportation

such as logistics and emergency rescues

for the

reasons ai technology is built on the

foundations of

improving efficiency and scientists are

like the wright brothers

being brave and cautious to improve this

technology

another concern is human employment it

may be intuitive to think that the

growing development of ai

will eventually take over many existing

jobs

however the world economic forum states

that automation will generate 60 million

more new jobs than existing jobs

and i.t companies gartner and the

mckinsey global institute

echoed that thought now if you watch a

lot of science fiction movies like me

you might be thinking what if the robots

from the matrix and the terminator team

up with ai to dominate

humanity well oxford university released

a study claiming that a.i will

outperform humans and many activities in

the next 10 years

such as translating languages by

boosting efficiency

ai will allow us to see a decrease in

manual labor

and an increase in new jobs by

developing new products

and services our relationship with ai

should not be like

that seen in the terminator but rather

like that we

see in star wars with r2d2

in fact mit professor eric brynjolfsson

says that we can use the power of ai

to work on the elimination of global

poverty disease reduction

and the provision of better education he

tells us

not to ask what will happen but rather

what will we choose to do with this

technology thank you

[音乐]

[鼓掌]

hey

siri 今天天气晴朗,

83 度高。

好吧,你能不能给我讲个笑话,

确定为什么学生们会吃掉他们的

作业,

因为他们的老师说这是

小菜一碟,

让我试着问一些更

抽象的东西 嘿 siri

生命的意义是什么

所有证据都表明它是巧克力

刚才我和 siri 进行了一些交谈

,我相信你们中的许多人都曾

与某种数字助理交谈过,

无论是 siri 还是 alexa bixby 你的名字

它太方便了 有时我们

觉得他们甚至比真人更聪明,

直到我们给他们一些创造性的

问题 siri 和她的亲戚背后的技术

是人工智能或

今天的人工智能 从使用人工智能

为我们提供更好的音乐推荐的Spotify到

使用人工智能的verizon 改进互联网

服务

到 Instagram 使用人工智能解决网络

欺凌

人工智能就在我们身边

,事实上,人工智能现在被称为继 stea 之后的第四次

工业革命

电力和

互联网,

但对太多人来说,

人工智能似乎很可怕,

但不要担心今天我会谈谈我

人工智能核心机制的研究,并讨论

人工智能

对未来的影响。人工智能的概念

首次被看穿

1956 年 1920 年代大都会等电影中对机器人的描绘 人工智能

一词是

达特茅斯

哈佛和 ibm 的一组科学家在达特茅斯夏季研究会议上正式创造

的 人工智能是由一个大胆的愿景发起的,

即计算机可以被用来执行

智能任务,

但是当我们谈论人工智能时,我们经常会

想到那些很酷的机器人,

但实际上人工智能和机器人技术是

不同的研究领域

机器人技术涉及

与物理世界交互的机器

另一方面,人工智能通过传感器处理学习

和逻辑

逻辑感知,而人工智能则

充当大脑 机器人充当身体,

但两个领域的发展

仍在继续

,人工智能和机器人之间的界限正在

模糊,

因为当结合起来你可以得到一个

人工智能机器人

,2018 年,SpaceX 实际上将一个

名为西蒙的人工智能机器人

飞到了国际空间站,以

更好地实现 了解人工智能是如何工作的

让我们看一个简单的例子,说明

计算机是如何学习的

这是一个简短的 Python 程序,称为

世界首都

资本

现在 如果之前没有报告过回复,

那么它会提示

你现在输入正确的答案

,你们中的一些人可能会说哦,

我从小学就知道国家资本,

我不需要人工智能来这样做

然而我这只是最简单的

学习过程这里是另一个例子

钻石的价格可以

根据我们人类的七个不同特征

来确定,考虑到两个或三个 ee

功能可能很容易,

但添加更多功能可能会伤害我们的

大脑,

因为 AI 考虑

到要计算五个甚至十个以上的

功能只需几毫秒的时间

,您可以看到 AI 可以在

极其复杂的

水平上进行计算,以了解 AI 的工作原理

在引擎盖下,

我们可以将 AI 分解为两个主要

子集,

窄 AI 和机器学习窄

AI 擅长执行预定

动作 siri

是窄 AI 在收到您的请求时的一个很好的应用程序

siri 记录您声音中的声波

并将它们转换为

然后,她会分解代码以

识别特定的模式

和关键字,当你听到她的

回应时,

这不是幕后的人,

而只是

她庞大数据库中的搜索结果,当

siri 没有答案时,

她只是向你显示一个

与您的关键字匹配的网络搜索

,最后她将结果转换为

可听见的响应,

但对于更复杂的问题,我们

没有 swers 并且没有

可搜索的数据库,

因此没有预先确定的

动作,因此我们人类

通过反复试验来学习经验,并

通过

我们大脑中的神经元做出有根据的猜测 猜测 AI 学到了什么,

就像

科学家使用人工神经网络复制这个过程一样

, 这是人工智能的核心,称为

机器学习

,这非常酷,当无法判断什么是正确的时候,

谁不想做出有根据的

猜测,

我当然

在学校这样做了,我共同创立了一个

预测俱乐部

分析预测未来事件的通用技能

我们从有趣的事件开始,例如美国

公开赛,我们将继续

即将到来的选举,

我们使用微软的基本软件

,只是在

玩数据,

但我不得不承认 如此多的活动

部件很难考虑所有

因素

因此向前推进我们将邀请当地

大学教授

和人工智能专家来帮助和 一路教我们

现在机器学习是如何

工作的有四个主要组成部分

首先是

收集数据供人工智能学习的邮递员

然后我们有建造模型的建筑工人

现在不是时装模特而是一组

功能 计算输出

这是人脑的模拟

这就是为什么这些模型也

被称为

神经网络 然后

警察使用损失函数来

监控性能

计算错误并将它们重新分配

回要重新计算的模型中

,最后是经理 谁

通过称为优化器的花哨术语使计算过程更快

,这个过程重复了

几轮,直到

错误最小化 好奇如何更好地

使用

AI 去年夏天我参加了

国家大气研究中心

人工智能暑期学校

与 斯坦福

大学和美国国家

海洋和大气局的科学家们,我了解到这一点 y 机器

学习正在将

输入和输出拟合到一个算法中,

也称为

曲线拟合,现在让我们看一个

来自夏季降雨预测学校的例子

,看看整个机器

学习过程是如何发挥作用的,

我们使用温度压力和

湿度数据作为

输入 预测下雨的机会我们使用

过去的降雨事件来训练 ai 并获得

一条曲线

,然后我们使用曲线和

函数集来计算输出

,同时我们使用

质量和能量等物理函数,

因为计算仍然需要

遵循 质量守恒定律等物理定律

,这个过程重复

几轮,直到

曲线拟合,正如你所见,这个机器

学习过程需要

今天该领域的人工智能专家

与其他学科如

环境科学医学

工程金融业务

等 创建推进每个

领域的模型

,团队合作是未来发展的关键,

而 人工智能的好处是巨大的

新技术会

像人类历史上的所有其他创新一样引起关注

存在的风险 考虑汽车和飞机的发明

我们都知道乘坐这些交通工具旅行的风险,

但它们让旅行

变得如此容易

甚至影响到基础交通以外的领域,

例如物流和紧急救援

因为人工智能技术建立在提高效率的

基础上

,科学家

就像莱特兄弟

一样勇敢和谨慎地改进这项

技术

另一个问题是人类

就业 直觉认为,

人工智能的不断发展

最终将取代许多现有

工作,

但世界经济论坛指出

,自动化

将比现有工作多创造 6000 万个新工作

,如果你看的话,它公司 gartner 和麦肯锡全球研究所现在也赞同这一想法

很多科幻电影都喜欢 我

你可能在想,如果

来自矩阵和终结者的机器人

与人工智能合作来统治

人类,牛津大学发布

了一项研究,声称人工智能将

在未来 10 年内超越人类和许多活动,

例如通过提高效率来翻译语言。

将允许我们通过开发新产品和服务看到体力劳动的减少

和新工作的增加

我们与人工智能的关系

不应该像

终结者中看到的

那样,而是像我们

在星球大战中看到的那样

,实际上是 mit 教授 eric brynjolfsson

说,我们可以利用人工智能的力量

来消除全球

贫困疾病

并提供更好的教育,他

告诉我们

不要问会发生什么,而是

我们将选择用这项技术做什么

谢谢