How to tackle the AI inequality

[Music]

what would you do

if i give you one trillion dollar

right now and i guess

buy an island you would still have 990

billion dollars

during the pandemic of 2020 tech

billionaires guy

get richer by around 2 trillion

while 200 million people lost

their jobs but how is this large sum of

money

related to ai

in 2012 the ai wave of deep learning

started when a deep network called

alexnet revolutionized how to tackle

a core problem of image classification

or enabling the computer to identify the

content of an image automatically

this was a massive leap forward for ai

it was followed by

several leaps in the following years in

2015

alphago an ai developed by google

deepmind

defeated the world champion in go a game

like chess

by observing and learning from previous

games

in 2020 alpha fold another ai developed

by google deep mind made a breakthrough

in a 50 year old problem in biology to

predict

protein folding nowadays

the ai that run self-driving vehicles

face filters language to language

translation

even play chess or rely on deep learning

i witnessed this huge development in ai

myself

doing graduate studies focused in deep

learning here at kaust

after four years of hard work that paid

off in 2020 when i received national and

international hours in ai

i realized there is a big problem in the

ai field

there is huge ai inequality

from what i observe it can be

categorized into three main

aspects international industrial

and lack of diversity

on the international aspect we see this

ai inequality manifested in the gap

between countries

in terms of ai investment and ai

development

for example in 2019 the total investment

in ai companies were at 30 billion

dollar in the us

and china and only 5 billion in the rest

of the world

pwc projects that ai will add up to

f up to 15 trillion dollar

by 2030 to the world gdp that’s a lot of

money

but 10 trillion will be to the us and

china as predicted

and 5 trillion to the rest of the world

the same gap is observed in the science

wealth and money created by ai

across all different sectors

the future consequence is concentrated

wealth in these countries that dominate

ai today

the second aspect of ai inequality is

the industrial aspect we see i think

some tech companies that dominate

industries and create ai monopolies

these businesses are largely driven by

deep learning algorithms that are fed

enormous amount of data

the data are used to develop predictive

models

and train they call it in ai train

models and these giant models are used

to enhance digital products

the products are used to collect more

data

and then the data is fed back again to

the models for fine tuning

for better products so this is a vicious

cycle is created

and it is self-reinforcing once it’s

created

it keeps growing

companies that own large amount of data

from the beginning today

are in better position to benefit from

this vicious cycle

i keep calling it vicious cycle and not

virtuous

because ai is a wealth generation

machine

and this ai inequality leads to

economic inequality

and it is known like predicted that it

will add

a lot of wealth to very few who own the

data

and necessary tools for them for example

with its autopilot program and hundreds

of millions of recording hours by its

fleet of cars

the world top electric car company has

an advantage in the race to achieve

self-driving cars

another example is social media

platforms

that know more about us than we do know

about ourselves

and that’s why we see this huge

addiction to social social media

platforms

it fit into this vicious cycle the last

aspect

i would like to discuss today about ai

inequality

is lack of diversity in ai if you look

at the internet content

which is a major source of data used to

develop

ai tools today sixty percent of it is in

english

language compared to one percent in

arabic language

for example in comparison native arabic

speakers account for

five percent of the world population

that means

the data used to train and develop ai

tools

are actually is actually biased not only

that

but also the training and educational

content about these new technologies is

are inaccessible

to a large portion of these less

represented groups

this has a direct impact in both

technology development

and the diversity of the ai workforce

for example

not a single arabic speaking country is

among the top 20 countries

according to nature research index in ai

if you look at industry there are bigger

disparities in the number

with some diversity reports showing that

blacks arabs and other minorities

account for less than five percent

of the ai workforce in some of these big

companies

compared to let’s say 40 of asians and

40 of whites

this is lack of diversity in ai today

and you might ask me you might be asking

yourself

is there something that can be done on

the individual level to tackle this

i’m here to say yes we can do something

at least with this

last aspect in 2019

a group of mind and i started fahim dot

ai

fihim is an arabic word it means

understanding

so ai or understanding ai is an online

platform

that helped the arab community a

disadvantaged group in the ai landscape

to learn and utilize the recent ai

technologies we felt that this arabic

region is deeply disconnected

from the recent technologies in ai and

usually ignored by big tech companies

when it comes to technology development

they are fond of this region when it

comes as a consumer

but not in terms of technology

development

so we started this initiative that

provides

free high quality ai and machine

learning educational content

in all levels in arabic language

the team have pushed through to publish

more than 100 articles

listens videos tutorials

organized workshops all in arabic

language and that benefited more than

200 000 people from the arab world

basically what fame ai is doing it’s

demystifying

ai for arabic audiences allowing

this these new technologies like deep

learning to be accessible

it helped many to take the first baby

steps in their ai adoption

whether in the organization if they have

an organization and want to adopt ai

or in their careers if their students

are looking for new career opportunities

our goal at fehem.ti is to become a

catalyst

for widespread adoption of ai in this

arabic region

fortunately we are not the only ones

tackling this systemic

inequality in the ai ecosystem today

other global organizations are pursuing

similar paths

for example blacks in ai and latin xai

try to help less represented groups to

thrive in ai research

a very competitive area of research

another example is ai for all a u.s

nonprofit dedicated

to increase the inclusion and diversity

in the ai research development education

and policy

so this is what i’ve been working on for

the last

couple of years on the side now beside

my graduate study

of course and i would do this because i

work in the ais and i saw this

problem and i’m doing my best to tackle

it but you might be asking yourself what

can you do

you might be working in a different

domain so this might

not look very relevant to you

but it doesn’t matter because you can

always support the initiatives

that help that try to address this

inequality problem in ai

by sharing the knowledge and helping

others

i’m not here to say ai is bad i work in

on the field

so actually i would argue it’s one of

the greatest inventions of humanity

however it has this big inequality

problem and

we should not ignore it as the latest

scientific writer isaac asimov

who is familiar with his stories about

robots

and actually inspired generation of

scientists in the field

used to say if knowledge can create

problems

it’s not through ignorance we can solve

them

thank you

you

[音乐]

如果我现在给你一万亿美元,你会怎么做

,我猜

你会买一个岛,

在 2020 年科技亿万富翁大流行期间,你仍然有 9900 亿美元,这

家伙

变得更富有约 2 万亿美元,

而 2 亿人失去

了工作 但是,这笔巨款

2012 年的 AI 有

什么关系? 当一个名为 alexnet 的深度网络

彻底改变了如何解决

图像分类的核心问题

或使计算机能够

自动识别图像的内容时,深度学习的 AI 浪潮开始了。

人工智能实现了巨大飞跃,

随后在

2015 年

实现了

几次飞跃

谷歌 Deep Mind

突破了 50 年前的生物学难题

预测

蛋白质折叠 如今

运行自动驾驶汽车的人工智能

面部过滤器语言到语言

翻译

甚至下棋或依赖深度学习

我亲眼目睹了人工智能的巨大发展,

我自己

在考斯特攻读研究生,专注于深度学习,

经过四年的努力,

在 2020 年获得了国家和

国际小时的回报 在人工智能中,

我意识到人工智能领域存在一个大问题,据

我观察,人工智能存在巨大的不平等,可以

分为三个主要

方面,国际产业

国际方面缺乏多样性我们看到这种

人工智能不平等表现在

以人工智能投资和人工智能

发展为例,2019年美国和中国

对人工智能公司的总投资为300亿

美元,

世界其他地区只有50亿美元

,普华永道人工智能项目加起来

f达

到 2030 年,世界 GDP 将达到 15 万亿美元,这是很多

钱,

正如预测的那样

,美国和中国将达到 10 万亿美元,而 t 将达到 5 万亿美元 在世界其他地区,

科学

财富和人工智能创造的金钱

在所有不同领域都存在同样的差距

未来的结果是

财富集中在今天主导人工智能的这些国家

人工智能不平等的第二个方面是

我们看到的工业方面 我认为

一些主导

行业并创造人工智能垄断的科技公司

这些业务

主要由深度学习算法驱动,这些算法提供

大量数据,

这些数据用于开发预测

模型

和训练,他们称之为人工智能训练

模型,这些巨型模型

用于 增强数字

产品产品用于收集更多

数据

,然后将数据再次反馈

到模型以进行微调

以获得更好的产品,因此这是一个恶性

循环,

一旦创建它就会自我强化,

它会使公司不断发展

从一开始就拥有大量数据,现在

更有利于从

这个恶性循环中受益,

我保持愈伤组织 ng 它是恶性循环而不是良性循环,

因为 AI 是一台财富生成

机器

,这种 AI 不平等会导致

经济不平等

,众所周知,它会

为极少数拥有

数据

和必要工具的人增加大量财富,例如

凭借其自动驾驶程序和

数亿小时的车队记录

,世界顶级电动汽车公司

在实现自动驾驶汽车的竞赛中具有优势

另一个例子是社交媒体

平台

,它对我们的了解比我们所知道的还要多

我们自己

,这就是为什么我们看到这种

对社交媒体

平台的巨大成瘾

它符合这个恶性循环

我今天想讨论的关于人工智能

不平等的最后一个方面

是如果你看一下

作为主要来源的互联网内容,人工智能缺乏多样性 今天用于开发人工智能工具的数据中有

60% 是

英语,

而 1% 是

阿拉伯语

,例如比较母语 讲阿拉伯语的

占世界人口的 5%,

这意味着

用于培训和开发人工智能

工具

的数据实际上是有偏差的

代表群体

这对

技术发展

和人工智能劳动力的多样性都有直接影响,

例如,

根据人工智能的自然研究指数,没有一个讲阿拉伯语的国家

跻身前 20 名国家之列 数字

和一些多样性报告显示,在这些大公司中,

阿拉伯黑人和其他少数族裔

占人工智能劳动力的比例不到 5%

而假设亚洲有

40 人,白人有 40 人,

这是当今人工智能缺乏多样性

,你可能 问我你可能会问

自己

有没有可以

在个人层面上做的事情来解决 他

我在这里说是的,我们至少可以

在 2019 年的最后一个方面做

一些事情,我开始了 fahim dot

ai

fihim 是一个阿拉伯语单词,它的意思是

理解

所以 AI 或理解 AI 是一个在线

平台

,可以帮助 阿拉伯社区

人工智能领域的弱势群体

学习和利用最新的人工智能

技术 我们认为这个阿拉伯

地区与

人工智能的最新技术严重脱节,在技术开发方面

通常被大型科技公司忽视,

他们喜欢这一点

地区作为消费者,

但不是在技术开发方面,

因此我们启动了这项计划,

以阿拉伯语提供所有级别的免费高质量人工智能和机器学习教育内容,

团队已推动发布

100 多篇文章

听视频教程

以阿拉伯语组织的讲习班

,使

来自阿拉伯世界的 200 000 多人受益,

基本上什么名气 人工智能正在

为阿拉伯观众揭开人工智能的神秘面纱,让

这些新技术(如深度

学习)可以访问

它帮助许多人

迈出了他们采用人工智能的第一步,

无论是在组织中,如果他们有

一个组织并想要采用人工智能,

还是在他们的 如果他们的学生

正在寻找新的职业机会,

我们在 fehem.ti 的目标是成为

在这个阿拉伯地区广泛采用人工智能的催化剂

幸运的是,我们并不是当今唯一

解决

人工智能生态系统中这种系统性不平等问题的人,

其他全球组织也在 追求

类似的道路

,例如 AI 和 latin xai 中的黑人

试图帮助代表性较少的群体

在 AI 研究中茁壮成长

一个竞争非常激烈的研究领域

另一个例子是 AI for all 美国

非营利组织,

致力于增加 AI 研究发展教育的包容性和多样性

和政策,

所以这就是我过去几年一直在做

的事情,现在除了

当然,我的研究生学习,我会这样做,因为我

在 ais 工作,我看到了这个

问题,我正在尽我所能解决

它,但你可能会问自己

你能做什么

你可能在不同的

领域工作 所以这

对你来说可能看起来不太相关,

但这没关系,因为你

总是可以支持

那些通过分享知识和帮助他人来帮助

解决人工智能不平等问题的倡议

我不是在这里说人工智能不好 我在这个领域工作,

所以实际上我认为它

是人类最伟大的发明之一,

但是它有这个巨大的不平等

问题,

我们不应该忽视它作为最新的

科学作家艾萨克·阿西莫夫

,他熟悉他关于

机器人的故事

并真正受到启发

该领域的一代科学家

曾经说过,如果知识可以创造

问题,

那不是通过无知,我们可以解决

它们,

谢谢