AI如何助力企业管理
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When I think about the rise of AI,
I'm reminded by the rise of literacy.
A few hundred years ago,
many people in society thought
that maybe not everyone needed to be able to read and write.
Back then, many people were tending fields or herding sheep,
so maybe there was less need for written communication.
And all that was needed
was for the high priests and priestesses and monks
to be able to read the Holy Book,
and the rest of us could just goto the temple or church
or the holy building
and sit and listen to the high priest and priestesses read to us.
Fortunately, it was since figured out that we can build a much richer society
if lots of people can read and write.
翻译
当我思考人工智能的兴起时，
我想起了读写能力的普及。
几百年前，
社会上的许多人认为
也许并不是每个人都需要能够读写。
那时候，许多人都在耕田或放羊，
所以也许对书面交流的需求较少。
而所需的一切
只是高级祭司和女祭司以及僧侣们
能够阅读圣书，
我们其他人只需要去寺庙或教堂
或者神圣的建筑
坐下来听高级祭司和女祭司们读给我们听。
幸运的是，后来人们意识到
如果很多人能够读写，
我们可以建立一个更加丰富多彩的社会。
Today, AI is in the hands of the high priests and priestesses.
These are the highly skilled AI engineers,
many of whom workin the big tech companies.
And most people have access only to the AI that they build for them.
I think that we can build a much richer society
if we can enable every one to help to write the future.
But why is AI largely concentrated in the big tech companies?
Because many of these AI projects have been expensive to build.
They may require dozens of highly skilled engineers,
and they may cost millions or tens of millions of dollars
to build an AI system.
And the large tech companies,
particularly the one with hundreds of millions
or even billions of users,
have been better than anyone elseat making these investments pay off
because, for them,a one-size-fits-all AI system,
such as one that improves web search
or that recommends better productsfor online shopping,
can be applied to [these] verylarge numbers of users
to generate a massive amount of revenue.
翻译
如今，人工智能掌握在高级祭司和女祭司的手中。
他们就是那些技能高超的人工智能工程师，
其中许多人在大型科技公司工作。
而大多数人只能使用他们为其构建的人工智能。
我认为，如果我们能让每个人都参与到书写未来的过程中，
我们可以建立一个更加丰富多彩的社会。
但为什么人工智能在大型科技公司中高度集中呢？
因为许多这些人工智能项目构建起来成本高昂。
它们可能需要数十名高技能工程师，
并且可能需要花费数百万甚至数千万美元
来构建一个人工智能系统。
而大型科技公司，
尤其是那些拥有数亿甚至数十亿用户的公司，
在使这些投资获得回报方面比任何人都做得更好，
因为对他们来说，一个通用的人工智能系统，
比如一个改进网络搜索的系统，
或者一个为在线购物推荐更好产品的系统，
可以应用于这些非常庞大的用户群体，
从而产生大量的收入。
But this recipe for AI does not work
once you go outside the tech and internet sectors to other places
where, for the most part,
there are hardly any projects that apply to 100 million people
or that generate comparable economics.
Let me illustrate an example.
Many weekends, I drive a few minutes from my house to a local pizza store
to buy a slice of Hawaiian pizza
from the gentleman that owns this pizza store.
And his pizza is great,
but he always has a lot of cold pizzas sitting around,
and every weekend some different flavor of pizza is out of stock.
But when I watch him operate his store,
I get excited,
because by selling pizza,
he is generating data.
And this is data that he can take advantage of
if he had access to AI.
AI systems are good at spotting patterns when given access to the right data,
and perhaps an AI system could spot if Mediterranean pizzas sell really well
on a Friday night,
maybe it could suggest to him to make more of it on a Friday afternoon.
Now you might say to me,"Hey, Andrew, this is a small pizza store.
What's the big deal?"
And I say, to the gentleman that owns this pizza store,
something that could help him improve his revenues
by a few thousand dollars a year, that will be a huge deal to him.
I know that there is a lot of hype aboutAI's need for massive data sets,
and having more data does help.
But contrary to the hype,
AI can often work just fine
even on modest amounts of data,
such as the data generated by a single pizza store.
翻译
但这种人工智能的配方一旦走出科技和互联网领域，进入其他领域，就不再有效了，因为在大多数情况下，几乎没有项目能够适用于1亿人，或者产生可比的经济效益。让我举一个例子来说明。
许多周末，我会开车从我家出发几分钟，去当地的比萨店买一片夏威夷比萨，这家店是由一位男士经营的。他的比萨非常好吃，但他总是有很多冷比萨闲置着，每个周末都有不同口味的比萨缺货。但当我观察他经营店铺时，我感到很兴奋，因为他通过卖比萨，正在产生数据。而这些数据，如果他能够使用人工智能，就可以加以利用。
人工智能系统擅长在获得正确数据的情况下发现模式，也许一个人工智能系统能够发现地中海比萨在周五晚上卖得非常好，也许它可以建议他在周五下午制作更多的地中海比萨。
你可能会对我说：“嘿，安德鲁，这只是一家小比萨店。有什么大不了的？”而我会说，对于经营这家比萨店的男士来说，任何能帮助他每年增加几千美元收入的事情，对他来说都是一件大事。我知道关于人工智能需要大量数据集的炒作有很多，而拥有更多的数据确实有帮助。但与炒作相反，人工智能即使在数据量适中的情况下，比如由单个比萨店产生的数据，通常也能很好地工作。
So the real problem is not
that there isn’t enough data from the pizza store.
The real problem is that the small pizza store
could never serve enough customers
to justify the cost of hiring an AI team.
I know that in the United States
there are about half a million in dependent restaurants.
And collectively, these restaurants do serve tens of millions of customers.
But every restaurant is different with a different menu,
different customers, different ways of recording sales
that no one-size-fits-all AI would work for all of them.
What would it be like if we could enable small businesses
and especially local businesses to use AI?
Let's take a look at what it might look like
at a company that makes and sells T-shirts.
I would love if an accountant working for the T-shirt company
can use AI for demand forecasting.
Say, figure out what funny memes to prints on T-shirts
that would drive sales,
by looking at what's trending on social media.
翻译
所以真正的问题不在于比萨店没有足够的数据。真正的问题是，这家小比萨店永远无法服务足够多的客户来证明雇佣一个人工智能团队的成本是合理的。我知道在美国，大约有五十万家独立餐厅。这些餐厅总体上确实服务了数千万的客户。但是每家餐厅都有不同的菜单、不同的客户、不同的销售记录方式，没有一种通用的人工智能能够适用于所有餐厅。
如果我们能让小企业，尤其是本地企业使用人工智能，那将会是什么样子呢？让我们看看一家制作和销售T恤的公司可能会是什么样子。如果T恤公司的会计师能够使用人工智能进行需求预测，那就太好了。比如说，通过观察社交媒体上的趋势，来确定哪些有趣的模因印在T恤上能够推动销售。
Or for product placement,
why can’t a front-of-store manager take pictures of what the store looks like
and show it to an AI
and have an AI recommend where to place products to improve sales?
Supply chain.
Can an AI recommend to a buyer whether or not they should pay 20 dollars
per yard for a piece of fabric now,
or if they should keep looking
because they might be able to find it cheaper elsewhere?
Or quality control.
A quality inspector should be able to use AI
to automatically scan pictures of the fabric they use to make T-shirts
to check if there are any tears or discolorations in the cloth.
Today, large tech companies routinely use AI to solve problems like these
and to great effect.
But a typical T-shirt company or a typical auto mechanic
or retailer or school or local farm
will be using AI for exactly zero of these applications today.
Every T-shirt maker is sufficiently different from every other T-shirt maker
that there is no one-size-fits-all AI that will work for all of them.
And in fact, once you go outside the internet and tech sectors
in other industries, even large companies
such as the pharmaceutical companies,
the car makers, the hospitals,
also struggle with this.
This is the long-tail problem of AI.
If you were to take all current and potential AI projects
and sort them in decreasing order of value and plot them,
you get a graph that looks like this.
翻译
或者对于产品摆放，为什么商店经理不能拍摄商店的照片，然后展示给人工智能，并让人工智能推荐如何摆放产品以提高销售额呢？
供应链问题。人工智能能否建议采购员现在是否应该以每码20美元的价格购买一块布料，或者他们是否应该继续寻找，因为他们可能在别处能以更低的价格找到？
或者质量控制。质量检查员应该能够使用人工智能自动扫描他们用来制作T恤的布料的照片，检查是否有任何撕裂或布料变色。
如今，大型科技公司经常使用人工智能来解决这样的问题，并取得了巨大的成效。但是，一个典型的T恤公司、汽车修理工、零售商、学校或当地农场今天将会完全不会使用人工智能来处理这些应用中的任何一个。
每一个T恤制造商与其他T恤制造商都足够不同，以至于没有一种通用的人工智能能够适用于所有他们。实际上，一旦你走出互联网和科技领域，进入其他行业，即使是大型公司，如制药公司、汽车制造商、医院，也会在这方面遇到困难。这就是人工智能的长尾问题。
如果你将所有当前和潜在的人工智能项目按照价值递减的顺序排序并绘制出来，你会得到一个像这样的图表。
Maybe the single most valuable AI system
is something that decides what ads to show people on the internet.
Maybe the second most valuable is a web search engine,
maybe the third most valuable is an online shopping product recommendation system.
But when you goto the right of this curve,
you then get projects like T-shirt product placement
or T-shirt demand fore casting or pizzeria demand forecasting.
And each of these is a unique project that needs to be custom-built.
Even T-shirt demand forecasting,
if it depends on trending memes on social media,
is a very different project than pizzeria demand forecasting,
if that depends on the pizzeria sales data.
So today there are millions of projects
sitting on the tail of this distribution that no one is working on,
but whose aggregate value is massive.
So how can we enables mall businesses and individuals
to build AI systems that matter to them?
For most of the last few decades,
if you wanted to build an AI system, this is what you have to do.
You have to write pages and pages of code.
And while I would love for everyone to learn to code,
and in fact, online education and also offline education
are helping more people than ever learn to code,
unfortunately, not everyone has the time to do this.
But there is an emerging new way
to build AI systemsthat will let more people participate.
翻译
或许最有价值单一的人工智能系统是决定在互联网上向人们展示什么广告的系统。也许第二有价值的是网络搜索引擎，第三有价值的是在线购物产品推荐系统。但当你走到这条曲线的右侧时，你将得到像T恤产品摆放、T恤需求预测或比萨店需求预测这样的项目。每一个都是需要定制构建的独特项目。
即使是T恤需求预测，如果它依赖于社交媒体上的趋势模因，也是一个非常不同的项目，与比萨店需求预测不同，后者可能依赖于比萨店的销售数据。因此，如今有数以百万计的项目位于这个分布的尾部，没有人在进行这些项目，但它们的总价值是巨大的。
那么我们如何让小企业和个人构建对他们有意义的人工智能系统呢？在过去几十年的大部分时间里，如果你想要构建一个人工智能系统，这是你必须做的事情：你必须编写一页又一页的代码。虽然我希望大家都能学会编程，事实上，无论是在线教育还是线下教育，都比以往任何时候都帮助更多的人学会编程，但不幸的是，并不是每个人都有时间去做这件事。但是，一种新兴的构建人工智能系统的方式将让更多人参与其中。
Just as pen and paper,
which are a vastly superior technology to stone tablet and chisel,
were instrumental to widespread literacy,
there are emerging newAI development platforms
that shift the focus from asking you to write lots of code
to asking you to focus on providing data.
And this turns out to be much easier for a lot of people to do.
Today, there are multiple companies working on platforms like these.
Let me illustrate a few of the concepts using one that my team has been building.
Take the example of an inspector
wanting AI to help detect defects in fabric.
An inspector can take pictures of the fabric
and upload it to a platform like this,
and they can go in to show the AI what tears in the fabric look like
by drawing rectangles.
And they can also go in to show the AI
what discoloration on the fabric looks like
by drawing rectangles.
So these pictures,
together with the green and pink rectangles
that the inspector's drawn,
are data created by the inspector
to explain to AI how to find tears and discoloration.
翻译
正如钢笔和纸张这种技术远远优于石板和凿子，对普及读写能力起到了重要作用一样，新兴的人工智能开发平台正在改变焦点，从要求你编写大量代码，转变为要求你专注于提供数据。这对许多人来说，要容易得多。
如今，有多家公司正在开发这样的平台。让我用我的团队一直在构建的一个平台来说明一些概念。以一个检查员为例，他希望人工智能帮助检测布料中的缺陷。检查员可以拍摄布料的照片并上传到这样的平台，他们可以进入平台向人工智能展示布料上的撕裂是什么样子，通过绘制矩形来标识。他们还可以通过绘制矩形来向人工智能展示布料上的变色是什么样子。
所以这些照片，连同检查员绘制的绿色和粉色矩形，是由检查员创建的数据，用来向人工智能解释如何找到撕裂和变色。
After the AI examines this data,
we may find that it has seen enough pictures of tears,
but not yet enough pictures of discolorations.
This is akin to if a junior inspector had learned to reliably spot tears,
but still needs to further hone their judgment about discolorations.
So the inspector can go back and take more pictures of discolorations
to show to the AI,
to help it deepen this understanding.
By adjusting the data you give to the AI,
you can help the AI get smarter.
So an inspector using an accessible platform like this
can, in a few hours to a few days,
and with purchasing a suitable camera set up,
be able to build a custom AI system to detect defects,
tears and discolorations in all the fabric
being used to make T-shirts through out the factory.
And once again, you may say,
"Hey, Andrew, this is one factory.
Why is this a big deal?"
And I say to you,
this is a big deal to that inspector whose life this makes easier
and equally, this type of technology can empower a baker to use AI
to check for the quality of the cakes they're making,
or an organic farmer to check the quality of the vegetables,
or a furniture maker to check the quality of the wood they're using.
翻译
在人工智能检查了这些数据之后，我们可能会发现它已经看过足够多的撕裂照片，但还没有看过足够多的变色照片。这就像一个初级检查员已经学会了可靠地发现撕裂，但仍然需要进一步提高他们对变色的判断力。因此，检查员可以回去拍摄更多变色的照片给人工智能看，以帮助它加深这种理解。通过调整你给人工智能的数据，你可以帮助人工智能变得更聪明。
因此，使用这样一个易于访问的平台的检查员可以在几个小时到几天内，并且购买合适的摄像设备后，能够构建一个定制的人工智能系统，来检测整个工厂用于制作T恤的布料中的缺陷、撕裂和变色。再次，你可能会说，“嘿，安德鲁，这只是一家工厂。为什么这是一件大事？”我对你说，这对于让生活变得更轻松的检查员来说是一件大事，同样，这种类型的技术可以赋予面包师使用人工智能检查他们正在制作的蛋糕的质量的能力，或者有机农民检查他们种植的蔬菜的质量，或者家具制造商检查他们使用的木材的质量的能力。
Platforms like these will probably still need a few more years
before they're easy enough to use for every pizzeria owner.
But many of these platforms are coming along,
and some of the mare getting to be quite useful
to someone that is tech savvy today,
with just a bit of training.
But what this means is that,
rather than relying on the high priests and priestesses
to write AI systems for everyone else,
we can start to empower every accountant,
every store manager,
every buyer and every quality inspector to build their own AI systems.
I hope that the pizzeria owner
and many other small business owners like him
will also take advantage of this technology
because AI is creating tremendous wealth
and will continue to create tremendous wealth.
And it's only by democratizing access to AI
that we can ensure that this wealth is spread far and wide across society.
Hundreds of years ago.
I think hardly anyone understood the impact
that widespread literacy will have.
Today, I think hardly anyone understands
the impact that democratizing access to AI will have.
Building AI systems has been out of reach for most people,
but that does not have to be the case.
In the coming era for AI,
we’ll empower everyone to buildAI systems for themselves,
and I think that will be incredibly exciting future.
Thank you very much.
(Applause)
翻译
像这样的平台可能仍然需要几年的时间，才能变得足够简单，让每一个比萨店老板都能轻松使用。但许多这样的平台正在发展中，其中一些对于今天已经具备一定技术知识的人来说，经过一点培训就能够变得相当有用。
但这意味着，我们不再依赖高级祭司和女祭司为每个人编写人工智能系统，而是可以开始赋予每一个会计师、每一个商店经理、每一个采购员和每一个质量检查员构建他们自己的人工智能系统的能力。
我希望比萨店老板和许多像他这样的小企业主也能利用这项技术，因为人工智能正在创造巨大的财富，并且将继续创造巨大的财富。只有通过普及人工智能的获取，我们才能确保这些财富在社会中广泛传播。
几百年前，我认为几乎没有人理解普及读写能力将会产生的影响。今天，我认为几乎没有人理解普及人工智能获取将会产生的影响。构建人工智能系统对于大多数人来说一直是遥不可及的，但情况不必如此。在即将到来的人工智能时代，我们将赋予每个人为自己构建人工智能系统的能力，我认为这将是一个令人难以置信的激动人心的未来。
非常感谢大家。
（掌声）
高频单词
- AI (Artificial Intelligence)
中文意思：人工智能
生活中的句子：
"I use AI to manage my daily tasks and reminders."（我使用人工智能来管理我的每日任务和提醒。）
"AI is revolutionizing the way we interact with our devices."（人工智能正在改变我们与设备互动的方式。）
学术句子：
"The integration of AI in healthcare is a topic of extensive research and debate."（人工智能在医疗保健中的整合是一个广泛研究和讨论的话题。）
"Machine learning, a subset of AI, has seen significant advancements in recent years."（作为人工智能的一个子集，机器学习近年来取得了显著进展。）
- literacy
中文意思：识字能力；文化素养
生活中的句子：
"Financial literacy is essential for managing personal finances effectively."（财务文化素养对于有效管理个人财务至关重要。）
"Digital literacy is becoming increasingly important in today's technology-driven world."（在当今技术驱动的世界中，数字文化素养变得越来越重要。）
学术句子：
"Literacy rates are often used as an indicator of a country's level of education and development."（识字率常被用作衡量一个国家教育和发展水平的指标。）
"The concept of media literacy has gained traction in educational circles to address the challenges of the information age."（媒体文化素养的概念在教育界获得了关注，以应对信息时代的挑战。）
- technology
中文意思：技术
生活中的句子：
"Technology has made it possible to work remotely from almost anywhere."（技术使得几乎可以从任何地方远程工作。）
"We rely on technology for communication, entertainment, and information."（我们依赖技术进行沟通、娱乐和获取信息。）
学术句子：
"The impact of technology on society is a complex issue that requires interdisciplinary study."（技术对社会的影响是一个需要跨学科研究的复杂问题。）
"Technological determinism is a theory that suggests that technology drives social change."（技术决定论是一种理论，认为技术推动社会变革。）
- data
中文意思：数据
生活中的句子：
"You can't make informed decisions without analyzing the relevant data."（没有分析相关数据，你无法做出明智的决策。）
"Data privacy is a concern for many users of social media platforms."（数据隐私是许多社交媒体平台用户的担忧。）
学术句子：
"The collection and interpretation of data are fundamental to the scientific method."（数据的收集和解释是科学方法的基础。）
"Big data analytics is reshaping the field of market research."（大数据分析正在重塑市场研究领域。）
- system
中文意思：系统
生活中的句子：
"The new ticketing system at the train station has streamlined the booking process."（火车站的新售票系统简化了订票流程。）
"Our heating system is not working properly, and we need to call a repairman."（我们的供暖系统出了问题，我们需要叫一个修理工。）
学术句子：
"The ecosystem is a complex network of plants, animals, and microorganisms."（生态系统是一个由植物、动物和微生物组成的复杂网络。）
"The bureaucratic system in many organizations can hinder innovation and efficiency."（许多组织的官僚体制可能阻碍创新和效率。）
长难句
- "And the large tech companies, particularly the ones with hundreds of millions or even billions of users, have been better than anyone else at making these investments pay off because, for them, a one-size-fits-all AI system, such as one that improves web search or that recommends better products for online shopping, can be applied to [these] very large numbers of users to generate a massive amount of revenue.”
中文翻译： 而大型科技公司，尤其是那些拥有数亿甚至数十亿用户的公司，在使这些投资获得回报方面比其他任何人都做得更好，因为对他们来说，一个通用的人工智能系统，比如一个改进网络搜索或为在线购物推荐更好产品的系统，可以应用于这些非常庞大的用户群体，从而产生大量的收入。
句子结构分析： 这个复杂句以"And"开头，连接了前面的内容，并引入了主语"the large tech companies"。"particularly"用于进一步限定主语。"have been better than anyone else at making these investments pay off"是谓语部分，说明了这些公司的表现。"because"引导原因状语从句，解释了为什么这些公司能够成功。"a one-size-fits-all AI system"是这个从句中的主语，后面跟着两个并列的定语从句，分别描述了这种系统可以做什么。"can be applied to [these] very large numbers of users"是被动语态，说明了系统的应用范围。"to generate a massive amount of revenue"是目的状语，说明了应用的最终结果。
类似结构的例句： "The pharmaceutical industry, especially the companies with a wide range of products, has been successful in marketing their drugs globally because, for them, a targeted marketing strategy that focuses on patient education and awareness can lead to increased sales and brand loyalty among a diverse population."
中文翻译： 制药行业，尤其是那些产品线广泛的公司，在全球范围内成功地营销了他们的药品，因为对他们来说，一个专注于患者教育和意识提高的针对性营销策略可以导致销售增加和在多样化人口中的品牌忠诚度提高。
- "But contrary to the hype, AI can often work just fine even on modest amounts of data, such as the data generated by a single pizza store.”
中文翻译： 但与炒作相反，人工智能即使在数据量适中的情况下，比如由单个比萨店产生的数据，通常也能很好地工作。
句子结构分析： 这个句子以"But"开头，表示转折。"contrary to the hype"是一个介词短语，用来修饰整个句子。"AI can often work just fine"是主句的核心，说明了人工智能的能力。"even on modest amounts of data"是一个状语，强调了数据量的不重要性。"such as the data generated by a single pizza store"是一个非限制性定语从句，用来举例说明前文提到的"modest amounts of data"。
类似结构的例句： "Despite common beliefs, a healthy lifestyle doesn't necessarily mean giving up all your favorite foods; instead, it's about finding a balance and making smart choices that suit your individual needs."
中文翻译： 尽管有普遍的信念，健康的生活方式并不一定意味着放弃所有你最喜欢的食物；相反，它是关于找到一个平衡点，并做出适合你个人需求的明智选择。
- "But the real problem is not that there isn’t enough data from the pizza store. The real problem is that the small pizza store could never serve enough customers to justify the cost of hiring an AI team.”
中文翻译： 但真正的问题不在于比萨店没有足够的数据。真正的问题是这家小比萨店永远无法服务足够多的客户来证明雇佣一个人工智能团队的成本是合理的。
句子结构分析： 这个复合句由两个并列句组成，用"not"来强调对比。第一个句子"The real problem is not that there isn’t enough data from the pizza store"中，"The real problem is not"是主句的核心，"that there isn’t enough data from the pizza store"是一个宾语从句，作为真正的主语。第二个句子"The real problem is that the small pizza store could never serve enough customers to justify the cost of hiring an AI team"结构相似，"The real problem is"作为主句的核心，"that the small pizza store could never serve enough customers to justify the cost of hiring an AI team"是一个宾语从句，详细说明了问题所在。
类似结构的例句： "The challenge is not the lack of resources, but the lack of innovative ideas that can utilize the resources effectively. The real issue is that we often overlook the potential of simple solutions to complex problems."
中文翻译： 挑战不在于资源的缺乏，而在于缺乏能够有效利用资源的创新想法。真正的问题是我们经常忽视简单解决方案对复杂问题的潜力。
- "If you were to take all current and potential AI projects and sort them in decreasing order of value and plot them, you get a graph that looks like this.”
中文翻译： 如果你将所有当前和潜在的人工智能项目按照价值递减的顺序排序并绘制出来，你会得到一个像这样的图表。
句子结构分析： 这个条件句以"If you were to"开头，是一个虚拟语气的表达，用来提出一个假设的情况。"take all current and potential AI projects"是这个假设情况的宾语从句，说明了要执行的动作。"and sort them in decreasing order of value and plot them"是并列的动词短语，进一步描述了这个动作的细节。"you get a graph that looks like this"是结果状语从句，说明了上述动作的结果。
类似结构的例句： "If you were to analyze all the data from the experiment carefully, you would find correlations that were not evident at first glance."
中文翻译： 如果你仔细分析实验的所有数据，你会发现一些乍看之下不明显的相关性。
- "Building AI systems has been out of reach for most people, but that does not have to be the case. In the coming era for AI, we’ll empower everyone to build AI systems for themselves, and I think that will be incredibly exciting future.”
中文翻译： 构建人工智能系统对于大多数人来说一直是遥不可及的，但情况不必如此。在即将到来的人工智能时代，我们将赋予每个人为自己构建人工智能系统的能力，我认为这将是一个令人难以置信的激动人心的未来。
句子结构分析： 第一个句子"Building AI systems has been out of reach for most people"是一个简单句，说明了当前的情况。"but that does not have to be the case"是一个并列句，提供了一个转折。第二个句子"In the coming era for AI, we’ll empower everyone to build AI systems for themselves"是一个复合句，其中包含一个时间状语从句"In the coming era for AI"，主句是"we’ll empower everyone to build AI systems for themselves"，说明了未来的情况。"and I think that will be incredibly exciting future"是一个并列句，表达了演讲者对未来的看法。
类似结构的例句： "Creating a sustainable future may seem daunting at first, but with collective effort and innovative solutions, we can make a significant impact. In the years to come, we will see a shift in how we approach environmental conservation, and I believe that will lead to a greener, more sustainable planet."
中文翻译： 创建一个可持续的未来一开始可能看起来令人生畏，但通过集体努力和创新解决方案，我们可以产生重大影响。在未来几年中，我们将看到我们如何对待环境保护的方式发生变化，我相信这将导致一个更绿色、更可持续的星球。