And it seems like all I'm hearing about lately is...

最近我似乎一直一直聽到……。

Artificial intelligence.

人工智慧。

Artificial intelligence.

人工智慧。

Artificial intelligence.

人工智慧。

It's all about artificial intelligence.

全都在講人工智慧。

I think it's time we talk about AI.

我想是時候來談談 AI 了。

Okay, I love my Google Assistant.

我必須說我超愛我的 Google 助理。

And I even say goodnight to it.

我甚至會跟它說晚安。

Which is mildly embarrassing.

這有點令人臉紅。

But I'm here for it because then it tells me the weather for tomorrow.

但我還是要幫它說話，因為它會告訴我明天天氣如何。

And it plays cricket sounds which I think are actually helping me sleep better.

還播放蟋蟀的聲音使我更好入睡。

And it's also getting to know me, right.

此外，它愈來愈熟悉我的一切。

So it tells me when the train's gonna leave in the morning, and it's even recommending songs I might like.

它會提醒我明天早上火車幾點開，甚至推薦我可能會喜歡的歌曲。

And for a long time companies have been pushing this off as artificial intelligence.

很長一段時間，各家公司都為它冠上人工智慧的名號。

Which I always thought of as a marketing term.

我以前老是覺得那是行銷手法。

Like I don't think we think that Siri is intelligent.

我並不認為人們覺得 Siri 有智慧。

But then Google showed us this.

但後來 Google 發布了這樣的成果。

That's the voice of Google Assistant making calls for you.

那是 Google 助手為你打電話的聲音。

This made me think that maybe artificial intelligence is closer to replicating human intelligence than I thought.

這讓我不禁想，也許人工智慧與人類智慧間的差距比我想的還要近。

So I called James Vincent.

於是我打電話給 James Vincent。

And he's a reporter here at the Verge that covers all things AI.

他是 the Verge 這邊的記者，負責一切關於 AI 的報導。

I wanted to know were AI is right now and where it's going.

我想了解 AI 現在的近程以及未來的走向。

So AI as a field is a term, it's sort of an umbrella and it includes lots of different types of AI that kind of come into fashion over the years, and then they test them, they get to the limitations and they move on to the next one.

AI 是個概括性的詞彙，包含各種不同類型的 AI，在這幾年才慢慢流行起來，經過不斷測試、推展技術限制、推進到下一項挑戰。

And the thing that's very much dominate in the field at the moment is machine learning.

而目前 AI 領域最核心的發展就是機器學習。

Which is all about giving a system a lot of data.

簡單來說就是給機器一堆數據資料。

And then it goes through that data.

讓它處理那些資料。

And it learns the patterns within it.

並從其中的模式學習。

And then a flavor of machine learning is what's called deep learning.

機器學習的其中一個分支是所謂的深度學習。

Deep learning is basically, you know, basically machine learning but it uses a lot of data.

深度學習基本上類似於機器學習，但它使用大量的數據資料。

And it uses a lot of computing power.

以及運算資源。

Which we now have access thanks to the internet and thanks to cheaper chips.

多虧有了網路連線以及更便宜的微晶片，這已經不難做到。

Okay James, real talk though.

好 James，嚴肅的部分差不多到這裡吧。

Have you seen Terminator?

妳看過《 魔鬼終結者》嗎？

I mean how close are we to the Skynets of the world?

我們距離遍布全球的「天網」還很遠嗎？

Like is that coming?

它真的會成真嗎？

It's not really.

事實上不太可能。

I mean, so what you get in films, popular culture.

在電影裡、流行文化裡看到的。

That is what's usually called artificial general intelligence.

通常都是「通用人工智慧」。

Which is a huge step forward from what we got at the present moment.

距離我們今天的發展，還有很長的距離得跨越。

So AI is a field, it was kind of founded on this belief you could build computers that were just humans essentially.

AI 這個領域的發展基礎是渴望打造能夠媲美人類的電腦。

That just kind of thought like humans, acted, reacted like humans.

讓電腦可以像人類一樣思考、行為表現與反應。

And as it's gone on we've kind of realized that oh wait no no this is a really difficult task,

然而一步步有所進展之後，噢不等等，這是項浩大的工程。

And we're not near it basically.

而我們距離這項目標還非常遠。

So while Google Duplex sounds very human.

所以即使 Google Duplex 聽起來就像人類。

It's actually what we call narrow AI.

事實上它只是所謂的「狹義人工智慧」。

Now at the moment everything that's being sold to you as AI is narrow AI.

現在市面上所有 AI 商品都是運用狹義人工智慧。

And it was built under a limited predetermined set of functions.

而且它的運作只限定於事先規劃好的一套功能。

It pops up on your phone.

它在你的手機畫面彈出。

Your Google Home or your Echo.

你要用的 Google Home 或 Echo。

And it's how Facebook recognizes your face.

它也讓 Facebook 辨認你的臉。

And automatically takes photos of you.

並自動為你拍下照片。

This form of AI is designed to complete very specific tasks, and it's incapable of doing anything else.

這種形式的 AI 被設計成完成特定一項任務，對於其他任務則無能為力。

Now that doesn't mean it can't do impressive things.

但這並不代表它不令人驚豔。

Take for example Deep Mind's Alpha Go.

以 Deep Mind 公司開發的 Alpha Go 為例。

Which is an AI program trained to play the game Go.

它是一個 AI 程式，專門被設計來玩 Go 這個遊戲。

Now Go is like a strategy game, sort of like chess, except it has way more possible outcomes.

Go 是一種策略遊戲，有點像下棋，然而 Go 有更多樣的可能結果。

And in 2016, the AI system battled against legendary Go player Lee Sedol defeating him four to one.

在 2016 年，該系統對戰傳奇性的 Go 玩家李世乭，並以四比一的比數擊敗對方。

And in 2017, Deep Mind retired the AlphaGo AI after it defeated the world's best Go player, three to zero.

2017 年以三比零的比數擊潰 Go 的全球冠軍選手之後，Deep Mind 讓 AlphaGo AI 退役。

But I'd like to point out that this program AlphaGo would continue playing Go even if the building it was in was on fire.

但別忘了 AlphaGo 這個程式會持續進行遊戲，即便所處的建築物失火了。

Even if the room was on fire.

甚至所處的房間著火了。

That's Dr. Oren Etzioni.

這位是 Oren Etzioni 博士。

CEO of Allen Institute for Artificial Intelligence and professor at the University of Washington in Seattle.

艾倫人工智慧研究所的執行長，他也是西雅圖華盛頓大學的教授。

It's an excellent symbol because it shows that in these very narrow, very well structured tasks.

這是非常出色的實例，因為它在這麼精細、有組織的任務裡。

Like a board game.

例如一場桌遊。

We can achieve superhuman performance.

它展現出超越普通人類的表現。

But on things that are more nuanced.

但一旦牽扯到差別更細微的事項。

Things that have to do with language.

例如語言相關。

We are actually very far from even the abilities of a child.

這技術還遠遠比不上孩童。

So common sense could be thought of as the missing link between AI and AGI.

換言之，人類常理可能是狹義 AI 與通用 AI 之間的重要分水嶺。

And when referring to common sense.

而當我們提到「常理」。

We're referring to the whole range of capabilities that humans have and computers just don't.

指的是所有一切人類所具備、 電腦卻缺乏的心智功能。

Dr. Etzioni is one of many researchers that are working on programs to teach computers common sense.

Etzioni 博士是許多正教導電腦人類常理的研究者之一。

But within the community there's a large debate over whether these smarter computers could be dangerous.

但這在研究社群裡其實引發了激烈的論辯：萬一電腦變得太聰明，會不會帶來危險？

Today narrow AI is being used to solve very real, very serious problems like helping doctors diagnose cancer, or predicting future weather disasters.

今天狹義人工智慧被用來解決非常真實、非常迫切的問題，例如幫助醫生診斷癌症，或是預測未來的天氣災難。

But it's also being used for things that people find worrying and that's understandable.

但它同時也被用在令人擔心的地方，而這是可以理解的。

I mean these are things like facial recognition for mass surveillance or augmenting weapons.

例如運用臉部辨識監視大眾，或者提升武器效能。

Which raises the question.

這引發了一個問題。

How if at all is AI going to be regulated?

究竟人工智慧該不該受到管制？又該如何管制？

This world evolves so rapidly that it's very hard to put these policies in place.

這個世界以非常快的速度向前邁進，也因此想在所有地方部署警力非常困難。

What I would suggest instead is that we identify applications of AI.

我反而會建議先得辨明人工智慧的應用範疇。

For example, AI cars or AI weapons.

例如 AI 車輛或武器。

And that we define very careful regulations around those specific applications.

再針對這些應用，謹慎地訂下管制規定。

So yeah I was creeped out by Google Duplex.

所以的確我有被 Google Duplex 嚇到。

But it's nowhere near the AI we have in the movies.

但它離電影裡的 AI 還有大段差距。

This is very specific pointed AI.

我們談的是非常精確狹義的 AI。

And when that's applied to security or weapons.

此外，當我們談到安全或武器的人工智慧應用。

It can be scary but all we can do is educate ourselves.

可能會感到害怕，但只要我們主動去認識與了解。

And be in the know about what AI we have right now.

人工智慧現在的發展進度到哪裡。

And maybe where it's going.

或許再加上它未來的發展方向。

These are still early days, and we should assess the exuberance people have and some of the fears that they have with a note of caution and skepticism.

我們尚處於早期階段，必須注意到人性的豐富多樣，同時也必須小心處理可能引發的恐懼。

If you want to know what AI can really do and what it can't, just have a chat with Siri and Alexa.

如果你想知道 AI 能否做到某件事，只要去跟 Siri 和 Alexa 聊聊。

And you'll quickly see where reality stops and the hype begins.

你就能清楚知道實際發展走到哪裡，哪些又只是媒體炒作。

Hey thanks for watching.

謝謝觀賞。

Let us know on the comments below your favorite uses of AI so far.

請在留言區分享你最喜歡的 AI 應用。

What's helping you on your day to day?

哪一種是你每天不可或缺的幫手？

And thanks for two million subscribers.

同時也感謝我們有了 200 萬名訂閱者。

That's hella cool and I'll see you next time.

超酷的！我們下回見！