Why AI Is Incredibly Smart and Shockingly Stupid | Yejin Choi | TED

But first, let's get philosophical哲學的 by starting with this quote by Voltaire, an 18th century Enlightenment philosopher, who said, "Common sense is not so common."

但在開始之前，讓我們先探討一下哲學問題，從伏爾泰的一句名言說起：這位 18 世紀啟蒙運動哲學家曾說，“常識其實並不常見。”

Turns out this quote couldn't be more relevant to artificial intelligence today.

事實證明，這句名言對當今的人工智慧來說再貼切不過了。

Despite that, AI is an undeniably不可否認地 powerful tool, beating the world-class "Go" champion, acing college admission tests and even passing the bar exam.

儘管如此，人工智慧無疑是一個強大的工具，它擊敗了世界級的圍棋冠軍，在大學入學考試中取得高分，甚至透過了律師資格考試。

I’m a computer scientist of 20 years, and I work on artificial intelligence.

我是一名擁有 20 年經驗的電腦科學家，專門從事人工智慧工作。

I am here to demystify使……不再神秘 AI.

我的目標是揭開人工智慧的神秘面紗。

So AI today is like a Goliath.

所以，當今的人工智慧就像歌利亞巨人一樣龐大。

It is literally very, very large.

它的規模 literally（字面上）非常、非常大。

It is speculated推測 that the recent ones are trained on tens of thousands of GPUs and a trillion words.

據推測，最近的一些模型是在數萬個 GPU 和一萬萬億個單字的資料上訓練出來的。

Such extreme-scale AI models, often referred to as "large language models," appear to demonstrate證明 sparks of AGI, artificial general intelligence.

這種超大規模的人工智慧模型，通常被稱為“大型語言模型”，似乎展現出了通用人工智慧（AGI）的火花。

Except when it makes small, silly mistakes, which it often does.

除非它犯下一些微小而愚蠢的錯誤，而這正是它經常做的事。

Many believe that whatever mistakes AI makes today can be easily fixed with brute force, bigger scale and more resources.

許多人認為，無論人工智慧今天犯什麼錯誤，都可以透過蠻力、更大的規模和更多的資源輕鬆解決。

What possibly could go wrong?

究竟會有什麼不對勁呢？

So there are three immediate challenges we face already at the societal level.

因此，我們在社會層面已經面臨三個緊迫的挑戰。

First, extreme-scale AI models are so expensive to train, and only a few tech companies can afford to do so.

首先，超大規模人工智慧模型的訓練成本極高，只有少數科技公司負擔得起。

we already see the concentration專注力 of power.

我們已經看到了權力的集中化。

But what's worse for AI safety, we are now at the mercy of those few tech companies because researchers in the larger community do not have the means to truly inspect and

但更糟糕的是，就人工智慧安全而言，我們現在受制於那幾家科技公司，因為社群中的研究人員沒有能力真正去檢查並

dissect仔細分析 these models.

剖析這些模型。

And let's not forget their massive carbon footprint and the environmental impact.

別忘了它們巨大的碳足跡及其對環境的影響。

And then there are these additional intellectual questions.

此外，還存在這些額外的智力層面的問題。

Can AI, without robust強健的 common sense, be truly safe for humanity?

如果沒有健全的常識，人工智慧真的能對人類安全嗎？

And is brute-force scale really the only way and even the correct way to teach AI?

而且，依靠蠻力擴大規模真的是唯一的方式，甚至是正確的方式來教導人工智慧嗎？

So I’m often asked these days whether it's even feasible可行的 to do any meaningful research without extreme-scale compute.

所以，如今我經常被問及，在不具備超大規模計算能力的情況下，是否還能進行任何有意義的研究。

And I work at a university and nonprofit research institute, so I cannot afford a massive GPU farm to create enormous language models.

我在大學和非營利研究機構工作，因此無法負擔龐大的 GPU 農場來建立巨型語言模型。

Nevertheless, I believe that there's so much we need to do and can do to make AI sustainable and humanistic人本的.

儘管如此，我相信我們有許多需要去做且能夠去做的事情，以使人工智慧更具可持續性和人文關懷。

We need to make AI smaller, to democratize使大眾化 it.

我們需要讓人工智慧變得更小，以實現其普及化。

And we need to make AI safer by teaching human norms and values.

我們需要透過教授人類的規範和價值觀，讓人工智慧更安全。

Perhaps we can draw an analogy類比 from "David and Goliath," here, Goliath being the extreme-scale language models, and seek inspiration from an old-time classic, "The Art of War," which tells us, in

也許我們可以從“大衛與歌利亞”的故事中汲取類比：將極端規模的語言模型比作歌利亞，並從《孫子兵法》這一古老經典中尋找靈感，因為書中告訴我們……

my interpretation解釋, know your enemy, choose your battles, and innovate your weapons.

依我之見，那就是要知己知彼，選擇戰場，並革新武器。

Let's start with the first, know your enemy, which means we need to evaluate AI with scrutiny仔細審查.

讓我們先從第一條開始：知己知彼，這意味著我們需要審慎地評估人工智慧。