Machine learning is another hot topic.

機器學習是另一個熱門話題。

Are they the same thing or are they different?

它們是一回事還是不同？

And if so, what are those differences?

如果有，這些差異是什麼？

And deep learning is another one that comes into play.

深度學習是另一個發揮作用的方法。

I actually did a video on these three, artificial intelligence, machine learning, and deep learning, and talked about where they fit.

實際上，我曾做過一個關於人工智能、機器學習和深度學習這三者的視頻，並談到了它們的契合點。

And there were a lot of comments on that, and I read those comments, and I'd like to address some of the most frequently asked questions so that we can clear up some of the myths and misconceptions around this.

我讀了這些評論，我想談談一些最常被問到的問題，以便我們能夠澄清一些關於這個問題的神話和誤解。

In addition, something else has happened since that video was recorded, and that is this, the absolute explosion of this area of generative AI.

此外，自錄製這段視頻以來，還發生了另一件事，那就是生成式人工智能領域的絕對爆炸性發展。

Things like large language models and chat bots have seemed to be taking over the world.

大型語言模型和哈拉機器人等東西似乎正在席捲全球。

We see them everywhere.

我們到處都能看到它們。

Really interesting technology.

非常有趣的技術。

And then also things like deep fakes.

此外，還有深度偽造等問題。

These are all within the realm of AI, but how do they fit within each other?

這些都屬於人工智能的範疇，但它們之間如何相互配合？

How are they related to each other?

它們之間有什麼關係？

We're going to take a look at that in this video and try to explain how all these technologies relate and how we can use them.

我們將在本視頻中對此進行介紹，並嘗試解釋所有這些技術之間的關係以及我們如何使用它們。

First off, a little bit of a disclaimer.

首先，我要聲明一點。

I'm going to have to simplify some of these concepts in order to not make this video last for a week.

為了不讓這段視頻持續一週，我必須簡化其中的一些概念。

So those of you that are really deep experts in the field, apologies in advance, but we're going to try to make this simple, and that will involve some generalizations.

是以，對於那些在這一領域有很深造詣的專家來說，我在此先向你們道歉，但我們還是要儘量把這個問題簡單化，這就涉及到一些概括性的問題。

First of all, let's start with AI.

首先，讓我們從人工智能說起。

Artificial intelligence is basically trying to simulate with a computer something that would match or exceed human intelligence.

人工智能基本上就是試圖用計算機模擬出某種能與人類智能相媲美或超越人類智能的東西。

What is intelligence?

什麼是智力？

Well, it could be a lot of different things, but generally we tend to think of it as the ability to learn, to infer, and to reason, things like that.

嗯，它可以是很多不同的東西，但一般來說，我們傾向於認為它是一種學習能力、推理能力和推理能力，諸如此類。

So that's what we're trying to do in the broad field of AI, of artificial intelligence.

這就是我們在廣泛的人工智能領域要做的事情。

And if we look at a timeline of AI, it really kind of started back around this timeframe.

如果我們看一下人工智能的時間軸，它確實是在這個時間段前後開始的。

And in those days, it was very premature.

而在那個年代，這還很不成熟。

Most people had not even heard of it.

大多數人甚至都沒聽說過。

And it basically was a research project.

這基本上是一個研究項目。

But I can tell you as an undergrad, which for me was back during these times, we were doing AI work.

但我可以告訴你，作為一名大學生，對我來說，當時我們正在做人工智能方面的工作。

In fact, we would use programming languages like Lisp or Prolog.

事實上，我們會使用 Lisp 或 Prolog 等編程語言。

And these kinds of things were kind of the predecessors to what became later expert systems.

這類東西就是後來專家系統的前身。

And this was a technology, again, some of these things existed previous, but that's when it really hit kind of a critical mass and became more popularized.

同樣，其中一些技術以前就有，但這是它真正達到臨界品質並得到普及的時候。

So expert systems of the 1980s, maybe in the 90s.

是以，80 年代的專家系統，也許是 90 年代的專家系統。

And again, we use technologies like this.

同樣，我們也使用了這樣的技術。

All of this was something that we did before we ever touched in to the next topic I'm going to talk about.

所有這些都是我們在接觸下一個話題之前做的。

And that's the area of machine learning.

這就是機器學習領域。

Machine learning is as its name implies, the machine is learning.

機器學習顧名思義就是機器在學習。

I don't have to program it.

我不需要編程。

I give it lots of information and it observes things.

我給它很多資訊，它就會觀察事物。

So for instance, if I started doing this, if I give you this and then ask you to predict what's the next thing that's going to be there, well, you might get it, you might not.

所以，舉個例子，如果我開始這樣做，如果我給你這個，然後讓你預測下一個會出現的東西是什麼，那麼，你可能會得到它，也可能不會。

You have very limited training data to base this on.

你所依據的訓練數據非常有限。

But if I gave you one of those and then ask you what to predict would happen next, well, you're probably going to say this, and then you're going to say it's this.

但如果我給你其中一個，然後問你預測接下來會發生什麼，那麼，你可能會說是這個，然後你又會說是這個。

And then you think you got it all figured out.

然後你就以為自己都想通了。

And then you see one of these.

然後你就會看到這些東西。

And then all of a sudden I give you one of those and throw you a curve ball.

然後，我突然給你一個這樣的機會，給你一個曲線球。

So this, in fact, and then maybe it goes on like this.

所以，事實上，也許還可以這樣繼續下去。

So a machine learning algorithm is really good at looking at patterns and discovering patterns within data.

是以，機器學習算法非常善於在數據中尋找模式和發現模式。

The more training data you can give it, the more confident it can be in predicting.

給它提供的訓練數據越多，它就越有信心進行預測。

So predictions are one of the things that machine learning is particularly good at.

是以，預測是機器學習特別擅長的事情之一。

Another thing is spotting outliers like this and saying, oh, that doesn't belong in, it looks different than all the other stuff because the sequence was broken.

另一件事是發現像這樣的異常值，然後說，哦，這不屬於這裡，它看起來和其他東西不一樣，因為序列被破壞了。

So that's particularly useful in cybersecurity, the area that I work in, because we're looking for outliers.

是以，這對我所從事的網絡安全領域特別有用，因為我們正在尋找異常值。

We're looking for users who are using the system in ways that they shouldn't be or ways that they don't typically do.

我們正在尋找那些以不該用的方式或通常不會用的方式使用系統的用戶。

So this technology, machine learning is particularly useful for us.

是以，機器學習這項技術對我們特別有用。

And machine learning really came along and became more popularized in this timeframe, in the 2010s.

而機器學習真正出現和普及是在 2010 年代。

And again, back when I was an undergrad, riding my dinosaur to class, we were doing this kind of stuff.

再說一遍，當我還是個大學生，騎著我的恐龍去上課時，我們就在做這樣的事情。

We never once talked about machine learning.

我們從未談論過機器學習。

It might have existed, but it really hadn't hit the popular mindset yet.

它可能已經存在，但還沒有真正深入人心。

But this technology has matured greatly over the last few decades.

但在過去的幾十年裡，這項技術已經非常成熟。

And now it becomes the basis of a lot we do going forward.

現在，它已成為我們今後工作的基礎。

The next layer of our Venn diagram involves deep learning.

維恩圖的下一層涉及深度學習。

Well, it's deep learning.

這就是深度學習。

We use these things called neural networks.

我們使用這些被稱為神經網絡的東西。

Neural networks are ways that in a computer, we simulate and mimic the way the human brain works, at least to the extent that we understand how the brain works.

神經網絡是我們在計算機中模擬和模仿人腦工作方式的一種方法，至少在我們瞭解人腦工作方式的範圍內是這樣。

And it's called deep because we have multiple layers of those neural networks.

之所以稱之為深度，是因為我們有多層神經網絡。

And the interesting thing about these is they will simulate the way a brain operates.

有趣的是，它們可以模擬大腦的運行方式。

But I don't know if you've noticed, but human brains can be a little bit unpredictable.

但我不知道你是否注意到，人類的大腦可能有點難以捉摸。

You put certain things in, you don't always get the very same thing out.

你把某些東西放進去，不一定能得到同樣的東西出來。

And deep learning is the same way.

深度學習也是如此。

In some cases, we're not actually able to fully understand why we get the results we do because there are so many layers to the neural network.

在某些情況下，我們實際上無法完全理解為什麼我們會得到這樣的結果，因為神經網絡的層級太多了。

It's a little bit hard to decompose and figure out exactly what's in there.

要分解並弄清裡面到底有什麼東西有點困難。

But this has become a very important part and a very important advancement that also reached some popularity during the 2010s.

但這已成為一個非常重要的部分，也是一個非常重要的進步，在 2010 年代也達到了一定的普及程度。

And as something that we use still today as the basis for our next area of AI.

今天，我們仍將其作為下一個人工智能領域的基礎。

The most recent advancements in the field of artificial intelligence, all really are in this space, the area of generative AI.

人工智能領域的最新進展，其實都在這一領域，即生成式人工智能領域。

Now, I'm going to introduce a term that you may not be familiar with.

現在，我要介紹一個大家可能不太熟悉的術語。

It's the idea of foundation models.

這就是基礎模型的理念。

Foundation models is where we get some of these kinds of things.

基礎模型就是我們獲得此類資訊的地方。

For instance, an example of a foundation model would be a large language model, which is where we take language and we model it.

例如，基礎模型的一個例子就是大型語言模型，我們將語言作為模型。

And we make predictions in this technology, where if I see certain types of words, then I can sort of predict what the next set of words will be.

我們利用這項技術進行預測，如果我看到某些類型的單詞，我就可以預測下一組單詞是什麼。

I'm going to oversimplify here for the sake of simplicity.

為了簡單起見，我在這裡把事情說得過於簡單。

But think about this as a little bit like the autocomplete.

不過，你可以把它想象成有點像自動完成功能。

When you start typing something in, and then it predicts what your next word will be.

當你開始輸入內容時，它就會預測你的下一個單詞是什麼。

Except in this case, with large language models, they're not predicting the next word.

只是在這種情況下，使用大型語言模型時，它們無法預測下一個單詞。

They're predicting the next sentence, the next paragraph, the next entire document.

他們在預測下一個句子、下一個段落、下一整篇文檔。

So there's a really an amazing exponential leap in what these things are able to do.

是以，這些東西所能做的事情真的是驚人的指數級飛躍。

And we call all of these technologies generative because they are generating new content.

我們稱所有這些技術為 "生成性 "技術，因為它們正在生成新的內容。

Some people have actually made the argument that the generative AI isn't really generative, that these technologies are really just regurgitating existing information and putting it in different format.

實際上，有些人認為，生成式人工智能並不是真正的生成式，這些技術實際上只是在重複現有的資訊，並將其轉換成不同的格式。

Well, let me give you an analogy.

我給你打個比方吧。

If you take music, for instance, then every note has already been invented.

以音樂為例，每一個音符都已經被髮明出來。

So in a sense, every song is just a recombination, some other permutation of all the notes that already exist already, and just putting them in a different order.

是以，從某種意義上說，每首歌都是對已有音符的重組和變體，只是將它們按照不同的順序排列而已。

Well, we don't say new music doesn't exist.

我們不會說新音樂不存在。

People are still composing and creating new songs from the existing information.

人們仍在根據現有資訊創作新歌。

I'm going to say AI is similar.

我要說人工智能也差不多。

It's an analogy, so there'll be some imperfections in it, but you get the general idea.

這是一個類比，所以會有一些不完美的地方，但你會明白其中的大概意思。

Actually, new content can be generated out of these.

事實上，新內容可以從這些內容中產生。

And there are a lot of different forms that this can take.

這可以有很多不同的形式。

Other types of models are audio models, video models, and things like that.

其他類型的模型包括音頻模型、視頻模型等。

Well, in fact, these we can use to create deepfakes.

事實上，我們可以利用這些來創建深度偽造。

And deepfakes are examples where we're able to take, for instance, a person's voice and recreate that and then have it seem like the person said things they never said.

而深度偽造就是這樣的例子，例如，我們可以獲取一個人的聲音並進行再創作，然後讓它看起來像是這個人說了他們從未說過的話。

Well, it's really useful in entertainment situations, in parodies and things like that.

在娛樂場合、模仿秀和諸如此類的活動中，它真的很有用。

Or if someone's losing their voice, then you could capture their voice and then they'd be able to type and you'd be able to hear it in their voice.

或者，如果有人失聲了，你可以捕捉到他的聲音，然後他就能打字，你也能聽到他的聲音。

But there's also a lot of cases where this stuff could be abused.

但在很多情況下，這東西也可能被濫用。

The chatbots, again, come from this space.

哈拉機器人也來自這個領域。

The deepfakes come from this space.

贗品就來自這個空間。

But they're all part of generative AI and all part of these foundation models.

但它們都是生成式人工智能的一部分，都是這些基礎模型的一部分。

And this, again, is the area that has really caused all of us to really pay attention to AI.

同樣，這也是真正引起我們所有人關注人工智能的領域。

The possibilities of generating new content, or in some cases, summarizing existing content and giving us something that is bite-sized and manageable.

產生新內容的可能性，或者在某些情況下，對現有內容進行總結，為我們提供小而易懂的內容。

This is what has all of the attention.

這就是所有關注的焦點。

This is where the chatbots and all of these things come in.

這就是哈拉機器人和所有這些東西的用武之地。

In the early days, AI's adoption started off pretty slowly.

在早期，人工智能的應用起步相當緩慢。

Most people didn't even know it existed.

大多數人甚至不知道它的存在。

And if they did, it was something that always seemed like it was about five to 10 years away.

即使有，似乎也是五到十年後的事。

But then machine learning, deep learning, and things like that came along, and we started seeing some uptick.

但後來，機器學習、深度學習和類似的東西出現了，我們開始看到一些起色。

Then foundation models, gen AI, and the like came along, and this stuff went straight to the moon.

後來，基礎模型、基因人工智能等技術出現了，這些東西就直接登月了。

These foundation models are what have changed the adoption curve, and now you see AI being adopted everywhere.

正是這些基礎模型改變了人工智能的應用曲線，現在人工智能已被廣泛採用。

And the thing for us to understand is where this is, where it fits in, and make sure that we can reap the benefits from all of this technology.

我們需要了解的是這是什麼，它適合什麼，並確保我們能從所有這些技術中獲益。

If you like this video and want to see more like it, please like and subscribe.

如果您喜歡這段視頻並希望看到更多類似內容，請點贊並訂閱。

If you have any questions or want to share your thoughts about this topic, please leave a comment below.

如果您有任何疑問或想分享您對這一話題的看法，請在下面留言。

Thank you.

謝謝。