跟讀練習: AI, Machine Learning, Deep Learning and Generative AI Explained - 通過影片學習英語口語

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Everybody's talking about artificial intelligence these days, AI.

Machine learning is another hot topic.

Are they the same thing or are they different?

And if so, what are those differences?

And deep learning is another one that comes into play.

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

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.

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

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

We see them everywhere.

Really interesting technology.

And then also things like deepfakes.

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

How are they related to each other?

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.

First off, a little bit of a disclaimer.

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

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.

First of all, let's start with AI.

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

What is intelligence?

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.

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

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

And in those days, it was very premature.

Most people had not even heard of it.

And it basically was a research project.

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

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

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

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.

Expert systems of the 1980s, maybe in the 90s, and again, we used technologies like this.

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

That's the area of machine learning.

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

I don't have to program it.

I give it lots of information and it observes things.

For instance, if I start 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.

You have very limited training data to base this on.

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, and then you think you got it all figured out,

and then you see one of these, and then all of a sudden I give you one of those and throw you a curve ball.

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

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

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

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

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.

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

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.

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

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

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

We never once talked about machine learning.

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

But this technology has matured greatly over the last few decades, and now it becomes the basis of a lot we do going forward.

The next layer of our Venn diagram involves deep learning.

Well it's deep learning in the sense that with deep learning we use these things called neural networks.

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.

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

The interesting thing about these is they will simulate the way a brain operates, but I don't know if you've noticed,

but human brains can be a little bit unpredictable.

You put certain things in, you don't always get the very same thing out and deep learning is the same way.

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

But this has become a very important part and a very important advancement.

That also reached some popularity during the 2010s and as something

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

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

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

It's the idea of foundation models.

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

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

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.

I'm going to oversimplify here for the sake of simplicity, but think about this as a little bit like the auto-complete.

When you start typing something in and then it predicts what your next word will be, except in this case with large language models, they're not predicting the next word,

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

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

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

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.

Let me give you an analogy.

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

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.

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

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

I'm going to say Gen.AI is similar.

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

Actually new content can be generated out of these and there are a lot of different forms that this can take.

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

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

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.

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

where 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.

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

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The chatbots, again, come from this space.

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The deepfakes come from this space.

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

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

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

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This is what has gotten all of the attention.

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

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

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Most people didn't even know it existed, and if they did, it was something that always seemed like it was about five to 10 years away.

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

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

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

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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.

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

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

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本課的詞彙與口語要點

這節 C1 級別的口語課以影片“AI, Machine Learning, Deep Learning and Generative AI Explained”為素材。影片中反覆出現的詞有：learning, model, machine, deep, intelligence。這段影片共有 114 個句子、1738 個單詞可供跟讀。講話部分時長為 9:55。說話人語速自然，每分鐘約 175 個詞，接近日常對話。 87% 的單詞屬於英語最常用的 3,000 詞，其餘的詞建議在練習前先學一下。

影片中的重點詞彙

影片中最難的 15 個單詞，附發音和釋義：

單詞	發音	釋義
predict 動詞	/pɹɪˈdɪkt/	預言 /預言
deepfake 名詞	/ˈdipˌfeɪk/	深偽 /深偽, 深度偽造 /深度偽造
chatbot 名詞	/ˈtʃætbɑt/	聊天機器人 /聊天機器人
simulate 動詞	/ˈsɪm.jə.lət/	模擬
analogy 名詞	/əˈnæləd͡ʒi/	類推 /類推, 類比 /類比
prediction 名詞	/pɹɪˈdɪkʃən/	預言 /預言, 預估 /預估
compose 動詞	/kəmˈpəʊz/	創作 /創作, 整理
cybersecurity 名詞	/saɪbəsɪˈkjʊəɹɪtɪ/	網路安全 /網路安全, 網安 /網安
decompose 動詞	/ˌdiːkəmˈpəʊz/	分解
disclaimer 名詞	/dɪsˈkleɪm.ɚ/	免責者 /免責者
exponential 形容詞	/ˌɛk.spəˈnɛn.t͡ʃəl/	指數的 /指數的
misconception 名詞	/ˌmɪs.kənˈsɛp.ʃən/	錯覺 /錯覺
permutation 名詞	/ˌpɝ.mjʊˈteɪ.ʃ(ə)n/	排列
simplify 動詞	/ˈsɪmplɪfaɪ/	簡化 /簡化
summarize 動詞	/ˈsʌməˌɹaɪz/	摘要, 總結 /總結

影片中出現的短語動詞

單詞	釋義
come along 動詞	陪伴, 陪同
figure out 動詞	弄清楚
going forward 副詞	放眼未來 /放眼未來
take over 動詞	接管, 接手

影片中的語法

說話人最常用的結構，並附上影片中的原話：

結構	影片中的用法
現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事	has happened · have seemed · has matured
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	was recorded · was broken · been invented

需要注意的發音

說話人用了 33 次縮略和弱讀形式，例如 we're, don't, I'm。請按聽到的簡短形式來說。

“th” 音: algorithm /ˈælɡəɹɪðm̩/, myth /mɪθ/
“sh” 和 “zh” 音: prediction /pɹɪˈdɪkʃən/, imperfection /ɪmpɚˈfɛkʃən/, misconception /ˌmɪs.kənˈsɛp.ʃən/, permutation /ˌpɝ.mjʊˈteɪ.ʃ(ə)n/
長單詞——注意重音位置: generative /ˈd͡ʒɛnəɹətɪv/, analogy /əˈnæləd͡ʒi/, cybersecurity /saɪbəsɪˈkjʊəɹɪtɪ/, exponential /ˌɛk.spəˈnɛn.t͡ʃəl/, imperfection /ɪmpɚˈfɛkʃən/

中文母語者容易讀錯的音：

/v/ — 不要讀成 /w/，上齒輕觸下唇: generative /ˈd͡ʒɛnəɹətɪv/, advancement /ædˈvæns.mənt/, invent /ɪnˈvɛnt/, observe /əbˈzɜːv/
詞尾子音 — 要讀清楚，後面不要加母音: predict /pɹɪˈdɪkt/, generative /ˈd͡ʒɛnəɹətɪv/, deepfake /ˈdipˌfeɪk/, chatbot /ˈtʃætbɑt/, simulate /ˈsɪm.jə.lət/
/r/ 和 /l/ 的區別: neural /ˈnʊɹəl/, outlier /ˈaʊtˌlaɪə(ɹ)/, algorithm /ˈælɡəɹɪðm̩/

如何用這段影片練習

先完整聽一遍影片，不要開口，記下不認識的單詞。
先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
錄下自己的聲音並與原聲對比，特別注意 predict, deepfake, chatbot 這類單詞。

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: AI, Machine Learning, Deep Learning and Generative AI Explained - 通過影片學習英語口語

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