【2023.4.30】六分钟英语 培训人工智能 Training artific

Introduction

How clever is artificial intelligence? It currently helps us in many ways from car satnavs to detecting cancer cells but it's yet to be cleverer than us humans. We still need to train AI to do things but should we fear that it eventually learns too much?

This week's question

In terms of brain cell count, what level of intelligence is AI currently working at? Is AI as smart as:

a) a frog

b) an earthworm

c) a bumblebee

Transcript

Note: This is not a word for word transcript 

Neil

Hello. This is 6 Minute English from BBC Learning English. I’m Neil.

Sam

And I’m Sam.

Neil

Do you like cooking, Sam? There’s a new recipe I’ve been trying out - it’s for ‘frosted oysters’.

Sam

Frosted oysters?! Sounds… unusual. How do you make it?

Neil

Well, take a pound of chicken, then some cubed pork and half a crushed garlic.

Sam

Eh? I thought you said it was for ‘frosted oysters’, whatever they are.

Neil

Yes, that’s right. Now heat it up until boiling and serve with custard.

Sam

Ugh, that sounds disgusting! Who on earth told you that recipe?

Neil

It’s not ‘who’ told me, Sam, but ‘what’. In fact, that recipe was made by computers using artificial intelligence, or AI, which is the topic of today’s programme. In real life, AI is making huge progress - from car satnavs to detecting cancer cells. But as you can see from that revolting recipe, things don’t always go according to plan.

Sam

So, just how intelligent is artificial intelligence? I mean, it definitely needs some cooking lessons!

Neil

Right. AI is not as intelligent as we tend to think. AI programmes use artificial brain cells to roughly imitate real brain cell activity, but they’re still a long way behind human levels of intelligence. And that’s my quiz question – in terms of brain cell count, what level of intelligence is AI currently working at? Is AI as smart as:

a)    a frog

b)    an earthworm

c)     a bumblebee

Sam

Well, I don’t think anyof those are good cooks either, to be honest. I’ll say c) a bumblebee, because at least they can make honey!

Neil

Nice guess, Sam. We’ll find out the answer later. But first let’s find out more about how AI misunderstandings like the oyster recipe can happen. Janelle Shane is the author of ‘You Look Like a Thing and I Love You’ in which she tells her amusing experiences and bizarre experiments with AI.

Sam

You Look Like a Thing and I Love You – that’s a strange title for a book, Neil.

Neil

Yes. It’s another example of AI miscommunication. The book title is what a AI produced when asked to write chat-up lines – remarks men and women make to start up a conversation with someone they don’t know but find attractive.

Here she is talking to the BBC World Service programme More or Less:

Janelle Shane

‘Machine learning’ is what most programmers mean when they say ‘AI’. In the programme that we’re used to, if you want to have a computer programme solve a problem you have to have a human programmer write down exhaustive step-by-step instructions on how to do everything. But with ‘machine learning’ you just give it the goal, and then the programme figures out via trial and error how it’s going to solve that problem.

Sam

So even though we’re talking about machines learning for themselves, there still need to be humans involved at the start of the journey. This human teaching is done by computer programmers – people who write, or code, the computer programmes used by AI.

Neil

Right. These programmers write algorithms – a set of rules or procedures to be followed in problem-solving exercises. So, for example, the AI that wrote that oyster recipe read thousands of other recipes before coming up with its own version.

Sam

In other words, Artificial intelligence uses a process of trial and error – repeating the same task over and over until finding the most successful way. Only in the case of the oyster recipe, there was more ‘error’ than ‘trial’!

Neil

Well, according to Janelle Shane, we can learn a lot about something by seeing how it goes wrong. Here she is, talking about an AI which had been told to solve maths problems:

Janelle Shane

It seemed to be that it was getting scored on how many wrong answers it got, and it was supposed to be minimising the number of wrong answers, and just by a stroke of luck as part of its trial and error flailing around, one of the flails it did accidentally deleted the solutions list, and then it and everybody else got a perfect score.

Sam

So, AIs learn by minimising their errors – reducing them as much as possible. And sometimes, these algorithms only discover the right answer by a stroke of luck – when something unexpected happens by good luck or chance. It seems to me that they’re not so intelligent after all!

Neil

Well, let’s settle it once and for all by answering today’s quiz question. Remember I asked you how intelligent AI was in terms of brain cell count and you said, as intelligent as…

Sam

I said c) a bumblebee.

Neil

Well, here’s Janelle again with the answer…

Janelle Shane

If you’re looking at rough computing power, the algorithms we’re working with are probably somewhere around the level of an earthworm.

Sam

So, the correct answer was b) as clever as an earthworm! No wonder AIs can’t cook!

Neil

Or take a maths test without cheating! In this programme we’ve been looking at artificial intelligence, or AI, and seeing how programmers – that’s people who write instructions for computers to follow create algorithms – sets of rules used in problem-solving.

Sam

AI learns through trial and error – repeating the same activity again and again until discovering the best way, and minimising – reducing as much as possible, the number of errors it makes.

Neil

And success can be the result of a stroke of luck, when something unexpected happens purely by chance, although so far that hasn’t helped AIs to write good chat-up lines – the flattering remarks people make to get to know someone they find attractive.

Sam

And AIs don’t know much about cooking oysters either!

Neil

That’s all from us from this programme. Be sure to join us again for more topical discussion and vocabulary at 6 Minute English for BBC Learning English. Bye for now!

Sam

Bye.

Vocabulary

chat-up lines

remarks men and women make to start up a romantic conversation with someone they don’t know but find attractive

computer programmers

people who write, or code, computer programmes

algorithms

a set of rules or procedures to be followed by computers in problem-solving exercises

trial and error

repeating the same task over and over until finding the most successful way

minimising

reducing as much as possible

a stroke of luck

when something unexpected happens by good luck or chance

双语版Transcript

Neil

Hello. This is 6 Minute English from BBC Learning English. I’m Neil.

大家好，这里是BBC教学英语的六分钟英语，我是尼尔。

Sam

And I’m Sam.

我是山姆。

Neil

Do you like cooking, Sam? There’s a new recipe I’ve been trying out - it’s for ‘frosted oysters’.

你喜欢做饭吗？我正在尝试一种新食谱- “冻牡蛎”

Sam

Frosted oysters?! Sounds… unusual. How do you make it?

冻牡蛎？听起来……不寻常。你怎么做的？

Neil

Well, take a pound of chicken, then some cubed pork and half a crushed garlic.

好吧，取一磅鸡肉，然后放一些猪肉块和半个碎大蒜。

Sam

Eh? I thought you said it was for ‘frosted oysters’, whatever they are.

啊？我以为你说的是“冻牡蛎”

Neil

Yes, that’s right. Now heat it up until boiling and serve with custard.

是的，没错。现在将其加热至沸腾，然后与卡仕达一起食用。

Sam

Ugh, that sounds disgusting! Who on earth told you that recipe?

听起来真恶心！到底是谁告诉你那个食谱？

Neil

It’s not ‘who’ told me, Sam, but ‘what’. In fact, that recipe was made by computers using artificial intelligence, or AI, which is the topic of today’s programme. In real life, AI is making huge progress - from car sat navs to detecting cancer cells. But as you can see from that revolting recipe, things don’t always go according to plan.

不是“谁”，而是“什么”。实际上，该配方是使用人工智能的计算机完成的，这是今天节目的主题。在现实生活中，人工智能正在取得巨大进步-从汽车卫星导航到检测癌细胞。但是，从令人反感的食谱中可以看出，事情并非总是按计划进行。

Sam

So, just how intelligent is artificial intelligence? I mean, it definitely needs some cooking lessons!

人工智能有多智能？我的意思是，它肯定需要一些烹饪课程！

Neil

Right. AI is not as intelligent as we tend to think. AI programmes use artificial brain cells to roughly imitate real brain cell activity, but they’re still a long way behind human levels of intelligence. And that’s my quiz question – in terms of brain cell count, what level of intelligence is AI currently working at? Is AI as smart as:

a) a frog

b) an earthworm

c) a bumblebee

对。人工智能并不像我们认为的那样聪明。人工智能程序使用人工脑细胞粗略地模仿真实脑细胞活动，但距离人类的智力水平还有很长的路要走。这是我的测试问题–就脑细胞数量而言，人工智能目前正在哪种智能水平？人工智能像

a）青蛙

b）蚯蚓

c）大黄蜂

Sam

Well, I don’t think any of those are good cooks either, to be honest. I’ll say c) a bumblebee, because at least they can make honey!

好吧，老实说，我也不认为这些都是好厨师。我会说c）大黄蜂，因为至少它们可以酿造蜂蜜！

Neil

Nice guess, Sam. We’ll find out the answer later. But first let’s find out more about how AI misunderstandings like the oyster recipe can happen. Janelle Shane is the author of ‘You Look Like a Thing and I Love You’ in which she tells her amusing experiences and bizarre experiments with AI.

猜的好。我们稍后会找到答案。但是首先让我们更多地了解AI如何发生像牡蛎食谱这样的误解。Janelle Shane是《You Look Like a Thing and I Love You》的作者，在书中讲述了她的有趣经历和AI的奇异实验。

Sam

You Look Like a Thing and I Love You – that’s a strange title for a book, Neil.

你看起来像个东西，我爱你-这本书的标题很奇怪。

Neil

Yes. It’s another example of AI miscommunication. The book title is what a AI produced when asked to write chat-up lines – remarks men and women make to start up a conversation with someone they don’t know but find attractive.Here she is talking to the BBC World Service programme More or Less:

是的，这是AI错误沟通的另一个例子。本书标题是AI被要求编写聊天记录时产生的–男女之间与有魅力的陌生人开始交流时的言论。这是她在BBC More or Less节目的谈话：

Janelle Shane

‘Machine learning’ is what most programmers mean when they say ‘AI’. In the programme that we’re used to, if you want to have a computer programme solve a problem you have to have a human programmer write down exhaustive step-by-step instructions on how to do everything. But with ‘machine learning’ you just give it the goal, and then the programme figures out via trial and error how it’s going to solve that problem.

“机器学习”是大多数程序员说“ AI”时的意思。在我们惯用的程序中，如果要使用计算机程序来解决问题，则必须让人类程序员写下详尽的逐步的指令，说明如何进行所有操作。但是通过“机器学习”，你只需给它一个目标，然后程序就会通过反复试验弄清楚它将如何解决该问题。

Sam

So even though we’re talking about machines learning for themselves, there still need to be humans involved at the start of the journey. This human teaching is done by computer programmers – people who write, or code, the computer programmes used by AI.

即使我们正在谈论的机器学习是机器的，在旅程的开始仍需要人类参与。这种人工教学是由computer programmers计算机程序员完成的，他们编写AI使用的计算机程序。

Neil

Right. These programmers write algorithms – a set of rules or procedures to be followed in problem-solving exercises. So, for example, the AI that wrote that oyster recipe read thousands of other recipes before coming up with its own version.

这些程序员编写algorithms算法 -解决问题练习中要遵循的一组规则或过程。因此，例如，编写牡蛎食谱的AI读取数千种其他食谱之后提出自己的版本。

Sam

In other words, Artificial intelligence uses a process of trial and error – repeating the same task over and over until finding the most successful way. Only in the case of the oyster recipe, there was more ‘error’ than ‘trial’!

换句话说，人工智能使用trial and error试验和犯错 -一遍又一遍地重复相同的任务，直到找到最成功的方式。只是在牡蛎食谱的情况下，“错误”多于“试验”！

Neil

Well, according to Janelle Shane, we can learn a lot about something by seeing how it goes wrong. Here she is, talking about an AI which had been told to solve maths problems:

根据Janelle Shane的说法，我们可以通过观察问题出在哪里，从而学到很多东西。在这里，她谈论解决数学问题的AI。

Janelle Shane

It seemed to be that it was getting scored on how many wrong answers it got, and it was supposed to be minimising the number of wrong answers, and just by a stroke of luck as part of its trial and error flailing around, one of the flails it did accidentally deleted the solutions list, and then it and everybody else got a perfect score.

似乎正在对它得到多少错误答案进行评分，并且应该将错误答案的数量减至最少，而在实验和错误的过程中，碰巧走运，偶然删除了方案列表，然后它和其他所有人得到了满分。

Sam

So, AIs learn by minimising their errors – reducing them as much as possible. And sometimes, these algorithms only discover the right answer by a stroke of luck – when something unexpected happens by good luck or chance. It seems to me that they’re not so intelligent after all!

人工智能通过minimising最小化错误来学习- 尽可能减少错误。有时，这些算法只能通过a stroke of luck运气来找到正确的答案-因好运或偶然发生意外事情时。在我看来，他们毕竟不是那么聪明！

Neil

Well, let’s settle it once and for all by answering today’s quiz question. Remember I asked you how intelligent AI was in terms of brain cell count and you said, as intelligent as…

我们通过回答今天的测验问题一次性解决它。还记得我问过你在脑细胞数量方面AI的智能程度如何，你说过，像...一样聪明。

Sam

I said c) a bumblebee.

我说过c）大黄蜂。

Neil

Well, here’s Janelle again with the answer…

再听听Janelle的答案

Janelle Shane

If you’re looking at rough computing power, the algorithms we’re working with are probably somewhere around the level of an earthworm.

如果你考虑粗略的计算能力，那么我们正在使用的算法可能大约处于蚯蚓的水平。

Sam

So, the correct answer was b) as clever as an earthworm! No wonder AIs can’t cook!

所以，正确答案为B）像蚯蚓一样聪明！难怪AI不会做饭！

Neil

Or take a maths test without cheating! In this programme we’ve been looking at artificial intelligence, or AI, and seeing how programmers – that’s people who write instructions for computers to follow ,create algorithms – sets of rules used in problem-solving.

参加数学考试而不作弊！在本期节目中，我们一直在研究人工智能即AI，并观察programmers程序员-为计算机编写遵循的指令。创造algorithms算法-解决问题的系列规则。

Sam

AI learns through trial and error – repeating the same activity again and again until discovering the best way, and minimising – reducing as much as possible, the number of errors it makes.

AI学习是通过trial and error试验犯错-一次又一次地重复相同的活动，直到找到最好的方法为止，并且minimising最小化 - 尽可能地减少犯错数量。

Neil

And success can be the result of a stroke of luck, when something unexpected happens purely by chance, although so far that hasn’t helped AIs to write good chat-up lines – the flattering remarks people make to get to know someone they find attractive.

成功可能是a stroke of luck碰运气的结果，这是纯粹因偶然而发生的意外事件，尽管到目前为止，这还没有帮助AI编写良好的chat-up lines聊天记录 –人们用讨喜的话来结识有魅力的人。

Sam

And AIs don’t know much about cooking oysters either!

AI也不了解牡蛎的烹饪知识！

Neil

That’s all from us from this programme. Be sure to join us again for more topical discussion and vocabulary at 6 Minute English for BBC Learning English. Bye for now!

这就是本期节目的全部内容了！下次再见！