跟读练习: Scientists still don't know the answer to this infamous question - Charles Wallace & Dan Kwartler - 通过YouTube学习英语口语
C1
After waking up alone in a locked room, two documents are slipped under your door: a note in an alien language and a detailed instruction manual in your language.
37 句
如果句子过短或过长，请点击 Edit 进行调整。
1
After waking up alone in a locked room, two documents are slipped under your door: a note in an alien language and a detailed instruction manual in your language.
2
The manual explains that for each alien character in the note, you should write an indicated corresponding symbol.
3
Following this chart, you write a response that you slip out the door.
4
And for the next several days, this exchange continues.
5
Outside the room, alien scientists are thrilled because they believe you’re conversing with them.
6
But you still have no idea what these characters mean.
7
This scenario isn’t just a bizarre misunderstanding— it’s a valuable thought experiment for understanding artificial intelligence.
8
Philosopher John Searle developed the original version of this premise in 1980, as a response to some of the AI work being done at the time.
9
But while modern AI models don't work like those outdated machines or the prisoner in Searle’s hypothetical, the question motivating his thought experiment is still relevant.
10
To quote Searle, he wanted to interrogate whether an “appropriately programmed computer literally has cognitive states.” In other words, if a computer looks like it understands something, does that mean it actually understands the way a human does?
11
Searle’s question falls into a long tradition of exploring whether or not AI could have a mind like ours.
12
But answering these inquiries is incredibly difficult because, as philosophers and cognitive scientists will tell you, we still don’t know how our minds work.
13
Even our fundamental definitions are slippery!
14
Theorists generally agree that concepts like understanding, sentience, and consciousness are all different, but also that they’re related, and we don’t know how.
15
Worse still, our usual scientific tools struggle to help us understand these experiences.
16
Consider drinking a cup of coffee.
17
Scientists can observe the physical process of ingesting the coffee, and we can measure the chemical impacts of caffeine on your body.
18
These things are objective realities.
19
But your collective sensation of smelling, sipping, evaluating, and experiencing a morning routine is more than the sum of its parts.
20
This is consciousness— your subjective experience of being alive.
21
And despite major leaps in psychology, cognitive science, and neurology, researchers still don’t know how various firing neurons bring about this experience.
22
So if we can't define consciousness and understanding or identify what's uniquely human about them, how can we possibly test for these states in computers?
23
Assessments like the Turing Test propose that if a human can't tell they're conversing with a computer, that computer could be seen as having some internal cognition.
24
But this scenario is exactly what Searle was criticizing!
25
This computer might just have the appearance of understanding.
26
And just like cognitive scientists struggling to map consciousness onto brain activity, today’s AI researchers know how they trained their creations, but not how AIs reach their exact conclusions.
27
There are some ways in which modern machine learning models are less mysterious than their predecessors.
28
Approaches like neural networks and deep learning are designed to mimic known elements of human cognition.
29
Like us, these models excel at pattern recognition— they learn by becoming familiar with information and forming connections across data sets.
30
This kind of processing arguably approaches Searle’s definition of understanding— but it also reveals a bias in his original question.
31
Since humans learn through pattern recognition and we believe ourselves to be conscious, we might also be predisposed to think other beings who learn the same way are somehow closer to consciousness as well.
32
To combat this bias, some theorists have developed a different metric; specifically, that a fully conscious AI could draw connections beyond the information in its data set.
33
For example, one lab’s “artificial consciousness test” probes AIs that have no data about consciousness for information they could only acquire from being conscious.
34
This might involve asking an AI if it understands dreaming, or can report having had dreams itself.
35
Can it understand a story about body swapping, where consciousnesses are shuffled between physical forms?
36
It’s unclear when or if an AI will be able to understand us the way we understand each other.
37
But whatever happens, it’s up to us already conscious creatures to chart the path forward.
下载应用
AI 为你说出的每个句子打分
TRENDING
热门
背景与语境
在这个视频中，科学家们讨论了关于人工智能理解能力的深刻哲学问题。通过一个形象的实验场景，展现了我们与机器之间的沟通困境。虽然机器能够模仿人类的交流方式，但它们是否真正理解我们所表达的内容仍然是一个悬而未决的问题。这一讨论源自哲学家约翰·希尔所提出的思考实验，旨在探讨计算机是否具有真正的认知状态。这对于希望提高英语口语能力的学习者来说，理解交流的本质至关重要。
日常沟通的五个常用短语
- What does it mean?（这是什么意思？）
- I don't understand.（我不明白。）
- Can you explain it again?（你可以再解释一遍吗？）
- Let's discuss this further.（让我们进一步讨论这个。）
- Could you give me an example?（你能给我一个例子吗？）
这些短语对于进行有效的对话至关重要，尤其是在谈论复杂主题如人工智能时。不断运用这些句子，可以帮助你在英语口语练习中变得更加自信。
逐步跟读指南
为了提升你的英语口语能力，采用影子跟读（英语影子跟读， shadow speak）的方式尤其有效。以下是使用视频内容的具体步骤：
- 选择段落：从视频中挑选出你感兴趣的对话段落，最好是那些与人工智能或哲学相关的讨论。
- 聆听对照：反复听视频中的对话，注意发音和语调。
- 尝试模仿：在听的同时，尝试用自己的声音进行跟读（shadow speech），此时要尽量保持与视频中的语音同步。
- 录音对比：录下自己的声音，再与视频中的原声进行对比，找出差异并加以改正。
- 反复练习：坚持每天进行这种跟读练习，可以有效提高你的英语口语表达能力和理解能力。
总之，影子跟读不仅能够帮助你掌握语言，更能加深你对英语交流的理解。通过这种方法，无论是日常对话还是针对复杂主题的讨论，你都能更加自如地表达自己。不断练习，进步将在不知不觉中到来！
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。