ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: We're Not Ready for Biocomputing
เลือกโหมดฝึก
แต่ละโหมดจะเปิดห้องฝึกแบบเต็มจอ
224 ประโยค
หากประโยคสั้นหรือยาวเกินไป กดที่ Edit เพื่อปรับแก้
1
In a few seconds I'm going to show you one of the most disturbing things happening in science right now
2
and almost no one is talking about it.
3
That's why we made this video.
4
You're looking at a computer in a laboratory called Cortical Labs.
5
It's playing Doom.
6
But look closer.
7
There's nobody playing it.
8
Or actually, there is.
9
They just can't get out.
10
They are trapped there because this computer is running on living human brain cells And those cells are the ones playing.
11
So why would anyone build a computer out of brain cells?
12
It basically comes down to power.
13
AI uses a massive amount of electricity, and that number keeps going up.
14
Amazon investing another $15 billion.
15
Meta continuing its massive investments in AI, this time in the form of compute power.
16
The computing power it takes to train these models roughly doubles every six months.
17
Almost nothing in the real world grows that fast, but you can't build power plants that quickly.
18
So there's a growing gap between what AI needs and what we can actually supply, and it keeps getting wider.
19
You can see the panic in what the big companies are doing.
20
They're going after energy now, not just better software.
21
Old nuclear plants that got shut down years ago are being turned back on just to power them.
22
Companies are signing deals to lock up huge chunks of the grid years ahead of time.
23
It's not really a race for smarter AI anymore.
24
It's turning into a race for electricity.
25
So a few researchers went a totally different direction.
26
They looked at the brain.
27
Because the brain figured this out a long time ago.
28
Think about what your brain is doing right now and how little it runs on.
29
Everything you're thinking runs on about 20 watts.
30
We're using whole power plants just to copy a small piece of what your brain does on basically nothing.
31
And it learns way better than our machines too.
32
To teach a computer what a dog is, you have to show it thousands of photos.
33
A little kid sees a dog once or twice and just gets it.
34
So how did we get from that to a dish playing Doom?
35
It took three steps.
36
Back in 2008, researchers grew a sheet of rat neurons and hooked it up to a little robot.
37
And the cells actually drove it around.
38
It sounds bigger than it was.
39
The neurons just sat flat on a grid of electrodes and connected to their neighbors.
40
That was pretty much it.
41
This was nowhere near a real brain.
42
And it wasn't even the first time someone had wired living tissue to a machine.
43
One team even did it with the brain of a lamprey.
44
The footage always looked a little like a horror movie.
45
And from the start, people had one real doubt.
46
Were the cells actually driving, or was the computer just taking their random signals and making it look that way?
47
Keep that question in mind.
48
We're coming back to it.
49
The second step came in 2013.
50
A scientist named Shinya Yamanaka showed you could reset a grown-up cell all the way back to the start.
51
He found four proteins that turn a regular cell into a blank, brand new one.
52
And from there, you can turn it into almost anything, including a neuron.
53
That was huge.
54
It meant you didn't have to take neurons out of a brain anymore.
55
You could just take a bit of skin and grow them.
56
There was another big step too.
57
If you grow neurons on a flat dish, they only connect to the ones right next to them.
58
But if you let them grow in 3D, they start to organize on their own.
59
They spread out, fold into layers, and form the rough beginnings of real brain regions, kind of like an embryo does.
60
One of those tiny balls of tissue is called an organoid.
61
Then, in 2022, a company called Cortical Labs put all of this together into something they called DishBrain.
62
They grew about 800,000 neurons, part mouse and part human, and taught them to play Pong.
63
Two things about this were a big deal.
64
First, a dish of cells was actually working toward a goal.
65
Second, and this is the weird part, They'd figured out how to teach it.
66
They could ask the cells a question and get an answer back.
67
You're not supposed to be able to do that to a blob of tissue in a dish.
68
So how do you teach a neuron?
69
You can't give it a treat.
70
So they used a basic fact about neurons.
71
They hate chaos.
72
If you give them a signal they can't predict, it stresses them out.
73
So the setup was simple and kind of mean.
74
When they missed the ball, they got hit with scrambled random noise.
75
When they hit it back, the signal went calm and steady.
76
The neurons have no idea what Pong is.
77
They're not playing to win.
78
They're just trying to make the chaos stop.
79
And they didn't pick that method at random.
80
They based it on an idea of the free energy principle.
81
Basically, it says every living thing is always trying to avoid surprises and keep its world predictable.
82
Because being surprised usually means something's wrong.
83
So that urge to calm things down isn't something the scientists added.
84
It might be the most basic thing a living cell does.
85
But there's a big catch.
86
We can grow brain tissue in 3D and we can talk to living neurons.
87
We just can't do both at the same time yet.
88
So in order to talk to them, DishBrain had to keep the cells flat because the chip that reads them is a flat grid.
89
If you put a round organoid on it, you only touch the bottom layer.
90
So they had to flatten it out and give up the brain's real shape just to talk to it.
91
And remember, this is the crude version.
92
Half the design is missing.
93
Which brings us to a big new.
94
Cortical Labs showed off a computer that runs on brain cells.
95
They call it CL1 and it plays Doom.
96
This one was grown from about 200,000 human neurons.
97
They took skin cells, reset them, turned them into neurons and spread them across a bed of electrodes.
98
Then they tied the signals to moving and shooting the same way as before.
99
When it did well, the signal stayed calm.
100
When it took damage or died, the cells got flooded with chaos.
101
And within about a week, it had learned the basics of moving around and shooting.
102
I'm not going to pretend to be calm about this, and I don't think you should be either.
103
It's amazing.
104
And it kind of makes me sick.
105
And I think both of those reactions are fair, Because that computer came from a real person.
106
It's literally made of human cells.
107
I keep imagining what it would be like to be the thing stuck in there, playing the same little piece of game over and over forever,
108
not remembering anything before it, not knowing that anything else even exists.
109
For these cells, there's nothing else, just the game.
110
Are we making a piece of a human being whose whole world is getting shocked inside a violent video game
111
just to save some electricity?
112
For years, we worried about the opposite thing.
113
Humans turning into machines, chips in our heads, that whole idea.
114
Almost nobody expected it to happen the other way around.
115
But that's kind of what's going on here.
116
Now, to be fair, there was a big backlash to all of this.
117
And it was kind of fair.
118
When Cortical Labs published the Pong word, the title used a heavy word.
119
It said the neurons showed sentience.
120
Normally, that word means someone's in there having an experience.
121
But the scientists meant it in a really narrow, technical way.
122
Basically, anyway, the story blew up, and a lot of scientists were angry too.
123
About 30 of them signed a letter saying the company had twisted the word just to get headlines
124
and oversold the whole thing to help the business.
125
Their worry was pretty practical.
126
Too much hype leads to backlash and enough backlash could shut down a young field like this.
127
And this research really matters.
128
Scientists use these same cells to build living models of human diseases.
129
They can watch how a disease develops and test drugs on tissue that's way closer to ours than a mouse's.
130
That could save real lives and replace a lot of animal testing.
131
It would be a real loss if a stunt with Pong and Doom dragged it all down.
132
Doom really was kind of a stunt.
133
The audience asked for it and it made great marketing.
134
But it brings us right back to that doubt from 2008.
135
Is the brain really playing or is the computer just cleaning up random noise and making it look good.
136
Almost 20 years later, we can finally check.
137
The developer behind the demo, who goes by Shun Call, released the code so anyone can read it.
138
And what's inside is not just a brain in a box.
139
There are three parts.
140
First, a normal computer chip running an AI that watches the game and decides when to reward and when to punish.
141
Second, the 200,000 human neurons.
142
And third, a translator in the middle, passing messages both ways.
143
Here's the uncomfortable part.
144
Almost all of the actual smarts are on the chip, not in the cells.
145
Reading the screen, running the learning, telling a win from a loss, that's all the computer.
146
The developer was even worried his AI would get so good it wouldn't need the neurons at all.
147
So he ran a simple test.
148
He took the cells out and replaced them with pure random noise.
149
and the learning just stopped.
150
So the neurons are doing something real, even if it's a small part.
151
So the truth is somewhere in the middle.
152
It's not a brain playing on its own, but the cells aren't just for show either.
153
It's mostly a normal computer doing the work, with living cells adding a small but real piece.
154
That's where we are today, and here's where I think it goes next.
155
I don't think that small biological parts stay small.
156
all over are racing to grow this tissue in full 3D and connect to it properly.
157
And it's already turning into a business, a huge business.
158
A Swiss company called FinalSpark rents out living human neurons over the internet.
159
You pay a monthly fee, log in from anywhere in the world and run experiments on 16 tiny human mini-brains that they keep alive in their lab.
160
And their whole pitch is the same thing we started with, energy.
161
They say these living chips can run on up to a million times less power than a normal one.
162
So this weird one-off experiment is basically becoming a product you can subscribe to.
163
And I believe, sooner or later, someone's going to connect a full 3D mini-brain and close that last gap.
164
But let's go back to that Doom machine for a second.
165
Most of the smarts are in the computer, the cells only do a little, so it would be easy to hear that and just ignore it.
166
But But being smart was never the scary part here.
167
Being smart and being awake are two different things.
168
You can be great at solving problems and still not feel anything at all.
169
So the real question isn't whether those cells are clever, it's whether anyone's actually in there.
170
And it turns out being able to feel might not need a big smart brain at all.
171
A renowned neuroscientist, Mark Solms, argues almost the opposite of what we were taught.
172
Solmes says feeling isn't something that sits on top of intelligence.
173
He thinks it comes first, underneath everything else, and that it comes straight out of the basic drive to stay alive.
174
Think about what came first in evolution.
175
It wasn't logic or language.
176
The earliest living things weren't solving anything.
177
They were just doing something much simpler and older, moving toward what kept them alive and away from what didn't.
178
So something like feeling was already around long before thinking showed up.
179
Now think back to how these cells get trained.
180
Chaos when they fail, calm when they succeed.
181
That's the free energy principle again.
182
That same drive every living thing has to avoid surprise and keep itself steady.
183
And if Songs is right, that drive isn't just close to feeling.
184
It might be where feeling actually starts.
185
So maybe we've had it backwards this whole time.
186
We're not slowly building a mind and waiting for it to wake up at the top.
187
We might be poking at the exact thing feeling comes from, right at the bottom, from the very first moment we turn the dish on.
188
And if something in there did start to feel, we'd probably miss it completely.
189
We can barely spot it in each other.
190
In 2006, a scientist named Adrian Owen studied a young woman who everyone had given up on.
191
They'd called her vegetative.
192
He put her in a brain scanner and asked her to imagine playing tennis.
193
And her brain lit up exactly like yours would.
194
She was completely awake and aware.
195
She just couldn't move or speak to show it.
196
And a later study found out that about one in four patients written off like that are actually still aware.
197
So if we could miss a fully conscious person lying right in front of us, what chance do we have of spotting it in a clump of cells?
198
We like to tell ourselves that feeling needs the right parts.
199
Pain receptors, certain brain structures, but even that is shaky.
200
Insects don't have a visual cortex, and they clearly see, so feeling might run on parts Parts that look nothing like ours, parts we'd never even think to check.
201
When the noise in the dish gets too loud, the cells turn their response down like they're trying to block it out.
202
Something in pain would do that, but so would something that feels nothing at all.
203
Backing away from a bad signal isn't the same as suffering.
204
And from the outside, there's no test that can tell the two apart.
205
This is what the philosopher David Chalmers called the hard problem.
206
We can map every single cell in a brain and still have no idea how it turns into an actual feeling.
207
So let me be honest about both sides of this.
208
Think about what this research could give us.
209
It could help us finally understand diseases.
210
It could replace a lot of animal testing.
211
And all of it comes from a tiny few cells sitting in a dish, playing a game that's older than most of the people in the lab.
212
It looks like nothing, but it might be the start of something huge.
213
For almost the entire history of the universe, there was just matter and nobody around to feel any of it.
214
Then at some point, some of that matter arranged itself in a way that did something completely new.
215
It started to feel.
216
We've always thought of that as the rarest thing there is, something it took billions of years of evolution to make.
217
And now we're basically trying to do it again in a lab on purpose.
218
We take some cells, we wire them up, we train them with calm and chaos, and we watch to see what happens.
219
And don't forget where those cells came from.
220
They started as ordinary skin from a real living person who's still out there right now.
221
We were never really trying to copy human thinking.
222
That was never the scary part.
223
The scary part is that somewhere in that dish, a piece of that person might start to feel And nothing on the outside will change.
224
The game will just keep playing and nobody would ever know.
ดาวน์โหลดแอป
ทุกอย่างที่ต้องใช้เพื่อพูดได้คล่อง
AI ให้คะแนนการออกเสียงให้คะแนนทุกประโยค
ฝึก IPAเชี่ยวชาญทุกเสียง
คำศัพท์สร้างคลังคำของคุณ
เกมคำศัพท์เล่นไปเรียนไป
FOR YOU
วิดีโอแนะนำ
B21
211 ประโยค
The Biggest Problem No One Is Talking About
IELTS PrepTOEFL PrepUniversity Students
C17
13 ประโยค
Our Brains Are Not Special. But Why Are We?
IELTS PrepTOEFL PrepUniversity Students
C1295
13 ประโยค
AI Can Literally Lend You a Hand
IELTS PrepTOEFL PrepUniversity Students
C13
32 ประโยค
Lucy: Time is Unity (HD CLIP)
IELTS PrepTOEFL PrepUniversity Students
C18
30 ประโยค
Technology Made Us Human
IELTS PrepTOEFL PrepUniversity Students
C16
452 ประโยค
Discover The Mysteries of the Human Brain | BBC Earth Science
IELTS PrepTOEFL PrepUniversity Students
C12
240 ประโยค
Why This Is the Most Exciting Time to Be Human | Ken Ono, Axiom Math
IELTS PrepTOEFL PrepUniversity Students
C11
242 ประโยค
🎧 Luyện Nghe Tiếng Anh Level C1- TRÍ TUỆ NHÂN TẠO - | Listening English Podcast |🎯
IELTS PrepTOEFL PrepUniversity Students
C11
40 ประโยค
The Engineering that Runs the Digital World 🛠️⚙️💻 How do CPUs Work?
Daily ConversationUniversity StudentsWorking Adults
C16
32 ประโยค
Anti-matter & nuclear weapons: Why technology is always a double-edge sword | Don Lincoln
IELTS PrepTOEFL PrepUniversity Students
B23
75 ประโยค
Pause AI Development NOW
IELTS PrepTOEFL PrepUniversity Students
C1121
29 ประโยค
The Neuroscience of Creativity
IELTS PrepTOEFL PrepUniversity Students
สถานการณ์
ในวิดีโอนี้ เราได้เห็นความก้าวหน้าทางวิทยาศาสตร์ที่น่าตกใจเกี่ยวกับการใช้เซลล์สมองในการสร้างคอมพิวเตอร์ที่ทำงานได้เหมือนสมองของมนุษย์ โดยเซลล์สมองเหล่านี้สามารถเล่นเกมอย่าง Doom ได้โดยไม่ต้องมีมนุษย์ควบคุม นี่คือแนวทางใหม่ในการพัฒนาเทคโนโลยีที่อาจเปลี่ยนแปลงอนาคตของปัญญาประดิษฐ์และการคอมพิวเตอร์อย่างสิ้นเชิง
กลุ่มคำและการใช้ร่วมที่มีประโยชน์
- living human brain cells - เซลล์สมองของมนุษย์ที่มีชีวิต
- computing power - พลังการคอมพิวเตอร์
- investment in AI - การลงทุนในปัญญาประดิษฐ์
- learning ability - ความสามารถในการเรียนรู้
- trapped in a game - ติดอยู่ในเกม
ความท้าทายในการฝึกพูด
ลองฝึกพูดตามวิดีโอโดยใช้คำศัพท์และวลีที่เราได้เรียนรู้ในวันนี้ ฟังเสียงของผู้พูดแล้วพยายามเลียนแบบการออกเสียงและการเน้นเสียงของพวกเขา คุณสามารถเลือกช่วงที่คุณชอบในวิดีโอแล้วฝึกพูดตามแบบ shadowspeak หรือ shadow speak เพื่อปรับปรุงการออกเสียงภาษาอังกฤษของคุณให้ดีขึ้น ทุกครั้งที่คุณฝึกพูด จำไว้ว่าความสามารถในการเรียนรู้ของคุณจะพัฒนาขึ้นตามการฝึกฝนอย่างสม่ำเสมอ!
ไวยากรณ์ในวิดีโอนี้
โครงสร้างที่ผู้พูดใช้บ่อยที่สุด พร้อมคำพูดจริงจากวิดีโอ:
| โครงสร้าง | ในวิดีโอ |
|---|---|
| Passive voice be + กริยาช่อง 3 — เน้นสิ่งที่เกิดขึ้น ไม่ใช่ผู้กระทำ | are trapped · being turned · is called |
| Present perfect have/has + กริยาช่อง 3 — เหตุการณ์ในอดีตที่ยังเกี่ยวข้องกับปัจจุบัน | we've had · We've always thought |
เทคนิค Shadowing คืออะไร?
Shadowing เป็นเทคนิคการเรียนรู้ภาษาที่ได้รับการรับรองทางวิทยาศาสตร์ พัฒนาขึ้นสำหรับการฝึกนักแปลมืออาชีพ วิธีการนี้เรียบง่ายแต่ทรงพลัง: คุณฟังเสียงภาษาอังกฤษจากเจ้าของภาษาและพูดตามทันที — เหมือนเงาที่ตามผู้พูดด้วยช่วงเวลาห่าง 1-2 วินาที การวิจัยแสดงว่าเทคนิคนี้ปรับปรุงความแม่นยำในการออกเสียง ทำนองเสียง จังหวะ การเชื่อมเสียง การฟังเข้าใจ และความคล่องแคล่วในการพูดได้อย่างมีนัยสำคัญ