シャドーイング練習: We're Not Ready for Biocomputing - 動画で英語スピーキングを学ぶ

練習モードを選ぶ

各モードは全画面の練習ルームで開きます。

224 文

文が短すぎたり長すぎる場合は、Editをタップして調整してください。

In a few seconds I'm going to show you one of the most disturbing things happening in science right now

and almost no one is talking about it.

That's why we made this video.

You're looking at a computer in a laboratory called Cortical Labs.

It's playing Doom.

But look closer.

There's nobody playing it.

Or actually, there is.

They just can't get out.

They are trapped there because this computer is running on living human brain cells And those cells are the ones playing.

So why would anyone build a computer out of brain cells?

It basically comes down to power.

AI uses a massive amount of electricity, and that number keeps going up.

Amazon investing another $15 billion.

Meta continuing its massive investments in AI, this time in the form of compute power.

The computing power it takes to train these models roughly doubles every six months.

Almost nothing in the real world grows that fast, but you can't build power plants that quickly.

So there's a growing gap between what AI needs and what we can actually supply, and it keeps getting wider.

You can see the panic in what the big companies are doing.

They're going after energy now, not just better software.

Old nuclear plants that got shut down years ago are being turned back on just to power them.

Companies are signing deals to lock up huge chunks of the grid years ahead of time.

It's not really a race for smarter AI anymore.

It's turning into a race for electricity.

So a few researchers went a totally different direction.

They looked at the brain.

Because the brain figured this out a long time ago.

Think about what your brain is doing right now and how little it runs on.

Everything you're thinking runs on about 20 watts.

We're using whole power plants just to copy a small piece of what your brain does on basically nothing.

And it learns way better than our machines too.

To teach a computer what a dog is, you have to show it thousands of photos.

A little kid sees a dog once or twice and just gets it.

So how did we get from that to a dish playing Doom?

It took three steps.

Back in 2008, researchers grew a sheet of rat neurons and hooked it up to a little robot.

And the cells actually drove it around.

It sounds bigger than it was.

The neurons just sat flat on a grid of electrodes and connected to their neighbors.

That was pretty much it.

This was nowhere near a real brain.

And it wasn't even the first time someone had wired living tissue to a machine.

One team even did it with the brain of a lamprey.

The footage always looked a little like a horror movie.

And from the start, people had one real doubt.

Were the cells actually driving, or was the computer just taking their random signals and making it look that way?

Keep that question in mind.

We're coming back to it.

The second step came in 2013.

A scientist named Shinya Yamanaka showed you could reset a grown-up cell all the way back to the start.

He found four proteins that turn a regular cell into a blank, brand new one.

And from there, you can turn it into almost anything, including a neuron.

That was huge.

It meant you didn't have to take neurons out of a brain anymore.

You could just take a bit of skin and grow them.

There was another big step too.

If you grow neurons on a flat dish, they only connect to the ones right next to them.

But if you let them grow in 3D, they start to organize on their own.

They spread out, fold into layers, and form the rough beginnings of real brain regions, kind of like an embryo does.

One of those tiny balls of tissue is called an organoid.

Then, in 2022, a company called Cortical Labs put all of this together into something they called DishBrain.

They grew about 800,000 neurons, part mouse and part human, and taught them to play Pong.

Two things about this were a big deal.

First, a dish of cells was actually working toward a goal.

Second, and this is the weird part, They'd figured out how to teach it.

They could ask the cells a question and get an answer back.

You're not supposed to be able to do that to a blob of tissue in a dish.

So how do you teach a neuron?

You can't give it a treat.

So they used a basic fact about neurons.

They hate chaos.

If you give them a signal they can't predict, it stresses them out.

So the setup was simple and kind of mean.

When they missed the ball, they got hit with scrambled random noise.

When they hit it back, the signal went calm and steady.

The neurons have no idea what Pong is.

They're not playing to win.

They're just trying to make the chaos stop.

And they didn't pick that method at random.

They based it on an idea of the free energy principle.

Basically, it says every living thing is always trying to avoid surprises and keep its world predictable.

Because being surprised usually means something's wrong.

So that urge to calm things down isn't something the scientists added.

It might be the most basic thing a living cell does.

But there's a big catch.

We can grow brain tissue in 3D and we can talk to living neurons.

We just can't do both at the same time yet.

So in order to talk to them, DishBrain had to keep the cells flat because the chip that reads them is a flat grid.

If you put a round organoid on it, you only touch the bottom layer.

So they had to flatten it out and give up the brain's real shape just to talk to it.

And remember, this is the crude version.

Half the design is missing.

Which brings us to a big new.

Cortical Labs showed off a computer that runs on brain cells.

They call it CL1 and it plays Doom.

This one was grown from about 200,000 human neurons.

They took skin cells, reset them, turned them into neurons and spread them across a bed of electrodes.

Then they tied the signals to moving and shooting the same way as before.

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.

アプリをダウンロード

流暢に話すために必要なすべて

AI発音採点文ごとに採点

IPA練習すべての音をマスター

単語学習自分の単語帳を作る

単語ゲーム遊びながら覚える

QRをスキャン

Download on theApp Store Get it onGoogle Play

FOR YOU

このレッスンについて

このレッスンでは、「バイオコンピューティングの時代に私たちは準備できていない」というテーマのビデオを使い、英語のスピーキング練習をします。ビデオでは、脳細胞を使ったコンピューターの驚くべき進化や、AIとエネルギー問題の関係などが語られています。カジュアルで説得力のあるトーン、少し速めのスピードが特徴で、リスニングと同時に、自然な発音やイントネーションを身につけるのに最適です。

重要な語彙とフレーズ

biocomputing：バイオコンピューティング（生物を利用したコンピューティング技術）
massive amount of electricity：大量の電気（AIが消費する電力量を表すフレーズ）
gap：ギャップ（必要なものと供給できるものの間の隔たり）
neurons：ニューロン（神経細胞）
reset：リセット（細胞を初期状態に戻すこと）

練習のヒント

このビデオのスピードは少し速いですが、shadow speech（シャドーイング）の練習で効果的に対応できます。まずはビデオを1文ずつ止めながら、すぐ後に同じように発音してみましょう。特に「massive amount of electricity」のような長いフレーズは、リズムを捉えることが重要です。英語シャドーイングのコツは、発音だけでなくイントネーションやストレスも真似ることです。ビデオの中で話者が驚きを表すときの声の上がり方や、説明するときの落ち着いたトーンを観察し、それを再現してみましょう。何度も繰り返すことで、shadowspeakの技術が上達し、英語スピーキング練習の成果が実感できるようになります。

この動画の文法

話し手がよく使っている文型を、動画の実際の表現とともに紹介します。

文型	動画での表現
受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる	are trapped · being turned · is called
現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している	we've had · We've always thought

シャドーイングとは？英語上達に効果的な理由

シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。

シャドーイングのやり方: ステップ別の完全ガイドを読む →

シャドーイング練習: We're Not Ready for Biocomputing - 動画で英語スピーキングを学ぶ

練習モードを選ぶ

アプリをダウンロード

おすすめ動画

The Biggest Problem No One Is Talking About

Our Brains Are Not Special. But Why Are We?

AI Can Literally Lend You a Hand

Lucy: Time is Unity (HD CLIP)

Technology Made Us Human

Discover The Mysteries of the Human Brain | BBC Earth Science

Why This Is the Most Exciting Time to Be Human | Ken Ono, Axiom Math

🎧 Luyện Nghe Tiếng Anh Level C1- TRÍ TUỆ NHÂN TẠO - | Listening English Podcast |🎯

The Engineering that Runs the Digital World 🛠️⚙️💻 How do CPUs Work?

Anti-matter & nuclear weapons: Why technology is always a double-edge sword | Don Lincoln

Pause AI Development NOW

The Neuroscience of Creativity

このレッスンについて

重要な語彙とフレーズ

練習のヒント

この動画の文法

シャドーイングとは？英語上達に効果的な理由