シャドーイング練習: How AI uses our drinking water - BBC World Service - YouTubeで英語スピーキングを学ぶ
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1/15 of a teaspoon. That's how much water the average single interaction with ChatGPT uses, according to Sam Altman, the boss of OpenAI. So if you type, can you help me solve this maths problem? That's a drop. Or can I put lime instead of lemon in this recipe? That's a drop. Or why is the sky blue?
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1/15 of a teaspoon. That's how much water the average single interaction with ChatGPT uses, according to Sam Altman, the boss of OpenAI. So if you type, can you help me solve this maths problem? That's a drop. Or can I put lime instead of lemon in this recipe? That's a drop. Or why is the sky blue?
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Help me write this email. Help me improve my website code.
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Mr. Altman claims there are 1 billion messages sent to ChatGPT every day, and ChatGPT is just one AI bot. Chuck in Gemini, DeepSeek, Claude and others. It's clear that the AI revolution is a thirsty one. Striking though it is, some experts are more than a little sceptical of Sam Altman's estimate on water usage.
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At this point, there was just not enough information for me to agree with or trust the number. Their number was perhaps referring to some tiny models. We're considering a medium sized large language model that's the size of GPT-3.
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Basically, if you write an email or ask some questions, if you have 10 to 50 queries, you're going to be consuming roughly 500ml of water.
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This calculation includes water used in cooling and electricity generation. The BBC asked OpenAI for more details about Sam Altman's estimate, but the company declined. Either way, it's clear AI uses a lot of water. But why? Every time you send a prompt to an AI, it has to run complex calculations to understand and respond. This work is done by the most powerful and specialised computer chips in the world, housed inside enormous data centres. Even before users can send prompts, the training process for the models uses the chips to carry out intense work. And all that extra power means the hardware can overheat and become damaged if not cooled properly. Most data centres use air cooling systems, which was fine until I came along.
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But now, because these data centres and the infrastructure that's going in is so much more energy intensive, there are liquid cooling approaches that are now being implemented.
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For liquid cooling the water must be clean to prevent bacteria growing or clogs and corrosion in the system, which means using mostly drinking water. Here's how the most common liquid cooling process works. It begins by piping coolant over the processing chips within the servers. This cooling liquid absorbs the heat and takes it away from the electrics to a heat exchange unit. Water is used to reduce the temperature of the coolant. The coolant then recirculates back to cool the servers.
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Meanwhile, the now hot water is piped to cooling towers, where a combination of fans and water vapour dissipate the heat, cooling the water. Some of the water evaporates in that process, while the rest is recirculated through the cooling process several times before being discharged back into the nearby water source. Overall, up to 80% of the water evaporates.
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What it means is that this type of water is gone, and that we are extracting water from a water circuit that is necessary for irrigation, for human consumption and hygiene.
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Communities around the world concerned about data centres putting stress on water sources and electricity grids are pushing back. Protests have been held in Spain, India, Chile, Uruguay and parts of the US. And it's not just the operations within the data centre that need water. Generating the electricity to run them requires a lot of water too, because power plants like coal, gas and nuclear heat water to create steam, which drives a turbine. The International Energy Agency has said electricity demand for AI optimised data centres is expected to increase by 400% by 2030 to 300 terawatt hours.
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That's roughly the electricity consumption of the whole of the UK for a year. And aside from electricity, water is also needed when manufacturing the semiconductor chips used to run AI.
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So water is both used directly and indirectly in the whole supply and creation chain of AI technologies. It is used for the refination of the critical raw materials that are needed to create the hardware of AI.
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Getting accurate figures on how much water it takes to build AI systems and run them is difficult. Google, Meta and Microsoft release annual figures showing that their data centres use billions of litres of water every year from local sources, but none of them indicate how much of it is due to AI. Most tech giants recognise the impact it's having. Many, including Google, Microsoft and Meta, have pledged to be water neutral by 2030.
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S5 We hope that can happen, there is a long way to go to get to those kind of numbers. Part of what we hope to see is, across the industry, a range of innovations that allow us to maybe minimise the use of water.
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Companies are trialing, for example, ways to cool data centres without evaporating any water at all. And to use the heat that's generated to warm homes. There are also experiments to move data centres away from communities entirely under the sea, to the Arctic, or even off the planet.
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Can we actually put capacity out in space? It's very, very early stage. So what, you know, we at NTT are looking at is, can we launch satellites that can at least do some more backup-oriented or other oriented tasks?
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Though skeptics point to the many hurdles that need to be overcome, there is optimism, too, about a more sustainable future.
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Let's remember that that GenAI capability is still very, very young. It's moved exponentially fast, but as an industry and as a use, it is still young. Ideally, we can learn together as as a society and as a world society, how do we minimise against the use of water and energy? Because this is all, you know, a world resource when we talk about water.
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この動画で話す練習をする理由は？
このBBCの動画では、AIが私たちの飲み水をどのように利用しているかについて解説しています。AI技術が急速に発展する中で、実際の世界における影響に気づくことが重要です。そのため、この動画を通じて英語を学ぶことは、AIに関する基本的な理解を深めるだけでなく、現代の技術に対する批判的な思考を育む助けとなります。特に、数字や統計が使われる場面が多いので、実用的な英語のスキルを高める絶好の機会となります。動画に頻出するフレーズや表現を反復して学ぶことで、より自信を持って英会話に臨むことができるでしょう。これは、shadowspeakやshadow speechを通じて特に効果的です。
文法とコンテキスト内の表現
- used to ～: 過去の習慣や状態を表す表現です。例文：AI技術は以前はこのように使われていなかった。
- every time + 文: 「～するたびに」という意味で、条件や状況を説明する際に使います。例文：AIに入力を送信するたびに計算が必要です。
- it's clear that: 「～は明らかである」という表現で、何かの事実を強調する際に使用されます。例文：AIが水を多く使用することは明らかです。
- reduce + 名詞: 「～を減少させる」という意味です。例文：温度を減らすために水が用いられます。
これらの文法構造は、動画の内容を理解する手助けとなり、効果的なコミュニケーションスキルを磨くために重要です。
一般的な発音の罠
動画の中では、いくつかの発音が難しい単語やフレーズが出てきます。特に“data”や“evaporate” という言葉は、発音のアクセントが異なるため注意が必要です。また、“cooling” や“electricity” も早口で言われると聞き取るのが難しい可能性があります。これらの単語を、YouTubeで英語学習の際に何度もリピートして、正しい発音を身につけていきましょう。
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。