Shadowing-Übung: How AI uses our drinking water - BBC World Service - Englisch Sprechen Lernen mit YouTube

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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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Warum das Sprechen mit diesem Video üben?

Das Video "Wie KI unser Trinkwasser nutzt" von der BBC verdeutlicht, wie KI-Anwendungen massive Mengen an Wasser benötigen. Beim Üben des Englischsprechens mit diesem Video wirst du in zahlreiche kontextuelle Szenarien eintauchen, die das Verständnis komplexer Themen fördern. Indem du wiederholt mit den Inhalten interagierst, kannst du nicht nur dein Hörverstehen verbessern, sondern auch deinen Wortschatz erweitern und deine Ausdrucksweise verfeinern. Englisch sprechen üben ist entscheidend, um flüssiger zu werden und dein Selbstvertrauen in Gesprächen über technische und gesellschaftliche Themen zu stärken.

Grammatik und Ausdrücke im Kontext

In diesem Video gibt es mehrere interessante grammatikalische Strukturen und Ausdrücke, die du in deinen eigenen Gesprächen anwenden kannst:

  • Every time you send a prompt - Diese Struktur hilft dabei, Bedingungen auszudrücken. Übe ähnliche Satzstrukturen, um deinen Ausdruck zu variieren.
  • It’s clear that AI uses a lot of water - Nutze diese Phrase, um sichere Annahmen auszudrücken. Solche Formulierungen sind besonders nützlich, wenn du Argumente unterstützt.
  • Many, including Google, Microsoft and Meta, have pledged - Die Nutzung des Begriffs „have pledged“ zeigt eine Verpflichtung. Dies kann in Diskussionen über Verpflichtungen oder Absichten genutzt werden.

Gemeinsame Aussprachefallen

Einige Wörter und Phrasen in diesem Video könnten für nicht-muttersprachliche Englischlerner herausfordernd sein. Achte auf Folgendes:

  • Data centres - Das ‚a‘ in „data“ kann leicht falsch betont werden. Übe, indem du es langsam nachsprichst und darauf achtest, wie die Vokale klingen.
  • Electricity demand - Die Kombination dieser Wörter kann das Reden über technische Themen erschweren. Nutze shadowing, um die richtige Intonation zu erfassen.
  • Evaporates - Die Endung kann oft übersehen werden. Achte darauf, das „tes“ klar auszusprechen.

Indem du die Techniken des shadowspeak anwendest, kannst du nicht nur dein Sprechen verbessern, sondern auch ein tieferes Verständnis für komplexe Themen entwickeln. Nutze diese shadowing site als wertvolles Werkzeug, um deine Fähigkeiten im Englisch Shadowing zu optimieren. Das regelmäßige Üben mit solch konkreten Beispielen wird dich in deiner Reise zum fließenden Englisch erheblich unterstützen.

Was ist die Shadowing-Technik?

Shadowing ist eine wissenschaftlich fundierte Sprachlerntechnik, die ursprünglich für die professionelle Dolmetscherausbildung entwickelt und durch den Polyglotten Dr. Alexander Arguelles populär gemacht wurde. Die Methode ist einfach aber wirkungsvoll: Du hörst englisches Audio von Muttersprachlern und wiederholst es sofort laut — wie ein Schatten, der dem Sprecher mit nur 1–2 Sekunden Verzögerung folgt. Anders als passives Hören oder Grammatikübungen zwingt Shadowing dein Gehirn und deine Mundmuskulatur, gleichzeitig echte Sprachmuster zu verarbeiten und zu reproduzieren. Studien zeigen, dass es Aussprachegenauigkeit, Intonation, Rhythmus, verbundene Sprache, Hörverständnis und Sprechflüssigkeit signifikant verbessert — was es zu einer der effektivsten Methoden für die IELTS Speaking-Vorbereitung und reale englische Kommunikation macht.

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