शैडोइंग अभ्यास: How AI uses our drinking water - BBC World Service - YouTube के साथ अंग्रेजी बोलना सीखें

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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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लोकप्रिय

इस वीडियो के साथ बोलने का अभ्यास करने के लाभ क्या हैं?

जब आप "How AI uses our drinking water" को देखते हैं, तो यह न केवल आपको सूचना प्रदान करता है, बल्कि बोलने का अभ्यास करने का एक अनूठा साधन भी है। इस वीडियो के माध्यम से आप प्राकृतिक और तकनीकी दोनों क्षेत्रों में जुड़ी जटिलताएं समझ सकते हैं। बोलने के लिए ऐसे विषयों पर चर्चा करना जो समाज और पर्यावरण पर प्रभाव डालते हैं, आपकी शब्दावली को बढ़ाता है और आपको विश्व स्तर पर चर्चित मुद्दों के बारे में सोचने पर मजबूर करता है। यूट्यूब से अंग्रेजी सीखें का यह तरीका आपके संचार कौशल को निखारने में मदद करेगा।

विपणन और अभिव्यक्तियाँ संदर्भ में

  • "AI uses a lot of water" - यह वाक्यांश एक निश्चित तथ्य को स्पष्ट रूप से बताता है।
  • "The cooling liquid absorbs the heat" - यह क्रिया-वाक्य हमें क्रियाओं और क्रियाविशेषणों का उपयोग करते समय सटीकता सिखाता है।
  • "Data centres are putting stress on water sources" - यह संबोधन हमें अन्य शब्दों के साथ समस्याओं को व्यक्त करने में मदद करता है।

इन संरचनाओं को बोलने में शामिल करके, आप अपने उच्चारण और संप्रेषण कौशल को विकसित कर सकते हैं, साथ ही मजबूत एवं प्रभावी संवाद स्थापित कर सकते हैं। shadowspeak तकनीक का उपयोग करते हुए इन वाक्यांशों का अभ्यास करें और अंग्रेजी उच्चारण में सुधार लाएँ।

सामान्य उच्चारण की बाधाएँ

वीडियो में कुछ शब्दों का उच्चारण करना चुनौतीपूर्ण हो सकता है, जैसे "evaporates", "circulate", और "algorithm". इन शब्दों पर ध्यान केंद्रित करना और उन्हें बार-बार बोलना आपको उनके सही उच्चारण में मदद कर सकता है। इस प्रकार के शब्दों का सही उच्चारण सीखना न केवल आपके बोलने के कौशल को सुधारता है, बल्कि आपको अन्य अंग्रेजी बोलने वालों के साथ संवाद करने में भी आत्मविश्वास प्रदान करता है। shadowspeaks में, यह महत्वपूर्ण है कि आप इन कठिन शब्दों को सही तरीके से उच्चारित करें।

शैडोइंग तकनीक क्या है?

शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।

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