The European Union is negotiating an Artificial Intelligence Act, the world’s first comprehensive law that aims to regulate artificial intelligence (AI) based on the risk it poses to individuals, society and the environment.
歐盟正在商議一項《人工智慧法案》，這是旨在根據人工智慧(AI)，對個人、社會及環境造成之風險，進行管理的世界首部綜合性法律。
However, discussions of AI overlook one significant environmental risk: a potential increase in energy consumption from using it in everyday activities. Without acknowledging this risk, the development of AI may contribute to the climate emergency.
不過，人工智慧的討論忽略了，一項重大的環境風險：也就是，於日常活動中，來自使用人工智慧之能源消耗，潛在性的增加。不承認此風險，人工智慧的發展，會是氣候非常時刻的部分原因。
AI can be a double-edged sword. It can be a powerful tool for climate action, improving the efficiency of the energy grid, modelling climate change predictions or monitoring climate treaties. But the infrastructure needed to run AI is energy- and resource-intensive.
人工智慧會是一把雙面刃。它會是就氣候動態、提高能源網路效率、製作氣候變遷預測模型或監測氣候條約的一種強大工具。不過，運作人工智慧所需的基礎設施，是能源及資源密集的。
“Training” a large language model such as OpenAI’s GPT-3, a popular AI-powered chatbot, requires lots of electricity to power data centres that then need lots of water to cool down.
“訓練”大型語言模型，譬如OpenAI的GPT-3(一種流行的人工智慧聊天機器人)需要大量電力來為，之後需要大量水進行冷卻的數據中心，供給電力。
In fact, the true scale of AI’s impact on the environment is probably underestimated, especially if we focus only on the direct carbon footprint of its infrastructure.
實際上，人工智慧對環境影響的真實規模，可能被低估。特別是，倘若我們僅著重於，其基礎設施的直接碳足跡。
Today, AI permeates almost all aspects of our digitalised daily lives. Businesses use AI to develop, market and deliver products, content and services more efficiently, and AI influences how we search, shop, socialise and organise our everyday lives.
目前，人工智慧幾乎普及到，咱們數位化之日常生活的各種層面。企業利用人工智慧更有效地開發，行銷與交付產品、內容及服務。因此，人工智慧影響我們，如何搜尋、購物、社交及安排咱們的日常生活。
These changes have massive implications for our total energy consumption at a time when we need to actively reduce it. And it’s not yet clear that AI will support us in making more climate-positive choices.
當我們需要積極減少能源消耗時，這些改變同時對咱們總能源消耗，具有巨大的密切關係。因此，目前仍然不詳，在做出更有利於氣候的選擇上，人工智慧是否能支持我們。
AI can indirectly change how much energy we use by changing our activities and behaviour – for instance, by completing tasks more efficiently or by substituting analogue tools like physical maps with their digital equivalents.
藉由改變我們的活動及行為，人工智慧能間接改變我們使用多少能源量。譬如，透過更有效地完成任務，或透過替代的類比工具，諸如使用其數位式同等工具的實體地圖。
However, things can backfire if convenience and lower costs simply spur demand for more goods or services. This is known as a “rebound effect”, and when the rebound effect is larger than the energy saving, it leads to greater energy use overall. Whether AI leads to more or less energy use will depend on how we adapt to using it.
不過，倘若便利性及較低成本，僅刺激對更多商品或服務的需求，事情會適得其反。這被通稱為一種“反彈效應”，當反彈效應大於節能時，會導致整體更大的能源使用。是否人工智慧會導致更多或更少的能源消耗，將取決於我們採取如何使用它。
For example, AI-powered smart home systems can improve energy efficiency by controlling heating and appliances. A smart heating system is estimated to reduce gas consumption by around 5%. Home energy management and automation could even reduce households’ CO₂ consumption by up to 40%.
譬如，人工智慧驅動的智慧型住宅系統，能藉由控制暖氣設備及電器製品，來改善能源效率。據估計，智慧型暖氣設備系統，減少瓦斯消耗量，達大約5%。家庭能源管理及自動化，甚至能減少家庭的二氧化碳排放量高達 40%。
However, a more efficient and comfortably heated home can make people stay at home more often with the heating on. People may also have increased comfort expectations of a warmer house and pre-warming of spaces. A study on smart homes found that people purchase and use additional smart devices to increase control and comfort, rather than to use less energy.
不過，更有效率且加溫舒適的住宅，會使人們更頻繁開著暖氣，待在家裡。人們也可能已經增加了，對較溫暖房子及空間預熱的舒適期望。一項有關智慧型住宅的研究發現，人們購買及使用額外的智慧型設備，來提升控制及舒適度，而不使用較少的能源。
圖1. 萬一節能供暖只是意味著我們習慣了額外的溫暖，怎麼辦？
What if energy-efficient heating just means we get used to extra warmth?
In the transport sector, ride-hailing apps that use AI to optimise routes can reduce travel time, distance and congestion. Yet they are displacing more sustainable public transportation and increasing travel demand, resulting in 69% more climate pollution.
在交通領域，使用人工智慧最佳化路線的叫車應用程式，能減少行程時間、距離及擁塞。不過，它們正在取代較能永續的大眾運輸工具，及增加行程需求，而導致69%更多的氣候污染。
As AI in the transportation sector becomes more advanced, the effect may escalate. The convenience of an autonomous vehicle may increase people’s travel and in a worst-case scenario, double the amount of energy used for transport.隨著人工智慧，於交通運輸領域中，變得更為先進，影響可能逐步擴大。自動駕駛汽車的便利性，可能增加人們的行程。因此，在最壞情況的腳本下，會加倍供交通使用的能源數量。
In retail, AI-powered advertising and search functions, personalised recommendations or virtual personal assistants may encourage overconsumption rather than sustainable shopping.
在零售業，人工智慧驅動的廣告及搜尋功能、個人化推薦或虛擬的個人輔助器，可能鼓勵過度消費，而不是可持續的購物。
Rebound effects can also transpire through time use and across sectors. Research predicts that AI could take over 40% of our time spent doing domestic chores within the next ten years. That idle time is now available for other activities which may be more energy-intensive, such as additional travel.
反彈效應也能透過時間的使用及跨部門發生。 研究預測，未來10年內，人工智慧可能承當，我們花於作家務時間的40%以上。那麼，那空閒時間可資，用於諸如額外的旅行等，其他或許更耗能的活動。
At a larger scale, AI will also have systemic impacts that threaten climate action. We are aware of AI’s risks of exacerbating misinformation, bias and discrimination, and inequalities.
於更大規模時，人工智慧也會具有，威脅氣候動態的系統性影響。我們察覺，人工智慧有加劇錯誤訊息、偏見與歧視及不平等的風險。
These risks will have knock-on effects on our ability to take action on climate change. Erosion of people’s trust, agency and political engagement may undermine their desire to cut emissions and adapt to climate change.
此些風險對我們採取行動，來應對氣候變遷的能力，會具有連鎖反應。人們的信任、使然作用及政治保證的減少，可能削弱他們削減排放及適應氣候變遷的渴望。
As we grapple with the potential risks of AI, we have to broaden our understanding of how it will affect our behaviour and our environment. Scientists have called for more work to improve and standardise accounting methodologies for reporting the carbon emissions of AI models. Others have proposed best-practice solutions to reduce energy and carbon emissions from machine learning.
當我們努力解決人工智慧的潛在風險時，我們必須擴大瞭解，人工智慧會如何影響，我們的行為及環境。科學家們一直呼籲，更多的研究來改善及標準化，說明報告人工智慧模型碳排放的方法論。其他人已經提出，諸多最佳實踐的解決方案，來減少源自機器學習的能源及碳排放。
These efforts tackling the direct carbon footprint of AI infrastructure are important, but not enough. When considering the true environmental impacts of AI, its indirect impact on everyday life should not be ignored.
此些解決人工智慧基礎設施之直接碳足跡的努力很重要，不過並不夠。當考慮人工智慧對環境的真正影響時，其對日常生活的間接影響，不應該被忽視。
As the technology becomes ever more embedded in our lives, its developers need to think more about human behaviour and how to avoid unintended consequences of AI-driven efficiency savings. Eventually, they’ll have to somehow embed that into the design of AI itself, so that a world in which humans rely on AI isn’t a world which uses extra energy unnecessarily.
隨著該項技術越來越融入我們的生活，其開發人員們需思考，更多有關人類行為，及如何避免人工智慧驅動之效率節約，非預期的後果。最終，他們將必須以某種方式，將那嵌入人工智慧本身的設計中。以便人類依賴人工智慧的世界，不是一個無謂地使用額外能源的世界。
網址：https://theconversation.com/ai-is-supposed-to-make-us-more-efficient-but-it-could-mean-we-waste-more-energy-220990
翻譯：許東榮
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