A Critical Look at Generative AI’s Carbon Footprint
In Brief
Image processing and chatbot creation are common tasks for data centers. These tasks are performed by thousands of computers.
AI-powered chatbots and image generators run on thousands of computers in data centers worldwide. These data centers are designed to handle massive amounts of data and perform complex computations at lightning-fast speeds. As the demand for AI-powered technologies continues to grow, so will the need for these powerful data centers.
Generative AI is the technology behind chatbots and image generators. Generative AI creates complex outputs like a sentence, a paragraph, or an image. In applications like smart speakers or autocomplete, it can generate audio responses or suggest a search query. Chatbots and image generators are becoming more popular, and Google and Microsoft will use AI language models to improve search results. It requires more energy to build powerful models. This is because the more powerful an AI is, the more energy it takes. It is unclear how this will impact society’s future carbon footprint. Researchers are exploring ways to make AI models more energy-efficient. One approach is to use specialized hardware that can perform computations more efficiently than traditional processors.
Researchers estimate that creating a generative AI model called BERT with 110 million parameters consumed the energy of a transcontinental flight for one person in 2019. This energy usage is a serious environmental problem as the development of AI models continues to accelerate. As a result, more ecological and energy-efficient approaches to AI development are required. The carbon footprint of AI models can also be greatly reduced if renewable energy sources like solar or wind power are used.
ChatGPT is a model released by OpenAI in November 2022. It had over 1.5 billion visits in March 2023. Microsoft incorporated ChatGPT into its search engine, Bing, and made it available to everyone on May 4, 2023. A chatbot is an AI assistant that can do many different things, such as writing documents, solving math problems, and creating marketing campaigns. A model needs to be updated regularly to keep up with new data. The carbon footprint of creating ChatGPT is not known, but it is likely higher than that of GPT3. Based on the hardware and infrastructure being used, ChatGPT’s efficiency in terms of power consumption and processing speed may change. Therefore, when creating and utilizing AI models like ChatGPT, it’s crucial to take the environment’s impact and resource usage into account. It is much more expensive than a regular query to a search engine, with one chat in ChatGPT costing 2 cents and a regular Google request costing seven times less because of energy consumption.
Large generative AI models are here to stay, and people will turn to them for information. For example, if a student needs help solving a math problem, they will ask a tutor or a friend or consult a textbook. In the future, they will probably ask a chatbot. This is because a single large AI model will not ruin the environment, but if a thousand companies develop slightly different AI bots for different purposes, each used by millions of customers, energy use could become an issue. The good news is that AI can run on renewable energy. By bringing the computation to where green energy is more abundant or scheduling it for times of day when renewable energy is more available, it could be more efficient.
The public can use data about AI models’ carbon footprints to choose a “greener” chatbot. The future could be used to choose a “greener” AI system.
- The world is getting excited about the potential of Generative AI, which researchers say is impressive, but it might be more advanced than we realize. Researchers at Microsoft and Columbia University have shown that chatbots can show evidence of AGI.
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About The Author
Damir is the team leader, product manager, and editor at Metaverse Post, covering topics such as AI/ML, AGI, LLMs, Metaverse, and Web3-related fields. His articles attract a massive audience of over a million users every month. He appears to be an expert with 10 years of experience in SEO and digital marketing. Damir has been mentioned in Mashable, Wired, Cointelegraph, The New Yorker, Inside.com, Entrepreneur, BeInCrypto, and other publications. He travels between the UAE, Turkey, Russia, and the CIS as a digital nomad. Damir earned a bachelor's degree in physics, which he believes has given him the critical thinking skills needed to be successful in the ever-changing landscape of the internet.
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Damir is the team leader, product manager, and editor at Metaverse Post, covering topics such as AI/ML, AGI, LLMs, Metaverse, and Web3-related fields. His articles attract a massive audience of over a million users every month. He appears to be an expert with 10 years of experience in SEO and digital marketing. Damir has been mentioned in Mashable, Wired, Cointelegraph, The New Yorker, Inside.com, Entrepreneur, BeInCrypto, and other publications. He travels between the UAE, Turkey, Russia, and the CIS as a digital nomad. Damir earned a bachelor's degree in physics, which he believes has given him the critical thinking skills needed to be successful in the ever-changing landscape of the internet.
