Resource Consumption Of #AI Remains A Constraint To Evolution
Artificial intelligence, especially the more advanced deep learning systems, require a great deal of energy and processing power.
Some considerations in this regard:
—The most complex AI models, such as large language models, can have billions or even trillions of parameters, which demands an enormous amount of computational resources to train and run them.
—Training these models often takes place in specialized data centers with tens or hundreds of GPUs and TPUs working in parallel for weeks or months.
—Once trained, AI models still require considerable computational power to perform inference and generate results, especially for real-time tasks.
—The power consumption of AI systems can be very high, with some models consuming up to megawatts of electricity during training.
—There are ongoing efforts to improve the energy efficiency of AI, such as the use of specialized hardware, model compression techniques, and more efficient learning approaches. But there is still work to be done in this regard.
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2024-05-19 12:57:05
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npub1wpzpvzfkn4m754fasp0wnt6ck20ycww4kz9nj4n5rquu9ul7a0xq4hzs7pPublished at
2024-05-19 12:57:05Event JSON
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"content": "Resource Consumption Of #AI Remains A Constraint To Evolution\n\nArtificial intelligence, especially the more advanced deep learning systems, require a great deal of energy and processing power.\n\nSome considerations in this regard:\n\n—The most complex AI models, such as large language models, can have billions or even trillions of parameters, which demands an enormous amount of computational resources to train and run them.\n\n—Training these models often takes place in specialized data centers with tens or hundreds of GPUs and TPUs working in parallel for weeks or months.\n\n—Once trained, AI models still require considerable computational power to perform inference and generate results, especially for real-time tasks.\n\n—The power consumption of AI systems can be very high, with some models consuming up to megawatts of electricity during training.\n\n—There are ongoing efforts to improve the energy efficiency of AI, such as the use of specialized hardware, model compression techniques, and more efficient learning approaches. But there is still work to be done in this regard.",
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