The artificial intelligence (AI) market is growing fast. One powerful subset of AI is machine learning (ML), which involves not predefined instructions and fixed algorithms but learned patterns over artificial neural networks. Developers have used ML to solve mission-critical problems with high speed and accuracy in a wide range of domains, including agriculture, e-commerce, education, finance, manufacturing, medicine, networking, transportation and more.
For many IT practitioners and consultants, ML is a matter of not if but when and how. They are asking themselves questions such as, "What's my use case, design and scale? Which ML techniques will I deploy (language processing, classification, anomaly detection, etc.)? How will I deploy—DIY or with external help—and train the model?"
Recently, however, another set of questions have arisen concerning the external costs and environmental impact of ML. That applies especially to deep learning—neural networks with several layers of interconnected nodes.
Recent Studies
One of the early alerts on this topic was a 2019 paper by three scientists from the University of Massachusetts, Amherst, who estimated that the training alone of one natural language processing (NLP) deep learning model emitted five times more CO2 than an average car consumes across its entire lifetime. (Overall, in commercial deployments to date, the energy consumed while training ML is small compared to the energy required for inference, so the total carbon impact of a single model can be very large.) More studies followed.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/04/13/ai-and-ml-think-green/?sh=2bcdf79341a9
For many IT practitioners and consultants, ML is a matter of not if but when and how. They are asking themselves questions such as, "What's my use case, design and scale? Which ML techniques will I deploy (language processing, classification, anomaly detection, etc.)? How will I deploy—DIY or with external help—and train the model?"
Recently, however, another set of questions have arisen concerning the external costs and environmental impact of ML. That applies especially to deep learning—neural networks with several layers of interconnected nodes.
Recent Studies
One of the early alerts on this topic was a 2019 paper by three scientists from the University of Massachusetts, Amherst, who estimated that the training alone of one natural language processing (NLP) deep learning model emitted five times more CO2 than an average car consumes across its entire lifetime. (Overall, in commercial deployments to date, the energy consumed while training ML is small compared to the energy required for inference, so the total carbon impact of a single model can be very large.) More studies followed.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/04/13/ai-and-ml-think-green/?sh=2bcdf79341a9
