This week wintry weather battered the southern US and parts of Europe. Researchers suggest this could be a counterintuitive effect of the climate crisis where the warming of the Arctic causes the jet stream to shift. “The energy escaping from the jet stream bangs into the polar vortex so it starts to wobble and move all over the place,” said Judah Cohen, the director of seasonal forecasting at Atmospheric and Environmental Research, to the Guardian. “Where the polar vortex goes, so goes the cold air.”
While many of us do our part to avoid common climate blunders –– avoiding plastic bags, opting for walking instead of driving, using recycling bins, etc –– we don’t always see the impact of our technology use. In the field of AI, computations required for deep learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018. Several factors impact the carbon emitted by neural network computations including the location of the server used for training, the energy grid that it plugs into, the size of the dataset, and the hardware where the training takes place. Tools are available to help individuals calculate their machine learning carbon impact.
While 1 percent of worldwide electricity consumption can be attributed to data centres, usage is forecasted to increase to 15 percent of global electricity consumption by 2025 due to the growth of inference deployments. The use of increasingly energy-efficient processing units, as well as expanding efficiencies in servers, storage, devices and hyperscale data centres, offer optimism for the future. New inference chips are coming to market that offer significant leaps in efficiency. For example, Untether AI, a Radical Ventures portfolio company, launched a new inference chip that is 8 times more energy efficient than the current market leader.
While machine learning carries an environmental footprint, it also plays a critical role in bringing efficiencies to Green House Gas-emitting industries. Some of the highest potential areas identified by researchers include forecasting supply and demand for electricity scheduling, accelerating materials science for better energy storage, preventing methane leakage from natural gas pipelines, reducing transport activity, improving urban planning, optimizing supply chains, precision agriculture, carbon dioxide removal and more accurate climate prediction.
At Radical, we will continue to look for investments that are great AI businesses, and also good for the health of our global climate.