"The net effect [of DeepSeek] should be to significantly increase the pace of AI development, since the secrets are being let out and the models are now cheaper and easier to train by more people." ~ Associate Professor Phillip Isola
In a two-part series, MIT News explores the environmental implications of generative AI. In this article, we look at why this technology is so resource-intensive. A second piece will investigate what experts are doing to reduce genAI’s carbon footprint and other impacts.
MIT professor Stefanie Mueller’s group has spent much of the last decade developing a variety of computing techniques aimed at reimagining how products and systems are designed. Much in the way that platforms like Instagram allow users to modify 2-D photographs with filters, Mueller imagines a world where we can do the same thing for a wide array of physical objects.
Try taking a picture of each of North America's roughly 11,000 tree species, and you’ll have a mere fraction of the millions of photos within nature image datasets. These massive collections of snapshots — ranging from butterflies to humpback whales — are a great research tool for ecologists because they provide evidence of organisms’ unique behaviors, rare conditions, migration patterns, and responses to pollution and other forms of climate change.
The Irish philosopher George Berkely, best known for his theory of immaterialism, once famously mused, “If a tree falls in a forest and no one is around to hear it, does it make a sound?”
What does sustainable fashion design have in common with Tetris? For both, an intriguing puzzle awaits, where you must configure unique shapes in a way that fills up the available space.
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The findings, based on a single electrochemical process, could help cut emissions from the hardest-to-decarbonize industries, such as steel and cement.
