With healthy populations of animals that disperse seeds, tropical forests can absorb up to four times more carbon. Deforestation, hunting, and wildlife trade threaten the hornbill's ability to disperse seeds throughout Asian tropical forests. A lot of attention has been paid to how climate change can reduce biodiversity. Now MIT researchers have shown that the reverse is also true: Loss of biodiversity can jeopardize regrowth of tropical forests, one of Earth's most powerful tools for mitigating climate change. Combining data from thousands of previous studies and using new tools for quantifying interconnected ecological processes, the researchers analyzed numerous tropical sites where deforestation was being followed by natural regrowth, focusing on the role of animals such as birds and monkeys that spread plant seeds by eating them in one place and then defecating someplace else. Evan Fricke, a research scientist in the MIT Department of Civil and Environmental Engineering and the lead author of a paper on the work, has studied such animals for 15 years, showing that without their role, trees have lower survival rates and a harder time keeping up with environmental changes.

Network pruning is used to reduce inference latency and power consumption in large neural networks. However, most existing methods struggle to accurately assess the importance of individual weights due to their inherent interrelatedness, leading to poor performance, especially at extreme sparsity levels. We introduce Hyperflows, a dynamic pruning approach that estimates each weight's importance by observing the network's gradient response to the weight's removal. A global pressure term continuously drives all weights toward pruning, with those critical for accuracy being automatically regrown based on their flow, the aggregated gradient signal when they are absent. We explore the relationship between final sparsity and pressure, deriving power-law equations similar to those found in neural scaling laws. Empirically, we demonstrate state-of-the-art results with ResNet-50 and VGG-19 on CIFAR-10 and CIFAR-100.

Generative artificial intelligence offers an opportunity for Japan to achieve regrowth, Jun Uchikawa, chief information officer at Sumitomo Mitsui Financial Group, said in a recent interview. "The biggest challenge facing Japanese companies is the lack of talent and labor. Generative AI can resolve this," Uchikawa said. The Japanese banking group in April started a trial use of generative AI based on the technology of the ChatGPT chatbot for searching information and creating documents. This could be due to a conflict with your ad-blocking or security software.

Forest loss is being driven by various factors, including commodity production, forestry, agriculture, wildfire, and urbanization. Curtis et al. used high-resolution Google Earth imagery to map and classify global forest loss since 2001. Just over a quarter of global forest loss is due to deforestation through permanent land use change for the production of commodities, including beef, soy, palm oil, and wood fiber. Despite regional differences and efforts by governments, conservationists, and corporations to stem the losses, the overall rate of commodity-driven deforestation has not declined since 2001. Global maps of forest loss depict the scale and magnitude of forest disturbance, yet companies, governments, and nongovernmental organizations need to distinguish permanent conversion (i.e., deforestation) from temporary loss from forestry or wildfire.