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Inter-plant communication through mycorrhizal networks mediates complex adaptive behaviour in plant communities
Adaptive behaviour of plants, including rapid changes in physiology, gene regulation and defence response, can be altered when linked to neighbouring plants by a mycorrhizal network (MN). Mechanisms underlying the behavioural changes include mycorrhizal fungal colonization by the MN or interplant communication via transfer of nutrients, defence signals or allelochemicals. We focus this review on our new findings in ectomycorrhizal ecosystems, and also review recent advances in arbuscular mycorrhizal systems. We have found that the behavioural changes in ectomycorrhizal plants depend on environmental cues, the identity of the plant neighbour and the characteristics of the MN. The hierarchical integration of this phenomenon with other biological networks at broader scales in forest ecosystems, and the consequences we have observed when it is interrupted, indicate that underground ‘tree talk’ is a foundational process in the complex adaptive nature of forest ecosystems
PaLM: Scaling Language Modeling with Pathways
Large language models have been shown to achieve remarkable performance
across a variety of natural language tasks using few-shot learning, which
drastically reduces the number of task-specific training examples needed to
adapt the model to a particular application. To further our understanding of
the impact of scale on few-shot learning, we trained a 540-billion parameter,
densely activated, Transformer language model, which we call Pathways Language
Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML
system which enables highly efficient training across multiple TPU Pods. We
demonstrate continued benefits of scaling by achieving state-of-the-art
few-shot learning results on hundreds of language understanding and generation
benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough
performance, outperforming the finetuned state-of-the-art on a suite of
multi-step reasoning tasks, and outperforming average human performance on the
recently released BIG-bench benchmark. A significant number of BIG-bench tasks
showed discontinuous improvements from model scale, meaning that performance
steeply increased as we scaled to our largest model. PaLM also has strong
capabilities in multilingual tasks and source code generation, which we
demonstrate on a wide array of benchmarks. We additionally provide a
comprehensive analysis on bias and toxicity, and study the extent of training
data memorization with respect to model scale. Finally, we discuss the ethical
considerations related to large language models and discuss potential
mitigation strategies