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Communication Algorithm-Architecture Co-Design for Distributed Deep Learning


"Abstract—Large-scale distributed deep learning training has enabled developments of more complex deep neural network models to learn from larger datasets for sophisticated tasks. In particular, distributed stochastic gradient descent intensively invokes all-reduce operations for gradient update, which dominates communication time during iterative training epochs. In this work, we identify th... » read more

Are Better Machine Training Approaches Ahead?


We live in a time of unparalleled use of machine learning (ML), but it relies on one approach to training the models that are implemented in artificial neural networks (ANNs) — so named because they’re not neuromorphic. But other training approaches, some of which are more biomimetic than others, are being developed. The big question remains whether any of them will become commercially viab... » read more

Using Machine Learning To Break Down Silos


Jeff David, vice president of AI solutions at PDF Solutions, talks with Semiconductor Engineering about where machine learning can be applied into semiconductor manufacturing, how it can be used to break down silos around different process steps, how active learning works with human input to tune algorithms, and why it’s important to be able to choose different different algorithms for differ... » read more

Even good bots fight: The case of Wikipedia (Oxford & Alan Turing Institute)


Source: University Of Oxford published via PLOS ONE, Milena Tsvetkova, Ruth García-Gavilanes, Luciano Floridi, Taha Yasseri "The research paper, published in PLOS ONE, concludes that bots are more like humans than you might expect as they appear to behave differently in culturally distinct online environments. The paper says the findings are a warning to those using artificial intelligence ... » read more