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As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger counterparts. While most prior work investigated the use of distillation for building task-specific models, we leverage knowledge distillation during the pre-training phase and show that it is possible to reduce the size of a BERT model by 40%, while retaining 97% of its language understanding capabilities and being 60% faster. To leverage the inductive biases learned by larger models during pre-training, we introduce a triple loss combining language modeling, distillation and cosine-distance losses. Our smaller, faster and lighter model is cheaper to pre-train and we demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative on-device study.

A significant fraction of software failures in large-scale Internet systems are cured by rebooting, even when the exact failure causes are unknown. However, rebooting can be expensive, causing nontrivial service disruption or downtime even when clusters and failover are employed. In this work we separate process recovery from data recovery to enable microrebooting -- a fine-grain technique for surgically recovering faulty application components, without disturbing the rest of the application. We evaluate microrebooting in an Internet auction system running on an application server. Microreboots recover most of the same failures as full reboots, but do so an order of magnitude faster and result in an order of magnitude savings in lost work. This cheap form of recovery engenders a new approach to high availability: microreboots can be employed at the slightest hint of failure, prior to node failover in multi-node clusters, even when mistakes in failure detection are likely; failure and recovery can be masked from end users through transparent call-level retries; and systems can be rejuvenated by parts, without ever being shut down.

Ford dealers were told about the new Escape-sized model; one of them spilled the beans

As gaming laptop prices continue to rise, it’s increasingly difficult to find affordable options that aren’t terrible。 Here are your best options based on performance and cost

A large body of literature suggests willingness‐to‐pay is overstated in hypothetical valuation questions as compared to when actual payment is required. Recently, “cheap talk” has been proposed to eliminate the potential bias in hypothetical valuation questions. Cheap talk refers to process of explaining hypothetical bias to individuals prior to asking a valuation question. This study explores the effect of cheap talk in a mass mail survey using a conventional value elicitation technique. Results indicate that cheap talk was effective at reducing willingness‐to‐pay for most survey participants; however, consistent with previous research, cheap talk did not reduce willingness‐to‐pay for knowledgeable consumers.

Nature, money, work, care, food, energy, and lives: these are the seven things that have made our world and will shape its future. In making these things cheap, modern commerce has transformed, governed, and devastated Earth. In A History of the World in Seven Cheap Things , Raj Patel and Jason W. Moore present a new approach to analyzing today’s planetary emergencies. Bringing the latest ecological research together with histories of colonialism, indigenous struggles, slave revolts, and other rebellions and uprisings, Patel and Moore demonstrate that throughout history, crises have always prompted fresh strategies to make the world cheap and safe for capitalism. At a time of crisis in all seven cheap things, innovative and systemic thinking is urgently required. This book proposes a radical new way of understanding—and reclaiming—the planet in the turbulent twenty-first century.

Economists often ask how private information is shared through markets, costly signaling, and other mechanisms. Yet most information sharing is done through ordinary, informal talk. Economists are inconsistent in their view of such ‘cheap talk’: sometimes it is supposed that communication generally leads to efficient equilibria; other times it is supposed that since ‘talk is cheap,’ it is never credible. The authors think both views are wrong. In this paper, they describe what some recent research in game theory teaches about when people will convey private information by cheap talk.

Deploying convolutional neural networks (CNNs) on embedded devices is difficult due to the limited memory and computation resources. The redundancy in feature maps is an important characteristic of those successful CNNs, but has rarely been investigated in neural architecture design. This paper proposes a novel Ghost module to generate more feature maps from cheap operations. Based on a set of intrinsic feature maps, we apply a series of linear transformations with cheap cost to generate many ghost feature maps that could fully reveal information underlying intrinsic features. The proposed Ghost module can be taken as a plug-and-play component to upgrade existing convolutional neural networks. Ghost bottlenecks are designed to stack Ghost modules, and then the lightweight GhostNet can be easily established. Experiments conducted on benchmarks demonstrate that the proposed Ghost module is an impressive alternative of convolution layers in baseline models, and our GhostNet can achieve higher recognition performance (e.g. 75.7% top-1 accuracy) than MobileNetV3 with similar computational cost on the ImageNet ILSVRC-2012 classification dataset. Code is available at https://github.com/huawei-noah/ghostnet.