Google’s Big Week: Quantum Supremacy, Boosting Search With BERT, Exploring Transfer Learning | Synced

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Hello Quantum World! Google Publishes Landmark Quantum Supremacy Claim
A Google research team says that its quantum computer carried out a specific calculation that is beyond the practical capabilities of regular, ‘classical’ machines. The same calculation would take even the best classical supercomputer 10,000 years to complete, Google estimates.
(Nature)/ (Google AI Blog)

Milestone: BERT Boosts Google Search
In what the company calls “the biggest leap forward in the past five years, and one of the biggest leaps forward in the history of Search,” Google announced that it has leveraged its pretrained language model BERT to dramatically improve the understanding of search queries.
(Google) / (Synced)

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Google researchers explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. This systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks.
(Google)

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Technology

AI Benchmark: All About Deep Learning on Smartphones in 2019
In this paper, researchers evaluate the performance and compare the results of all chipsets from Qualcomm, HiSilicon, Samsung, MediaTek and Unisoc that are providing hardware acceleration for AI inference. They also discuss the recent changes in the Android ML pipeline and provide an overview of the deployment of deep learning models on mobile devices.
(ETH Zurich & Google Research & Samsung & Huawei & Qualcomm & MediaTek & Unisoc)

Grounded Human-Object Interaction Hotspots From Video
Researchers propose an approach to learn human-object interaction “hotspots” directly from video. Rather than treat affordances as a manually supervised semantic segmentation task, the proposed approach learns about interactions by watching videos of real human behavior and anticipating afforded actions.
(Facebook Research)

Teacher Algorithms for Curriculum Learning of Deep RL in Continuously Parameterized Environments
Researchers consider the problem of how a teacher algorithm can enable an unknown Deep Reinforcement Learning (DRL) student to become good at a skill over a wide range of diverse environments. To do so, they study how a teacher algorithm can learn to generate a learning curriculum, whereby it sequentially samples parameters controlling a stochastic procedural generation of environments.
(Inria & Microsoft Research)