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Highlights of Google Cloud Next; Google BERT Wins NAACL’s Best Long Paper

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Google Cloud Next | New Hybrid Cloud ‘Anthos,’ Partnerships, Data Centres
To catch up with rivals Amazon Web Services and Microsoft Azure, Google is accelerating the development of its own cloud services. At the annual Google Cloud Next conference yesterday in San Francisco the company unveiled a handful of new AI-based services and partnerships for its smart analytics and machine learning tools.

Google Launches AI Platform, A Collaborative Model-Making Tool for Data Scientists
Google Cloud Platform is giving AI creators a new, shared, end-to-end environment for teams to test, train, and deploy models called the AI Platform. Google today also upgraded AutoML, its service for automating the creation of custom AI models, at the Cloud Next conference taking place this week in San Francisco.

NAACL 2019 | Google BERT Wins Best Long Paper
The annual conference of the North American Chapter of the Association for Computational Linguistics (NAACL) is a grand event in the field of natural language processing. NAACL 2019 received 1198 long paper and 757 short paper submissions for a total of 1955 papers. The conference accepted 424 papers for a 22.6 percent acceptance rate.
(Synced) / (Google Bert paper)

A Google Brain Program Is Learning How to Program
In a new paper, Google Brain researchers propose using neural networks to model human source code editing. Effectively this means treating code editing as a sequence and having a machine learn how to “write code” like in a natural language model — by analysing a short paragraph of editing, the model can extract intent and leverage that to generate subsequent edits.
(Synced) / (Google Brain)


Stanford Machine Learning Group Released The MRNet Dataset of 1000+ Annotated Knee MRIs
The MRNet dataset consists of 1,370 knee MRI exams performed at Stanford University Medical Center. The dataset contains 1,104 (80.6%) abnormal exams, with 319 (23.3%) ACL tears and 508 (37.1%) meniscal tears; labels were obtained through manual extraction from clinical reports.

Robots That Learn to Use Improvised Tools

Taking inspiration from how animals learn, the research team designed an algorithm that allows robots to learn tool-use skills through a similar paradigm of imitation and interaction. In particular, they show that, with a mix of demonstration data and unsupervised experience, a robot can use novel objects as tools and even improvise tools in the absence of traditional ones.
(Berkeley Artificial Intelligence Research)

Cross-Lingual Transfer Learning for Bootstrapping AI Systems Reduces New-Language Data Requirements
In a paper presenting at this year’s International Conference on Acoustics, Speech, and Signal Processing, Alexa team reports using cross-lingual transfer learning (a sub-category of transfer learning) to bring existing functions to a new language. Alexa is currently available in English, German, Japanese, French, Spanish, Italian, and an additional six variants of those languages.
(Amazon Alexa)

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Megvii Introduces Single-Path, One-Shot Method for NAS Design
Designing Neural Architecture Search (NAS) is an important but compute-intensive process that requires huge datasets. To identify promising architectures from complex search spaces and minimize resource demands has become a major challenge for researchers and enterprises alike.

A Google Brain Program Is Learning How to Program
To develop a good representation that captures the desired intent information and scales the sequence length, Google Brain researchers proposed two editorial characterizations: explicit characterization and implicit characterization.

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