Synced Global AI Weekly | 2018.10.20—10.26

Hot Topics in the ML Community Last Week

CVPR Paper Controversy; ML Community Reviews Peer Review

Today’s top Machine Learning (ML) conferences are heavily reliant on peer review as it allows them to gauge submitted academic papers’ quality and suitability. However, a series of unsettling incidents and heated discussions on social media have now put the peer review process itself under scrutiny.

(Synced)

Poll Results Released: NIPS Keeps Its Name

In April the NIPS organizing committee announced that it was considering a name change and began collecting opinions and suggestions from the AI research community. Yesterday, the final results of the poll were disclosed along with the announcement that there would be no name change.

(Synced)

Fei-Fei Li Named Co-Director of Stanford’s New Human-Centered AI Initiative

Stanford University has announced that the Director of the Stanford Artificial Intelligence Lab (SAIL) and Stanford Vision Lab Dr. Fei-Fei Li will co-direct the California university’s new Human-Centered AI Initiative (HAI).

(Synced)

Technology

Do Deep Generative Models Know What They Don't Know?

"A neural network deployed in the wild may be asked to make predictions for inputs that were drawn from a different distribution than that of the training data. A plethora of work has demonstrated that it is easy to find or synthesize inputs for which a neural network is highly confident yet wrong. "

(DeepMind)

Fluid Annotation: An Exploratory Machine Learning–Powered Interface for Faster Image Annotation

In “Fluid Annotation: A Human-Machine Collaboration Interface for Full Image Annotation", we explore a machine learning–powered interface for annotating the class label and outline of every object and background region in an image, accelerating the creation of labeled datasets by a factor of 3x.

(Google AI)

GANs Beyond Generation: 7 Alternative Use Cases

In this article I present 7 alternative use cases. With some of them I already have worked personally and can confirm their usefulness, some other are in research, but it doesn’t mean they’re not worth to give a try.

(Alexandr Honchar)

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Can A Canadian AI Startup Challenge Google in AutoML?

Enter DarwinAI, a Waterloo, Ontario based AI startup which recently released a beta version of an automated machine learning solution it says can generate models ten times more efficiently than comparable state-of-the-art solutions.

(Synced)

Tencent AI Lab Open-Sources 8M Word Chinese NLP Vector Dataset

Tencent AI Lab has announced an open-source NLP dataset comprising vector representations for eight million Chinese words and phrases. The dataset aims to provide large-scale and high-quality support for deep learning-based Chinese language NLP research in both academic and industrial applications.

(Synced)

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