DeepMind Loses $572M; KDD 2019 Best Papers; AI for Wildlife Conservation
DeepMind Takes on Billion-Dollar Debt and Loses US$572 Million
DeepMind, the artificial-intelligence company owned by Google parent Alphabet Inc., saw its revenue almost double last year, but gains were dwarfed by losses that increased to hundreds of millions of dollars.
(Bloomberg)
KDD 2019 Announces Best Paper Awards
A Cornell University research team took top honours in the research track for Network Density of States; while in the ADS track the winner was Actions Speak Louder than Goals: Valuing Player Actions in Soccer, from researchers at Katholieke Universiteit Leuven and SciSports.
(Synced)
How AI Is Helping Track Endangered Species
Now, technology is offering hope to scientists. Collecting better data and analyzing it more effectively with machine learning and AI allows conservationists to make more targeted and timely interventions. Here are five ways Microsoft and conservation efforts are coming together to help endangered species.
(Microsoft)
DeepMind’s New AI Tracks Serengeti Herds from Images Alone
DeepMind, the U.K.-based AI research subsidiary acquired by Alphabet in 2014 for $500 million, today detailed ecological research its science team is conducting to develop AI systems that’ll help study the behavior of animal species in Tanzania’s Serengeti National Park.
(VentureBeat)
Technology
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
Researchers present ViLBERT (short for Vision-and-Language BERT), a model for learning task-agnostic joint representations of image content and natural language. They extend the popular BERT architecture to a multi-modal two-stream model, processing both visual and textual inputs in separate streams that interact through co-attentional transformer layers.
(Georgia Institute of Technology & Facebook AI & Oregon State Univerisity)
Temporal Cycle-Consistency Learning
Researchers introduce a self-supervised representation learning method based on the task of temporal alignment between videos. The method trains a network using temporal cycleconsistency (TCC), a differentiable cycle-consistency loss that can be used to find correspondences across time in multiple videos.
(Google Brain & DeepMind)
FSA-Net: Learning Fine-Grained Structure Aggregation for Head Pose Estimation from a Single Image
This paper proposes a method for head pose estimation from a single image. Previous methods often predicts head poses through landmark or depth estimation and would require more computation than necessary. This method is based on regression and feature aggregation.
(Academia Sinica & National Taiwan University)
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South Korean Game Developer’s AI Turns Your Selfie Into an Anime Face
A team of South Korean researchers proposed a method that uses unsupervised image translation to transform a simple selfie into a classic Japanese-style anime face. The novel “U-GAT-IT” method generates visually superior results compared with previous cutting-edge techniques.
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Global AI Events
August 19–23: Knowledge Discovery and Data Mining (KDD2019) in London, United Kingdom
September 10–12: The AI Summit (Part of TechXLR8) in Singapore
September 24–28: Microsoft Ignite in Orlando, United States
October 27-November 3: International Conference on Computer Vision (ICCV) in Seoul, South Korea
Global AI Opportunities
Research Scientist, Google Brain Toronto
OpenAI Seeking Software Engineers and Deep Learning Researchers
DeepMind Scholarship: Access to Science
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