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LVMH and Google Cloud Create Strategic Partnership for AI and Cloud-Based Innovation

On June 16, LVMH and Google Cloud announced a strategic partnership to accelerate innovation and develop new cloud-based artificial intelligence solutions.

AI Machine Learning & Data Science Nature Language Tech Research

Google Researchers Merge Pretrained Teacher LMs Into a Single Multilingual Student LM Via Knowledge Distillation

A Google Research team proposes MergeDistill, a framework for merging pretrained teacher LMs from multiple monolingual/multilingual LMs into a single multilingual task-agnostic student LM to leverage the capabilities of the powerful language-specific LMs while still being multilingual and enabling positive language transfer.

AI Machine Learning & Data Science Research

Pieter Abbeel Team’s Decision Transformer Abstracts RL as Sequence Modelling

A research team from UC Berkeley, Facebook AI Research and Google Brain abstracts Reinforcement Learning (RL) as a sequence modelling problem. Their proposed Decision Transformer simply outputs optimal actions by leveraging a causally masked transformer, yet matches or exceeds state-of-the-art model-free offline RL baselines on Atari, OpenAI Gym, and Key-to-Door tasks.

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Beijing‘s Hardcore Vlogger Comes Up With Self-driving Bicycles

On April Fool’s Day in 2016, Google secretly uploaded a video of an autonomous bicycle roaming through the busy streets. Two days after the post went viral, Google clarified that it was actually the result of superb video editing. Five years later, Zhi Hui Jun, a vlogger from Beijing with more than 800,000 fans, spent four months (mainly on weekends) making the joke into a reality.

AI Machine Learning & Data Science Research

What Matters in Adversarial Imitation Learning? Google Brain Study Reveals Valuable Insights

A research team from Google Brain conducts a comprehensive empirical study on more than fifty choices in a generic adversarial imitation learning framework and explores their impacts on large-scale (>500k trained agents) continuous-control tasks to provide practical insights and recommendations for designing novel and effective AIL algorithms.

AI Machine Learning & Data Science Research

Microsoft & OneFlow Leverage the Efficient Coding Principle to Design Unsupervised DNN Structure-Learning That Outperforms Human-Designed Structures

A research team from OneFlow and Microsoft takes a step toward automatic deep neural network structure design, exploring unsupervised structure-learning and leveraging the efficient coding principle, information theory and computational neuroscience to design structure learning without label information.

AI Computer Vision & Graphics Machine Learning & Data Science Research

Google & Rutgers’ Aggregating Nested Transformers Yield Better Accuracy, Data Efficiency and Convergence

A research team from Google Cloud AI, Google Research and Rutgers University simplifies vision transformers’ complex design, proposing nested transformers (NesT) that simply stack basic transformer layers to process non-overlapping image blocks individually. The approach achieves superior ImageNet classification accuracy and improves model training efficiency.

AI Machine Learning & Data Science Research

NYU, Facebook & CIFAR Present ‘True Few-Shot Learning’ for Language Models Whose Few-Shot Ability They Say Is Overestimated

A research team from New York University, Facebook AI, and a CIFAR Fellow in Learning in Machines & Brains raise doubts regarding large-scale pretrained language models’ few-shot learning abilities. The researchers re-evaluate such abilities with held-out examples unavailable, which they propose constitutes “true few-shot learning.”

AI Machine Learning & Data Science Nature Language Tech Research

Study Shows Transformers Possess the Compositionality Power for Mathematical Reasoning

A research team from UC Davis, Microsoft Research and Johns Hopkins University extends work on training massive amounts of linguistic data to reveal the grammatical structures in their representations to the domain of mathematical reasoning, showing that both the standard transformer and the TP-Transformer can compose the meanings of mathematical symbols based on their structured relationships.

AI Machine Learning & Data Science Research

Yoshua Bengio Team’s Recurrent Independent Mechanisms Endow RL Agents With Out-of-Distribution Adaptation and Generalization Abilities

A research team from the University of Montreal and Max Planck Institute for Intelligent Systems constructs a reinforcement learning agent whose knowledge and reward function can be reused across tasks, along with an attention mechanism that dynamically selects unchangeable knowledge pieces to enable out-of-distribution adaptation and generalization.