AI Technology & Industry Review
Global machine intelligence updates.
A research team from MIT, Allen Institute for AI and Microsoft Research open-sources Python Programming Puzzles (P3), a novel programming challenge suite that captures the essence of puzzles and can be used to teach and evaluate an AI’s programming proficiency.
On June 16, LVMH and Google Cloud announced a strategic partnership to accelerate innovation and develop new cloud-based artificial intelligence solutions.
On June 16, General Motors Co. announced it will increase its electric vehicles (EV) and autonomous vehicles (AV) investments from 2020 through 2025 to USD 35 billion, representing a 75 percent increase from its initial commitment announced prior to the pandemic
A research team from New York University and Google Research explores whether knowledge distillation really works, showing that a surprisingly large discrepancy often remains between the predictive distributions of the teacher and student models, even when the student has the capacity to perfectly match the teacher.
A research team from Mila, McGill University, Université de Montréal, DeepMind and Microsoft proposes GFlowNet, a novel flow network-based generative method that can turn a given positive reward into a generative policy that samples with a probability proportional to the return.v
A research team from DeepMind and Google Brain proposes Launchpad, a programming model that simplifies the process of defining and launching instances of distributed computation.
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.
A research team from McGill University, Université de Montréal, DeepMind and Mila presents an end-to-end, model-based deep reinforcement learning (RL) agent that dynamically attends to relevant parts of its environments to facilitate out-of-distribution (OOD) and systematic generalization.
An IEEE team provides a comprehensive overview of the bottom-up and top-down design approaches toward neuromorphic intelligence, highlighting the different levels of granularity present in existing silicon implementations and assessing the benefits of the different circuit design styles in neural processing systems.
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.
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.
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.
A research team from Google Research combines the benefits of implicit differentiation and autodiff and proposes a unified, efficient and modular approach for implicit differentiation of optimization problems.
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.
A research team from Google proposes ByT5 architecture, a competitive token-free pretrained byte-to-byte transformer that can be straightforwardly adapted to process byte sequences without adding excessive computational cost.
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.
A research team from Georgia Tech, Microsoft Research and Microsoft Azure AI studies the collections of “lottery tickets” in extremely over-parametrized models, revealing the generalization performance pattern of winning tickets and proving the existence of “super tickets.”
On May 31, in Wuhan East Lake National Comprehensive Science Center， Huawei’s Wuhan Artificial Intelligence Computing Center was completed and putContinue Reading
Didi Autonomous Driving is to complete a USD 300 million new round of financing, with USD 200 million from the Guangzhou Automobile Group and its capital investment arm.
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.”
According to Nikkei reports, the Japanese government will provide half of the funds for TSMC’s relocation research facility in the Ibaraki Prefecture, which is north of Tokyo, and it is expected that R&D will be officially launched in 2022.
According to media reports on May 31, Shenzhen is building its tallest smart parking lot, which is expected to beContinue Reading
On May 30, media reported that the South Korean regulatory agency Fair Trade Commission has approved SK Hynix’s USD 9 billion acquisition of Intel’s NAND flash memory and SSD businesses.
On May 27th, SberAutoTech, a subsidiary of Russian state-owned banking company PJSC Sberbank, unveiled a self-driving taxi “Flip” developed byContinue Reading
The US government is advancing the “Artemis” moon landing plan in 2024, using a new type of autonomous rover, or Lunar Terrain Vehicle (LTV), to explore places that it has never set foot in before.
On May 24, SoftBank Vision Fund 2 announced exclusively investment of USD 175 million in Riiid, a South Korean AI tutor company.
South Korea’s largest telecommunications company will join hand with the Korea Institute of Science and Technology (KAIST) to establish a AI research institute.
An IEEE team proposes AngularGrad — a novel optimization algorithm that takes both gradient direction and angular information into consideration. The method successfully reduces the zig-zag effect in the optimization trajectory and speeds up convergence.
A team from Cornell University and NTT Research proposes Physical Neural Networks (PNNs), a universal framework that leverages a backpropagation algorithm to train arbitrary, real physical systems to execute deep neural networks.
On May 26, OpenAI unveiled a new $100 million startup fund called the “OpenAI Startup Fund”, focusing on early-stage startupsContinue Reading
On May 25, Tesla announced that it will abandon LiDARs and roll out camera-only Autopilot assisted driving system called “Tesla Vision” in its North American Model 3 and Model Y vehicles starting in June.
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.
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.
On May 21, leading IoT cloud platform Tuya Smart hosted a panel to discuss resilience and innovation for the IoT industry in North America during COVID-19 outbreak.
A research team from ETH Zürich and Microsoft presents a systematic, comparative study of distributed ML training over serverless infrastructures (FaaS) and “serverful” infrastructures (IaaS), aiming to understand the system tradeoffs of distributed ML training with serverless infrastructures.
A research team from ETH Zürich presents an overview of priors for (deep) Gaussian processes, variational autoencoders and Bayesian neural networks. The researchers propose that well-chosen priors can achieve theoretical and empirical properties such as uncertainty estimation, model selection and optimal decision support; and provide guidance on how to choose them.
Industry report unveiled that Waymo, the self-driving arm of Google, is seeking a USD 4 billion investment from external investors to fuel its autonomous vehicle business.
On May 20, Zhang Yiming, the founder of ByteDance and owner of TikTok since 2012, issued an internal letter to all employees announcing his resignation as CEO.
According to reports on May 17, Volkswagen said it will suspend production at its Chattanooga plant for two weeks beginning on June 7.
A research team from Adobe proposes Directional GAN (DGAN), a novel and simple approach for generating high-resolution images conditioned on expected semantic attributes, greatly simplifying the image content generating process for marketing campaigns, websites and banners.