AI Technology & Industry Review
A research team from Google Research and Northwestern University presents polynomial time and sample-efficient algorithms for learning an unknown depth-2 feedforward neural network with general ReLU activations, aiming to provide insights into whether efficient algorithms exist for learning ReLU networks.
A research team from MIT and Google Quantum AI presents a quantum algorithm for training classical neural networks in logarithmic time and provides numerical evidence of its efficiency on the standard MNIST image dataset.
A research team from the University of Melbourne, Facebook AI, and Twitter Cortex proposes a black-box test method for assessing and debugging the numerical translation of neural machine translation systems in a systematic manner. The approach reveals novel types of errors that are general across multiple state-of-the-art translation systems.
A research team from DeepMind presents epistemic neural networks (ENNs) as an interface for uncertainty modelling in deep learning, and proposes the KL divergence from a target distribution as a precise metric to evaluate ENNs.
ording to estimation by Loup Ventures, by 2032, full self-driving (FSD) purchases and subscriptions is expected to bring electric vehicles giant Tesla USD 102 billion in profits.
On July 20, Quanergy Systems announced its 3D LiDAR solution has been selected to support the development of an Information, Communication, and Technology (ICT) system in Busan, South Korea. The ICT system is a key component of the South Korean government’s strategy to build data driven IoT smart cities. Busan is one of the pilot cities for the initiative.
On July 19, SenseTime, China’s leading AI company, announced the launch of the SenseTime International AI Innovation Hub.
On July 21, the chip giant Intel announced its second-quarter financial report. The report shows that Intel’s revenue in the second quarter was USD 19.631 billion, and its net profit was USD 5.061 billion, a decrease of 0.9 percent compared to the same period last year.
On July 22, Argo AI, Ford Motors and Lyft joined hands to launch L4-level autonomous driving services in Miami by the end of this year.
On July 23, Spanish Prime Minister Pedro Sanchez stated that Apple will increase artificial intelligence in Spain, after meeting with company’s CEO Tim Cook during a four day trip to the US west coast.
A research team from Google Brain, Columbia University and University of Oxford proposes Graph Kernel Attention Transformers (GKATs), a new class of graph neural network that achieves greater expressive power than SOTA GNNs while reducing computation burdens.
A research team from Microsoft, Zhejiang University, Johns Hopkins University, Georgia Institute of Technology and University of Denver proposes Only-Train-Once (OTO), a one-shot DNN training and pruning framework that produces a slim architecture from a full heavy model without fine-tuning while maintaining high performance.
A Google Research team proposes Wordcraft, a text editor with a built-in AI-powered creative writing assistant. Wordcraft uses few-shot learning and the natural affordances of conversation to support a variety of user interactions; and can help with story planning, writing and editing.
In a new paper published in the prestigious scientific journal Nature, DeepMind presents AlphaFold2, a redesigned neural-network system based on last year’s AlphaFold that can predict protein structures with atomic-level accuracy.
A research team from Taichi Graphics, MIT CSAIL, Zhejiang University, Tsinghua University and Kuaishou Technology introduces a programming language and compiler for quantized simulation that achieves both high performance and significantly reduced memory costs by enabling flexible and aggressive quantization.
On July 15, tech conglomerate Google announced that it is broadening its cloud service infrastructure in India, by setting up a second “Cloud Region” in the Delhi-NCR area.
On July 7, the University of Texas at Austin announced that it has teamed up with major wireless companies to set up a new 6G research center, the 6G@UT. Founding affiliates Samsung, AT&T, NVIDIA, Qualcomm and InterDigital will each fund at least two projects for three years at the center.
On July 17, Baidu rolled out its Apollo Robotaxi in the streets of China’s southern city Guangzhou. Users can access the self-driving taxi service via the Baidu Map and Apollo GO apps, and board the vehicles from 9:30 to 23:00, at 237 stops across the city.
A research team from Baidu proposes ERNIE 3.0, a unified framework for pretraining large-scale, knowledge-enhanced models that can easily be tailored for both natural language understanding and generation tasks with zero-shot learning, few-shot learning or fine-tuning, and achieves state-of-the-art results on NLP tasks.
A research team from the University of Electronic Science and Technology of China, Chinese Academy of Sciences, School of Education Shaanxi Normal University, Japan Advanced Institute of Science and Technology and ETH Zurich encodes the basic belief assignment (BBA) into quantum states and implements them on a quantum circuit, aiming to utilize quantum computation characteristics to better handle belief functions.
On July 13, Alibaba announced the establishment of the Alibaba Artificial Intelligence Governance Laboratory (AAIG, https://s.alibaba.com/cn/aaig). The laboratory will promote sustainable, reliable, credible, and usable AI.
University of Washington and the Allen Institute for Artificial Intelligence researchers say human evaluations are no longer the gold standard for evaluating natural language generation models, as evaluators’ focus on surface-level text qualities degrades their ability to accurately assess current NLG models’ overall capabilities.
As the dynamic computational graph is widely supported by many machine learning frameworks, GPU memory utilization for training on a dynamic computational graph becomes a key specification of these frameworks. In the recently released v1.4, MegEngine provides a way to reduce the GPU memory usage by additional computation using Dynamic Tensor Rematerialization (DTR) technique and further engineering optimization, which makes large batch size training on a single GPU possible.
A research team from OpenAI proposes Codex, a specialized GPT model fine-tuned on publicly available code from GitHub that can produce functionally correct Python code bodies from natural language docstrings and could excel at a variety of coding tasks.
A research team from MIT proposes a unified framework for estimation and inference in the presence of various forms of economic data corruption such as measurement errors, missing values, discretization, and differential privacy.
On July 8, e-commerce giant Amazon announced its plan to set up a R&D centre in Helsinki, Finland, to develop technologies for its Scout unmanned delivery robot.
Intel plans to invest 20 billion U.S. dollars to build chip factories in a number of EU countries, and is actively lobbying governments, including France and Italy.
MIT and University of Waterloo researchers propose a machine learning model that outperforms the average student on problems from MIT’s 6.036 Introduction to Machine Learning course.
At the World Artificial Intelligence Conference (WAIC) held in Shanghai on July 9, Daosheng Tang, the senior vice exectuvie of Tencent and president of the Tencent cloud and smart industry group, said that the company’s Yangtze River AI Supercomputing Center with RMB 45 billion (approx. USD 7 billion) investment will soon commence operation.
A research team from Cross Compass Ltd, Massachusetts Institute of Technology, Tokyo Institute of Technology and University of Tokyo presents CLIPDraw, an algorithm that synthesizes drawings based on natural language input without the need for any training.
A research team from ByteDance AI Lab, University of Wisconsin–Madison and Nanjing University wins the ACL 2021 best paper award. Their proposed Vocabulary Learning via Optimal Transport (VOLT) approach leverages optimal transport to automatically find an optimal vocabulary without trial training.
A research team from Facebook AI and UC Berkeley finds a solution for vision transformers’ optimization instability problem by simply using a standard, lightweight convolutional stem for ViT models. The approach dramatically increases optimizer stability and improves peak performance without sacrificing computation efficiency.
On July 4, according to notice mandated by China’s Cyberspace Administration (CAC), ride-hailing giant “Didi Chuxing” app has serious violations of laws and regulations with its collecting and use of users’ personal information.
A recent paper proposes A New Hope (ANH), a set of novel blockchain platforms designed to enable the integration of large-scale deep neural networks (DNNs) into smart contracts.
A research team from Microsoft Research Asia, University of Science and Technology of China, Huazhong University of Science and Technology, and Tsinghua University takes advantage of the inherent spatiotemporal locality of videos to present a pure-transformer backbone architecture for video recognition that leads to a better speed-accuracy trade-off.
A research team from University of Cambridge, Imperial College London & Twitter, UCLA, MPI-MIS, and SJTU & UNSW proposes CW Networks (CWNs), a message-passing scheme that operates on regular cell complexes and achieves stronger expressive power than graph neural networks (GNNs).
On June 28, Shenzhen promulgated the draft of Regulations on the Promotion of Artificial Intelligence Industry of Shenzhen Special Economic Zone, which seeks to establish an overarching framework for AI, such as the approval framework for market entrance of AI products and services.
According to media reports on June 28, South Korea is investing KRW 1.1 trillion (USD 1 billion) on L4-level self-driving, involving the government’s science, transport and police units. The government task force also announced 53 projects for commercializing L4 and high levels of autonomous driving by 2027.
According to a securities report released on June 24, Toyota CEO Akio Toyoda is personally investing USD 45.3 million into Woven Planet Holdings, the smart city unit of his company spearheading autonomous driving research.
A research team from DeepMind and Amii extends the emphatic method to multi-step deep reinforcement learning (RL) targets, and demonstrates that combining emphatic trace with deep neural networks can improve performance on classic Atari video games.