In a joint effort with the Perimeter Institute for Theoretical Physics and Alphabet (Google) X, Google AI researchers recently announced a new open source library, TensorNetwork, which can greatly improve the efficiency of tensor calculations for tensor network algorithms.
By 2050 almost one-in-four humans will be aged 60 years and older, double today’s share. Moreover, the number of people aged 80 years and older will quadruple. This demographic shift is opening new vistas for AI technologies in elders’ daily healthcare management, and as a useful tool for healthcare professionals and institutions treating seniors.
The world’s biggest technical professional organization, the Institute of Electrical and Electronics Engineers (IEEE) issued a statement on May 22 that forbids its colleagues from Huawei and 68 of its affiliates from reviewing or accessing non-public papers submitted by other persons for publications.
Imagine the lips forming the Mona Lisa’s famous smile were to part, and she began “speaking” to you. This is not some sci-fi fantasy or a 3D face animation, it’s an effect achieved by researchers from Samsung AI lab and Skolkovo Institute of Science and Technology, who used adversarial learning to generate a photorealistic talking head model.
A group of AI experts from top US universities is organizing a sample-efficient reinforcement learning competition, MineRL, which will start on June 1, 2019. The organizers want to increase group participation in reinforcement learning and are encouraging people to “play to benefit science”.
Google’s deep learning TensorFlow platform has added Differentiable Graphics Layers with TensorFlow Graphics, a combination of computer graphics and computer vision. Google says TensorFlow Graphics can solve data labeling challenges for complex 3D vision tasks by leveraging a self-supervised training approach.
Automated machine learning (AutoML) is a hot topic in artificial intelligence. Researchers from German digital and software company USU Software AG and the University of Stuttgart recently published a review paper summarizing the latest academic and industrial developments in AutoML.
Google has achieved a milestone in machine learning research that will boost the company’s broader ambitions in healthcare. In a paper published today in Nature Medicine, Google researchers present an end-to-end deep learning model that can predict lung cancer comparably or better than human radiologists.
The annual meeting of the Association for Computational Linguistics (ACL) is world-leading conference in the field of natural language processing, Yesterday, conference organizers sent out author notifications on accepted papers for the 57th ACL gathering, which will take place in Florence, Italy from July 28 to August 2.
n the new paper Adversarial Examples Are Not Bugs, They Are Features, a group of MIT researchers propose that adversarial examples’ effectiveness can be attributed to non-robustness: “Adversarial vulnerability is a direct result of our models’ sensitivity to well-generalizing features in the data.”
Current state-of-the-art convolutional architectures for object detection tasks are human-designed. In a recent paper, Google Brain researchers leveraged the advantages of Neural Architecture Search (NAS) to propose NAS-FPN, a new automatic search method for feature pyramid architecture.
Traditional methods used to estimate 3D structure and camera motion in videos rely heavily on manual assumptions such as continuity and planarity. Google researchers have now presented an alternative deep learning method which is able to obtain these assumptions from unlabelled video.
The Seventh International Conference on Learning Representations (ICLR) kicked off today. One of the world’s major machine learning conferences, ICLR this year received 1591 main conference paper submissions — up 60 percent over last year — and accepted 24 for oral presentations and 476 as poster presentations.
Over 6,000 developers and computer scientists poured into Seattle’s Washington State Convention Center this morning for Day 1 of Microsoft Build 2019, the company’s annual developer conference. Microsoft used the occasion to announce a new AI-driven collaboration and hybrid-cloud innovations across Microsoft 365 and Microsoft Azure.
Designing accurate and efficient CNNs for mobile devices is challenging due to the large design space and expensive computational methods. Although many mobile CNNs are available for developers to train and deploy to mobile devices, existing CNN architecture may not be able to achieve the best results for some tasks on mobile devices.
Google today announced the release of a new and improved landmark recognition dataset. Google-Landmarks-v2 includes over 5 million images, doubling the number in the landmark recognition dataset the tech giant released last year. The dataset now covers more than 200 thousand different landmarks, a seven times increase over the first version.