AI Technology & Industry Review
Quicktron completed a new C+ financing round led by KION Group and Prosperity7 Ventures, and proceeds will be used for global market expansion and further technical development.
Synced invited Dr. Linchao Zhu, a lecturer at the ReLER lab, University of Technology Sydney whose works focus on video representation learning, to share his thoughts on the paper Text-to-Image Generation Grounded by Fine-Grained User Attention.
Synced has invited Mr. Brian Tse to share his insights about the current development and future trends of artificial intelligence.
Synced has invited Mr. Tatsuya Nagata, the COO of Navier Inc., to share his insights about the current development and future trends of artificial intelligence.
Synced has invited Prof. Johan A.K. Suykens to share his insights about the current development and future trends of artificial intelligence.
Synced has invited Mr. Sheldon Fernandez to share his insights about the current development and future trends of artificial intelligence.
Synced has invited Dr. Viral B. Shah to share his insights about the current development and future trends of artificial intelligence.
As part of the Women in AI special project, Synced spoke with Chelsea Finn, an assistant professor in Computer Science and Electrical Engineering at Stanford University.
In an exclusive interview with Synced, the 56-year-old professor shared his thoughts on humanoids, human nature, human-robot interaction, knowledge and intelligence.
Researchers have proposed a new adversarial attack approach, SemanticAdv, which generates adversarial perturbations by manipulating not pixels but rather an image’s semantic attributes.
Recently, Facebook AI Research (FAIR) researchers introduced a structured memory layer which can be easily integrated into a neural network to greatly expand network capacity and the number of parameters without significantly changing calculation cost.
Last month, Harvard Microrobotics Lab researchers introduced RoboBee X-Wing, a solar-powered micro-aerial vehicle that fits in the palm of your hand.
On the occasion of 2019 International Conference on Machine Learning last week in Long Beach, California, Synced spoke with Dr. Bengio on the Turing Award, ICML, global AI trends, etc.
Deep learning model performance has taken huge strides, allowing researchers to tackle tasks which were simply not possible for machines less than a decade ago.
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.
Microsoft Research Asia (MSRA) has been dubbed the “Whampoa Academy for AI” in reference the elite Chinese military school. MSRA is a bootcamp for NLP research and has trained more than 500 interns, 20 PhDs and 20 postdocs over the past two decades.
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.
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.
Neural networks for image recognition have matured from simple chain-like models to structures with multiple wiring paths. The emergence of Neural Architecture Search (NAS) can optimize models with more elaborate wiring and operation types.
In a paper recently published in Nature, IBM researchers propose using two quantum algorithms based on superconducting processors to provide a novel solution to classification problems.
DeepMind’s Research Platform Team has open-sourced TF-Replicator, a framework that enables researchers without previous experience with the distributed system to deploy their TensorFlow models on GPUs and Cloud TPUs. The move aims to strengthen AI research and development.
Model-free reinforcement learning can be used to learn effective strategies for complex tasks such as Atari games, but it usually requires a large amount of interaction, which adds significant time and cost.
Natural language processing has made significant progress in the past year, but few frameworks focus directly on NLP or sequence modeling. Google Brain recently released Lingvo, a deep learning framework based on TensorFlow. Synced invited Ni Lao, Chief Science Officer at Mosaix, to share his thoughts on Lingvo.
Machine learning models based on deep neural networks have achieved unprecedented performance on many tasks. These models are generally considered to be complex systems and difficult to analyze theoretically. Also, since it’s usually a high-dimensional non-convex loss surface which governs the optimization process, it is very challenging to describe the gradient-based dynamics of these models during training.
Last Monday US President Donald Trump signed the “American AI Initiative,” an executive order designed to spur US investment in artificial intelligence and boost the domestic AI industry. The initiative has five highlights: Investing in AI Research and Development (R&D), Unleashing AI Resources, Setting AI Governance Standards, Building the AI Workforce, International Engagement and Protecting our AI Advantage.
Synced spoke with AI pioneer Professor Yoshua Bengio at the Computing in the 21st Century Conference in Beijing, where he discussed his recent research and the current state of AI.
Synced is proud to present Gary Marcus as the last installment in our Lunar New Year Project — a series of interviews with AI experts reflecting on AI development in 2018 and looking ahead to 2019. (Read the previous articles on Clarifai CEO Matt Zeiler and Google Brain Researcher Quoc Le.)
The Synced Lunar New Year Project is a series of interviews with AI experts reflecting on AI development in 2018 and looking ahead to 2019. In this second installment (click here to read the previous article on Clarifai CEO Matt Zeiler), Synced speaks with Google Brain Researcher Quoc Le on his latest invention, AutoML, Google Brain’s pursuit of AI, and the secret of transforming lab technologies into real practices.
Uber AI Lab has created a buzz in the machine learning community with the publication of a paper introducing a new reinforcement learning algorithm called Go-Explore. The algorithm is designed to overcome the challenges of intelligence exploration in reinforcement learning to improve performance on hard-exploration tasks.
This is the first installment of the Synced Lunar New Year Project, a series of interviews with AI experts reflecting on AI development in 2018 and looking ahead to 2019. In this article, Synced chats with Clarifai Founder and CEO Matt Zeiler on recent progress in computer vision and his company’s plans for the future. Founded in New York in 2013, Clarifai produces advanced image recognition systems.
A cooperative research group from Facebook and the University of Southern California has introduced an alternative evaluation task for visual-grounding systems: BISON (Binary Image Selection).
To welcome 2019 with some fresh AI insights, Synced posed three questions to forty researchers and experts at last month’s NeurIPS 2018 conference in Montréal, Canada.
In an exclusive interview with Synced at NeurIPS, members of the University of Toronto and Vector Institute team led by Assistant Professor David Duvenaud discussed their winning submission Neural Ordinary Differential Equations — a math-based approach to designing deep learning models that is stimulating discussion across the machine learning community.
A founding member of Google Brain and the mind behind AutoML, Quoc Le is an AI natural: he loves machine learning and loves automating things. Le used millions of YouTube thumbnails to develop an unsupervised learning system that recognized cats when he was a Stanford University PhD in 2011.
As Chinese Internet giant Baidu has expanded from search to mobile apps, cloud services, and emerging business sectors like autonomous driving and voice assistants, it has correspondingly beefed up its research efforts, particularly in AI, to keep pace with growing security threats.
Robert S. Warren, MD is a Professor of Surgery and a specialist in gastrointestinal and liver cancer. Dr. Warren joined UCSF Medical Center in 1988. Highly respected by his peers, Dr. Warren was named to the list of U.S. News “America’s Top Doctors,” a distinction reserved for the top 1% of physicians in the nation for a given specialty.
MORE Health is a Silicon Valley-based company that provides access to top international physicians for patients faced with critical illnesses such as cancer or heart disease. The company was founded in 2013, and recently took a leap forward by partnering with Houston-based Melax Technologies…
A performance boost of less than one percent may not seem like much to most people, but for Microsoft Global Technical Fellow and Chief Speech Scientist Xuedong Huang, it’s cause for celebration.
While a Computer Science PhD candidate at the University of Washington, Hao focused on intelligent human-computer interaction research, and R&D and evaluation of intelligent systems and their tools.
Liulishuo is the AI English teacher on your phone. You don’t need to know how it works, yet it helps you learn English more efficiently than a human teacher,” says Yi Wang, Founder and CEO of Liulishuo — a Beijing-based “AI + language” company…