AI Technology & Industry Review
Intel today opened its first-ever AI Developer Conference, AIDevCon, a two-day gathering at San Francisco’s prestigious Palace of Fine Arts. While day one was short on hot announcements, the company did detail some aggressive plans for AI.
According to an AWS blog post, Apache MXNet now supports Keras 2 and the Keras-MXNet deep learning backend is now available to developers.
Cambricon today unveiled its Cambricon 1M chip for edge computing, and the MLU100, the first in a new chip series for cloud computing.
While Poggio the teacher has taught some extraordinary leaders in AI, Poggio the scientist is renowned for his theory of deep learning, presented in papers with self-explanatory names: Theory of Deep Learning I, II and III.
The US FDA defines five steps for the development of a new drug: discovery and development, preclinical research, clinical research, FDA review, and FDA post-market safety monitoring.
Founded by two Chinese AI experts in 2012, the company is particularly competent in recognizing human faces and vehicles. Such capability appeals to China’s public security departments.
At last month’s RE•WORK Deep Learning in Finance Summit in London, leading AI industry practitioners and academics from prestigious universities discussed their research, provided insights on business trends and real-life AI applications, and addressed current challenges facing the AI industry as a whole.
China’s computer vision company SenseTime today announced it had raised a staggering US$600 million in Series C funding, setting a world record for an AI company and bringing its value to an estimated US$4.5 billion to make it the world’s most valued AI startup.
Huang and his NVIDIA team pioneered the graphics processing chip (GPU) in 1999, revolutionizing the visual performance of device displays. But even Huang never dreamt that his GPU would one day become a driving force in the arena of AI.
As Facebook struggles with fallout from the Cambridge Analytica scandal, its research arm today delivered a welcome bit of good news in deep learning. Research Engineer Dr. Yuxin Wu and Research Scientist Dr. Kaiming He proposed a new Group Normalization (GN) technique they say can accelerate deep neural network training with small batch sizes.
Now Google has introduced a surprising new use for retinal images: combined with artificial intelligence, they can also predict a patient’s risk of heart attack or stoke…
Chinese netizens are all ears for the company’s “hearty” AI-powered music recommendations. In an interview with Synced, NetEase Data Scientist Jia Xu and Product Manager Bowen Shen explained the NetEase system, which learns how to predict what songs will resonate with a user’s particular taste in music…
The GDPR mandates that all customers or users are entitled to receive “specific information” regarding the decision. This raises the issue of AI interpretability, especially in deep learning systems.
It took a mere 72 hours for deep learning researchers to ignite the first AI Twitter debate of 2018.
One of the top minds in artificial intelligence, Andrew Ng believes “AI is the new electricity.” However few industries canContinue Reading
Google’s Ali Rahimi put it bluntly in his NIPS presentation: “Machine learning has become alchemy.” The words triggered Facebook’s Yann LeCun to fight back.
NIPS has become the world’s leading conference on machine learning and computational neuroscience, with 8,000 attendees flocking to Long Beach, California this week for NIPS 2017, the most popular yet.
At last week’s Collaborative Robotics and Advanced Vision Conference in San Jose, John Petry from Cognex stressed the need for robotic technology companies to provide maintainable deep learning-based solutions for their manufacturing customers.
With so many sales in so little time, it’s no surprise that artificial intelligence is playing a growing role in the largest online shopping event in human history.
This glossary currently includes a total of 743 specialized AI terms based on machine learning concepts and terminologies.
The success AI has achieved in image recognition has prompted tech companies to explore the new field of video understanding, in which machines are trained to answer questions like “Who is in this video?” or “What are the cats in the video doing?”
Synced recently attended a Baidu meet-up at the company’s newly-opened Silicon Valley offices, where details were released on the latest iteration of the Apollo platform, Apollo 1.5.
In applying the adversarial training, this paper adopts distributed word representation, or word embedding, as the input, rather than the traditional one-hot representation. The reason lies in the fact that the higher dimensionality the input has, the more likely it is to be disturbed by noise.
Recently NASA announced that their Frontier Development Laboratory (FDL) would cooperate with Intel to apply deep learning for space exploration.
China’s biggest domestic mobile phone maker, Huawei, is now powering its phones with artificial intelligence (AI) engines. The Chinese techContinue Reading
The blog author, Rob Farber, has been working on machine learning and related fields as a staff scientist, and participating in relating projects since the 1980s.
In the article “Memory, attention, sequences”, the author predicts that future work on neural networks will emphasize understanding complex spatio-temporal data from the real world, which is highly contextual and noisy.
In this report, we will touch on some of the recent technologies, trends and studies on deep neural network inference acceleration and continuous training in the context of production systems.
We interviewed Mr. Qiang Dong, chief scientist of Beijing YuZhi Language Understanding Technology Co. Ltd. and learned more about their YuZhi NLU Platform that conducts its unique semantic analysis based on concepts rather than words.
Andrew Ng, one of the top minds in deep learning, loves teaching. And so, the Stanford University professor decided toContinue Reading
Although food packaging comes with nutrition labels, it’s still not convenient for people to reference. Thus, scientists started to use machine learning algorithms in computer vision to help people determine the caloric value in the food they eat.
In contrast to traditional approaches that use infrared or structured light along with the aforementioned hardware, SensePose needs only ambient light and a regular RGB camera lens.
We explore top-notch Swiss AI facilities: starting with deep learning and neural network research at IDSIA in Lugano, to interdisciplinary research at École Polytechnique Fédérale de Lausanne and University of Basel, and ending with robotics innovations at ETH in Zurich and University of Zurich.
Multimodal convolutional neural networks for appearance-based gaze estimation that significantly outperforms state-of-the-art methods in the most challenging cross-dataset evaluation
Google introduced a new algorithm: ‘Dynamic Batching’, and developed a Tensorflow-based library called ‘TensorFlow Fold’, which solved the DCGs problem in both theoretical and empirical fields.
Canada is determined to build AI superclusters in Toronto-Waterloo, Montréal, and Edmonton.
A tweet from Google DeepMind’s Nenad Tomasev, concerning a paper from the prestigious Montreal Institute for Learning Algorithms has sparked a well-known natural language processing expert Yoav Goldberg to tweet back: “…I really dislike this work.”
In 2015, Anh Nguyen published a paper in CVPR that identified a limit in computer vision, where you can fool a deep neural network (DNN) by changing an image in a way that’s imperceptible to humans, but can cause the DNN to label the image as something else entirely.
“Scientists need passion to do research, and that is my passion.” – Yuandong Tian, Facebook Artificial Intelligence Researcher
Tim Sweeney predicts that video game companies will ultimately utilize advanced AI techniques when the VR/AR games (or so-called “The Metaverse” games) become popular, due to advancements of cameras and displays