AI Technology & Industry Review
Technical review of the newest machine intelligence research.
At the 10th Google I/O, held May 17-19 at the Shoreline Amphitheatre in Mountain View, California, Google took a different approach from unveiling exciting new products, by putting its focus on the convergence of existing products, aimed at providing a better user experience.
TensorFlow is one of the most popular open source AI libraries. Its high in computing efficiency, and the rich development resources make it widely adopted by companies and individual developers.
The main advantage of the acGAN is the “Identity-Preserving” latent vector optimization approach to maintain the original person’s identity in reconstruction.
Personal computers and mobile devices are in their heyday. Researchers are swarming standalone AI, focusing on how to automate self-learning intelligent systems. The interfaces for wearables meanwhile are evolving from smart screens to gesture commands, like those often seen in AR and VR commercials.
Nowadays, the use of agricultural robots are steadily rising, like the AgBot II, which helps detect and classify weeds, and Harvey, used for detecting and segmenting crops.
Thanks to AlphaGo, the term “Artificial Intelligence” (AI) came under the spotlight (again). However, Jean-Christophe Baillie doesn’t agree that AlphaGo is AI, for the reason that it’s not able to get us to full AI – an Artificial General Intelligence (AGI).
This is a comprehensive guide on how to calculate and visualize the receptive field information of a convolutional neural network.
Researchers introduce an automated synaptic connectivity inference pipeline (SyConn) to delivers a richly annotated wiring diagram, or components of a connectome.
RECOS model supply us with a signal analysis perspective of Convolution Neural Network. From this perspective, we can see the effectiveness of activation and deep architectures.
This paper proposes a general approach to achieve “Image-to-Image Translation” by using deep convolutional and conditional generative adversarial networks (GANs).
Authors proposed a general framework for learning a span of skills in a pre-training environment, which can be used in downstream tasks by training a high-level policy on top of the skills.
This work proposes a novel neural-evolution algorithm for large scale search.
The article discusses about the limitation of using existing GPU to train neural network and how one can improve to train larger model.
Dr. Eisaku Maeda, the director of NTT Communication Science Laboratories, was recently interviewed by The Huffington Post, sharing his opinions on AI.
In the spirit of April (Fools), let us see how far down is AI along the path of “fooling” humans.
“Why did I go to such extremes? Because I realized that the future of computing was as much about communications between people wearing computers as it was about preforming colossal calculations.” — Steve Mann
This paper proposed a computationally efficient convolutional layer to upscale the final low-resolution feature map to a high-resolution output.
In this article we will provide some insights of Intel’s recent deep learning products.
This paper proposes an adaptive learning rate algorithm, which utilizes stochastic curvature information of the loss function for automatically tuning the learning rates.
This review will introduce and compare the three commonly used machine learning frameworks: Matlab Neural Network Toolbox, Torch and Tensorflow.
Andrew McCallum explained how the use of Universal schema can improve knowledge representation and reasoning from natural language.
Neuromorphic computing, a promising direction for future of computing that emulates human brain to maintain energy efficiency and fast computing speed.
Researchers from Alibaba and UCL have recently published a new study, claiming that multiple game units controlled by artificial intelligence are now able to develop tactics similar to human players using unsupervised learning.
Synced had the opportunity for an exclusive interview with Tecent AI Lab’s senior manager, team lead for Fine Art, Liu Yongsheng.
Canada unveiled a budget of $125 million to launch Pan-Canadian Al Strategy, a new independent institute for AI, Vector Institute.
Google released a method for recovering pictures from low resolution to high resolution using pixel recursive super resolution model.
Compared to simplifying or approximating the solution of a complicated equation, deep learning provides a highly flexible model and might serve as a perfect walk-around method.
David Aslan uses neural network “Deep Style” to transform original photos or paintings into images with other styles.
PrOCTOR integrates chemical properties of a compound and its protein targets, to provide a measure about whether the drugs in clinical trials will fail for toxicity reasons.
The authors introduced a novel approach called deep meta-reinforcement learning (meta-RL) which can rapidly adapt to new tasks.
ArtGAN can generate images with abstract information like images with a certain art style.
This talk presents natural gradient, a new approach to approximate the Fisher matrix by adopting structured probabilistic models.
AI must be biased because the knowledge we used in the training process contains traces of our history, including our prejudices.
Between March 8-10, Synced was invited as media guest to attend the Google Cloud NEXT ’17 conference in San Francisco.
Graham Taylor from the University of Guelph gave a talk at the University of Toronto, summarizing current techniques used to address the issue of insufficient labeled data.
The team of Rob Fergus, who is currently a research scientist at Facebook AI Research, have devised two neural net models for handling unstructured data.
Summaries and recommendations of peer-reviewed papers that discuss various aspects of machine learning.
This talk describes the dialog system architecture and explains the three main steps of the architecture: understanding, generation, and dialog manager and their challenges for machine learning.
If thinking can be understood as the step-by-step process that it is, then we can build artificial intelligences to have the potential to be as conscious as we are
A glance at the state of the art research shows that neural networks would still serve us, and artificial general intelligence is not yet in sight.