There's no question that AI is redefining processes across a whole spectrum of businesses. There is, however, a question of what that means for the overall economy. Canada is now investing in AI research with the expectation that it will benefit the country in general. DeepMind, the London-based leader in artificial intelligence owned by Google's parent-company Alphabet, is now reaching across the pond to Canada. On July 5, Demis Hassabis, co-founder and CEO, DeepMind announced "the opening of DeepMind's first ever international AI research office in Edmonton, Canada, in close collaboration with the University of Alberta."

Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network.

Over the past half decade, sensors capable of precisely measuring distances have dropped in price dramatically. RGB-D (RGB Distance) cameras such as the Microsoft Kinect are able to assign distances to individual pixels, and LIDAR (Light Detection and Ranging) scanners are more effective and affordable. A combination of these advances in hardware and research into SLAM (Simultaneous Localization and Mapping) have allowed robots and self driving cars to stitch together individual images into maps of their environment. Whereas 2D image based object detection and segmentation has seen plenty of advancement, the processing of point cloud data is still slightly lagging. This can be attributed partly to the ubiquity of 2D images and relative scarcity of point cloud data, but also partly to the convenient nature of RGB images, as spatial relationships between pixels are encoded in the structure of the image itself by the indices of pixels in the matrix. CNNs exploit this efficiently, as individual pixels can be matched with individual weights, resulting in a computationally cheap operation. In a point cloud however, individual points can exist in any location in the array, and spatial information is encoded explicitly alongside other information. A map generated from an RGB-D camera would consist of points that would each be structured as such: [X,Y,Z,R,G,B].

Syllabification does not seem to improve word-level RNN language modeling quality when compared to character-based segmentation. However, our best syllable-aware language model, achieving performance comparable to the competitive character-aware model, has 18%-33% fewer parameters and is trained 1.2-2.2 times faster.

Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128x128 resolution image samples exhibiting global coherence. We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. These analyses demonstrate that high resolution samples provide class information not present in low resolution samples. Across 1000 ImageNet classes, 128x128 samples are more than twice as discriminable as artificially resized 32x32 samples. In addition, 84.7% of the classes have samples exhibiting diversity comparable to real ImageNet data.

One of the key challenges of artificial intelligence is to learn models that are effective in the context of planning. In this document we introduce the predictron architecture. The predictron consists of a fully abstract model, represented by a Markov reward process, that can be rolled forward multiple "imagined" planning steps. Each forward pass of the predictron accumulates internal rewards and values over multiple planning depths. The predictron is trained end-to-end so as to make these accumulated values accurately approximate the true value function. We applied the predictron to procedurally generated random mazes and a simulator for the game of pool. The predictron yielded significantly more accurate predictions than conventional deep neural network architectures.

The fear of super-intelligent machines is as real as it gets for Tesla and SpaceX CEO and founder Elon Musk. He's spoken about it so many times, but perhaps not in the strongest terms as when he told U.S. governors that artificial intelligence (AI) poses "a fundamental risk to the existence of human civilization." The comment caught the attention of not just the governors present, but also AI researchers -- and they're not very happy about it. "While there needs to be an open discussion about the societal impacts of AI technology, much of Mr. Musk's oft-repeated concerns seem to focus on the rather far-fetched super-intelligence take-over scenarios," Arizona State University computer scientist Subbarao Kambhampati told Inverse. Musk's megaphone seems to be rather unnecessarily distorting the public debate, and that is quite unfortunate."

Originally posted to LinkedIn Pulse by Chris Bradford, Executive Vice President of Sales & Marketing at FiberLight, LLC. According to a report from Markets and Markets, the global Artificial Intelligence (AI) space is expected to surge to $16 billion over the next five years, growing at a CAGR of nearly 63 percent from 2016 to 2022. AI is the development of smart systems that can perform tasks which normally require human intelligence. Machine and deep learning are subsets of AI that mimic activities in neural networks of the brain where thinking occurs. Deep learning software can be programmed to recognize patterns in the digital representations of sounds, images and other data.

Machines are becoming more intelligent every year thanks to advances being made by companies like Google, Facebook, Microsoft, and many others. AI agents, as they're sometimes known, can already beat us at complex board games like Go, and they're becoming more competent in a range of other areas. Now a London artificial-intelligence research lab owned by Google has carried out a study to make sure that we can pull the plug on self-learning machines when we want to. DeepMind, bought by Google for a reported 400 million pounds -- about $580 million -- in 2014, teamed up with scientists at the University of Oxford to find a way to make sure that AI agents don't learn to prevent, or seek to prevent, humans from taking control. The paper -- "Safely Interruptible Agents PDF," published on the website of the Machine Intelligence Research Institute (MIRI) -- was written by Laurent Orseau, a research scientist at Google DeepMind, Stuart Armstrong at Oxford University's Future of Humanity Institute, and several others.

According to IDC, artificial intelligence (AI) is expected to become pervasive across customer journeys, supply networks, merchandizing, and marketing and commerce because it provides better insights to optimize retail execution. One thing is clear: new analytic technologies are expected to radically change analytics – and retail – as we know them. AI is defined broadly as the ability of computers to mimic human thinking and logic. Machine learning is a subset of AI that focuses on how computers can learn from data without being programmed through the use of algorithms that adapt to change; in other words, they can "learn" continuously in response to new data. We're seeing these breakthroughs now because of massive improvements in hardware (for example, GPUs and multicore processing) that can handle Big Data volumes and run deep learning algorithms needed to analyze and learn from the data. Ivano Ortis, vice president at IDC, recently shared with me how he believes, "Artificial intelligence will take analytics to the next level and will be the foundation for retail innovation, as reported by one out of every two retailers globally.