Voters have a right to keep their political beliefs private. But according to some researchers, it won't be long before a computer program can accurately guess whether people are liberal or conservative in an instant. All that will be needed are photos of their faces. Michal Kosinski – the Stanford University professor who went viral last week for research suggesting that artificial intelligence (AI) can detect whether people are gay or straight based on photos – said sexual orientation was just one of many characteristics that algorithms would be able to predict through facial recognition. Using photos, AI will be able to identify people's political views, whether they have high IQs, whether they are predisposed to criminal behavior, whether they have specific personality traits and many other private, personal details that could carry huge social consequences, he said.

TORONTO and NEW YORK, Oct. 20, 2017 /CNW/ – AcuityAds Holdings Inc. (TSXV:AT) ("AcuityAds" or "Company"), a technology leader that provides targeted digital media solutions enabling advertisers to connect intelligently with audiences across video, mobile, social and online display campaigns, today announced that the Company's Chief Technology Officer, Dr. Nathan Mekuz, will be presenting at the StableView Asset Management TECH17 Conference on Thursday, October 26, 2017 in Toronto, Canada. Dr. Mekuz will be delivering a brief presentation on the topic of how organizations are powering business innovation with Artificial Intelligence (AI). The StableView TECH17 conference is being held at the Arcadian Loft, located at 401 Bay Street, Simpson Tower, 8th Floor with AcuityAds presenting at 9:15 am. As the only buy-side curated tech conference in Canada, StableView TECH17 sits at the junction of the public and private investing tech ecosystem in Canada and brings together participants that typically stay in their individual silos: institutional investors, pensions, VCs, broker-dealers, advisors, HNW investors and family offices with private and public tech companies in Canada. On October 26th, 2017, approximately 25 of Canada's leading technology companies will be presenting to an expected audience of over 450 professionals representing approximately $350B in AUM.

This paper describes a novel text-to-speech (TTS) technique based on deep convolutional neural networks (CNN), without any recurrent units. Recurrent neural network (RNN) has been a standard technique to model sequential data recently, and this technique has been used in some cutting-edge neural TTS techniques. However, training RNN component often requires a very powerful computer, or very long time typically several days or weeks. Recent other studies, on the other hand, have shown that CNN-based sequence synthesis can be much faster than RNN-based techniques, because of high parallelizability. The objective of this paper is to show an alternative neural TTS system, based only on CNN, that can alleviate these economic costs of training. In our experiment, the proposed Deep Convolutional TTS can be sufficiently trained only in a night (15 hours), using an ordinary gaming PC equipped with two GPUs, while the quality of the synthesized speech was almost acceptable.

In his session at 21st Cloud Expo, James Henry, Co-CEO/CTO of Calgary Scientific Inc., will introduce you to the challenges, solutions and benefits of training AI systems to solve visual problems with an emphasis on improving AIs with continuous training in the field. He will explore applications in several industries and discuss technologies that allow the deployment of advanced visualization solutions to the cloud. Speaker Bio James Henry is Co-CEO/CTO of Calgary Scientific Inc., a company specializing in bringing real time interactive software to cloud and mobile platforms. He has 25 years of experience leading software teams in many industries including the oil and gas, healthcare, telecommunication, geolocation, construction and simulation industries. His current interest is in enabling people, data and AIs to interact in real time to solve complex problems.

WAI," short for "Where Am I." A well-educated 29-year-old man without any history of disease or trauma, it took him four tries to produce a semi-accurate map of the house he had lived in for 15 years.1 Another patient, Jennifer, from San Francisco, always feels like she is facing north, regardless of which direction she is actually facing. Judy Bentley had her memory of her physical surroundings suddenly vanish one day in high school. She suddenly had no idea what was beyond the classroom door. These are just some of the subjects that have been identified by a field that was kicked off with what might be called patient one, whom we'll call Alice.2 In 2007, Alice approached the neuroscientist Giuseppe Iaria with a peculiar and vexing problem: She had extraordinary difficulty finding her way around. Sometimes she would even get lost in her own house. She had to rely on standardized routes, going from door to door along a carefully memorized path. To get to work she knew when to get off ...

Optimization algorithms for training deep models not only affects the convergence rate and stability of the training process, but are also highly related to the generalization performance of the models. While adaptive algorithms, such as Adam and RMSprop, have shown better optimization performance than stochastic gradient descent (SGD) in many scenarios, they often lead to worse generalization performance than SGD, when used for training deep neural networks (DNNs). In this work, we identify two problems of Adam that may degrade the generalization performance. As a solution, we propose the normalized direction-preserving Adam (ND-Adam) algorithm, which combines the best of both worlds, i.e., the good optimization performance of Adam, and the good generalization performance of SGD. In addition, we further improve the generalization performance in classification tasks, by using batch-normalized softmax. This study suggests the need for more precise control over the training process of DNNs.

Voters have a right to keep their political beliefs private. But according to some researchers, it won't be long before a computer program can accurately guess whether people are liberal or conservative in an instant. All that will be needed are photos of their faces. Michal Kosinski – the Stanford University professor who went viral last week for research suggesting that artificial intelligence (AI) can detect whether people are gay or straight based on photos – said sexual orientation was just one of many characteristics that algorithms would be able to predict through facial recognition. Using photos, AI will be able to identify people's political views, whether they have high IQs, whether they are predisposed to criminal behavior, whether they have specific personality traits and many other private, personal details that could carry huge social consequences, he said.

DeepMind Alberta will turbo-charge the research ecosystem, mirroring the partnerships DeepMind has nurtured with the top academic institutions in the UK,

Just one look at your smartphone is all it takes to remind you that digitization and automation in financial services is nothing new. But the recent heightened interest in artificial intelligence (AI) and banking is. "The explosive growth of structured and unstructured data, availability of new technologies such as cloud computing and machine learning algorithms, rising pressures brought by new competition, increased regulation and heightened consumer expectations"--all of these factors, he says--"have created a'perfect storm' for the expanded use of artificial intelligence in financial services." For many financial industry leaders, understanding how AI can be incorporated into their business operations can be a storm in and of itself. With this in mind, we reached out to industry thought leaders in advance of October's BAI Beacon financial services conference, where AI is going to be a huge topic of conversation in a slate of Innovation & FinTech sessions, and asked: "What can banks and financial institutions (FIs) do right now to get started with AI in their business?"

At a Calgary branch of ATB Financial, one of the bank's latest recruits educates customers about financial literacy, plays music, challenges them to an impromptu dance-off and, naturally, takes a selfie. A four-part look at how robots are changing the way we work. Pepper, the new hire, doesn't have the most sophisticated skill set at this point -- for one thing, she can't make financial transactions -- but she's made a big leap forward in becoming the first customer service robot in Canadian banking. At some point, Pepper, developed by SoftBank Robotics Corp., could be programmed to do more complicated tasks. But for now, Edmonton-based ATB Financial is more interested in gauging how people react and figure out which customer situations the robot best fits in.