When I tell people that I work at an AI company, they often follow up with "So what kind of machine learning/deep learning do you do?" This isn't surprising, as most of the market attention (and hype) in and around AI has been centered around Machine Learning, and its high profile subset, Deep Learning, and around Natural Language Processing, with the rise of the chatbot and virtual assistants. But while machine learning is a core component for artificial intelligence, AI is in fact more than just ML. So what does it really mean for an application to be "intelligent"? What does it take to create a system that is "artificially intelligent?

Glaucoma is the second leading cause of blindness all over the world, with approximately 60 million cases reported worldwide in 2010. If undiagnosed in time, glaucoma causes irreversible damage to the optic nerve leading to blindness. The optic nerve head examination, which involves measurement of cup-to-disc ratio, is considered one of the most valuable methods of structural diagnosis of the disease. Estimation of cup-to-disc ratio requires segmentation of optic disc and optic cup on eye fundus images and can be performed by modern computer vision algorithms. This work presents universal approach for automatic optic disc and cup segmentation, which is based on deep learning, namely, modification of U-Net convolutional neural network. Our experiments include comparison with the best known methods on publicly available databases DRIONS-DB, RIM-ONE v.3, DRISHTI-GS. For both optic disc and cup segmentation, our method achieves quality comparable to current state-of-the-art methods, outperforming them in terms of the prediction time.

This paper describes a new entropy-style of equation that may be useful in a general sense, but can be applied to a cognitive model with related processes. The model is based on the human brain, with automatic and distributed pattern activity. Methods for carrying out the different processes are suggested. The main purpose of this paper is to reaffirm earlier research on different knowledge-based and experience-based clustering techniques. The overall architecture has stayed essentially the same and so it is the localised processes or smaller details that have been updated. For example, a counting mechanism is used slightly differently, to measure a level of 'cohesion' instead of a 'correct' classification, over pattern instances. The introduction of features has further enhanced the architecture and the new entropy-style equation is proposed. While an earlier paper defined three levels of functional requirement, this paper re-defines the levels in a more human vernacular, with higher-level goals described in terms of action-result pairs.

This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. This lecture series provides a thorough introduction to the cutting-edge research in deep learning appli... more

Apple has been put on notice by Samsung's big Bixby virtual assistant in the Galaxy S8. But the iPhone maker apparently has some big changes of its own up its sleeve. Apple has filed for a patent on a deep learning technology that would turn Siri into an exceedingly powerful tool that could digitally "see" and deliver better assistance to users. The patent, which was earlier discovered by Patently Apple, describes how a device could use sensor to constantly analyze what's going on around the hardware. From there, it responds with something it believes, you'll want it to do.

One of required skills as an Artificial Intelligence engineer is ability to understand and explain highly technical research papers in this field. One of my projects as a student in AI Nanodegree classes is an analysis of seminal paper in the field of Game-Playing. The target of my analysis was Nature's paper about technical side of AlphaGo -- Google Deepmind system which for the first time in history beat elite professional Go player, winning by 5 games to 0 with European Go champion -- Fan Hui. The goal of this summary (and my future publications) is to make this knowledge widely understandable, especially for those who are just starting the journey in field of AI or those who doesn't have any experience in this area at all. AlphaGo is narrow AI created by Google DeepMind team to play (and win) board game Go. Before it was presented publicly, the predictions said that according to our state-of-art, we are about 1 decade away from having system with AlphaGo skills (capability to beat a human professional Go player).

Publicly funded and private technology ventures are tensing in anticipation of harsh budget cuts to research and H-1B visa restrictions. The Canadian government, joined by more than two dozen private institutions such as Google and Air Canada, will invest more than $150 million to create an AI research center within the University of Toronto, named the Vector Institute. Geoffrey Hinton, perhaps Canada's most famous AI researcher and co-author of the 1986 Nature paper that made modern deep learning possible, will serve as chief scientist for the center. Canada will contribute $50 million over five years, part of the $125 million Pan-Canadian Artificial Intelligence Strategy included in the country's budget that passed last week. Big tech names like Google and Nvidia are each contributing $5 million per year, while the Chan Zuckerberg Initiative (not Facebook itself) is committed to $20,000 annually.

WeightedRandomSampler has been added as a custom sampler for the DataLoader. It samples elements from [0,..,len(weights)-1] with the given probabilities and is useful to sample from unbalanced datasets where some classes have many more samples than others. WeightedRandomSampler has been added as a custom sampler for the DataLoader. It samples elements from [0,..,len(weights)-1] with the given probabilities and is useful to sample from unbalanced datasets where some classes have many more samples than others.

After all, it's been a popular focus in movies such as The Terminator, The Matrix, and Ex Machina (a personal favorite of mine). But you may have recently been hearing about other terms like "Machine Learning" and "Deep Learning," sometimes used interchangeably with artificial intelligence. I'll begin by giving a quick explanation of what Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) actually mean and how they're different. Then, I'll share how AI and the Internet of Things are inextricably intertwined, with several technological advances all converging at once to set the foundation for an AI and IoT explosion. First coined in 1956 by John McCarthy, AI involves machines that can perform tasks that are characteristic of human intelligence.

AI has forever changed digital advertising. As marketers, it already allows us to decide how to best engage potential customers and markets like never before. But there's room to grow. Deep learning tools are the next major area of AI-based research, and it will spark a wave of future innovation in every industry. Our interfaces have already adapted to fit a user's interest on a personal level, matching industry insights, behaviours, etc. with display ads – or personalization.