While recent neural encoder-decoder models have shown great promise in modeling open-domain conversations, they often generate dull and generic responses. Unlike past work that has focused on diversifying the output of the decoder at word-level to alleviate this problem, we present a novel framework based on conditional variational autoencoders that captures the discourse-level diversity in the encoder. Our model uses latent variables to learn a distribution over potential conversational intents and generates diverse responses using only greedy decoders. We have further developed a novel variant that is integrated with linguistic prior knowledge for better performance. Finally, the training procedure is improved by introducing a bag-of-word loss. Our proposed models have been validated to generate significantly more diverse responses than baseline approaches and exhibit competence in discourse-level decision-making.

It makes a certain kind of sense that the game's connoisseurs might have wondered if they'd seen glimpses of the occult in those three so-called ghost moves. Unlike something like tic-tac-toe, which is straightforward enough that the optimal strategy is always clear-cut, Go is so complex that new, unfamiliar strategies can feel astonishing, revolutionary, or even uncanny. Unfortunately for ghosts, now it's computers that are revealing these goosebump-inducing moves. As many will remember, AlphaGo--a program that used machine learning to master Go--decimated world champion Ke Jie earlier this year. Then, the program's creators at Google's DeepMind let the program continue to train by playing millions of games against itself.

Finally! Adobe has devised a method of adding a touch of color to black and white images without all the dimension-jumping time travel (looking at you Pleasantville). At the company's Adobe MAX 2017 event on Thursday, research scientist Jingwan Lu demonstrated Project Scribbler, an AI-driven program that can not only add color but also shading and image texture to grey-scale pictures in just seconds. Scribbler leverages Adobe's Sensei deep learning platform to automatically touch up images. Researchers trained the program on the various bits and pieces of the human face using tens of thousands of images, some monochromatic, others accurately colored. By comparing the two types of images, the program was able to work out the appropriate areas to color in (ie, not the teeth).

Last week, for the first time ever, RE•WORK brought together the'Godfathers of AI' to appear not only at the same event, but on a joint panel discussion. At the Deep Learning Summit in Montreal last week, we saw Yoshua Bengio, Yann LeCunand Geoffrey Hinton come together to share their most cutting edge research progressions as well as discussing the landscape of AI and the deep learning ecosystem in Canada. Joelle Pineau from McGill University who was moderating the discussion began by asking each pioneer to introduce their neighbour, which immediately generated a laugh from the packed auditorium. Yoshua kicked off by saying'here's Yann, I met him doing my masters and he was doing his post doc with Geoff and later Yann invited me to come and work with him and start working in convolutional neural networks, and it's still the hot thing today!' Yann went on to introduce Geoffrey and said'I'll be historical as well, when I was an undergrad I studied neural nets and realised there was no research published in the 70s. I saw a paper entitled Optimal Perceptual Inference and Geoff was one of the three authors.

While computer scientists have been touting artificial intelligence (AI) for more than half a century, the technology is just starting to reveal its potential. In spite of the hype, machine learning, deep learning, computer vision and natural language processing have quietly become entrenched in many people's daily routines. These innovations have brought with them new abilities to automate tasks, analyze data and connect dots, according to the eMarketer's latest report, "Artificial Intelligence for Marketers 2018: Finding Value Beyond the Hype." Nonsubscribers can purchase the report here.) Without even realizing it, people have become accustomed to interacting with AI. "When you use Facebook or Google or Apple, you're using it," said Karim Sanjabi, executive director of cognitive solutions at independent media agency Crossmedia.

In Lecture 8 we discuss the use of different software packages for deep learning, focusing on TensorFlow and PyTorch. We also discuss some differences between CPUs and GPUs. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka "deep learning") approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification.

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) – these are the three trending buzzwords that have created a great hype over the Internet and other media platforms for some time now. Irrespective of whether people hold a sound knowledge of the data science or not, everyone is actively making their own statements explaining the differences between these technologies, which thereby creating a mysterious situation for the newbies and laymen to understand the true differences between them. To make the things easy, this article will initially explain "what AI, ML, and DL are?", and later discusses the key differences between them. The definition of AI as per Wikipedia is – "the intelligence demonstrated by the In simple words, Artificial Intelligence (AI) can be referred as the'skill for a machine to exhibit its intelligent behavior'. Machine Learning as found in the Wikipedia is "the sub-field of computer science that gives computers the skill to learn without being explicitly programmed". Machine learning in simple words can be stated as'the ability of a machine to learn and achieve intelligence'. According to Wikipedia, "Deep Learning is a subset of Machine Learning, Deep learning is one of the best machine learning techniques that resembles how the human brain works (neural networks).

Artificial intelligence, Machine Learning, and Deep Learning are more than futuristic concepts. These technologies are impacting the insurance industry in a significant way right now and this impact is likely to increase in the near future. The idea of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) may fascinate consumers who enjoy talking to their digital while admiring a Nest thermostat. But for the insurance industry, these terms are business-changers that affect products and services offered and interactions with consumers and other industry partners. The definitions of these terms may be a bit confusing to the uninitiated (see sidebar).

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When I hear colleagues talk at a high level about their models, it has a very different feeling to it than people talking about more classical models. People talk about things in lots of different ways, of course – there's lots of variance in how people see deep learning – but there's often an undercurrent that feels very similar to functional programming conversations. It feels like a new kind of programming altogether, a kind of differentiable functional programming. One writes a very rough functional program, with these flexible, learnable pieces, and defines the correct behavior of the program with lots of data. Then you apply gradient descent, or some other optimization algorithm. The result is a program capable of doing remarkable things that we have no idea how to create directly, like generating captions describing images. It's the natural intersection of functional programming and optimization, and I think it's beautiful. I find this idea really beautiful. At the same time, this is a pretty strange article and I feel a bit weird posting it.