Deep learning is a field with intense computational requirements and the choice of your GPU will fundamentally determine your deep learning experience. With no GPU this might look like months of waiting for an experiment to finish, or running an experiment for a day or more only to see that the chosen parameters were off. With a good, solid GPU, one can quickly iterate over deep learning networks, and run experiments in days instead of months, hours instead of days, minutes instead of hours. So making the right choice when it comes to buying a GPU is critical. So how do you select the GPU which is right for you? This blog post will delve into that question and will lend you advice which will help you to make choice that is right for you. TL;DR Having a fast GPU is a very important aspect when one begins to learn deep learning as this allows for rapid gain in practical experience which is key to building the expertise with which you will be able to apply deep learning to new problems.

So to retain and attract top academic talent, and to increase the number of post-graduate trainees and researchers studying artificial intelligence and deep learning, our latest budget proposes to provide $125 million to launch a Pan-Canadian Artificial Intelligence Strategy for research and talent. The Strategy will promote collaboration between Canada's main centres of expertise in Montréal, Toronto-Waterloo and Edmonton and position Canada as a world-leading destination for companies seeking to invest in artificial intelligence and innovation. A leader in the area of artificial intelligence, the Canadian Institute for Advanced Research will be responsible for administering the funding for the new Strategy.

Artificial intelligence has reached peak hype. News outlets report that companies have replaced workers with IBM Watson and algorithms are beating doctors at diagnoses. New A.I. startups pop up every day and claim to solve all your personal and business problems with machine learning. Ordinary objects like juicers and wifi routers suddenly advertise themselves as "powered by AI". Not only can smart standing desks remember your height settings, they can also order you lunch.

Our system beats other approaches on Stanford Sentiment Treebank while using dramatically less data. The number of labeled examples it takes two variants of our model (the green and blue lines) to match fully supervised approaches, each trained with 6,920 examples (the dashed gray lines). Our L1-regularized model (pretrained in an unsupervised fashion on Amazon reviews) matches multichannel CNN performance with only 11 labeled examples, and state-of-the-art CT-LSTM Ensembles with 232 examples. We were very surprised that our model learned an interpretable feature, and that simply predicting the next character in Amazon reviews resulted in discovering the concept of sentiment. We believe the phenomenon is not specific to our model, but is instead a general property of certain large neural networks that are trained to predict the next step or dimension in their inputs.

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Google's DeepMind announced today that it was open sourcing Sonnet, its object-oriented neural network library. Sonnet doesn't replace TensorFlow, it's simply a higher-level library that meshes well with DeepMind's internal best-practices for research. Specifically, DeepMind says in its blog post that the library is optimized to make it easier to switch between different models when conducting experiments so that engineers don't have to upend their entire projects. To this avail, the team made changes to TensorFlow to make it easier to consider models as hierarchies. DeepMind also added transparency to variable sharing. It's in DeepMind's own interest to open source Sonnet.

Abstract: "Deep learning methods have provided us with remarkably powerful, flexible, and robust solutions in a wide range of passive perception areas: computer vision, speech recognition, and natural language processing. However, active decision making domains such as robotic control present a number of additional challenges, standard supervised learning methods do not extend readily to robotic decision making, where supervision is difficult to obtain. In this talk, I will discuss experimental results that hint at the potential of deep learning to transform robotic decision making and control, present a number of algorithms and models that can allow us to combine expressive, high-capacity deep models with reinforcement learning and optimal control, and describe some of our recent work on scaling up robotic learning through collective learning with multiple robots."

Artificial intelligence is gaining ground in healthcare. In 2012, there were fewer than 20 artificial intelligence startups focused on healthcare; last year there were almost 70, according to CB Insights. Additionally, the AI for healthcare sector is expected to drive overall AI market growth over the next six years, according to a MarketsandMarkets report. The overall AI market is expected to grow at a compound annual growth rate of 62.9 percent from 2016 to 2022, when it's projected to reach $16.6 billion. Here, six health IT company executives discuss how AI will impact healthcare over the next 20 years.

Here at The Next Platform, we tend to focus on deep learning as it relates to hardware and systems versus algorithmic innovation, but at times, it is useful to look at the co-evolution of both code and machines over time to see what might be around the next corner. One segment of the deep learning applications area that has generated a great deal of work is in speech recognition and translation--something we've described in detail via efforts from Baidu, Google, Tencent, among others. While the application itself is interesting, what is most notable is how codes and systems have shifted to meet the needs of new ways of thinking about some of the hardest machine learning problems. And when we stretch back to the underpinnings of machine translation and speech recognition, IBM has some of the longest history--even if that history doesn't have a true deep learning element in relatively recently. In his 36 years at IBM focusing on speech and language algorithms, Michael Picheny, senior manager for IBM's Watson Multimodal division (an area that focuses on language and image recognition, among other areas), much has changed for both code and the systems required to push speech recognition.

A type of machine learning that works well with small data sets holds promise for drug discovery and development. This methodology could be a useful tool for other areas of chemical research. One-shot learning, a kind of deep learning, differs from other machine-learning approaches in the amount of Vijay Pande. Credit: L.A. Cicerodata required to arrive at a solution. Most applications of machine learning, like image recognition, rely on training a set of algorithms with thousands to trillions of data points. One-shot learning can succeed with hundreds of data points.