BOSSES are more likely to groan than feel giddy about advances in artificial intelligence (AI). They need a strategy, but few companies can hope to own a unit like Google's DeepMind, whose algorithms not only beat the world's best Go players but made a 40% improvement in the energy efficiency of its parent's data centres. A Canadian startup, Element AI, wants to let all businesses tap into the world's best AI minds. The brain behind the new firm is Yoshua Bengio, a pioneer in "deep learning", a branch of AI. As firms such as Google and Facebook lured dozens of AI academics, some in the field expressed fears about a brain drain from academia.

At Qure, we're building deep learning systems which help diagnose abnormalities from medical images. Most of the deep learning models are classification models which predict a probability of abnormality from a scan. However, just the probability score of the abnormality doesn't amount much to a radiologist if it's not accompanied by a visual interpretation of the model's decision. Interpretability of deep learning models is very much an active area of research and it becomes an even more crucial part of solutions in medical imaging. In this post, I'll be giving a brief overview of the different perturbation based techniques for deep learning based classification models and their drawbacks.

Abstract--We address the problem of assisting human dispatchers in operating power grids in today's changing context using machine learning, with the aim of increasing security and reducing costs. Power networks are highly regulated systems, which at all times must meet varying demands of electricity with a complex production system, including conventional power plants, less predictable renewable energies (such as wind or solar power), and the possibility of buying/selling electricity on the international market with more and more actors involved at a European scale. This problem is becoming ever more challenging in an aging network infrastructure. One of the primary goals of dispatchers is to protect equipment (e.g. Using years of historical data collected by the French Transmission Service Operator (TSO) "Réseau de Transport d'Electricité" (RTE), we develop novel machine learning techniques (drawing on "deep learning") to mimic human decisions to devise "remedial actions" to prevent any line to violate power flow limits (so-called "thermal limits"). The proposed technique is hybrid. It does not rely purely on machine learning: every action will be tested with actual simulators before being proposed to the dispatchers or implemented on the grid. Electricity is a commodity that consumers take for granted and, while governments relaying public opinion (rightfully) request that renewable energies be used increasingly, little is known about what this entails behind the scenes in additional complexity for the Transmission Service Operators (TSOs) to operate the power grid in security. Indeed, renewable energies such as wind and solar power are less predictable than conventional power sources (mainly thermal power plants).

Generative modeling, a typical unsupervised learning that makes use of huge amount of unlabeled data, lies in the heart of rapid development of modern machine learning techniques [1]. Different from discriminative tasks such as pattern recognition, the goal of generative modeling is to model the probability distribution of input data and thus be able to generate new samples according to the distribution. At the research frontier of generative modeling, it was used for finding good data representation and dealing with tasks with missing data. Popular generative machine learning models include the Boltzmann Machines (BM) [2, 3] and their generalizations [4], variational autoencoders (VAE) [5], autoregressive models [6, 7], nonlinear density estimations [8-10], and the generative adversarial networks (GAN) [11]. For generative model design, one tries to balance the representational power and efficiency of learning and sampling. There is a long history of relation between generative modeling and physics, especially statistical physics. Some celebrated models, such as Hopfield model [12], and Boltzmann machine [2, 3], are closely related to the Ising model in statistical physics, and its inverse version which learns couplings in the Ising model based on given training configurations [13, 14]. The task of generative modeling also shares many similarities with quantum physics research in the sense that both of them try to model probability distributions in an enormously large space. In the past decades, tensor network (TN) states and algorithms have been shown to be an incredibly potent tool set for studying many-body quantum physics with its power in expressing quantum states relevant to realistic situations [15, 16].

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Washington D.C. – In its most basic form, artificial intelligence is an algorithm that is trained to learn via the data that is fed to it. But what happens what that data is full of bias? "In traditional model building, even with good data we can introduce biases by not constructing the right variables or picking up nuances. A model is a representative of the mechanism that generated the data. So if we don't represent that mechanism correctly, then we are not forecasting correctly, but forecasting something else," explains Oliver Schabenberger, chief technology officer and executive vice president of SAS Institute Inc. to Canadian media at the Analytics Experience 2017.

Lots of tech companies including Apple, Google, Microsoft, NVIDIA and Intel itself have created chips for image recognition and other deep-learning chores. However, Intel is taking another tack as well with an experimental chip called "Loihi." Rather than relying on raw computing horsepower, it uses an old-school, as-yet-unproven type of "nueromorphic" tech that's modeled after the human brain. Intel has been exploring neuromorphic tech for awhile, and even designed a chip in 2012. Instead of logic gates, it uses "spiking neurons" as a fundamental computing unit.

This is a PyTorch version of fairseq, a sequence-to-sequence learning toolkit from Facebook AI Research. The original authors of this reimplementation are (in no particular order) Sergey Edunov, Myle Ott, and Sam Gross. The toolkit implements the fully convolutional model described in Convolutional Sequence to Sequence Learning and features multi-GPU training on a single machine as well as fast beam search generation on both CPU and GPU. We provide pre-trained models for English to French and English to German translation.

AI/BI/CI/DI: Decision intelligence (DI) solves the world's most complex problems. It connects human decision makers to technologies like machine learning, AI, deep learning, visual decision modeling, complex systems modeling, big data, predictive analytics, UX design, statistical analysis, business intelligence, business process management, causal reasoning, evidence-based analysis, and more. For an overview, see the webinar at http://youtu.be/XRTJt3bVCaE, Also, my company offers DI and machine learning consulting services. See http://bit.ly/1X8O2zF to learn more.

This video shows a general review of the analytics capabilities of the KNIME Analytics Platform. Here we only mention: Random Forest, Deep Learning, Gradient Boosted Trees, Bagging and Boosting for ensemble methods, Decision Trees, Neural Networks, Logistic Regression, how to build your own ensemble model, and external integrations as Weka, H2O, R, and Python. This is what we show here, which for time reasons, is of course incomplete. Download and install KNIME Analytics Platform (https://www.knime.com/downloads) to explore the constantly growing set of machine learning and statistics algorithms available to analyze your data.