It is well established that neural networks with deep architectures perform better than shallow networks for many tasks in machine learning. In statistical physics, while there has been recent interest in representing physical data with generative modelling, the focus has been on shallow neural networks. A natural question to ask is whether deep neural networks hold any advantage over shallow networks in representing such data. We investigate this question by using unsupervised, generative graphical models to learn the probability distribution of a two-dimensional Ising system. Deep Boltzmann machines, deep belief networks, and deep restricted Boltzmann networks are trained on thermal spin configurations from this system, and compared to the shallow architecture of the restricted Boltzmann machine. We benchmark the models, focussing on the accuracy of generating energetic observables near the phase transition, where these quantities are most difficult to approximate. Interestingly, after training the generative networks, we observe that the accuracy essentially depends only on the number of neurons in the first hidden layer of the network, and not on other model details such as network depth or model type. This is evidence that shallow networks are more efficient than deep networks at representing physical probability distributions associated with Ising systems near criticality.

Deep learning techniques have been hugely successful for traditional supervised and unsupervised machine learning problems. In large part, these techniques solve continuous optimization problems. Recently however, discrete generative deep learning models have been successfully used to efficiently search high-dimensional discrete spaces. These methods work by representing discrete objects as sequences, for which powerful sequence-based deep models can be employed. Unfortunately, these techniques are significantly hindered by the fact that these generative models often produce invalid sequences. As a step towards solving this problem, we propose to learn a deep recurrent validator model. Given a partial sequence, our model learns the probability of that sequence occurring as the beginning of a full valid sequence. Thus this identifies valid versus invalid sequences and crucially it also provides insight about how individual sequence elements influence the validity of discrete objects. To learn this model we propose an approach inspired by seminal work in Bayesian active learning. On a synthetic dataset, we demonstrate the ability of our model to distinguish valid and invalid sequences. We believe this is a key step toward learning generative models that faithfully produce valid discrete objects.

Artificial Intelligence is changing the way we think of technology. It is radically changing the various aspects of our daily life. Companies are now significantly making investments in AI to boost their future businesses. Ericsson, in the sixth edition of its annual trend report, 'The 10 Hot Consumer Trends for 2017 and beyond,' predicts that 35 percent of advanced internet users want an AI advisor at work, and one in four would like an AI as their manager in the next one year. Another study performed by Forrester Research predicted an increase of 300% in investment in AI in the next one year. CXOToday finds out some of the most revolutionary artificial intelligence technologies that will rule in the next 1-2 years.

In this blog post, I am going to teach you how to train a Bayesian deep learning classifier using Keras and tensorflow. I will then cover two techniques for including uncertainty in a deep learning model and will go over a specific example using Keras to train fully connected layers over a frozen ResNet50 encoder on the cifar10 dataset. With this example, I will also discuss methods of exploring the uncertainty predictions of a Bayesian deep learning classifier and provide suggestions for improving the model in the future. This post is based on material from two blog posts (here and here) and a white paper on Bayesian deep learning from the University of Cambridge machine learning group. If you want to learn more about Bayesian deep learning after reading this post, I encourage you to check out all three of these resources. Thank you to the University of Cambridge machine learning group for your amazing blog posts and papers.

Operations: Companies like x.ai are already achieving near-perfect automation of meeting scheduling. And in 2017 will likely become household names inside medium and large enterprises. Similarly, recruitment chatbots like Mya will screen candidates and handle all communication with prospective talent, Saving companies time & valuable resources in the talent acquisition process. Tools like Clarke.ai will dial into our conference calls and send a summarized outline with action-points and to-do lists to all the participants afterwards.

About this course: Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images.

According to the new market research report "Artificial Intelligence in Manufacturing Market by Offering (Hardware, and Software), Technology (Deep Learning, Computer Vision, Context Awareness, and Natural Language Processing), Application, Industry, and Geography - Global Forecast to 2023", published by MarketsandMarkets, the artificial intelligence (AI) in manufacturing market is expected to grow from USD 272.5 Million in 2016 to USD 4,882.9 The growing usage big data technology, Industrial IoT in manufacturing, extensive usage of robotics in manufacturing, computer vision technology in manufacturing, cross-industry partnerships and collaborations, and significant increase in venture capital investments would propel the growth of the AI in manufacturing market. Browse 61 Market Data Tables and 58 Figures spread through 189 Pages and in-depth TOC on "Artificial Intelligence in Manufacturing Market - Global Forecast to 2023" In recent years, major software vendors such as IBM Corporation (US), Microsoft Corporation (US), and Alphabet Inc. (US) have started offering cognitive applications and cognitive software platforms. Moreover, many start-ups such as DataRPM (US), Clearpath Robotics Inc. (Canada), Sight Machine (US), Preferred Networks Inc. (Japan), SkyMind Inc. (US), Kespry Inc. (US), and AIBrain, Inc. (US) are bringing intelligent software solutions in manufacturing. The booming venture capital market for AI software-based start-ups and usage of big data technology are driving the growth of the AI in manufacturing market for the software segment.

You might not have noticed, but over the weekend a little coup took place. On Friday night, in front of a crowd of thousands, an AI bot beat a professional human player at Dota 2 -- one of the world's most popular video games. The human champ, the affable Danil "Dendi" Ishutin, threw in the towel after being killed three times, saying he couldn't beat the unstoppable bot. "It feels a little bit like human," said Dendi. The bot's patron was none other than tech billionaire Elon Musk, who helped found and fund the institution that designed it, OpenAI.

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. 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. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

The AI space is a crowded arena, but not all are solely focussed on competing, as many are providing research to the benefit of all. Artificial Intelligence (AI) has been progressing at a record rate in recent years, with its numerous names and voices becoming familiar in daily life and within the enterprise. These include Siri, Alexa, Cortana, Watson, Einstein and Coleman, to name but a few. Innovation has of course been driving the advancements and developments of AI, but a huge amount of investment has fuelled the progress, ensuring the continuation of exploration. Research and funding are almost symbiotic in requiring one another to progress in areas such as the AI space.