This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, dataviz, AI and many more. To keep receiving these articles, sign up on DSC. TensorFlow is an open source software library for machine learning across a range of tasks, and developed by Google to meet their needs for systems capable of building and training neural networks to detect and decipher patterns and correlations, analogous to the learning and reasoning which humans use (Source: Wikipedia.)

Artificial intelligence (AI) technologies are being deployed for an increasing variety of use cases across consumer, enterprise, and government markets around the world. AI is an umbrella term that includes multiple technologies, such as machine learning, deep learning, computer vision, natural language processing (NLP), machine reasoning, and strong AI. Tractica defines AI as an information system that is inspired by a biological system designed to give computers the human-like abilities of hearing, seeing, reasoning, and learning. AI has applications and use cases in almost every industry vertical and is considered the next big technological shift, similar to past shifts like the industrial revolution, the computer age, and the smartphone revolution. Tractica's market forecast is focused on identifying the software, hardware, and services revenue opportunity for AI, building a bottom-up, use case-based model that classifies and estimates the revenue potential of each use case and rolls it up by industry, technology, and world region to estimate the overall market.

However, there are new frameworks being developed that look to ease the burden associated with implementing and supporting these systems. Howdy's Slack Bot and Facebook's Wit.ai are both bringing point-and-click systems to developers, making the creation and customization of AI systems easier to manage. Other tools also aim to simplify the implementation of deep learning models. Options like TensorFlow, Keras, and Bonsai are just some of those looking to bring more advanced AI capabilities to a wider market. Cloud platforms are also lightening the load on business eliminating internal infrastructure concerns. Collectively, this makes AI more accessible to all.

Demis Hassabis, co-founder of Google's artificial intelligence (AI) startup DeepMind speaks during the AI forum of the Future of Go summit at Wuzhen internet international conference and exhibition center in Wuzhen, China's Zhejiang province, 24 May 2017. The summit is held from 23 to 27 May in Wuzhen.

We present two deep generative models based on Variational Autoencoders to improve the accuracy of drug response prediction. Our models, Perturbation Variational Autoencoder and its semi-supervised extension, Drug Response Variational Autoencoder (Dr.VAE), learn latent representation of the underlying gene states before and after drug application that depend on: (i) drug-induced biological change of each gene and (ii) overall treatment response outcome. Our VAE-based models outperform the current published benchmarks in the field by anywhere from 3 to 11% AUROC and 2 to 30% AUPR. In addition, we found that better reconstruction accuracy does not necessarily lead to improvement in classification accuracy and that jointly trained models perform better than models that minimize reconstruction error independently.

We describe the Customer LifeTime Value (CLTV) prediction system deployed at ASOS.com, a global online fashion retailer. CLTV prediction is an important problem in e-commerce where an accurate estimate of future value allows retailers to effectively allocate marketing spend, identify and nurture high value customers and mitigate exposure to losses. The system at ASOS provides daily estimates of the future value of every customer and is one of the cornerstones of the personalised shopping experience. The state of the art in this domain uses large numbers of handcrafted features and ensemble regressors to forecast value, predict churn and evaluate customer loyalty. Recently, domains including language, vision and speech have shown dramatic advances by replacing handcrafted features with features that are learned automatically from data. We detail the system deployed at ASOS and show that learning feature representations is a promising extension to the state of the art in CLTV modelling. We propose a novel way to generate embeddings of customers, which addresses the issue of the ever changing product catalogue and obtain a significant improvement over an exhaustive set of handcrafted features.

High-speed, low-latency obstacle avoidance that is insensitive to sensor noise is essential for enabling multiple decentralized robots to function reliably in cluttered and dynamic environments. While other distributed multi-agent collision avoidance systems exist, these systems require online geometric optimization where tedious parameter tuning and perfect sensing are necessary. We present a novel end-to-end framework to generate reactive collision avoidance policy for efficient distributed multi-agent navigation. Our method formulates an agent's navigation strategy as a deep neural network mapping from the observed noisy sensor measurements to the agent's steering commands in terms of movement velocity. We train the network on a large number of frames of collision avoidance data collected by repeatedly running a multi-agent simulator with different parameter settings. We validate the learned deep neural network policy in a set of simulated and real scenarios with noisy measurements and demonstrate that our method is able to generate a robust navigation strategy that is insensitive to imperfect sensing and works reliably in all situations. We also show that our method can be well generalized to scenarios that do not appear in our training data, including scenes with static obstacles and agents with different sizes. Videos are available at https://sites.google.com/view/deepmaca.

It is a machine-learning library using data flow graphs to build models. TensorFlow has been created for Deep Learning to let a user create a neural network architecture by himself (or herself, of course). Actually, tensors flow in the graph from node to node, thus making the name of the library sound logical. For some of you it may be interesting if there is any difference between TensorFlow and libraries like Theano, which also can make their own Deep Learning with multi-dimensional arrays and GPU.

Google's DeepMind artificial intelligence team has been based in the UK ever since it was acquired in 2014. However, it's finally ready to branch out -- just not to the US. DeepMind has announced that its first international research lab is coming to the Canadian prairie city of Edmonton, Alberta later in July. A trio of University of Alberta computer science professors (Richard Sutton, Michael Bowling and Patrick Pilarski) will lead the group, which includes seven more AI veterans. As Recode observes, you can chalk it up to a combination of familiarity and political considerations.

It's rare for Canadians to come out and assert global leadership in anything (barring hockey and winter coats), but here we are, on the brink of adding artificial intelligence (AI) to the list. This is no small measure. It requires us to move away from the understated modesty that often defines our national character and demands that we take action to be able to declare our place on the world stage. Thankfully, we have the goods to declare. Seminal breakthroughs such as deep learning and reinforcement learning, which have resulted in unprecedented technological transformation and are currently fuelling the AI engine, were brought to life by Canadian universities.