What is hardcore data science – in practice?


For example, for personalized recommendations, we have been working with learning to rank methods that learn individual rankings over item sets. Figure 1: Typical data science workflow, starting with raw data that is turned into features and fed into learning algorithms, resulting in a model that is applied on future data. This means that this pipeline is iterated and improved many times, trying out different features, different forms of preprocessing, different learning methods, or maybe even going back to the source and trying to add more data sources. Probably the main difference between production systems and data science systems is that production systems are real-time systems that are continuously running.

10 Ways Machine Learning Is Revolutionizing Manufacturing


Bottom line: Every manufacturer has the potential to integrate machine learning into their operations and become more competitive by gaining predictive insights into production. Machine learning's core technologies align well with the complex problems manufacturers face daily. From striving to keep supply chains operating efficiently to producing customized, built- to-order products on time, machine learning algorithms have the potential to bring greater predictive accuracy to every phase of production. Many of the algorithms being developed are iterative, designed to learn continually and seek optimized outcomes. These algorithms iterate in milliseconds, enabling manufacturers to seek optimized outcomes in minutes versus months.

Two Ways to Bring Shakespeare Into the Twenty-First Century

The New Yorker

For the four-hundredth anniversary of Shakespeare's death, Gregory Doran, the artistic director of the Royal Shakespeare Company, wanted to dazzle. He turned to "The Tempest," the late romance that includes flying spirits, a shipwreck, a vanishing banquet, and a masque-like pageant that the magician Prospero stages to celebrate his daughter's marriage. "The Tempest" was performed at the court of King James I, and it may have been intended in part to showcase the multimedia marvels of Jacobean court masques. "Shakespeare was touching on that new form of theatre," Doran told me recently, over the phone. "So we wanted to think about what the cutting-edge technology is today that Shakespeare, if he were alive now, would be saying, 'Let's use some of that.' " The politics behind Shakespeare and stage illusion are more fraught than usual these days.

Artificial Intelligence is now Intel's major focus


With technology governing almost every aspect of our lives, industry experts are defining these modern times as the "platinum age of innovation"; verging on the threshold of discoveries that could change human society irreversibly, for better or worse. At the forefront of this revolution is the field of artificial intelligence (AI), a technology that is more vibrant than ever due to the acceleration of technological progress in machine learning - the process of giving computers with the ability to learn without being explicitly programmed - as well as the realisation by big tech vendors of its potential. One major tech behemoth fuelling the fire of this fast moving juggernaut called AI is Intel, a company that has long invested in the science and engineering of making computers more intelligent. The Californian company held an'AI Day' in San Francisco showcasing its new strategy dedicated solely to AI, with the introduction of new AI-specific products, as well as investments for the development of specific AI-related tech. And Alphr were in town to hear all about it.

Questions To Ask When Moving Machine Learning From Practice to Production


With growing interest in neural networks and deep learning, individuals and companies are claiming ever-increasing adoption rates of artificial intelligence into their daily workflows and product offerings. Coupled with breakneck speeds in AI-research, the new wave of popularity shows a lot of promise for solving some of the harder problems out there. That said, I feel that this field suffers from a gulf between appreciating these developments and subsequently deploying them to solve "real-world" tasks. A number of frameworks, tutorials and guides have popped up to democratize machine learning, but the steps that they prescribe often don't align with the fuzzier problems that need to be solved. This post is a collection of questions (with some (maybe even incorrect) answers) that are worth thinking about when applying machine learning in production.

Large-scale machine learning at Criteo


At Criteo, machine learning lies at the core of our business. We use machine learning for choosing when we want to display ads as well as for personalized product recommendations and for optimizing the look & feel of our banners (as we automatically generate our own banners for each partner using our catalog of products). Our motto at Criteo is "Performance is everything" and to deliver the best performance we can, we've built a large scale distributed machine learning framework, called Irma, that we use in production and for running experiments when we search for improvements on our models. In the past, performance advertising was all about predicting clicks. That was a while ago.

Amazon Joins Tech Giants in Open Sourcing a Key Machine Learning Tool


"DSSTNE (pronounced "Destiny") is an open source software library for training and deploying deep neural networks using GPUs. Amazon engineers built DSSTNE to solve deep learning problems at Amazon's scale. DSSTNE is built for production deployment of real-world deep learning applications, emphasizing speed and scale over experimental flexibility. "Deep Scalable Sparse Tensor Network Engine, (DSSTNE), pronounced "Destiny", is an Amazon developed library for building Deep Learning (DL) machine learning (ML) models.

A Survey of Robotic Musicianship

Communications of the ACM

The term'robotic musicianship' may seem like an oxymoron. The first word often carries negative connotations in terms of artistic performance and can be used to describe a lack of expressivity and artistic sensitivity. The second word is used to describe varying levels of an individual's ability to apply musical concepts in order to convey artistry and sensitivity beyond the facets of merely reading notes from a score. To understand the meaning of robotic musicianship, it is important to detail the two primary research areas of which it constitutes: Musical mechatronics, which is the study and construction of physical systems that generate sound through mechanical means;15 and machine musicianship, which focuses on developing algorithms and cognitive models representative of various aspects of music perception, composition, performance, and theory.31 Robotic musicianship refers to the intersection of these areas.

Robot CEO: Your next boss could run on code


At the time it was predicted that it might take another hundred years until computers would beat top human players at the boardgame Go. But a few days ago, Google's AlphaGo beat the world's champion Go player in a five-game series. Business books and management consultants commonly list six functions that a CEO is responsible for: determine the strategic direction, allocate resources, build the culture, oversee and deliver the company's performance, be the face of the company, and juggle with everyday compromises. Human managerial decisions will switch focus to the "why" rather than the "how" as data-driven decisions slowly creep from scheduling to resource allocation to performance measurement and reporting, and finally to daily management tasks.

ANA by Factory Fifteen Science-fiction short film


Returning to Short of the Week for a third time, creative studio Factory Fifteen serve-up this enticing science-fiction short that centres around a worker in a futuristic car manufacturing plant as he starts to experience problems with the artificial intelligence that runs the production line. Produced as a way to entice an audience into the universe they are currently evolving into a longer piece, Factory Fifteen's Paul Nicholls spoke to Short of the Week about the aims of the project: "We had multiple ambitions with the film. With stellar VFX work combining with crip live-action in creating an on-screen world that feels both plausible and nightmarish, studio Co-Founder Nicholls reveals the short's impressive production process: "The film was made almost entirely in 3D pre-visualisation (with real locations made to scale), before shooting, something we always do. It was then shot on location in Coventry on a crazily ambitious 2-day shoot, stretching everyone involved, especially our camera team led by DOP Ben Kracun, and production design team led by Laura Tarrant Brown, both of whom did an unbelievable job.