New capabilities of analytics technology and machine learning are providing companies with greater insight and actionable intelligence than ever before.

The Second National Data Science Bowl, a data science competition where the goal was to automatically determine cardiac volumes from MRI scans, has just ended. We participated with a team of 4 members from the Data Science lab at Ghent University in Belgium and finished 2nd! The team kunsthart (artificial heart in English) consisted of Ira Korshunova, Jeroen Burms, Jonas Degrave (@317070), 3 PhD students, and professor Joni Dambre. It's also a follow-up of last year's team Deep Sea, which finished in first place for the First National Data Science Bowl. This blog post is going to be long, here is a clickable overview of different sections. The goal of this year's Data Science Bowl was to estimate minimum (end-systolic) and maximum (end-diastolic) volumes of the left ventricle from a set of MRI-images taken over one heartbeat. These volumes are used by practitioners to compute an ejection fraction: fraction of outbound blood pumped from the heart with each heartbeat.

All around us people are slowly (or sometimes quickly) transitioning into the future of work. The full-time job (9 to 5, traditional career, etc.) is about to become a rarity; only available to a select group of people who represent the core of an organization, or who possess a very specific skill set. Because we live in a society increasingly shaped by tech. Automation will take over many of the tasks previously assigned to people. And the youth of today (and tomorrow) will have no problem transitioning into that situation.

This post is the first in a series I'll be writing for Parallel Forall that aims to provide an intuitive and gentle introduction to deep learning. It covers the most important deep learning concepts and aims to provide an understanding of each concept rather than its mathematical and theoretical details. While the mathematical terminology is sometimes necessary and can further understanding, these posts use analogies and images whenever possible to provide easily digestible bits comprising an intuitive overview of the field of deep learning. I wrote this series in a glossary style so it can also be used as a reference for deep learning concepts. Part 1 focuses on introducing the main concepts of deep learning. Part 2 provides historical background and delves into the training procedures, algorithms and practical tricks that are used in training for deep learning. Part 3 covers sequence learning, including recurrent neural networks, LSTMs, and encoder-decoder systems for neural machine translation.

'Organizations are pivoting towards operational analytics as it can both increase the efficiency and performance of the back office as well as boost the customer experience in the front office.' comments Anne-Laure Thieullent, Head of Big Data in Europe, for Capgemini's Insights & Data global practice. 'However, despite the focus, there are factors limiting the success of these projects; specifically siloed datasets, fragile governance models, inability to harness third party data sources, and an absence of a strong mandate from leadership teams.' 'Going Big: Why Organizations Need to Focus on Operations Analytics' from Capgemini Consulting's Digital Transformation Institute mapped organizations based on the extent to which their analytics initiatives were integrated with core operations processes and their success rate with initiatives, identifying four stages of operational analytics maturity: Capgemini Consulting's Digital Transformation Institute applied the four stages of operational analytics maturity to build up a geographic picture of adoption and success rates around the world. US companies are not only the most advanced with their analytics initiatives but also the most successful; 50 percent have successfully realized the desired benefits from operational analytics compared to only 23 percent of Chinese respondents, despite China ranking highly for level of implementation. A strong contributing factor of the success of US companies is their focus on setting up effective data and governance processes. The prominence of US organizations tallies with a recent resurgence in US manufacturing and will drive US manufacturing competitiveness in the coming years.

A small step for Google may very soon become a giant step for mankind. An artificially intelligent computer system built by Google has just beaten the world's best human, Lee Sedol of South Korea, at an ancient strategy game called Go. Go originated in Asia about 2,500 years ago and is considered many, many times more complex than chess, which fell to AI back in 1997. Google's programmers didn't explicitly teach AlphaGo – that's what the system is called - to play the game. Instead, they built a sort of model brain called a neural network that learned how to play Go by itself. As it studied a database of about 100,000 human matches, and then continued by playing against itself millions of times, it constantly reprogrammed itself and improved.

Not so long ago, most businesses ran on mainframe computers. These computers were expensive to purchase and were typically stored in corporate headquarters. Internal staff had access to applications via a mainframe terminal. Data was typically stored in VSAM files. Individual fields were determined by the character position in the line of data.

Rosen Blatt, a freshman, joins The Perceptron, a school choir for women, which participates in an a capella competition. The choir girls inject some energy into their repertoire and start to compete with the male rivals. Surprisingly, they discover that girl with the most weight, Fat Amy, has the biggest influence on the quality of their singing. The girls master the repertoire through arduous training, and changing their team members (called Inputs) in order to achieve the best result.

Research reveals benefits of integrating machine learning into remote monitoring. Home healthcare software provider AlayaCare and home health provider We Care (part of the CBI Health Group)have released a white paper providing insight into how machine learning/artificial intelligence, when integrated into remote patient monitoring, can reduce hospital readmissions and emergency room visits. According to the study, Better Technology, Better Outcomes: The Effects of Machine Learning Powered Remote Patient Monitoring on Home Health Care, machine learning is a branch of artificial intelligence (AI) based on mathematical algorithms and automation, designed to automate the building of analytical models that use algorithms to learn from data in an iterative fashion. As the machine learns from its mistakes, it can improve its results to produce reliable, repeatable decisions. Machine learning algorithms have already been successfully applied in a range of industries from finance to retail and even healthcare.

A recent survey found that only 27 per cent of application problems were detected by application performanceParrot Bebop 2 is best drone in consumer market. Read more ... » monitoringBlockchain intelligence firm Elliptic completes 5m Series A. Read more ... » tools, leaving the majority unnoticed. This large gap could be closed by the use of anomalyManaging Internet of Things risks. However, as there are various ways of defining an anomaly, perhaps the different types can be distinguished with different labels. StatisticalResearchers are using AI to hack encrypted traffic so they can see when you're on YouTube.