Methods to find correlation among variables are of interest to many disciplines, including statistics, machine learning, (big) data mining and neurosciences. Parameters that measure correlation between two variables are of limited utility when used with multiple variables. In this work, I propose a simple criterion to measure correlation among an arbitrary number of variables, based on a data set. The central idea is to i) design a function of the variables that can take different forms depending on a set of parameters, ii) calculate the difference between a statistics associated to the function computed on the data set and the same statistics computed on a randomised version of the data set, called "scrambled" data set, and iii) optimise the parameters to maximise this difference. Many such functions can be organised in layers, which can in turn be stacked one on top of the other, forming a neural network. The function parameters are searched with an enhanced genetic algortihm called POET and the resulting method is tested on a cancer gene data set. The method may have potential implications for some issues that affect the field of neural networks, such as overfitting, the need to process huge amounts of data for training and the presence of "adversarial examples".

Markov Chain Monte Carlo (MCMC) sampling from a posterior distribution corresponding to a massive data set can be computationally prohibitive since producing one sample requires a number of operations that is linear in the data size. In this paper, we introduce a new communication-free parallel method, the Likelihood Inflating Sampling Algorithm (LISA), that significantly reduces computational costs by randomly splitting the dataset into smaller subsets and running MCMC methods independently in parallel on each subset using different processors. Each processor will be used to run an MCMC chain that samples sub-posterior distributions which are defined using an "inflated" likelihood function. We develop a strategy for combining the draws from different sub-posteriors to study the full posterior of the Bayesian Additive Regression Trees (BART) model. The performance of the method is tested using both simulated and real data.

The $k$-Means clustering problem on $n$ points is NP-Hard for any dimension $d\ge 2$, however, for the 1D case there exist exact polynomial time algorithms. Previous literature reported an $O(kn^2)$ time dynamic programming algorithm that uses $O(kn)$ space. We present a new algorithm computing the optimal clustering in only $O(kn)$ time using linear space. For $k = \Omega(\lg n)$, we improve this even further to $n 2^{O(\sqrt{ \lg \lg n \lg k})}$ time. We generalize the new algorithm(s) to work for the absolute distance instead of squared distance and to work for any Bregman Divergence as well.

Theoretical analysis of machine intelligence (MI) is useful for defining a common platform in both theoretical and applied artificial intelligence (AI). The goal of this paper is to set canonical definitions that can assist pragmatic research in both strong and weak AI. Described epistemological features of machine intelligence include relationship between intelligent behavior, intelligent and unintelligent machine characteristics, observable and unobservable entities and classification of intelligence. The paper also establishes algebraic definitions of efficiency and accuracy of MI tests as their quality measure. The last part of the paper addresses the learning process with respect to the traditional epistemology and the epistemology of MI described here. The proposed views on MI positively correlate to the Hegelian monistic epistemology and contribute towards amalgamating idealistic deliberations with the AI theory, particularly in a local frame of reference.

John Chambers, Chairman of Cisco System 3. Welcome to the Digital Economy Compass 3 Less talking, more facts – our idea behind creating the Digital Economy Compass. It contains facts, trends and key players, covering the entire digital economy. This very first edition will provide everything you need to know about the digital economy. In a global comparison, broadband speed is fastest in East Asia and Scandinavia Source: Akamai Technologies Note: Figure refers to Q3-2016; countries covered: broadband ranking – 147 1: Megabits per second 10 Average broadband speed in Top10 and selected countries (in Mbps1) South Korea 1 Hong Kong 2 Norway 3 23.6 18.2 Sweden 4 20.1 18.4 20.0 19.7 The sevenfold increase in global mobile data traffic is mainly driven by online video streaming Source: Cisco System; figures include only cellular mobile traffic (Wi-Fi or small cell from dual-mode devices are excluded) Note: Other mobile devices include Tablets, mobile PCs, M2M (incl. Pokémon Go was the most popular mobile game for iPhone users across the globe In cooperation with 19 Most downloaded iPhone apps per category and country in 2016 Categories covered: Shopping, Music, News, Gaming, Social Networks U.S. PandoraAmazon CNN Pokémon Go Messenger China Kugou Music Taobao Toutiao WeChat King of Glory Germany SpotifyKleinan zeigen Spiegel Online Pokémon Go WhatsApp U.K. SpotifyeBay BBC News Pokémon Go WhatsApp France DeezerWish Le Monde Pokémon Go Messenger Connectivity 21. "If you make customers unhappy in the physical world, they might each tell 6 friends. If you make customers unhappy on the Internet, they can each tell 6,000 friends."

Sean Lane, Chairman and CEO of CrossChx based in United States participates in Risk Roundup to discuss Artificial Intelligence based Healthcare System. Sean is a lifelong technologist who grew up in a small, rural town in Ohio and started his career in the U.S. intelligence community, holding leadership positions in the Department of Defense and the National Security Agency. Sean served five combat tours in Iraq and Afghanistan and was awarded the Defense Meritorious Service Medal and Bronze Star Medal for his service. After government service, Sean started his life as an entrepreneur and has founded several technology companies dealing with big data, advanced analytics, entity resolution, and most recently, artificial intelligence. Sean has raised over $40M in venture capital, and is now a leading expert in applying AI to healthcare.

Local differences: If steps are not taken to lessen the rate of warming from climate change, counties in the South and lower Midwest -- which on average tend to already be poorer and warmer -- may lose as much as 20% of their income and may experience higher mortality rates. However, areas of the Pacific Northwest, the Great Lakes region and New England -- which on average tend to be wealthier and cooler -- could benefit economically from the change and see lower mortality rates. A climate impact map by county is available here. Dire warning: The researchers predict mortality will increase by 5.4 deaths per 100,000 people for every one degree Celsius rise in temperature. "We show there are going to be as many additional deaths from climate change as there are car crashes, and possibly more. Of the sectors we looked at, the greatest costs by far to society are going to come from those additional deaths," Rising told Axios.

Keras is a powerful easy-to-use Python library for developing and evaluating deep learning models. It wraps the efficient numerical computation libraries Theano and TensorFlow and allows you to define and train neural network models in a few short lines of code. In this post, you will discover how to create your first neural network model in Python using Keras. Develop Your First Neural Network in Python With Keras Step-By-Step Photo by Phil Whitehouse, some rights reserved. There is not a lot of code required, but we are going to step over it slowly so that you will know how to create your own models in the future.

WIRE)--Glooko, the leader in diabetes data management, today announced $35 million in new funding to accelerate growth, expand international presence, and deepen expertise in data analytics. This round was led by Georgian Partners, a Toronto-based investor focused on applied analytics and machine learning. Other new investors include Insulet Corporation and Mayo Clinic who join existing investors Canaan Partners, Social Capital, Medtronic and Samsung NEXT in the round. Glooko will use the funds to accelerate growth by expanding its sales, marketing and development teams. The company will also increase commercialization efforts in France, Germany, the U.K., Asia and the Middle East, and further development efforts in data analytics and artificial intelligence to provide personalized insights that drive meaningful behavior change.

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