You've read about cities installing smart parking meters and noise- and air-quality sensors, but are you ready to embrace the idea of a city brain? The residents of Singapore are on track to do just that. Creating a centralized dashboard view of sensors deployed across a distributed network is nothing new, but it takes on a bigger -- perhaps ominous -- meaning when deployed across a major city. Many technologically advanced cities worldwide are exploring ways to build such comprehensive digital views for managing traffic and parking, monitoring water and air quality, and offering such citizen-facing services as web-based tools for interacting with government agencies. Some smart city experts call this system approach a "city brain" or, less glamorously, a "municipal backplane."

Tokyo, Japan – Ten months since the official release of Ginkan Inc.'s chat and dating app Festar, the app has seen high successful match rates with 53% of pairs from over 17,000 matches mutually liking each other and choosing to continue to talk after a 10 minute chat. Festar has ditched the dating app standard of picking based on appearances, and is proving just how important mutual interests and meaningful conversation are with thousands of users finding love and friendship through a live 10 minute chat. Festar is now available in 13 countries in English, Korean, and Japanese for both iOS and Android smartphones. How Festar Works: Unlike many dating apps that make users search for a partner, Festar starts by automatically connecting people for a 10 minute real time chat. Users are matched based on mutual interests and hobbies, instead of swiping and searching by looks or social status.

Future grid scenario analysis requires a major departure from conventional power system planning, where only a handful of most critical conditions is typically analyzed. To capture the inter-seasonal variations in renewable generation of a future grid scenario necessitates the use of computationally intensive time-series analysis. In this paper, we propose a planning framework for fast stability scanning of future grid scenarios using a novel feature selection algorithm and a novel self-adaptive PSO-k-means clustering algorithm. To achieve the computational speed-up, the stability analysis is performed only on small number of representative cluster centroids instead of on the full set of operating conditions. As a case study, we perform small-signal stability and steady-state voltage stability scanning of a simplified model of the Australian National Electricity Market with significant penetration of renewable generation. The simulation results show the effectiveness of the proposed approach. Compared to an exhaustive time series scanning, the proposed framework reduced the computational burden up to ten times, with an acceptable level of accuracy.

A company's logo is an important part of its identity, but the processes behind defining, registering, and protecting these trademarks is a convoluted and rather archaic one. A startup called TrademarkVision aims to simplify it by replacing that laborious and arcane process with what amounts to a machine-learning-powered reverse image search. This isn't in some lab, either: the EU just switched their whole image trademark system over to it. Most people probably haven't had to do many trademark and logo searches. Well, why don't you take the USPTO's version for a spin so you know what it's like? Try to find the Nike "Swoosh" or something.

You must understand algorithms to get good at machine learning. The problem is that they are only ever explained using Math. In this mega Ebook written in the friendly Machine Learning Mastery style that you're used to, finally cut through the math and learn exactly how machine learning algorithms work. Using clear explanations, simple pure Python code (no libraries!) and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement a suite of linear, nonlinear and ensemble machine learning algorithms from scratch. I live in Australia with my wife and son and love to write and code.

Brain tests at the age of three appear to predict a child's future chance of success in life, say researchers. Low cognitive test scores for skills like language indicate less developed brains, possibly caused by too little stimulation in early life, they say. These youngsters are more likely to become criminals, dependent on welfare or chronically ill unless they are given support later on, they add. Their study in New Zealand appears in the journal, Nature Human Behaviour. The US researchers from Duke University say the findings highlight the importance of early life experiences and interventions to support vulnerable youngsters.

With the Radeon Instinct line, AMD joins Nvidia and Intel in the race to put its chips into AI applications--specifically, machine learning for everything from self-driving cars to art. The company plans to launch three products under the new brand in 2017, which include chips from all three of its GPU families. The passively cooled Radeon Instinct MI6 will be based on the company's Polaris architecture. It will offer 5.7 teraflops of performance and 224GBps of memory bandwidth, and will consume up to 150 watts of power. The small-form-factor, Fiji-based Radeon Instinct MI8 will provide 8.2 teraflops of performance and 512GBps of memory bandwidth, and will consume up to 175 watts of power.

Artificial intelligence, machine and deep learning are some of the hottest areas in all of high-tech today. We've had a few generations of AI over the last 50 years, but in 2010, IBM kicked off the latest cycle with Watson, using brute-force, Big Data techniques to win jeopardy. The University of Toronto in 2012 pioneered Imagenet using deep learning to identify pictures. NVIDIA then began to drive the GPU-accelerated training technology of deep neural nets, and in the course of that, huge service providers opened up and announced initiatives beginning with Microsoft, Google, Apple, Samsung, and then Amazon. Chinese giants Baidu, Alibaba and Tencent are of course, involved.

thought is the contemplation of things beyond what we can sense.

In this tutorial, you discovered how to normalize and standardize time series data in Python. That some machine learning algorithms perform better or even require rescaled data when modeling. How to manually calculate the parameters required for normalization and standardization. How to normalize and standardize time series data using scikit-learn in Python. That some machine learning algorithms perform better or even require rescaled data when modeling. How to manually calculate the parameters required for normalization and standardization. How to normalize and standardize time series data using scikit-learn in Python. Do you have any questions about rescaling time series data or about this post? Ask your questions in the comments and I will do my best to answer.