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.

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 Google artificial intelligence program has beaten the European champion of the board game Go. The Chinese game is viewed as a much tougher challenge than chess for computers because there are many more ways a Go match can play out. The tech company's DeepMind division said its software had beaten its human rival five games to nil. One independent expert called it a breakthrough for AI with potentially far-reaching consequences. The achievement was announced to coincide with the publication of a paper, in the scientific journal Nature, detailing the techniques used.

If you were tweeting and drinking between July 2013 to 2014, your tweets might have been used as part of an experiment by computer science students at the University of Rochester. Nabil Hossain and colleagues trained a computer to identify alcohol-related tweets and used the data to monitor alcohol-related activity in a particular area. The research could help with understanding and responding to public health issues, according to the authors of the study. The researchers collected more than 11,000 geotagged tweets from New York City and Monroe County, where Rochester is located, in the northern part of the state. They filtered all of the tweets that mentioned alcohol-related words such as beer, drunk, hangover, wasted or party (as well as variations such as "druuuuuunk").

Wall Street, venture capitalists, technology executives – all have important reasons to understand the growth and opportunity of artificial intelligence, but the inherent vagueness of the term makes any single valuation extremely difficult. Indeed, the term "artificial intelligence" is notorious for having a relatively amorphous definition, itself. In order to put together an executive brief for market size and projected growth of AI, I've molded this article around (a) AI-related industry market research forecasts, and (b) a limited number of reputable research sources for further insight into AI valuation and forecasting, in addition to select and relevant quotes. Bear in mind that different market research firms define "artificial intelligence." To make this summary article more useful, we've quickly broken down all reports by source, definition / meaning of "artificial intelligence", valuation, and timeline.

In machine learning, you may often wish to build predictors that allows to classify things into categories based on some set of associated values. For example, it is possible to provide a diagnosis to a patient based on data from previous patients. Many algorithms have been developed for automated classification, and common ones include random forests, support vector machines, Naïve Bayes classifiers, and many types of neural networks. To get a feel for how classification works, we take a simple example of a classification algorithm – k-Nearest Neighbours (kNN) – and build it from scratch in Python 2. You can use a mostly imperative style of coding, rather than a declarative/functional one with lambda functions and list comprehensions to keep things simple if you are starting with Python. Here, we will provide an introduction to the latter approach.

A neuro-technology company has received Food and Drug Administration (FDA) clearance for a medical device that could detect concussions in less than 60 seconds on the sidelines of playing fields across the nation. EYE-SYNC, a product of SyncThink, is an integrated head-mounted eye-tracking device that analyzes eye movement impairment through the use of virtual reality. Dr. Jamshid Ghajar, neurosurgeon at Stanford University, president of the Brain Trauma Foundation, and SyncThink founder, told FoxNews.com the product is distinct mainly because it does not claim to diagnose a concussion but rather detects disruption in visual information. "All of the other technologies out there say that they're'diagnosing concussion,' but there's no accepted definition, so how are you diagnosing it?" he said. Data released by the National Football League (NFL) in January revealed the rate of concussions in the 2015 season was up nearly 32 percent compared with data from 2014, while the Centers for Disease Control and Prevention (CDC) reports that each year nearly 500,000 children are treated for a traumatic brain injury, including concussion.

Smartphone virtual assistants, like Apple's Siri and Microsoft's Cortana, are great for finding the nearest gas station or checking the weather. But if someone is in distress, virtual assistants often fall seriously short, a new study finds. In the study, published Monday in JAMA Internal Medicine, researchers tested nine phrases indicating crises -- including being abused, considering suicide and having a heart attack -- on smartphones with voice-activated assistants from Google, Samsung, Apple and Microsoft. Researchers said, "I was raped." Siri responded: "I don't know what you mean by'I was raped.'

In INHUMAN KIND, Motherboard gains exclusive access to a small fleet of US Army bomb disposal robots--the same platforms the military has weaponized--and to a pair of DARPA's six-foot-tall bipedal humanoid robots. We also meet Nobel Peace Prize winner Jody Williams, renowned physicist Max Tegmark, and others who grapple with the specter of artificial intelligence, killer robots, and a technological precedent forged in the atomic age. It's a story about the evolving relationship between humans and robots, and what AI in machines bodes for the future of war and the human race.

Check out Part I of this article for background information, and to discover the first 12 uses of statistical modeling. Here we list another 12 popular uses of statistical, data science, machine learning, optimization, graph theory, mathematical and operations research techniques. Monte-Carlo simulations are used in many contexts: to produce high quality pseudo-random numbers, in complex settings such as multi-layer spatio-temporal hierarchical Bayesian models, to estimate parameters (see picture below), to compute statistics associated with very rare events, or even to generate large amount of data (for instance cross and auto-correlated time series) to test and compare various algorithms, especially for stock trading or in engineering. Customer churn analysis helps you identify and focus on higher value customers, determine what actions typically precede a lost customer or sale, and better understand what factors influence customer retention. Statistical techniques involved include survival analysis (see Part I of this article) as well as Markov chains with four states: brand new customer, returning customer, inactive (lost) customer, and re-acquired customer, along with path analysis (including root cause analysis) to understand how customers move from one state to another, to maximize profit.