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.

In co-expression analysis, the correlation between pairs of genes is typically combined into a network model of the correlation structure, which facilitates secondary network analysis such as community structure or centrality [1]. However, the correlation between pairs of genes in a co-expression network typically is assumed to be uniform across all samples (e.g., tissue types, treatment conditions, disease status, etc.). Yet it is often inter-group differences in correlated data that are of biological or clinical interest. For example, a gene co-expression network in microarray data for chronic lymphocytic leukemia using known biomarkers was able to predict treatment outcomes in an independent sample [2]. A differential co-expression network approach that leverages the genetic network information may yield novel biomarkers and improved prediction. Differential expression methods compute the mean difference between groups for each gene but typically do not incorporate conditional variation from other genes in the data that may help explain the between-group variation.

If the business IT market in 2016 was defined by an increased focus on cybersecurity vulnerabilities (including from the Internet of Things), cloud adoption and a shift to hyperconverged infrastructure, what does that augur for 2017? Often, predictions about the year ahead are untethered from the year that was, and do not have much of a connection to underlying trends. The world of enterprise technology likely will not be radically different next year than it was in 2016. However, trends that have been ongoing may accelerate or evolve, as technologies mature and businesses get more acclimated to them. For example, Hardware as a Service may start to take off.

The 2017 Consumer Electronics Show in Las Vegas opens to the public on January 5 but will be preceded by press and preview days on January 3 and 4. This year's show will span across 2.5 million square feet of floor space spread across multiple venues and feature 3,800 exhibitors. "One of the big themes is going to be connectivity," Jeff Joseph, senior vice president for communications and strategic relationships at the Consumer Technology Association, which hosts CES. "For example, Internet of Things, vehicle-to-vehicle communication, voice-activated communication with things like Alexa and Google Home and higher-value content – 4K-produced content that you can move from device to device." In the past few years, more and more car companies and automotive suppliers have used CES to showcase their technological prowess, particularly in the area of self-driving cars.

Domain Mondo's weekly review of technology news: Feature • Tech Story of the Year: Tay, Microsoft's Artificial Intelligence (AI) Chatterbot: "As many of you know by now, on Wednesday [March 23, 2016] we launched a chatbot called Tay. We are deeply sorry for the unintended offensive and hurtful tweets from Tay, which do not represent who we are or what we stand for, nor how we designed Tay. Tay is now offline and we'll look to bring Tay back only when we are confident we can better anticipate malicious intent that conflicts with our principles and values ... The logical place for us to engage with a massive group of users was Twitter. Unfortunately, in the first 24 hours of coming online, a coordinated attack by a subset of people exploited a vulnerability in Tay. Although we had prepared for many types of abuses of the system, we had made a critical oversight for this specific attack. We take full responsibility for not seeing this possibility ahead of time. We will take this lesson forward as well as those from our experiences in China, Japan and the U.S. Right now, we are hard at work addressing the specific vulnerability that was exposed by the attack on Tay."--Learning from Tay's introduction blogs.microsoft.com

How long does it usually take you to pick out a new pair of glasses at the store? 10 minutes? When left unsupervised, I've admittedly taken over an hour. It's a big deal, as it is scientifically established that the type of glasses you wear impacts perception of your intelligence, success, and attractiveness. It's 2016; there must certainly be some sort of technology that has solved this problem. Of course there is! DITTO technologies developed a virtual mirror that allows customers to try on hundreds of products from the comfort of their homes.

Deep Learning compared to other Machine Learning methods is remarkably modular. This modularity gives it unprecedented capabilities that places Deep Learning head and shoulders above any other conventional Machine Learning approach. Recent research however is pointing to even greater modularity than previously. It is likely that quite soon, monolithic Deep Learning systems will become a thing of the past. Before I discuss what is coming in the future, let me first discuss the concept of modularity.

Janitorial services vendors and workers, through the years, have been plagued by inefficient and labor intensive processes that often result in an insufficient level of cleanliness and sanitization, a challenge that is currently the focus of several artificial intelligence developers and robotic cleaning tool manufacturers. Everyone, to some degree, is familiar with the Roomba robot vacuum cleaner line, manufactured by iRobot. Love them or hate them, the robots represent the steady march of humanity toward the automation of time-consuming tasks that often lead to repetitive motion injuries in operators. However, the manner in which the systems go about their duties, as well as the reasoning behind automated decisions, is not without its critics. A commonly cited issue with current technologies is the lack of transparency and understanding regarding the underlying instructions, performance, and functionality of the systems.

I want to create music generated by mathematical algorithms, or even turning big data files into sound files, just like NASA turned electromagnetic signals from space into music. Producing artificially generated music is a popular subject, see for instance Composing Music With Recurrent Neural Networks, or Using Machine Learning to Generate Music. My question is how to access my laptop's speaker from a script written in Python or Perl. I used to do it long ago in C language, using the command sound available in the Borland package. Today I tried various system calls from within Perl, or directly from the command line, to non avail.