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'We can presume that the goal of the EAGIs is to emancipate themselves from human control while also participating in the world economy which would be the most efficient way to acquire necessary resources (using the economic means or political means or a mix of both?).' The last phrase refers to an observation made by Franz Oppenheimer in The State. Oppenheimer noted that there are only two means of acquiring the resources necessary for survival, the political means or the economic means. The political means involve the threat or use of violence and/or fraud. The economic means is peaceful, voluntary exchange.

If the machines are taking all the jobs, how come so many people are working? The unemployment rate is at 4.9 percent. There are 143.6 million Americans with payroll jobs, a record. The number of first-time unemployment claims (pdf) is down more than 10 percent from last year, and is bumping along at levels not seen since the 1970s. Oh, and the Bureau of Labor Statistics says there are 5.5 million job openings in America, close to a record.

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.

Are there any efficient (the forward speed is much faster than AlexNet) models that attain at least the same performance as AlexNet for image classification? Look up for model compression: there were discussions on this subreddit where people much more competent than me suggested literature for that. First paper that comes to mind: http://arxiv.org/abs/1504.04788 Check out SqueezeNet, although the focus here is more on the number of parameters/deployability rather than inference speed: http://arxiv.org/abs/1602.07360

They took the NYC Data Science Academy 12-week full-time data science bootcamp program from Sep. 23 to Dec. 18, 2015. The post was based on their fourth in-class project (due after the 8th week of the program). Walmart uses trip type classification to segment its shoppers and their store visits to better improve the shopping experience. Walmart's trip types are created from a combination of existing customer insights and purchase history data. The purpose of the Kaggle competition is to use only the purchase data provided to derive Walmart's classification labels.

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.