Do you know that many of the artificially-intelligent things we use in our everyday, quotidian lives "learned" how to "think" to varying degrees by studying the emails of some of the most craven and degraded capitalists in our deeply weird corporate history? Brooklyn's Sam Lavigne and Tega Brain have a new piece of internet art out called The Good Life (Enron Simulator). We first told you about it back in August, right after it won a Rhizome Net Art Microgrant. You input your email into a very Windows 95-looking website and the site sends you each of the 500,000 publicly-available emails from the Enron archives in the order they were sent. You can choose to receive these emails over the course of seven days, 30 days, one year or seven years. Depending on your choice, you'll receive somewhere between 100,000 and 196 emails per day.

Pokeman Go has been a raging success. But its launch was marred by frequent downtimes and dropped connections. In a recent chat at the Google Cloud Platform Next Conference, Niantic CTO Phil Keslin talked about the "hair on fire" experience where the team had to firefight and upgrade key components on the live production system in order to handle the unexpected surge in new users joining in. Among the various upgrades made to the system, Niantic had to replace the network load balancer with a much more sophisticated HTTP/S load balancing system that could handle a larger overall throughput and offer faster connections. Keslin says that this timely upgrade made it possible for his team to launch in Japan without an incident although the number of new user signups at this point was triple what it was during their earlier US launch.

We've already published the top big data presentations on slideshare, as well as great Github list of public data sets, or top machine learning projects, or top R packages. We've asked our readers to share a list of top Data Science videos on YouTube. Here, we share a list of top data science presentations from VideoLectures.net. These presentations received 5 to 20 times fewer page views than those on Slideshare, because they are far more technical, and attract a different, truly technical audience. You can check the entire list here.

This was the subject of a question asked on Quora: What are the top 10 data mining or machine learning algorithms? Some modern algorithms such as collaborative filtering, recommendation engine, segmentation, or attribution modeling, are missing from the lists below. Algorithms from graph theory (to find the shortest path in a graph, or to detect connected components), from operations research (the simplex, to optimize the supply chain), or from time series, are not listed either. And I could not find MCM (Markov Chain Monte Carlo) and related algorithms used to process hierarchical, spatio-temporal and other Bayesian models. For the last one I'd let you pick one of the following: For the last one I'd let you pick one of the following: My point of view is of course biased, but I would like to also add some algorithms developed or re-developed at the Data Science Central's research lab: These algorithms are described in the article What you wont learn in statistics classes.

The federal government's highway safety agency agrees with Google: Computers that will control the cars of the future can be considered their driver. The redefinition of "driver" is an important break for Google. Companies like Visteon (NYSE: VC), which makes infotainment systems and connected car solutions, is already working with leading automakers like Ford, Mazda, Renault/Nissan, GM, Jaguar Land Rover, Honda, BMW, Daimler, PSA and Volkswagen. The company is working on making it easier for automakers to update apps in cars but also add a layer of cyber security to the infotainment system. More apps in cars mean more cybersecurity threats.

Infrastructure investment is on the forefront of political agendas around the world. From the Asian Infrastructure Investment Bank (AIIB) spearheaded by China to the pro-infrastructure themes that united an otherwise divisive presidential campaign in the United States, the policy focus is on transportation, telecommunications, utilities and so on around us. The expected surge in such infrastructure projects is estimated to fill a gap of $8 trillion across Asia alone, and around $1 trillion in the U.S. But this policy spotlight must also now be lifted above us to outer space, where newspace industries are building, extending and connecting a space-based infrastructure to the planet as never before. Newspace is taking it another step forward, transforming the prospects for the economics, societal and national security domains.

Machine learning has tremendous potential to help organizations create powerful and unique customer experiences, as well as make better and more informed decisions. So in October 2016 we conducted a survey to find out what is the state of machine learning and artificial intelligence. Download the infographic to discover some of the highlights.

Algorithms are now used throughout the public and private sectors, informing decisions on everything from education and employment to criminal justice. But despite the potential for efficiency gains, algorithms fed by big data can also amplify structural discrimination, produce errors that deny services to individuals, or even seduce an electorate into a false sense of security. Indeed, there is growing awareness that the public should be wary of the societal risks posed by over-reliance on these systems and work to hold themaccountable. Various industry efforts, including a consortium of Silicon Valley behemoths, are beginning to grapple with the ethics of deploying algorithms that can have unanticipated effects on society. Algorithm developers and product managers need new ways to think about, design, and implement algorithmic systems in publicly accountable ways.

One of the basic features of artificial intelligence (AI) is the ability to recognize images and process them. Companies like Microsoft and Google have debuted tools to show how accurate their image recognition platforms are. Now it seems that Amazon wants in as well as they have announced Amazon Rekognition. This is an image recognition service that is part of a suite of deep-learning services that Amazon has recently announced for developers. For the most part it does what most image recognition services do, which is to identify human faces, identify emotions, and label objects just by looking at it.

Machine learning is an exciting space in the field of artificial intelligence (AI) and has been a stalwart of computing machine efforts since the beginning of integrating computers with enterprises. Yet some organizations are increasingly overwhelmed with too much "big data", new technologies and figuring out how to keep everything secure. As one top IT security expert who wished to remain anonymous noted, "The increase in communications and information accumulation will require not just one program to filter the financial data professionals need from the avalanche of data available but automation of automation of such processes." Such a program, or rather a platform, could scan fundamental information to compile complex datasets together and determine what info is important. Some examples would be determining influencing factors for price movement in global logistics and how weather impacts supply chains. Then the organization could attack weaknesses in that chain to make sure they meet upcoming product demand.