From time to time, the Singularity Hub editorial team unearths a gem from the archives and wants to share it all over again. It's usually a piece that was popular back then and we think is still relevant now. This is one of those articles. It was originally published August 10, 2010. We hope you enjoy it!

Making sense of data can involve a wide variety of tools, and IBM is hoping to make data scientists' lives easier by putting them all in one place. The company on Tuesday released what it calls Data Science Experience, a new development environment in the cloud for real-time, high-performance analytics. Based on data-processing framework Apache Spark, Data Science Experience is designed to speed and simplify the process of embedding data and machine learning into cloud applications. Included in the new offering are tools such as RStudio and Jupyter Notebooks. Developers can tap Python, R and Scala; they can also view sample notebooks and watch tutorials while they code. Additional tools focus on data preparation and cleaning, visualization, prescriptive analytics, data connections, and scheduling jobs.

Mayhem ruled the day when seven AIs clashed here last week -- a bot named Mayhem that, along with its competitors, proved that machines can now quickly find many types of security vulnerabilities hiding in vast amounts of code. Sponsored by the Defense Advanced Research Projects Agency, or DARPA, the first-of-its-kind contest sought to explore how artificial intelligence and automation might help find security and design flaws that bad actors use to penetrate computer networks and steal data. Mayhem, built by the For All Secure team out of Carnegie Mellon University, so outclassed its competition that it won even though it was inoperable for about half of the contest's 96 270-second rounds. Mayhem pivoted between two autonomous methods of finding bugs and developing ways to exploit them. Under one method, dubbed symbolic execution, Mayhem tries to figure out how a target program works by systematically replacing sample inputs with classes of inputs.

The Data Science Institute (DSI) at Columbia University and Bloomberg LP are pleased to announce a workshop on "Machine Learning in Finance". The workshop will be held at Columbia University under the auspices of the Financial and Business Analytics Center, one of the constituent centers in the DSI, and the Center for Financial Engineering.

Machine Learning sounds like something out of an 1980s movie about a deceiving robot we end up fighting against to defend the existence of the planet. That may explain the distrust people have with it. How understandable is this distrust? Computers monitoring and absorbing information, learning and adapting to become better? Add to this distrust the purported technological singularity: the moment when artificial intelligence surpasses the human in any practical competency.

Deep learning neural networks are very easy to create and evaluate in Python with Keras, but you must follow a strict model life-cycle. In this post you will discover the step-by-step life-cycle for creating, training and evaluating deep learning neural networks in Keras and how to make predictions with a trained model. Deep Learning Neural Network Life-Cycle in Keras Photo by Martin Stitchener, some rights reserved. Below is an overview of the 5 steps in the neural network model life-cycle in Keras that we are going to look at. Deep Learning gets state-of-the-art results and Python hosts the most powerful tools.

' }); * // ' script ' * * // using the "evaluate" delimiter to execute JavaScript and generate HTML * var compiled _.template(' ' * * // using the internal print function in "evaluate" delimiters * var compiled _.template('!'); " : __t) '!'; * // return __p; * // * * // using the source property to inline compiled templates for meaningful * // line numbers in error messages and a stack trace * fs.writeFileSync(path.join(cwd, 'jst.js'),

Anagog, the leading provider of mobility status services and solutions for mobile apps and Holaverse, a leading mobile app publisher with a portfolio of games and utility apps, announced today their collaboration to include Anagog's Mobility Status SDK in the entire Holaverse portfolio of apps. Omni Swipe, Holaverse's popular accessibility app with over 100 million Google Play downloads worldwide, is the first app to be integrated with Anagog's innovative machine-learning algorithms to provide context awareness and activity classification. Holaverse hopes such smart technology will provide better functionality, convenience, and experiences for its global userbase. "We are very happy to work with Anagog," said Collin Huang, Co-Founder at Holaverse. "We found their technology to be a diffenetiator when it comes to providing the right service to our users at the right time and in the right place."

Over recent years, we've seen artificial intelligence systems designed to write software, compose music, paint works of art, and even pen news articles, but the machines have been notably quiet in the medium of fiction storytelling. Designing an A.I. system that can write the screenplay for a movie, or compose a great novel, has posed a big challenge for researchers. So just how close are we to having machines pen our blockbuster films? In June, a bizarre short film entitled Sunspring premiered. Filled with incoherent non-sequiturs and inexplicably surreal tangents, the film could be considered either a compelling dream-like fugue or an amateurish mess.

I'll respond more specifically for deep learning. You can get a broad idea of deep what deep learning is about through tutorial lectures that are available from the Web.