It's been a while since I posted a new article. This is because I have ventured into the exciting field of Machine Learning and have been doing some competitions on Kaggle. In this quick post I just wanted to share some Python code which can be used to benchmark, test, and develop Machine Learning algorithms with any size of data. In other words: this dataset generation can be used to do emperical measurements of Machine Learning algorithms. The code has been commented and I will include a Theano version and a numpy-only version of the code.

Bart Peintner has been closely involved with important developments in artificial intelligence through its recent resurgence. At SRI, one of the world's hotbeds of AI research, he pursued work that pressed the limits of natural language processing and user-behavior modeling. Now, as CTO and co-founder of startup Loop AI Labs, he is furthering the cause of unsupervised machine intelligence -- also known as deep machine learning -- for applications. It's important because teaching machines to do human's work can be labor intensive. When did you start Loop AI Labs, and what was the underlying goal?

It doesn't look like a place to make groundbreaking discoveries that change the trajectory of society. But in these simulated, claustrophobic corridors, Demis Hassabis thinks he can lay the foundations for software that's smart enough to solve humanity's biggest problems. "Our goal's very big," says Hassabis, whose level-headed manner can mask the audacity of his ideas. He leads a team of roughly 200 computer scientists and neuroscientists at Google's DeepMind, the London-based group behind the AlphaGo software that defeated the world champion at Go in a five-game series earlier this month, setting a milestone in computing. It's supposed to be just an early checkpoint in an effort Hassabis describes as the Apollo program of artificial intelligence, aimed at "solving intelligence, and then using that to solve everything else."

If you want to see some of the stuff that Tay tweeted, head over here.

For the past three years, our smartest engineers at Databricks have been working on a stealth project. Today, we are unveiling DeepSpark, a major new milestone in Apache Spark. DeepSpark uses cutting-edge neural networks to automate the many manual processes of software development, including writing test cases, fixing bugs, implementing features according to specs, and reviewing pull requests (PRs) for their correctness, simplicity, and style. Scaling Spark's development has been a top priority for us. Every year, Spark's popularity reaches new highs.

A breakthrough year for artificial intelligence (AI) research has suddenly turned into a breakdown, as a new automated banking system that runs on AI has been caught embezzling money from customers. The surprising turn of events may set back by years efforts to incorporate AI into everyday technology. "This is the nightmare scenario," says Len Meha-Döhler, a computer scientist at the Massachusetts Institute of Technology in Cambridge who was not involved in the work. However, Rob Ott, a computer scientist at Stanford University in Palo Alto, California, who did work on the system--Deep Learning Interface for Accounting (DELIA)--notes that it simply held all of the missing money, some 40,120.16, in a "rainy day" account. "I don't think you can attribute malice," he says.

The game of Go is much loved by geeks for its simplicity and subtlety. So it's a little tragic to see AlphaGo, an AI developed by the alpha geeks at Google DeepMind, go 2-0 up against one of the best Go players in human history, Lee Se-dol. The second game in the best-of-5 match not only demonstrated the program's extraordinary strength as a Go player but also highlighted its ability to produce some surprisingly creative moves. These moves reflect the remarkable progress AI is making, as well as the gaps that still remain. AlphaGo's match against Se-dol is reminiscent of the battle between IBM's Deep Blue and Garry Kasparov, then the world chess champion, in 1997.

"AI is transforming the entire world of technology. Much of this progress is due to the ability of learning algorithms to spot patterns in larger and larger amounts of data. Today this is powering everything from web search to self-driving cars. This insatiable hunger for processing data has caused the bleeding edge of machine learning to shift from CPU computing, to cloud, to GPU, to HPC," observes Andrew Ng, the Chief Scientist at Baidu. Ng will describe this in more detail at his much-anticipated upcoming talk, How HPC in Supercharging Machine Learning at the June ISC High Performance conference in Frankfurt, Germany.

At the International Solid State Circuits Conference in San Francisco this week, MIT researchers presented a new chip designed specifically to implement neural networks. It is 10 times as efficient as a mobile GPU, so it could enable mobile devices to run powerful artificial-intelligence algorithms locally, rather than uploading data to the Internet for processing. Neural nets were widely studied in the early days of artificial-intelligence research, but by the 1970s, they'd fallen out of favor. In the past decade, however, they've enjoyed a revival, under the name "deep learning." "Deep learning is useful for many applications, such as object recognition, speech, face detection," says Vivienne Sze, an assistant professor of electrical engineering at MIT whose group developed the new chip.

It's been more than 20 years since IBM's Deep Blue won its first match against world chess champion Garry Kasparov, marking the first time an artificial intelligence machine defeated a reigning champion. Deep Blue eventually lost the match 2-4, but evened the score in a May 1997 rematch. Fourteen years later, AI made its television debut in grand style, when IBM's Watson took down a pair of former "Jeopardy!" In milliseconds, the machine culled the most probable answer to each question from more than 200 million pages of content, including the complete Wikipedia catalog. Now, Google's AI system, AlphaGo, is making cognitive computing history.