I find myself implementing some custom types/trees in online AI challenges, using Python. I know for example that it is important to be able to "copy" (or "undo") in a fast way. Whenever I have a custom type though, copying becomes really slow. Does anyone have suggestions, perhaps some posts or first hand experience?

A well-funded Tech start up in Bristol looking for a Data Scientist to join a growing team. You will be able to implement high level machine learning techniques and big data technologies on a unique platform. To apply for this job email your details to george@mbnsolutions.com

Solving real world problems with data science concepts is so exciting and it yields so fun. A famous dialogue you could listen from the data science people. It could be true if we add it's so challenging at the end of the dialogue. The foremost challenge starts from categorising the problem itself. The first level of categorising could be whether supervised or unsupervised learning.

Big Data and the IoT are quickly transforming the world of clinical research, including how trial sponsors find and retain patients. While the digital revolution has permeated the medical world more slowly than other industries, it's finally begun to make a real impact. PwC research found that while the healthcare industry has a relatively low "Digital IQ" score of 65%, it boasts more CEOs that actively champion digital than any other industry. For clinical trials, that advocacy is translating into the implementation of big data and the Internet of Things (IoT), which are transforming not only how research is conducted, but the strategies used to identify, attract, and retain qualified patients. As methods of data collection and analysis become more sophisticated, clinical trial sponsors stand to make unprecedented progress in patient recruitment and retention.

As we are entering the fourth Industrial Revolution, more and more of our jobs will be replaced by robots and algorithms. Some estimates claim that 5 million jobs or more will be lost to machines by 2020. And it's not science fiction or paranoia; as advances in big data, deep learning, robotics, and artificial intelligence increase exponentially, so does the number and variety of jobs that machines will be able to do -- not just well, but better. So how do you know if your job is one of the ones at risk? One way to think about your job and how vulnerable it is is to distinguish between Algorithmic and Heuristic work.

When I arrived at Qihan's IFA booth, a rainbow-colored trio of Sanbots were performing choreographed moves to some unrecognizable pop song. Because, well, when you want to get people to stop at your both in the middle of a giant show full of gadgety distractions, you pull out all the stops. The plucky little robot has flipper arms, a pair of wheels for feet and a body chock full of various sensors to help it perform a factotum of different jobs, from security guard to nursing home companion to educational assistant. According to the company, this iteration of Sanbot has already been deployed at various spots in China from retail establishments to airports. Sanbot's got a Kinect-style 3D camera and HD camera about its LED eyes, a touchscreen tablet in its chest and infrared sensors at its feet.

You wouldn't know it, but over the last few years, computers have increasingly (and quietly) written many of the sporting news, financial reporting, and weather forecasts you already read daily. Example 1: "The University of Michigan baseball team used a four-run fifth inning to salvage the final game in its three-game weekend series with Iowa, winning 7-5 on Saturday afternoon (April 24) at the Wilpon Baseball Complex, home of historic Ray Fisher Stadium." Example 2: "Things looked bleak for the Angels when they trailed by two runs in the ninth inning, but Los Angeles recovered thanks to a key single from Vladimir Guerrero to pull out a 7-6 victory over the Boston Red Sox at Fenway Park on Sunday." The technology works like this. Rules for data are paired with pre-canned words and phrases to say different things.If you guessed that the more emotional language of the second example was human, you guessed wrong, according to the New York Times.

The test for what is'fair' is a matter of degree and the impression the court forms of the defendant's conduct.

X, formerly known as the Google X lab before spinning off into a new unit under parent Alphabet after a company restructuring, has tapped artificial intelligence to drive one of its most popular projects. Project Loon, which looks to launch balloons into the stratosphere to provide internet access to users on Earth, made a major breakthrough recently. One of its balloons was able to stay up in Peruvian airspace for 98 days, an impressive feat considering the difficulty to keep a balloon at a certain spot for a long period of time. The breakthrough was announced by the Project Loon team through its official Google page, where it stated that it was hard at work in the development of the navigation technology that its balloons will use. The latest updates were put to the test this summer on one of Project Loon's flights, launching the balloon from a site in Puerto Rico and then having it travel to Peru.

Mad Street Den, an India-U.S. startup focused on artificial intelligence that was started by ex-Silicon Valley founders, has pulled in an undisclosed Series A round of funding from Sequoia Capital India. Existing investors Exfinity Ventures and growX Ventures also took part. The two-and-a-half-year-old company is registered in the U.S. but principally located in Chennai, India, with offices in London and San Francisco. Mad Street Den was founded by husband and wife duo Anand Chandrasekaran and Ashwani Asokan who spent more than 25 years combined working in the U.S. -- he is a neuroscientist who graduated Stanford, while she spent most of her career Stateside with Intel Labs. We wrote about Mad Street Den when it picked up a 1.5 million seed round in January 2015.