I previous posted the write-up on the first part of my project (Github repo) to predict how skaters would rank in the figure skating world championship from earlier scores that they earned in the season. The main idea is to separate the skater effect, the intrinsic ability of each skater, from the event effect, the influence of an event on a skater's performance, so that a more accurate ranking could be built. Unfortunately, this model does not have a closed-form solution to learn the parameters as opposed to the earlier models. Therefore, gradient descent was used to learn them, which resulted in this neat little animation that tracks how the model residuals, RMSE, as well as predicted ranking gets better and better as gradient descent runs. I also explore different strategies to reduce model overfit (so that it can predict skater ranking more accurately), using familiar methods such as model penalization and early stopping. Lastly, note that this hybrid model is nothing but factorizing the event-skater score matrix into an event-specific vector and skater-specific vector, which can multiply together to approximate the score matrix.

I still remember I used to feel uncomfortable when looking at those complicated mathematical concepts needed for machine learning.

As part of that strategy, the Library began seeking a collaboration to test machine learning across different materials, since the library's collections are …

In a recent survey, it is estimated that Machine Learning and AI alone have at least 1.4 million open jobs at the moment.

It's very abstract and it's something that deep neural networks can't do even in the simplest form. They can't recognize same… If you give them an example of something in which two things are the same versus two things are different in general, it can't do that task.

The first entry in the franchise, Shogun: Total War, balances the combat simulations that strive for realism and authenticity, alongside the political strategy that is aimed to give context and stakes to each conflict. The original Total War is set in 1530, during the Sengoku Jidai period of feudal Japan: a time largely popularised in contemporary fiction by the works of Japanese film director Akira Kurosawa, with films such as Kagemusha, Seven Samurai and Ran proving influential on the design and development of the game - with clips of the latter being used as part of the games cinematics. Both the player and opposing AI assume the role of the'Daimyo': local-lords who control provinces of Japan with a need to conduct both diplomatic strategy alongside military movements. When rival factions are drawn into conflict, players take control of the'Taisho' (General) and move hundreds if not thousands of troops across the battlefield. Sengoku Jidai made for an ideal period of history for the game, given the politics and even the economics of the period was built around the logistics of fielding armies in defence of the Daimyo and his ambitions. Total War deviates from many traditional real-time strategy games in that it removes mechanics such as Fog of War from combat gameplay as well as the need for resource management outside of unit counts from combat decision making.

Visual 1st, the annual Silicon-Valley imaging conference for industry leaders and upstarts, once again brought together a worldwide audience for a day-and-a-half executive conference. The event, held Oct. 2-3 at the Golden Gate Club in San Francisco, addresses topics as far-reaching as artificial intelligence and as every day as printing. As with most conferences, the real meat of the event is the hallway discussions and informal meetings over a beer or wine at the reception. Below are some photos from the conference, courtesy of sponsor, Sweet Escapes. Each year, a panel of high-powered industry experts presented the four Visual 1st Awards to the most outstanding among 30 products competing in this year's show-and-tell demo sessions.

By Dr. Rasmi Misra "Some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think we'll augment our intelligence." This has led to a widespread belief that introducing AI driven systems could help in lowering these costs and also make remote patient monitoring more efficient. With the advanced use of AI & ML in medicine, the ability to form relationships with patients, which was once touted as a cornerstone in the human element in treatment, is now being portrayed as its Achilles' heel. While the fear that AI & ML may make doctors obsolete is not completely unfounded, the belief that it would replace doctors and steal their jobs is slightly presumptuous.