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.

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.

We are going to the AIoT Summit on December 3rd with Ambiq Micro and TensorFlow, and we wanted to give you a sneak peek! On December 3rd, you will find SparkFun CTO Kirk Bennel and Creative Technologist Rob Reynolds at the AIoT Dev Summit! In collaboration with Ambiq Micro and TensorFlow, we are going to be running a hands-on workshop where you will be able to build your very own Harry Potter wand! That's right, all of us muggles out there can finally get a magic wand without ever having to break into Ollivanders like a certain dark wizard. This is an experiential and experimental workshop that focuses on the use of TensorFlow Lite on a low-power microcontroller to perform machine learning.

Have you ever wondered how a song might sound if performed by a different artist? In this work, we propose SCM-GAN, an end-to-end non-parallel song conversion system powered by generative adversarial and transfer learning that allows users to listen to a selected target singer singing any song. SCM-GAN first separates songs into vocals and instrumental music using a U-Net network, then converts the vocal segments to the target singer using advanced CycleGAN-VC, before merging the converted vocals with their corresponding background music. SCM-GAN is first initialized with feature representations learned from a state-of-the-art voice-to-voice conversion and then trained on a dataset of non-parallel songs. Furthermore, SCM-GAN is evaluated against a set of metrics including global variance GV and modulation spectra MS on the 24 Mel-cepstral coefficients (MCEPs). Transfer learning improves the GV by 35% and the MS by 13% on average. A subjective comparison is conducted to test the user satisfaction with the quality and the naturalness of the conversion. Results show above par similarity between SCM-GAN's output and the target (70\% on average) as well as great naturalness of the converted songs.

For those of you unfamiliar, Andrew Ng runs a weekly newsletter where he shares thoughts and new developments in deep learning. I was very interested in something he said in today's newsletter (which can be read here), in which he talks about how deep learning systems still fail in many real scenarios because they are not yet robust to changes in data quality/distributions One of the challenges of robustness is that it is hard to study systematically. How do we benchmark how well an algorithm trained on one distribution performs on a different distribution? Performance on brand-new data seems to involve a huge component of luck. That's why the amount of academic work on robustness is significantly smaller than its practical importance.