Google is using romance novels to teach its artificial intelligence (AI) system to better understand how people communicate. Researchers at Google Brain, the company's AI-focused deep learning project, presented a paper earlier this month that detailed techniques they used to teach its AI to write fiction -- and the results were unexpectedly haunting. SEE ALSO: Japanese team helps A.I. program pass first round of novel writing contest The paper, first reported by Quartz, details Google's method, which involved using thousands of romance novels and other works of fiction to "train" the AI. The company then fed the model two sentences to serve as a starting point and endpoint and tasked it with generating transition sentences in between. While some of the results were a bit nonsensical, others were surprisingly haunting (the researchers concede the source material is "often rather dramatic.")

First Posted: May 06, 2016 10:50 AM EDT Tags Deep Learning, contextual deep learning, artificial intelligence, AI, RAGE Frameworks Contextual deep learning allows artificial intelligence machines to react in a more natural and intelligent way to the real-world auditory, visual or other type of data. Information for and about the digital publishing industry from the leading trade association dedicated to representing the interests of high-quality digital publishers before the advertising community, the press, the government and the public. Includes recent news and latest research on the industry, as well as membership information. Or you might imagine the disembodied voice of HAL, the recalcitrant computer that wouldn't open the pod-bay doors in '2001: A Space Odyssey,' the ... For those betting on the 142nd Kentucky Derby on Saturday, there are several ways to approach the strategy. Last year, Jimmy Fallon's puppies took a stab at it-and correctly predicted the winner, American Pharoah.

A company owned by Google has been given access to the healthcare data of up to 1.6 million patients from three hospitals run by a major London NHS Trust. DeepMind, the tech giant's London-based company most famous for its innovative use of artificial intelligence, is being provided with the patient information as part of an agreement with the Royal Free NHS trust, which runs the Barnet, Chase Farm and Royal Free hospitals. It includes information about people who are HIV-positive as well as details of drug overdoses, abortions and patient data from the last five years, according to a report by the New Scientist. DeepMind announced in February that it was developing a software in partnership with NHS hospitals to alert staff to patients at risk of deterioration and death through kidney failure. The technology, which is run through a smartphone app, has the support of Lord Darzi, a surgeon and former health minister who is director of the Institute of Global Health Innovation at Imperial College London.

HLMI can enable a machine to "carry out most human professions at least as well as a typical human1, and possesses "the capability to reason, converse, and make rational decisions in an environment of imprecision, uncertainty, incompleteness of information, partiality of truth, and possibility.2 In your opinion, which of the following will be first to develop and widely adopt HLMI?

Which will win out – robots or human beings? This is a highly topical question. The South Koreans, and many more millions of curious people worldwide, became obsessed with this issue when a match was played between a professional Go player named Lee Se-Dol, and AlphaGo, a computer programme developed by Google subsidiary DeepMind. The match resulted in a 4-1 victory for the machine over the Korean star player. Given that Go was one of the last bastions of the games world to hold out against the learning and analysis techniques employed by cutting-edge computers, this defeat basically symbolises the considerable progress made by deep learning.

Kaggle is the best place for learning from other data scientists. Many companies provide data and prize money to set up data science competitions on Kaggle. Recently I had my first shot on Kaggle and ranked 98th ( 5%) among 2125 teams. Since this is my Kaggle debut, I feel quite satisfied. Because many Kaggle beginners set 10% as their first goal, here I want to share my experience in achieving that goal. Most Kagglers use Python and R. I prefer Python, but R users should have no difficulty in understanding the ideas behind tools and languages. First let's go through some facts about Kaggle competitions in case you are not very familiar with them. Different competitions have different tasks: classification, regression, recommendation, ordering… Training set and testing set will be open for download after the competition launches.

Unlike conventional learning theories and tenets, our doubts are "Do we really need so many different types of learning algorithms (SVM, BP, etc) for so many different types of networks (different types of SLFNs (RBF networks, polynomial networks, complex networks, Fourier series, wavelet networks, etc) and multi-layer of architecfures, different types of neurons, etc)? Is there a general learning scheme for wide type of different networks (SLFNs and multi-layer networks)? Neural networks (NN) and support vector machines (SVM) play key roles in machine learning and data analysis. Feedforward neural networks and support vector machines are usually considered different learning techniques in computational intelligence community. Both popular learning techniques face some challenging issues such as: intensive human intervene, slow learning speed, poor learning scalability. It is clear that the learning speed of feedforward neural networks including deep learning is in general far slower ...

I'm a fan of Valiant Comics, but I'll admit the one series I've never gotten into is Rai. I just never quite got it. With the new 4001 A.D. event going on, I got a chance to read the return of Rai today and I was pleasantly surprised. Rai #13 gives you the feeling that you are at the beginning of a great science fiction epic. It opens establishing the world of New Japan as it floats over the Earth.

At MWC 2016, Sony revealed its new rebranding and re-envisioning efforts that would practically mean the end of the previous Xperia Z line. It seems, however, that the "Z" series isn't the only one getting the ax. Chinese media reports that Sony is focusing its smartphone thrust around the new Xperia X generation, all the way up to 2018. Noticeably missing are the Xperia C, M, and E brands that made up the rest of Sony's lineup, implying that those are similarly being retired in favor of a single name. While the Xperia Z series represented Sony's best of the best, the Xperia C, M, and E flavors occupied more budget friendly tiers. Sometimes they focused on certain features, like waterproof rating or battery life.

Lloyd's and the London market has a long and well- earned reputation for product innovation. It created the first ever marine insurance product back in Edward Lloyd's famous coffee shop over 325 years and has remained at the forefront of product, if not technology, innovation ever since. Its continuing ability to remain at the forefront of product innovation will define its future. In a rapidly changing world, the demand for greater certainty increases and that's what insurance is designed for. But it follows that the increased speed of change requires from the insurance industry a commensurate evolution in insurance product and that requires understanding what the future holds and the role that insurance can play within it.