Major corporations use this kind of artificial intelligence to help with the complexities of serving a massive, often international audience -- and now Amazon is making its machine learning software open source. The company's Deep Scalable Sparse Tensor Network Engine -- otherwise known as DSSTNE and pronounced "destiny" -- is now available to anyone who's interested in tinkering with it. Amazon hopes that outside influences will help make the platform even more powerful than it already is, according to a report from Engadget. "DSSTNE is built for production deployment of real-world deep learning applications, emphasizing speed and scale over experimental flexibility," reads documentation that accompanies the files released by Amazon. Internally, DSSTNE is used to deliver purchase recommendations to consumers based on their order histories. Product recommendations are big business for Amazon, as having such a daunting catalog of merchandise is really rather worthless unless customers are able to discover items that are relevant to their interests.

Attention-based models are firstly proposed in the field of computer vision around mid 20141 (thanks for the remindar from @archychu). And then they spread into Natural Language Processing. In this post, I will mainly focus on a list of attention-based models applied in natural language processing. The first one should be the "guy" innovately and successfully bringing in attention mechanism from computer vision in to NLP. Although called RNNsearch by themselves in this paper, the model is in nature and inspired by the attention-based model as what they said in Section 3.1: Intuitively, this implements a mechanism of attention in the decoder.

Note: The title of this post is circular. But I use/abuse it because of the post linked below. I noticed on the Hacker News front page (and via multiple reshares on twitter), a discussion on why logistic regression uses a sigmoid. The article linked in the story talks about the log-odds ratio, and how it leads to the sigmoid (and gives a good intuitive plug on it). However, I think that the more important question is – Why do you care about log-odds?

Artificial intelligence, popularly known as AI is slowly taking over the industry. More and more companies and brands turn to AI to give their customers better service. According to new reports, eBay is also considering AI to improve product searches on their site. According to Forbes, eBay is very aggressive in fulfilling its goal of making its platform simple and more efficient. Their website is a popular buy and sell platform.

Google I/O Extended 2016 Denver, Colorado is a place where developers and Google fans can experience Google I/O remotely. Check out photos from the 2015 event put on by GDG Denver (Google Developers Group Denver). Google I/O 2016 Extended is made possible by an amazing Denver tech community, including hard working volunteers, smart GDG members, and really wonderful companies that support us ... We have a pretty good idea what is going to happen, but everything is subject to change. Note our new venue does not permit alcohol, so no keg like years past. But the proverbial beer mug is half full, we will have air conditioning, a gorgeous patio and panoramic window views, and sound that will not leave your ears buzzing!

Horse betting is harder than it looks. At the 142nd Kentucky Derby last week, only one of five experts from Churchill Downs Racetrack correctly predicted the winner. None of them correctly predicted the top four horses. Known as a superfecta, this latter bet came with 540 to 1 odds, meaning 100 down would return 540,000. And although the experts failed to predict the finishing order, an anonymous group of internet users did.

Cogitai, a new company aimed at developing and commercializing core artificial intelligence (AI) technologies, today announced a strategic investment from Sony Corporation. Tokyo: Japan's Sony Corp said it plans to build up its artificial intelligence (AI) business and eventually turn it into a major revenue source, beginning with an investment in a US startup. TOKYO (Reuters) -- Japan's Sony said it plans to build up its artificial intelligence (AI) business and eventually turn it into a major revenue source, beginning with an investment in a U.S. startup. Japan's Sony Corp said it plans to build up its artificial intelligence (AI) business and eventually turn it into a major revenue source, beginning with an investment in a U.S. start-up.

ARM has acquired Apical, a U.K. designer of embedded computer vision technology, and plans to incorporate that technology into future ARM microprocessor and system-on-chip designs, it said Wednesday. The move will open up new opportunities for designers of autonomous vehicles and security systems, among other connected things, according to ARM CEO Simon Segars. Computer vision is in its early stages, and Apical is at the forefront of embedding such technology, he said. Apical's technologies is already used in 1.5 billion smartphones, according to ARM, although many of those phones may be using nothing more sophisticated than a display brightness control Apical calls Assertive Display. That technology also turned up in Samsung Electronics' new laptop, the ATIV Book 9. Assertive Camera is another of Apical's developments: It's a range of software packages and silicon-based image signal processors for reducing image noise, managing color and shooting high dynamic range images.

The experiment, developed by physicists from ANU, University of Adelaide and UNSW ADFA, created an extremely cold gas trapped in a laser beam, known as a Bose-Einstein condensate, replicating the experiment that won the 2001 Nobel Prize. The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. The team cooled the gas to around 1 microkelvin, and then handed control of the three laser beams over to the artificial intelligence to cool the trapped gas down to nanokelvin. "It may be able to come up with complicated ways humans haven't thought of to get experiments colder and make measurements more precise.

Physicists are putting themselves out of a job, using artificial intelligence to run a complex experiment. The experiment, developed by physicists from ANU, University of Adelaide and UNSW ADFA, created an extremely cold gas trapped in a laser beam, known as a Bose-Einstein condensate, replicating the experiment that won the 2001 Nobel Prize. "I didn't expect the machine could learn to do the experiment itself, from scratch, in under an hour," said co-lead researcher Paul Wigley from ANU Research School of Physics and Engineering. "A simple computer program would have taken longer than the age of the universe to run through all the combinations and work this out." Bose-Einstein condensates are some of the coldest places in the Universe, far colder than outer space, typically less than a billionth of a degree above absolute zero.