Here's the thing about the Internet: there's a lot of it and not everything is gold. The job of a news aggregator is to sort through each day's daily dose of Internet and choose the most interesting and relevant stories and videos for you people. Different sites have taken different approaches to this problem; Google News uses its algorithms to deliver a personalized homepage, Reddit uses upvotes and downvotes to deliver the freshest stream of dog photos and entertaining AskReddit topics. Here at Digg, we use humans because our goal is not to recommend lots of good things, but to surface a small amount of the very best things. And while algorithms are good at lots of things, we think that humans have the advantage at recognizing this type of content. However, algorithms are useful when we need to make lots of decisions about what to show you, and this problem arises on our video pages.

The machine learning solution from Google offers learning services with pre-trained models as well as the option of generating your own tailor-made models. A neural net-based platform, it performs better and more accurately than other learning systems on the market. The technology is currently available for developers in limited preview phase. The cloud machine learning tool can be used with some of the technologies that Google employs in its services, such as voice searches and translations in Gmail, therefore speeding up the development process. Its main advantages include greater speed, scalability and usability for all the applications featured in these services.

Unsupervisedly learned word embeddings have been exceptionally successful in many NLP tasks and are frequently seen as something akin to a silver bullet. In fact, in many NLP architectures, they have almost completely replaced traditional distributional features such as Brown clusters and LSA features. Proceedings of last year's ACL and EMNLP conferences have been dominated by word embeddings, with some people musing that Embedding Methods in Natural Language Processing was a more fitting name for EMNLP. Semantic relations between word embeddings seem nothing short of magical to the uninitiated and Deep Learning NLP talks frequently prelude with the notorious \(king - man woman \approx queen \) slide, while a recent article in Communications of the ACM hails word embeddings as the primary reason for NLP's breakout. This post will be the first in a series that aims to give an extensive overview of word embeddings showcasing why this hype may or may not be warranted.

Clear your calendar - It's going down! Splash Blocks kicks off on April 21st, and you're invited to take part in the festivities. Splash HQ (122 W 26th St) is our meeting spot for a night of fun and excitement. Come one, come all, bring a guest, and hang loose. This is going to be epic!

Microsoft today announced that it is making it easier for developers to use its Computational Network Toolkit (CNTK) to build their own deep learning applications. The company first open sourced this toolkit in April 2015, but at the time, it was hosted on Microsoft's own CodePlex site and was only available under a restrictive academic license. Now, the team is moving the project to GitHub and to the MIT open source license. CNTK is an open-source deep-learning toolkit that became available back in April 2015. However, when it was still on CodePlex, it was restricted by an academic license, which means that it was virtually unused beyond scholarly use.

By now, you've probably met Siri. You speak to her, give her directions, and even hold conversations with her; basically you've made the most of her capabilities as a handy, smartphone-based digital assistant. The person behind this novel addition to smartphone technology is Dag Kittlaus. Since co-founding Siri, Kittlaus has gone on to become the director of iPhone Apps for Apple, heading the Siri and speech recognition division. From there, he left Apple to work on his vision of what the next generation of AI should be.

Google revealed in October that it uses artificial intelligence to help with 15% of search queries. Named RankBrain, the system analyzes vague, ambiguous queries and matches them with the most relevant results. In fact, Google's Greg Corrado told Bloomberg that RankBrain is now the third-highest signal contributing to a search-query result. Google – and similar search-engine services – are getting smarter. As marketers, we no longer can rely solely on traditional digital strategies such as link-building or social-media signaling.

If you lie awake at night worrying about the'threat' of Artificial Intelligence (AI) taking over the world, then take heart from a recent episode involving robot waiters in China. Three restaurants in the southern Chinese city of Guangzhou have been forced to fire all of their robot staff after their utter incompetence began costing them money. Two of the restaurants have closed completely after discovering the clumsy waiters could not perform simple tasks like taking orders, pouring drinks and carrying soup, reports say. The slacking robot team also kept breaking down and after a string of complaints the third restaurant mentioned above decided to sack all but one and bring back human employees, the Workers' Daily newspaper reports.

Facepunch is aware that some players don't enjoy the idea of playing Rust as a woman, creator Garry Newman says. He shared one recent player complaint on Twitter that reads, in part, "I got a dirty woman ... and everytime [sic] I see her I wanna threw [sic] up." "We understand this causes you distress and makes you not want to play the game anymore," Newman says. "Technically nothing has changed, since half the population was already living with those feelings. The only difference is that whether you feel like this is now decided by your SteamID instead of your real life gender." Facepunch is continually updating Rust and this isn't the end of the character model changes.

I built deepjazz in 36 hours for HackPrinceton, Spring 2016. It uses Keras & Theano, two deep learning libraries, to generate jazz music. Specifically, it builds a two-layer LSTM, learning from the given MIDI file. It uses deep learning, the AI tech that powers Google's AlphaGo and IBM's Watson, to make music -- something that's considered as deeply human.