- Google is trying to make Google Translate more accurate by expanding the number of languages that are supported by its neural machine translation software.
- The Californian search giant announced on Monday that Hindi, Russian, and Vietnamese will be powered by neural machine translation in the next couple of weeks.
- Eight other languages are already using neural machine translation technology.
- “Neural translation is a lot better than our previous technology, because we translate whole sentences at a time, instead of pieces of a sentence,” wrote Barak Turovsky, product lead on Google Translate, in the blog post.
- Turovsky added that Google will be rolling out neural machine translation to other languages in the coming weeks.
Google is trying to make Google Translate more accurate by expanding the number of languages that are supported by its neural machine translation software.
Continue reading “Google translate is now able to translate even more languages, thanks to AI”
- We think technology can help.Today, Google and Jigsaw are launching Perspective, an early-stage technology that uses machine learning to help identify toxic comments.
- Through an API, publishers — including members of the Digital News Initiative — and platforms can access this technology and use it for their sites.HOW IT WORKSPerspective reviews comments and scores them based on how similar they are to comments people said were “toxic” or likely to make someone leave a conversation.
- Each time Perspective finds new examples of potentially toxic comments, or is provided with corrections from users, it can get better at scoring future comments.Publishers can choose what they want to do with the information they get from Perspective.
- Publishers could even just allow readers to sort comments by toxicity themselves, making it easier to find great discussions hidden under toxic ones.We’ve been testing a version of this technology with The New York Times, where an entire team sifts through and moderates each comment before it’s posted — reviewing up to 11,000 comments every day.
- We’ve worked together to train models that allows Times moderators to sort through comments more quickly, and we’ll work with them to enable comments on more articles every day.WHERE WE GO FROM HEREPerspective joins the TensorFlow library and the Cloud Machine Learning Platform as one of many new machine learning resources Google has made available to developers.
Imagine trying to have a conversation with your friends about the news you read this morning, but every time you said something, someone shouted in your face, called you a nasty name or accused you…
Continue reading “WHEN COMPUTERS LEARN TO SWEAR: – Jigsaw – Medium”
- Unfortunately, this happens all too frequently online as people try to discuss ideas on their favorite news sites but instead get bombarded with toxic comments.
- According to the same report, online harassment has affected the lives of roughly 140 million people in the U.S., and many more elsewhere.
- News organizations want to encourage engagement and discussion around their content, but find that sorting through millions of comments to find those that are trolling or abusive takes a lot of money, labor, and time.
- As a result, many sites have shut down comments altogether.
- Through an API, publishers—including members of the Digital News Initiative—and platforms can access this technology and use it for their sites.
Google and Jigsaw announce the launch of Perspective, an early-stage technology that uses machine learning to identify toxic comments.
Continue reading “When computers learn to swear: Using machine learning for better online conversations”
- Artificial Neural Networks are inspired by some of the “computations” that occur in human brains—real neural networks.
- In the past 10 years, much progress has been made with Artificial Neural Networks and Deep Learning due to accelerated computer power (GPUs), Open Source coding libraries that are being leveraged, and in-the-moment debates and corroborations via social media.
- Hugo Larochelle shares his observations of what’s been made possible with the underpinnings of Deep Learning.
- Hugo Larochelle is a Research Scientist at Twitter and an Assistant Professor at the Université de Sherbrooke (UdeS).
- His professional involvement includes associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), member of the editorial board of the Journal of Artificial Intelligence Research (JAIR) and program chair for the International Conference on Learning Representations (ICLR) of 2015, 2016 and 2017.
Artificial Neural Networks are inspired by some of the “computations” that occur in human brains—real neural networks. In the past 10 years, much progress ha…
Continue reading “The Deep End of Deep Learning”
- An AI can use Google Street View to help you decide where to move
- He plots 10,000 randomized points throughout a city, and grabs images taken by Google Street View.
- The idea of extracting information from Google Street View was inspired by MIT Media Lab’s StreetScore project, Keskkula writes, where machine learning was used to rank the safety of 3,000 streets in New York and Boston.
- Now one Estonia-based startup, Teleport , is using this idea, coupled with images from Google Street View, to automatically look around cities and see if people will like them based on their lifestyle preferences.
- Keskkula’s example focuses on motorcycles: He owns two and is interested in a city that welcomes them.
Machine learning is at its best when there’s way too much information for any human to comb through manually, like making high-volume stock trades or surfacing the best posts from hundreds of friends on Facebook. Now one Estonia-based startup, Teleport, is using this idea, coupled with images from Google Street View, to automatically look around cities and see if people will…
Continue reading “Artificial intelligence can look at Google Street View to help you decide where to move — Quartz”
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