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Face ID not working for Apple iPhone XS Max users

Daily Mail

An apparent problem with new software means that some iPhone X users are not able to activate Face ID on their handsets. The mysterious bug means that for to unlock their devices they have to enter their pass code manually. Frustrated Apple users are taking to Twitter and Reddit to complain about the problem which causes a'not available' message to show up on the screen. The issue has been linked to the latest iOS 12.1 version upgrade, according to reports from customers on social media. Frustrated Apple users are taking to Twitter and Reddit to complain about the mysterious bug that causes a'not available' message to show up on the screen, pictured Then press and hold the Power button until you see the Apple logo on-screen.


DIY Tinkerers Harness the Power of Artificial Intelligence

WIRED

In late winter of 1975, a scrap of paper started appearing on bulletin boards around the San Francisco Peninsula. "Are you building your own computer?" it asked. If so, you might like to come to a gathering." The invite drew 32 people to a Menlo Park, California, garage for the first meeting of the Homebrew Computer Club, a community of hobbyists intrigued by the potential of a newly affordable component called the microprocessor. One was a young engineer named Steve Wozniak, who later brought a friend named Steve Jobs into the club.


How to Build a Reddit Bot – Chatbots Life

#artificialintelligence

At their core, internet forums like Reddit work because they are centered around a democratic ideal. The content that makes the front page is whatever is most liked by the community. In theory, each website user has one vote and majority rule decides what content wins and what content loses. However, as malcontents run bots on sites like Reddit (as well as Instagram, Facebook, and Twitter), the process that makes these websites great is being tested. As a single user votes 150 times or automates thousands of comments to shift public opinion, the democratic procedure becomes eroded.


Gradient descent, how neural networks learn Deep learning, chapter 2

#artificialintelligence

Subscribe for more (part 3 will be on backpropagation): http://3b1b.co/subscribe Funding provided by Amplify Partners and viewers like you. His post on Neural networks and topology is particular beautiful, but honestly all of the stuff there is great. And if you like that, you'll *love* the publications at distill: https://distill.pub/ For more videos, Welch Labs also has some great series on machine learning: https://youtu.be/i8D90DkCLhI


Is AI Automated Coding the Next Era of Programming? - DZone AI

#artificialintelligence

Automation is a complex topic that has received a lot more focus in recent years. Many experts have been predicting that many unskilled jobs could soon be replaced by automatons. However, the impact could be much further reaching than that. Newer reports suggest that AI algorithms could soon start replicating computer code, which will possibly put coders out of work. These predictions seem very far-fetched to some people.


From TED Talks to Snoo, 15 Histories of the Future

WIRED

We may take the #hashtag for granted today, but it didn't emerge fully formed from Biz Stone's head. The Large Hadron Collider hasn't collapsed (or collapsed the space-time continuum), but that wasn't a given when scientists first turned the thing on. Nobody thought the sweaty geeks who sent their supposedly self-driving cars into concrete barriers instead of across the Mojave Desert would soon threaten to upend the way we move through the world. Oh, and remember the Microsoft trial? When we started planning our 25th birthday party more than a year ago, we knew that not all readers (okay, not even most readers) would have been following us since day one, and they certainly wouldn't recall every story we've told.


Google Brain, Microsoft plumb the mysteries of networks with AI ZDNet

#artificialintelligence

We live in an age of networks. From the social graph of Facebook to the interactions of proteins in the body, more and more of the world is being conceived of and represented as the connections in a network. And understanding of those connections can sometimes have stunning business implications, such as when Larry Page and Sergey Brin of Stanford University first proposed modeled networks of webpages, called "PageRank," the foundation of Google. Some heavy hitters in artificial intelligence have been working on ways to make machine learning techniques smarter about understanding networks. Late last week, a group of those researchers reported progress in having a neural network figure out the structure of a various networks without having full knowledge of all of a network.


Google Brain, Microsoft Plumb the Mysteries of Networks with AI

ZDNet

We live in an age of networks. From the social graph of Facebook to the interactions of proteins in the body, more and more of the world is being conceived of and represented as the connections in a network. And understanding of those connections can sometimes have stunning business implications, as when Larry Page and Sergey Brin of Stanford University first proposed modeled networks of Web pages, called "PageRank," the foundation of Google. Some heavy hitters in artificial intelligence have been working on ways to make machine learning techniques smarter about understanding networks. Late last week, a group of those researchers reported progress in having a neural network figure out the structure of a various networks without having full knowledge of all of a network.


Modeling Online Discourse with Coupled Distributed Topics

arXiv.org Machine Learning

In this paper, we propose a deep, globally normalized topic model that incorporates structural relationships connecting documents in socially generated corpora, such as online forums. Our model (1) captures discursive interactions along observed reply links in addition to traditional topic information, and (2) incorporates latent distributed representations arranged in a deep architecture, which enables a GPU-based mean-field inference procedure that scales efficiently to large data. We apply our model to a new social media dataset consisting of 13M comments mined from the popular internet forum Reddit, a domain that poses significant challenges to models that do not account for relationships connecting user comments. We evaluate against existing methods across multiple metrics including perplexity and metadata prediction, and qualitatively analyze the learned interaction patterns.


Reddit - MachineLearning - [P] Keras Implementation of Multi-gate Mixture-of-Experts for Multi-task Learning

#artificialintelligence

I have recently read this paper from KDD 2018 and wanted to implement the paper and tried to see if I can reproduce the results. This is my first time implementing a paper and I don't think my implementation was perfect. However, I'm excited about this work and I would really appreciate it if y'all can take a look at it and give some feedback on the implementation! Please feel free to submit issues/PRs and I'm more than happy to discuss them and make the implementation better:)