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But what *is* a Neural Network? Deep learning, chapter 1

#artificialintelligence

Subscribe to stay notified about new videos: http://3b1b.co/subscribe Additional funding provided by Amplify Partners. Typo correction: At 14:45, the last index on the bias vector is n, when it's supposed to in fact be a k. Thanks for the sharp eyes that caught that! For those who want to learn more, I highly recommend the book by Michael Nielsen introducing neural networks and deep learning: https://goo.gl/Zmczdy


OkCupid review: A fun, hip dating site that's way less lame than the competition

Mashable

Let's just cut the mushy bullshit and get straight to the point: Online dating can truly suck. Don't get me wrong -- the help is seriously great when you start to feel like you're #ForeverAlone, but the entire process of creating a dating profile and dealing with less than ideal matches can be cumbersome. You're forced to answer fake "deep" questions that everyone lies about anyway, deal with creepy dudes and girls who think it's cute to terrorize your inbox, and navigate sites that just do not have what you're looking for. Here's the thing though: OkCupid is about to be your new best friend. SEE ALSO: Which dating app is right for you? Use this guide to figure it out. Everyone knows the name, but what tons of people (especially young people) may not realize is that it's not just another eharmony or Match. OkCupid is the perfect happy medium that you might have thought didn't exist.


Alexa's advice to 'kill your foster parents' fuels concern over Amazon Echo

The Guardian

An Amazon customer got a grim message last year from Alexa, the virtual assistant in the company's smart speaker device: "Kill your foster parents." The user who heard the message from his Echo device wrote a harsh review on Amazon's website, Reuters reported - calling Alexa's utterance "a whole new level of creepy". An investigation found the bot had quoted from the social media site Reddit, known for harsh and sometimes abusive messages, people familiar with the investigation told Reuters. The odd command is one of many hiccups that have happened as Amazon tries to train its machine to act something like a human, engaging in casual conversations in response to its owner's questions or comments. The research is helping Alexa mimic human banter and talk about almost anything she finds on the internet.


Learning Dynamic Embeddings from Temporal Interactions

arXiv.org Machine Learning

Modeling a sequence of interactions between users and items (e.g., products, posts, or courses) is crucial in domains such as e-commerce, social networking, and education to predict future interactions. Representation learning presents an attractive solution to model the dynamic evolution of user and item properties, where each user/item can be embedded in a euclidean space and its evolution can be modeled by dynamic changes in embedding. However, existing embedding methods either generate static embeddings, treat users and items independently, or are not scalable. Here we present JODIE, a coupled recurrent model to jointly learn the dynamic embeddings of users and items from a sequence of user-item interactions. JODIE has three components. First, the update component updates the user and item embedding from each interaction using their previous embeddings with the two mutually-recursive Recurrent Neural Networks. Second, a novel projection component is trained to forecast the embedding of users at any future time. Finally, the prediction component directly predicts the embedding of the item in a future interaction. For models that learn from a sequence of interactions, traditional training data batching cannot be done due to complex user-user dependencies. Therefore, we present a novel batching algorithm called t-Batch that generates time-consistent batches of training data that can run in parallel, giving massive speed-up. We conduct six experiments on two prediction tasks---future interaction prediction and state change prediction---using four real-world datasets. We show that JODIE outperforms six state-of-the-art algorithms in these tasks by up to 22.4%. Moreover, we show that JODIE is highly scalable and up to 9.2x faster than comparable models. As an additional experiment, we illustrate that JODIE can predict student drop-out from courses five interactions in advance.


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.


Multi-channel discourse as an indicator for Bitcoin price and volume movements

arXiv.org Machine Learning

This research aims to identify how Bitcoin-related news publications and online discourse are expressed in Bitcoin exchange movements of price and volume. Being inherently digital, all Bitcoin-related fundamental data (from exchanges, as well as transactional data directly from the blockchain) is available online, something that is not true for traditional businesses or currencies traded on exchanges. This makes Bitcoin an interesting subject for such research, as it enables the mapping of sentiment to fundamental events that might otherwise be inaccessible. Furthermore, Bitcoin discussion largely takes place on online forums and chat channels. In stock trading, the value of sentiment data in trading decisions has been demonstrated numerous times [1] [2] [3], and this research aims to determine whether there is value in such data for Bitcoin trading models. To achieve this, data over the year 2015 has been collected from Bitcointalk.org, (the biggest Bitcoin forum in post volume), established news sources such as Bloomberg and the Wall Street Journal, the complete /r/btc and /r/Bitcoin subreddits, and the bitcoin-otc and bitcoin-dev IRC channels. By analyzing this data on sentiment and volume, we find weak to moderate correlations between forum, news, and Reddit sentiment and movements in price and volume from 1 to 5 days after the sentiment was expressed. A Granger causality test confirms the predictive causality of the sentiment on the daily percentage price and volume movements, and at the same time underscores the predictive causality of market movements on sentiment expressions in online communities


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.