The era of generic services is waning. Customers want to get offers tailored to their needs. You can see the numbers for yourself: half of customers are likely to switch brands if a company doesn't meet their needs, and 57 percent are willing to share data with companies that send personalized offers. Technological progress and digital improvements have created new chances for vendors to attract and retain customers. Yet there's a huge gap between mass personalization and true individualization: you can hardly delight your target audience with a mobile app that lacks any prominent feature or that contains bothersome pop-up ads.

If it's started to feel like all summer blockbuster movies are being written by robots [INSERT FORMER PRO WRESTLER, INSERT GIANT CGI ANIMAL], you'll be disquieted to learn that that future may not be too far off. The meditation app Calm teamed up with the tech team at Botnik to write a new Brothers Grimm-style fairy tale entirely through artificial intelligence. By inputting the data from existing Brothers Grimm stories and using predictive text technology (and with a few human writers stitching things together), the group at Botnik crafted "The Princess and the Fox," a story about "a talking fox [who] helps the lowly miller's son to rescue the beautiful princess from the fate of having to marry a dreadful prince who she does not love." "We're doing for the Brothers Grimm what Jurassic Park did for dinosaurs," says Michael Acton Smith, co-founder of Calm, in a press press release. "We're bringing them back from the dead, with modern science."

I saw a post of some time ago where somebody wanted to make use of variable length input vectors where the input would represent the pixels of multiple pictures and the resolution of the pictures would change thus the input. It seems a bit impossible as removing, changing or adding input nodes makes an impact on the network it self as weights and biases would have to be readjusted. I am doing research about this topic so if you have any insights or interesting papers to read it would be appreciated. Would the impact of removing or adding one input node on a relatively small network make a huge impact? If you keep memory of previous input nodes and place them back into the network after a few time steps would it worsen or better the network?

Over the last few years, the wave of machine learning and artificial intelligence APIs has been cresting as more and more businesses see the potential for differentiation and more and more API providers look to service that need. IBM clearly recognized the potential of these APIs when it acquired AlchemyAPI back in 2015. Alchemy specialized in machine-learning driven APIs like sentiment analysis and image/language processing. Those APIs are now a part of IBM's Watson portfolio. Now, a few years later, everyone is getting into the game.

It's Saturday night and you've just finished watching the last episode of a Swedish crime drama that you somehow stumbled upon, although you can't quite remember how. It's late and probably time for bed, but--without prompting--your Netflix screen fills with promotional shots for more shows. There's one about a female detective in Denmark and another about a British inspector who weaves between both sides of the law. It's a familiar scenario to any Netflix watcher--when the service seems to magically suggest programs that fit your latest pop-culture craze. These days, the computer algorithms that allow Netflix or Amazon to make purchasing suggestions are a normal part of life.

Main contributor of the repo here- shameless plug but some might find it useful. I wrote up a pretty simple version of Alpha Zero that works with any framework (PyTorch, Tensorflow, Keras) and any game (currently Othello, Gobang, TicTacToe, Connect4). Certainly not written to scale to large games, but hopefully clean code that can be fun to hack around with for smaller projects. Also a tutorial that goes with it here: http://web.stanford.edu/

We are pleased to announce that in "The Forrester New Wave: Conversational Computing Platforms, Q2 2018,"[1] IBM Watson Assistant is named as a Leader in conversational computing. It has become increasingly important for businesses to build engaging interactions that deliver value to their customers, and IBM is proud to offer technologies that help developers and enterprises enhance those experiences. The report evaluated the most significant conversational computing platforms, diving into each vendor's current offering and strategy and including customer feedback. IBM was cited for its developer-friendly tools and enterprise expertise requirements, which give developers access to the tools and technologies they need while providing industry and enterprise support for their businesses. Our customers also appreciated IBM's thorough understanding of enterprise requirements and Watson Assistant's readiness for integration into a broader architecture.

Estimator mostly saves you boilerplate. In the single host case, this is the train and eval loops, summaries, and checkpointing. You said you've had your pipeline for 2 years, so you've likely already implemented this yourself (in fact, 2 years ago Estimator barely existed IIRC). So it would likely bring you little benefit to switch now, except to save some code you have to maintain, maybe. In the distributed setting, it also helps you with graph replication.

The United States and the United Kingdom are leading this initiative. Denmark, Germany, the Netherlands, Norway and Spain are also involved (see go.nature.com/2hebxnt). Artificial intelligence (AI) is poised to revolutionize this activity.

Bias in journalism is nothing new, but there are growing concerns technology is pushing us into echo chambers where we only hear one side of the story. Now a startup says it's using AI to bring us a truly impartial source of news. Knowhere launched earlier this month, alongside an announcement that it had raised $1.8 million in venture capital. The site uses AI to aggregate news from hundreds of sources and create three versions of each story: one skewed to the left, one skewed to the right, and one that's meant to be impartial. Natural language processing algorithms trawl through more than a thousand news sources to identify popular stories, the company told Motherboard.