Despite what mainstream media wants to led us to believe, the current state of Artificial Intelligence is not yet capable of replacing humans.

The key to successfully applying artificial intelligence in the real world is to get up close and personal with the people who'll to use it, executives agreed during a roundtable discussion about A.I. during Fortune's Brainstorm Design conference in Singapore last Thursday. "The reason we all wear black shirts, by the way, is that you can't see the blood splatters on them, because we need to be so close to the action that we're getting covered in blood," said Sean Carney with a smile. Carney, who indeed was wearing a black dress shirt, is chief design officer for Philips, which claims substantial market share for A.I.-driven healthcare tools. Many clinicians worry that new technologies will put them out of a job. It's the wrong mindset, Carney said.

Machine learning models of music typically break up the task of composition into a chronological process, composing a piece of music in a single pass from beginning to end. On the contrary, human composers write music in a nonlinear fashion, scribbling motifs here and there, often revisiting choices previously made. In order to better approximate this process, we train a convolutional neural network to complete partial musical scores, and explore the use of blocked Gibbs sampling as an analogue to rewriting. Neither the model nor the generative procedure are tied to a particular causal direction of composition. Our model is an instance of orderless NADE (Uria et al., 2014), which allows more direct ancestral sampling. However, we find that Gibbs sampling greatly improves sample quality, which we demonstrate to be due to some conditional distributions being poorly modeled. Moreover, we show that even the cheap approximate blocked Gibbs procedure from Yao et al. (2014) yields better samples than ancestral sampling, based on both log-likelihood and human evaluation.

StellarGraph is a Python 3 library. The StellarGraph library implements several state-of-the-art algorithms for applying machine learning methods to discover patterns and answer questions using graph-structured data. Added GraphConvolution layer, GCN class for a stack of GraphConvolution layers, and FullBatchNodeGenerator class for feeding data into GCN (from version 0.5.0) We provide examples of using StellarGraph to solve such tasks using several real-world datasets.

Artificial intelligence can identify people in pictures, find the next TV series you should binge watch on Netflix, and even drive a car. But on Friday, when a suspected terrorist in New Zealand streamed live video to Facebook of a mass murder, the technology was of no help. The gruesome broadcast went on for at least 17 minutes until New Zealand police reported it to the social network. Recordings of the video and related posts about it rocketed across social media while companies tried to keep up. Why can't AI, which is already used by major social networks to help moderate the status updates, photos, and videos users upload, simply be deployed in greater measures to remove such violence as swiftly as it appears? A big reason is that whether it's hateful written posts, pornography, or violent images or videos, artificial intelligence still isn't great at spotting objectional content online.

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Reuters reports the former Texas congressman once belonged to Cult of the Dead Cow, an influential group "jokingly named after an abandoned Texas slaughterhouse." While there's no evidence that O'Rourke really got his hands dirty with what we'd consider nefarious "hacking," he was a member, which might help explain some of the policies he could champion during a presidential run. Music royalties can be confusing, but this piece breaks down what's happening with Spotify, Google, Pandora and Amazon. And most importantly, why it matters. A profile of Demis Hassabis, a co-founder of DeepMind, examines the origins of the AI startup and asks how much longer it can retain its independence from Google.

The musical score was developed in collaboration with kling klang klong guided by the intention to enhance the emotional, complex and unpredictable nature of the visual aesthetic. Through a research of various computational strategies, the musical composition was created using the Google Magenta Performance RNN learning model and a custom-built procedural system. To collect training datasets, piano players were invited to improvise musical phrases based on the diverse visual sceneries of the animation. The range of auditive interpretations they produced served as a large-scale polyphonic inventory for the Artificial Intelligence to learn from. To reflect the performative nature of the work, values outputted directly from the river simulation such as the length, curvature and events of connections of the meandering river were analyzed and interpreted in real-time to influence musical parameters like tempo, note strength, intensity, pitch range.

BEIJING: As a journalist, I always try to ask smart questions. But I could not beat the questions that a group of five-year-olds posed to China's top artificial intelligence (AI) guru, like the ones in the headline of this article. People pay attention to what Dr Lee Kai-Fu says – lots of people. Fifty million people in China follow him on social media, which means he has more fans than American talk show host Oprah Winfrey. One of his tag lines is "AI is reshaping the world as we know it".

BEIJING: As a journalist, I always try to ask smart questions. But I could not beat the questions that a group of five-year-olds posed to China's top artificial intelligence (AI) guru, like the ones in the headline of this article. People pay attention to what Dr Lee Kai-Fu says – lots of people. Fifty million people in China follow him on social media, which means he has more fans than American talk show host Oprah Winfrey. One of his tag lines is "AI is reshaping the world as we know it".