Until a few years ago, people queued at the bank, booked their holidays off brochures, paid their bills in cash, bought their music on CDs and waited in line at outpatient departments to see a doctor. People bank and book holidays online, use Revolut and PayPal to settle bills, and Spotify their music. Yet, they still wait in line at outpatient departments to see a doctor. The question practically asks itself. Why hasn't the'consumer revolution' reached the healthcare doorstep?

Will androids write novels about electric sheep? The dream, or nightmare, of totally machine-generated prose seemed to have come one step closer with the recent announcement of an artificial intelligence that could produce, all by itself, plausible news stories or fiction. It was the brainchild of OpenAI – a nonprofit lab backed by Elon Musk and other tech entrepreneurs – which slyly alarmed the literati by announcing that the AI (called GPT2) was too dangerous for them to release into the wild, because it could be employed to create "deepfakes for text". "Due to our concerns about malicious applications of the technology," they said, "we are not releasing the trained model." Are machine-learning entities going to be the new weapons of information terrorism, or will they just put humble midlist novelists out of business?

Stuart Russell has a rule. "I won't do an interview until you agree not to put a Terminator on it," says the renowned British computer scientist, sitting in a spare room at his home in Berkeley, California. "The media is very fond of putting a Terminator on anything to do with artificial intelligence." The request is a tad ironic. Russell, after all, was the man behind Slaughterbots, a dystopian short film he released in 2017 with the Future of Life Institute.

TRANSFERRING NEURAL SPEECH W A VEFORM SYNTHESIZERS TO MUSICAL INSTRUMENT SOUNDS GENERA TION Yi Zhao null Xin W ang null Lauri Juvela † Junichi Y amagishi null null National Institute of Informatics, Tokyo, Japan † Department of Signal Processing and Acoustics, Aalto University, Finland ABSTRACT Recent neural waveform synthesizers such as WaveNet, WaveGlow, and the neural-source-filter (NSF) model have shown good performance in speech synthesis despite their different methods of waveform generation. The similarity between speech and music audio synthesis techniques suggests interesting avenues to explore in terms of the best way to apply speech synthesizers in the music domain. This work compares three neural synthesizers used for musical instrument sounds generation under three scenarios: training from scratch on music data, zero-shot learning from the speech domain, and fine-tuning-based adaptation from the speech to the music domain. The results of a large-scale perceptual test demonstrated that the performance of three synthesizers improved when they were pre-trained on speech data and fine-tuned on music data, which indicates the usefulness of knowledge from speech data for music audio generation. Among the synthesizers, WaveGlow showed the best potential in zero-shot learning while NSF performed best in the other scenarios and could generate samples that were perceptually close to natural audio. Index T erms -- Neural waveform synthesizer, musical instrument sounds synthesis, zero-shot adaptation, fine-tuning 1. INTRODUCTION Many technological parallels can be drawn between the synthesis of speech and musical instruments, both historically and in the present deep learning era. Previously, concatenative techniques had been widely applied in text-to-speech (TTS) [1] and musical sound synthesis [2].

Of the few limited works which differentiate between trusted vs other types of news article (satire, propaganda, hoax), none of them model sentence interactions within a document. We observe an interesting pattern in the way sentences interact with each other across different kind of news articles. To capture this kind of information for long news articles, we propose a graph neural network-based model which does away with the need of feature engineering for fine grained fake news classification. Through experiments, we show that our proposed method beats strong neural baselines and achieves state-of-the-art accuracy on existing datasets. Moreover, we establish the generalizability of our model by evaluating its performance in out-of-domain scenarios. Code is available at https: //github.com/MysteryVaibhav/

Will the Third World War be fought by robots? This short film is a light-hearted comedy that aims to trigger an interesting discussion and reflexion on the terrifying killer-robot stories that increasingly fill us with dread when we read the news headlines. The fictional scenario takes inspiration from current scientific research and describes a future where robots are asked by humans to join the war. Robots are divided, sparking protests in robot society... will robots join the conflict or will they refuse to be employed in human warfare? Food for thought for engineers, roboticists and anyone imagining what the upcoming robot revolution could look like. We let robots pop on camera to tell a story, taking on the role of actors playing in the film, instructed through code on how to "act" for each scene.

Abe's lab shared a coffee pot with the machine learning group of Geoffrey Hinton -- inventor of deep neural networks. That's also where he met his …

It's called deep learning or machine learning. I am interested in harnessing the power of deep learning to improve our models.