Models for audio source separation usually operate on the magnitude spectrum, which ignores phase information and makes separation performance dependant on hyper-parameters for the spectral front-end. Therefore, we investigate end-to-end source separation in the time-domain, which allows modelling phase information and avoids fixed spectral transformations. Due to high sampling rates for audio, employing a long temporal input context on the sample level is difficult, but required for high quality separation results because of long-range temporal correlations. In this context, we propose the Wave-U-Net, an adaptation of the U-Net to the one-dimensional time domain, which repeatedly resamples feature maps to compute and combine features at different time scales. We introduce further architectural improvements, including an output layer that enforces source additivity, an upsampling technique and a context-aware prediction framework to reduce output artifacts. Experiments for singing voice separation indicate that our architecture yields a performance comparable to a state-of-the-art spectrogram-based U-Net architecture, given the same data. Finally, we reveal a problem with outliers in the currently used SDR evaluation metrics and suggest reporting rank-based statistics to alleviate this problem.

Art depicting Norman, a "psychopath" AI created by researchers at MIT. (Photo: Massachusetts Institute of Technology) Researchers at the Massachusetts Institute of Technology created an artificial intelligence labeled a "psychopath," using disturbing image captions found on Reddit. The AI is named Norman, after the character in the Alfred Hitchcock classic "Psycho." Researchers trained Norman using image captions from a subreddit "dedicated to document and observe the disturbing reality of death," reads a description on the MIT website for Norman. Because of technical and ethical concerns, the team at MIT used captions and not actual images of people dying. "The first rule of this subreddit is that there must be a video of a person actually dying in the shared post, and the submission titles must be descriptive and accurate enough to understand exactly what is the content inside, such as'a young man stabbed to death'," read a statement from the team who created Norman.

Amazon's other voice-controlled Fire TV devices require a push of the remote's mic button or a separate Echo device with Amazon's Alexa voice assistant. Unlike the other devices, the Cube will let viewers switch between streaming services like Netflix and regular cable channels with such voice commands as "Alexa, turn on ESPN." The new device can also do typical Alexa tasks, such as playing "Jeopardy!" or fetching the weather, even when the TV is off.

The National Transportation Safety Board also says crash data shows the SUV did not brake or steer away from the barrier in the three seconds before the crash. The agency says it now will examine whether the Tesla Autopilot system performed as designed.

Get ready to ditch your remote. Amazon on Thursday announced the Fire TV Cube, a hands-free streaming box that comes with Alexa built in and lets you control your TV. Unlike Amazon's other entertainment-focused offering, the Fire Stick, the Fire TV Cube is meant to be hub for not just playing Netflix and Hulu, but also turning on cable TV and smart home devices. The Fire TV Cube starts at $119.99 and is available for pre-order beginning on Thursday, before shipping on June 21. It's unclear if or when the device will be available globally, but Amazon is running a few different promotions alongside the Fire TV Cube's release.

On May 9, Netflix launched its own research website. This highlights the focus Netflix has on Deep Learning and Data Science. The site is extremely well designed showing vertical classification of the different areas that Netflix research works on along with the horizontal business areas where Data Science is deployed at Netflix. It has some great articles with everything from video encoding to A/B testing where they use Data Science. I found the website to be very comprehensive making it a go to destination for things Netflix Data Science from different verticals to jobs.

Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards. However, for many real-world natural language commands that involve a degree of underspecification or ambiguity, such as "tidy the room", it would be challenging or impossible to program an appropriate reward function. To overcome this, we present a method for learning to follow commands from a training set of instructions and corresponding example goal-states, rather than an explicit reward function. Importantly, the example goal-states are not seen at test time. The approach effectively separates the representation of what instructions require from how they can be executed.

Artificial intelligence, robots, and automation are rising in importance in many areas. As noted in the recent book, " The Future of Work: Robots, AI, and …