Arguably the most memorable scene in "The Devil Wears Prada" was Meryl Streep's searing monologue reprimanding the doe-eyed Anne Hathaway for having undermined fashion's profound influence on mass culture. In the movie, she's able to draw from her prolific knowledge of the industry to trace the origins of a certain cerulean blue hue, from the runways of Oscar de la Renta and St Laurent to the racks of department stores. Today, however, no one needs an impeccable memory to understand the life cycle of a colour du jour. In partnership with BoF, Google unveiled Thursday at the VOICES Conference an interactive online tool, free to use, to take a colour palette and pinpoint runway looks with the same colour schemes, drawn from nearly 4,000 fashion shows. Spearheaded by Google's artist-in-residence Cyril Diagne and announced in VOICES 2017, the project uses machine learning to map out these fashion palettes, and allows users to upload their own photos.

Splice is blowing up like a hit song. The audio sample marketplace has doubled revenue and user count in a year, and now reaches 3 million musicians. Many pay $7.99 for unlimited access, and 70% of subscribers visit weekly to hunt down the freshest and trendiest sounds to give their tracks that special something. But words can't always describe music. Searching by genre and subjective tags can take forever and leave artists frustrated when the sounds they find they don't resonate right.

This is a talk from GOTO Chicago 2019 by Doug Lenat, Award-winning AI pioneer who created the landmark Machine Learning program, AM, in 1976 and CEO of Cycorp. I've dropped the full talk abstract below for a read before diving into the talk: Almost everyone who talks about Artificial Intelligence, nowadays, means training multi-level neural nets on big data. Developing and using those patterns is a lot like what our right brain hemispheres do; it enables AI's to react quickly and – very often – adequately. But we human beings also make good use of our left brain hemisphere, which reasons more slowly, logically, and causally. I will discuss this "other type of AI" – i.e., left brain AI, which comprises a formal representation language, a "seed" knowledge base with hand-engineered default rules of common sense and good domain-specific expert judgement written in that language, and an inference engine capable of producing hundreds-deep chains of deduction, induction, and abduction on that large knowledge base.

The real power of machine learning may have nothing to with automating music making, and everything to do with making sound tools hear the way you do. While not a broadly known topic, the problem of source separation has interested a large community of music signal researchers for a couple of decades now. Wait a second – sure, you may not call it "source separation," but anyone who has tried to make remixes, or adapt a song for karaoke sing-alongs, or even just lost the separate tracks to a project has encountered and thought about this problem. You can hear the difference between the bassline and the singer – so why can't your computer process the sound the way you hear? Splitting stems out of a stereo audio feed also demonstrates that tools like EQ, filters, and multiband compressors are woefully inadequate to the task.

A recently released study explains how News Robots and journalism can benefit from Artificial Intelligence. According to the study, Artificial Intelligence can give reports more time to focus on some new ways to produce and deliver news to viewers. According to a report by a joint effort by Polis from the London School of Economics and Political Science and the Google News Initiative, it states that Artificial Intelligence could enable the journalists to simply leave the ordinary and repetitive tasks to machines and focus on other important tasks. News Organizations and AI According to a recent study that surveyed around 71 news organizations available in 32 different countries reveals that there are so many newsrooms already using the AI to find videos and images and also transcribe it into multiple languages. According to LSE, some news organizations like Finnish Public broadcaster Yle are already using news robots, these news robot journalists available at YLe are producing hundreds of pieces of textual content and illustrations within a week.

Newswire) VSBLTY Groupe Technologies Corp. (CSE: VSBY) (5VS.F) (VSBGF), a leading retail software and technology company, announced today that-in partnership with Onyx-Cognivas Pty.-it is launching two privately-led security deployments in South Africa to support community safety initiatives. The state-of-the-art security technology will protect two prominent high-rise residential apartment buildings in the upmarket Sandton area, a high income residential, financial and business suburb of Johannesburg with a population of 225,000. The rollout plan is to deploy this technology across several apartment blocks, a hotel and commercial properties in the precinct-with the objective of deploying a "private Smart City". In addition, advanced custom sensory applications are planned to be installed in a well-known petroleum group with convenience stores/service stations throughout South Africa. The announcement was made by Jay Hutton, VSBLTY co-founder and CEO, who said, "We are excited to provide complete Smart City-like security solutions in Sandton. This state-of-the-art technology uses the power of machine learning and computer vision."

Much of it is the same as Value Prediction Networks, which proposes that instead of training a model to minimize L2 prediction-loss, you just train it to get the long-term reward/value right for a start state and a series of actions. That gets around a lot of the difficulty of using MBRL for Atari-like things, where it's very hard to accurately predict next pixels. They pretty much simulate a dense tree to some short depth, assign estimated values to the nodes, and use that for action selection. One is that you're probably simulating a lot of states that your value-function would tell you are DEFINITELY not worthwhile. Atari has 16 actions -- it's unfeasible to simulate more than 3 states deep. And since you're simulating in all directions, but only taking the best (e-greedy) action, you're not going to gather training data on most of the transitions you're estimating.

Machine learning gained momentum in the 90s with a shift away from earlier symbolic approaches and towards exploiting probability and statistics.

With drone photography, "we can track all of the trash in a creek, river, or stream, examine how it's distributed, and then apply machine–learning …