I definitely understand that, though I am a bit surprised to see MSR papers being referenced often in an RL workshop. I don't quite remember any RL papers of theirs that weren't strongly theoretical (other than that HRL paper by Hoang et Daume). I'm also a PhD student that until recently was doing RL, but have become tired of working in a field that's moving this fast and seems to be taking a'fast science' approach. It feels almost impossible to have any sort of real impact. I'm also just bored with reading RL papers, about 10% of the time I spend reading them is understanding the novelty, and the rest studying their experimentation to get a feel for whether it's worth anything.

This is a 30 minute talk from GOTO Copenhagen 2019 by Feynman Liang - Creator of BachBot. I've dropped the full talk abstract below for a read before diving into the talk: Can musical creativity, something believed to be deeply human, be codified into an algorithm? While most music theorists are hesitant to claim a "correct" algorithm for composing music like Bach, recent advances in machine learning and computational musicology may help us reach an answer. In this talk, we describe BachBot: an artificial intelligence which uses deep learning and long short term memory (LSTM) to compose music in the style of Bach. We train BachBot on all known Bach chorale harmonisations and carry out the largest musical Turing test to date.

This video explores the recent study on Deep Double Descent! This is an interesting phenomenon of increasing and decreasing test error with respect to scaling up model size, training data, and epochs. The lens of bias-variance tradeoff may lead you to interpret increasing test error as overfitting, however the double descent phenomenon shows remarkable cases of a second descent when continuing to increase model size / epoch count. Awareness of this phenomenon may help you interpret your training curves for hyperparameter optimization and architectures search!

Avigilon Corporation, a Motorola Solutions company, presents the newest version of its video management software, Avigilon Control Center (ACC) 7.4, which incorporates artificial intelligence-powered facial recognition technology. FEATURES OF AVIGILON CONTROL CENTER (ACC) 7.4 The new "appearance alerts" capability will help commercial organizations, such as educational institutions and hospitals, accelerate response times by identifying people of interest in enterprise settings. For example, the technology can alert the security team at a local high school when a banned or flagged individual has entered the campus. People of interest are identified based on a secure, controlled watch list created and maintained by authorized users at the commercial organization. For organizations that use the new ACC software and license their Avigilon cameras for facial recognition, cameras will seek to identify potential matches based on the watch list.

The Decade, Reviewed looks back at the 2010s and how it changed human society forever. From 2010 to 2019, our species experienced seismic shifts in science, technology, entertainment, transportation, and even the very planet we call home. This is how the past ten years have changed us. Bots are a lot like humans: Some are cute. Some are annoying ... and a little racist.

Chinese artist Ai Weiwei announced a new documentary at Art Basel Miami Beach, where he has several pieces on display, the Art Newspaper reports. With one film already in the works on animals and the environment, Ai sent a camera team to the Brazilian states of Rondônia, Mato Grosso, and Amazonas to capture footage of the ongoing fires in the Amazon Rainforest, along with another team which went to Pará to shoot cattle farms. This footage will be used for a separate documentary on the fires, as well as in next year's production of Turandot at the Teatro dell'Opera di Roma, which Ai is directing. Agribusiness and the deforestation of the Amazon are inextricably linked issues. Ai said in his announcement: "We can clearly see that the fires are a part of a wide-ranging and premeditated plan to cause deforestation to increase land use for agriculture and cattle farming."

In short, onnx provides an Open Neural Network Exchange format. This format, describes a huge set of operators, that can be mixed to create every type of machine learning model that you ever heard of, from a simple neural network to complex deep convolutional networks. Some examples of operators are: matrix multiplications, convolutions, adding, maxpool, sin, cosine, you name it! They provide a standardised set of operators here. So we can say that onnx provides a layer of abstraction to ML models, which makes all framework compatible between them.

Yoshua Bengio is known as one of the "three musketeers" of deep learning, the type of artificial intelligence (AI) that dominates the field today.

In this paper we propose a deep learning method for performing attributed-based music-to-image translation. The proposed method is applied for synthesizing visual stories according to the sentiment expressed by songs. The generated images aim to induce the same feelings to the viewers, as the original song does, reinforcing the primary aim of music, i.e., communicating feelings. The process of music-to-image translation poses unique challenges, mainly due to the unstable mapping between the different modalities involved in this process. In this paper, we employ a trainable cross-modal translation method to overcome this limitation, leading to the first, to the best of our knowledge, deep learning method for generating sentiment-aware visual stories. Various aspects of the proposed method are extensively evaluated and discussed using different songs.