TMMI, high resolution video technology pioneer, expands into artificial intelligence with co-development agreement. TMMI President, Michael Kozole made the announcement stating, "TMMI has always focused on high quality, cutting-edge, video technology since its beginning in 1990. Over the recent months, TMMI has assembled this opportunity to bring together an association of top-level talent and shared technology development with CodeBaby, that will create emotionally intelligent avatars that deliver all-new experiences in artificial intelligence with greater access to a broad base of businesses and consumers". The CodeBaby teams come with over 20 years of experience in animation, gaming and artificial intelligence. Founded in 2001, CodeBaby attracted the attention of two doctors in Alberta, Canada, who had co-founded a video game company (Bioware) in 1995.

In fact, though, the world that Varoufakis has created feels strangely foreign, partly as a result of the rather essayistic writing style. There are constant references to somebody or something: Plato, Odysseus, futurists or even just the Eurovision Song Contest. It's not always clear what the references are meant to convey. When told that it wasn't particularly easy to read, he says: "My wife also tells me that it was hard to read. She said she loves it, but needed to read it again."

In an age of conspiracies, here is a striking example, preposterous as it may sound. Highly intelligent robots--general artificial intelligence--surround us, undetected but fundamentally in charge, and human beings are just following instructions that they receive from these elusive entities. Or, a little less preposterously, imagine that the world is alive with consciousness and intelligence, and human thought reflects these processes. Does it sound like something out of The Matrix? The science fiction classic is not science fiction but a parable of something very real--namely, cinema itself. When you enter the dark room of a movie theater, a radical transformation takes place. You become the screen, and the mind that perceives, thinks, and connects ideas is fully contained in the celluloid roll, or the digital file. In a movie, everything has already been perceived in exactly the sequence that it is intended to be perceived; in the dark room, those images leave the hidden mind of the celluloid and get projected onto your mind--they get force-fed into your mind and the minds of the other viewers, where the images and ideas properly unfold.

Recent advances in deep learning have expanded possibilities to generate music, but generating a customizable full piece of music with consistent long-term structure remains a challenge. This paper introduces MusicFrameworks, a hierarchical music structure representation and a multi-step generative process to create a full-length melody guided by long-term repetitive structure, chord, melodic contour, and rhythm constraints. We first organize the full melody with section and phrase-level structure. To generate melody in each phrase, we generate rhythm and basic melody using two separate transformer-based networks, and then generate the melody conditioned on the basic melody, rhythm and chords in an auto-regressive manner. By factoring music generation into sub-problems, our approach allows simpler models and requires less data. To customize or add variety, one can alter chords, basic melody, and rhythm structure in the music frameworks, letting our networks generate the melody accordingly. Additionally, we introduce new features to encode musical positional information, rhythm patterns, and melodic contours based on musical domain knowledge. A listening test reveals that melodies generated by our method are rated as good as or better than human-composed music in the POP909 dataset about half the time.

Can machines learn to use a search engine as an interactive tool for finding information? That would have far reaching consequences for making the world's knowledge more accessible. This paper presents first steps in designing agents that learn meta-strategies for contextual query refinements. Our approach uses machine reading to guide the selection of refinement terms from aggregated search results. Agents are then empowered with simple but effective search operators to exert fine-grained and transparent control over queries and search results. We develop a novel way of generating synthetic search sessions, which leverages the power of transformer-based generative language models through (self-)supervised learning. We also present a reinforcement learning agent with dynamically constrained actions that can learn interactive search strategies completely from scratch. In both cases, we obtain significant improvements over one-shot search with a strong information retrieval baseline. Finally, we provide an in-depth analysis of the learned search policies.

Existing audio-language task-specific predictive approaches focus on building complicated late-fusion mechanisms. However, these models are facing challenges of overfitting with limited labels and low model generalization abilities. In this paper, we present a Cross-modal Transformer for Audio-and-Language, i.e., CTAL, which aims to learn the intra-modality and inter-modality connections between audio and language through two proxy tasks on a large amount of audio-and-language pairs: masked language modeling and masked cross-modal acoustic modeling. After fine-tuning our pre-trained model on multiple downstream audio-and-language tasks, we observe significant improvements across various tasks, such as, emotion classification, sentiment analysis, and speaker verification. On this basis, we further propose a specially-designed fusion mechanism that can be used in fine-tuning phase, which allows our pre-trained model to achieve better performance. Lastly, we demonstrate detailed ablation studies to prove that both our novel cross-modality fusion component and audio-language pre-training methods significantly contribute to the promising results.

Deciding which of Netflix's thousands of shows and movies to grant your all-important click can be a paralyzing task for many of us, so there was something brilliant, or cynical--or in all likelihood, both--about the streaming service coming out with a show called Clickbait. It's announcing itself as potentially dishonest and exploitative and daring you to click anyway, and the gambit clearly worked: As of Tuesday, the limited series, which premiered on the streaming service last week, was topping Netflix's most-watched list. Whether you don't want to give Clickbait the satisfaction of your click or you've already clicked many times over, let's talk about it--and there is a lot to talk about--spoilers and all. In the first episode of the eight-episode series, a video surfaces online of Nick Brewer (Adrian Grenier), an improbably perfect husband and father, being held hostage and holding a series of signs: One says he abuses women. Another says that if the video gets to 5 million views, he will die.

This is the third blog post in a series covering churn and lifetime customer value (Introduction to Churn & Introduction to LTV). There are many ways to predict churn rate on the individual customer level. The full code is available in the Jupyter notebook. In this dataset, we have users of the KKBOX music streaming service along with their attributes, transaction histories and churn label (whether a customer will churn out in the next 30 days). Due to the nature of the business, customers can put subscriptions on pause or change subscription intervals, which makes this dataset both contractual and non-contractual simultaneously.

When I lived in the Bay Area, I used to get together with my friend Jaron Lanier to explore the implications of spectacularly weird thought experiments. Outlandish thought experiments have been essential in the intellectual history of science, but the point isn't the weirdness itself. The payoff of thinking about strange things like Schrödinger's cat, the infamous cat that is alive and dead at the same time, is not necessarily that we should then "believe" in the existence of such a cat. Instead, we can hope that uncommon ideas will shed light on the murky margins of our thoughts; in the case of Schrodinger's cat, in dealing with the question of superposition. The point is not to confuse or bamboozle people, but to eventually find a way to think that makes more sense and is a little less murky.

The Shadoks were "anthropomorphic creatures with the appearance of chubby birds, with long, filiform legs, tiny and prehensile wings, and original hair." Living on a planet with uncertain contours, their main life goal was to build a rocket to land on the earth. To achieve this, they invented the "Cosmopump" intended to pump the "Cosmogol 999" to fuel their rocket. Let's forget the Shadoks for a moment and focus on the effort of reading that led us to these lines. Our brains consumed energy during this turmoil.