I've been paranoid about posting anything about my personal life publicly since a bruising experience about a decade ago. My images and personal information were splashed across an online forum, then dissected and ridiculed by people who didn't like a column I'd written for a Finnish newspaper. Up to that point, like many people, I'd carelessly littered the internet with my data: personal blog posts, embarrassing photo albums from nights out, posts about my location, relationship status, and political preferences, out in the open for anyone to see. OpenAI has provided limited access to its famous large language model, GPT-3, and Meta lets people play around with its model OPT-175B though a publicly available chatbot called BlenderBot 3. I decided to try out both models, starting by asking GPT-3: Who is Melissa Heikkilä? When I read this, I froze.

Netflix is pioneering content creation at an unprecedented scale. Our catalog of thousands of films and series caters to 195M members in over 190 countries who span a broad and diverse range of tastes. Content, marketing, and studio production executives make the key decisions that aspire to maximize each series' or film's potential to bring joy to our subscribers as it progresses from pitch to play on our service. Our job is to support them. The commissioning of a series or film, which we refer to as a title, is a creative decision. Executives consider many factors including narrative quality, relation to the current societal context or zeitgeist, creative talent relationships, and audience composition and size, to name a few.

Cadences are complex structures that have been driving music from the beginning of contrapuntal polyphony until today. Detecting such structures is vital for numerous MIR tasks such as musicological analysis, key detection, or music segmentation. However, automatic cadence detection remains challenging mainly because it involves a combination of high-level musical elements like harmony, voice leading, and rhythm. In this work, we present a graph representation of symbolic scores as an intermediate means to solve the cadence detection task. We approach cadence detection as an imbalanced node classification problem using a Graph Convolutional Network. We obtain results that are roughly on par with the state of the art, and we present a model capable of making predictions at multiple levels of granularity, from individual notes to beats, thanks to the fine-grained, note-by-note representation. Moreover, our experiments suggest that graph convolution can learn non-local features that assist in cadence detection, freeing us from the need of having to devise specialized features that encode non-local context. We argue that this general approach to modeling musical scores and classification tasks has a number of potential advantages, beyond the specific recognition task presented here.

Recent years have seen considerable advances in audio synthesis with deep generative models. However, the state-of-the-art is very difficult to quantify; different studies often use different evaluation methodologies and different metrics when reporting results, making a direct comparison to other systems difficult if not impossible. Furthermore, the perceptual relevance and meaning of the reported metrics in most cases unknown, prohibiting any conclusive insights with respect to practical usability and audio quality. This paper presents a study that investigates state-of-the-art approaches side-by-side with (i) a set of previously proposed objective metrics for audio reconstruction, and with (ii) a listening study. The results indicate that currently used objective metrics are insufficient to describe the perceptual quality of current systems.

Existing singing voice synthesis models (SVS) are usually trained on singing data and depend on either error-prone time-alignment and duration features or explicit music score information. In this paper, we propose Karaoker, a multispeaker Tacotron-based model conditioned on voice characteristic features that is trained exclusively on spoken data without requiring time-alignments. Karaoker synthesizes singing voice and transfers style following a multi-dimensional template extracted from a source waveform of an unseen singer/speaker. The model is jointly conditioned with a single deep convolutional encoder on continuous data including pitch, intensity, harmonicity, formants, cepstral peak prominence and octaves. We extend the text-to-speech training objective with feature reconstruction, classification and speaker identification tasks that guide the model to an accurate result. In addition to multitasking, we also employ a Wasserstein GAN training scheme as well as new losses on the acoustic model's output to further refine the quality of the model.

Musical Metacreation tries to obtain creative behaviors from computers algorithms composing music. In this paper I briefly analyze how this field evolved from algorithmic composition to be focused on the search for creativity, and I point out some issues in pursuing this goal. Finally, I argue that hybridization of algorithms can be a useful direction for research.

The recent Hulu movie Prey, a prequel to the 1987 sci-fi horror film Predator, pits a young Comanche woman against a brutal alien hunter. Science fiction author Zach Chapman loved the new movie. "It's definitely my favorite Predator film," Chapman says in Episode 524 of the Geek's Guide to the Galaxy podcast. "I think it's the only one in the franchise that has a theme--or at least that commits to a theme in a meaningful way--and the action is super awesome." Prey has been a hit with audiences and critics alike, a much-needed boost for the franchise after flops like The Predator and Alien vs. Predator: Requiem.

Let's find out about the cons of artificial intelligence to understand if an error cause chaos or devastation. The development and growth of humanity depend heavily on AI technology, and there is no doubt about that. Are you scared of AI jargon? We have already created a detailed AI glossary for the most commonly used artificial intelligence terms and explained the basics of artificial intelligence as well as the risks and benefits of artificial intelligence for organizations and others. So, it's time to explore the cons of artificial intelligence.

I like this output, but I'm not too happy about the Space Needle drifting left. Since it seems to float around with different seeds, let's just keep it, and later on pick a seed that positions it better.

At this point in the film, with artificial intelligence (AI) creating art, music and chronicles, directing ships and planes, creating deepfakes that put the most experienced of views to the test and passing almost as human --or without the almost, that depending on who you ask--the question seems quite pertinent: why not let it set the course for governments? It sounds bizarre, but in the Kingdom of Denmark there are those who believe that it would be a barbaric idea. So much so, in fact, that he has already set in motion the administrative machinery to achieve it. At the end of May, the artist collective Computer Lars launched Det Syntetiske Parti, which translated into Spanish would be equivalent to El Partido Sintético. The name may be somewhat cryptic, but it captures part of the essence of the Danish formation: its artificial character and its effort to synthesize ideas, something that it has achieved by using precisely the possibilities offered by AI.