In the intrinsically motivated skills acquisition problem, the agent is set in an environment without any pre-defined goals and needs to acquire an open-ended repertoire of skills. To do so the agent needs to be autotelic (deriving from the Greek auto (self) and telos (end goal)): it needs to generate goals and learn to achieve them following its own intrinsic motivation rather than external supervision. Autotelic agents have so far been considered in isolation. But many applications of open-ended learning entail groups of agents. Multi-agent environments pose an additional challenge for autotelic agents: to discover and master goals that require cooperation agents must pursue them simultaneously, but they have low chances of doing so if they sample them independently. In this work, we propose a new learning paradigm for modeling such settings, the Decentralized Intrinsically Motivated Skills Acquisition Problem (Dec-IMSAP), and employ it to solve cooperative navigation tasks. First, we show that agents setting their goals independently fail to master the full diversity of goals. Then, we show that a sufficient condition for achieving this is to ensure that a group aligns its goals, i.e., the agents pursue the same cooperative goal. Our empirical analysis shows that alignment enables specialization, an efficient strategy for cooperation. Finally, we introduce the Goal-coordination game, a fully-decentralized emergent communication algorithm, where goal alignment emerges from the maximization of individual rewards in multi-goal cooperative environments and show that it is able to reach equal performance to a centralized training baseline that guarantees aligned goals. To our knowledge, this is the first contribution addressing the problem of intrinsically motivated multi-agent goal exploration in a decentralized training paradigm.

This study aims to determine the appropriate size of the Mongolian general corpus. This study used the Heaps function and Type Token Ratio to determine the appropriate size of the Mongolian general corpus. The sample corpus of 906,064 tokens comprised texts from 10 domains of newspaper politics, economy, society, culture, sports, world articles and laws, middle and high school literature textbooks, interview articles, and podcast transcripts. First, we estimated the Heaps function with this sample corpus. Next, we observed changes in the number of types and TTR values while increasing the number of tokens by one million using the estimated Heaps function. As a result of observation, we found that the TTR value hardly changed when the number of tokens exceeded from 39 to 42 million. Thus, we conclude that an appropriate size for a Mongolian general corpus is from 39 to 42 million tokens.

While the term `art' defies any concrete definition, this paper aims to examine how digital images produced by generative AI systems, such as Midjourney, have come to be so regularly referred to as such. The discourse around the classification of AI-generated imagery as art is currently somewhat homogeneous, lacking the more nuanced aspects that would apply to more traditional modes of artistic media production. This paper aims to bring important philosophical considerations to the surface of the discussion around AI-generated imagery in the context of art. We employ existing philosophical frameworks and theories of language to suggest that some AI-generated imagery, by virtue of its visual properties within these frameworks, can be presented as `readymades' for consideration as art.

We introduce VampNet, a masked acoustic token modeling approach to music synthesis, compression, inpainting, and variation. We use a variable masking schedule during training which allows us to sample coherent music from the model by applying a variety of masking approaches (called prompts) during inference. VampNet is non-autoregressive, leveraging a bidirectional transformer architecture that attends to all tokens in a forward pass. With just 36 sampling passes, VampNet can generate coherent high-fidelity musical waveforms. We show that by prompting VampNet in various ways, we can apply it to tasks like music compression, inpainting, outpainting, continuation, and looping with variation (vamping). Appropriately prompted, VampNet is capable of maintaining style, genre, instrumentation, and other high-level aspects of the music. This flexible prompting capability makes VampNet a powerful music co-creation tool. Code and audio samples are available online.

Roman Numeral analysis is the important task of identifying chords and their functional context in pieces of tonal music. This paper presents a new approach to automatic Roman Numeral analysis in symbolic music. While existing techniques rely on an intermediate lossy representation of the score, we propose a new method based on Graph Neural Networks (GNNs) that enable the direct description and processing of each individual note in the score. The proposed architecture can leverage notewise features and interdependencies between notes but yield onset-wise representation by virtue of our novel edge contraction algorithm. Our results demonstrate that ChordGNN outperforms existing state-of-the-art models, achieving higher accuracy in Roman Numeral analysis on the reference datasets. In addition, we investigate variants of our model using proposed techniques such as NADE, and post-processing of the chord predictions. The full source code for this work is available at https://github.com/manoskary/chordgnn

This paper describes the methodology followed and the lessons learned from employing crowdsourcing techniques as part of a homework assignment involving higher education students of computer science. Making use of a platform that supports crowdsourcing in the cultural heritage domain students were solicited to enrich the metadata associated with a selection of music tracks. The results of the campaign were further analyzed and exploited by students through the use of semantic web technologies. In total, 98 students participated in the campaign, contributing more than 6400 annotations concerning 854 tracks. The process also led to the creation of an openly available annotated dataset, which can be useful for machine learning models for music tagging. The campaign's results and the comments gathered through an online survey enable us to draw some useful insights about the benefits and challenges of integrating crowdsourcing into computer science curricula and how this can enhance students' engagement in the learning process.

Fans and casual viewers alike are stumbling upon'Unknown: Killer Robots' the chilling latest installment of Netflix's new documentary series'Unknown' -- and discovering that they can't unknow what they've just learned. 'I can only conclude that we have created a psychopathic demi-god and unleashed it on the world,' as one viewer tweeted about the streamer's in-depth look at AI's lethal potential and the military arms race for more autonomous weapons of war. Everyone from refugee groups that serve war-torn countries, to the very same scientific experts who appear in'Killer Robots' themselves, have voiced concern over the documentary's alarming revelations. In the words of one fan, the Netflix expose is'fascinating, thought-provoking, and the stuff of nightmares.' One fan said the Netflix doc is'fascinating, thought-provoking, and the stuff of nightmares' Everyone from refugee groups serving war-torn countries, to the very same scientific experts who appeared in Netflix's'Killer Robots,' have voiced concern over the doc's dark revelations One nonprofit devoted to helping refugees of the Taliban and the war in Afghanistan, Afghans For A Better Tomorrow, praised the film as'a groundbreaking documentary exploring the rise of AI-powered robots on the battlefield.'

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A year after saving the summer box office with the smash hit Top Gun: Maverick, Tom Cruise is back for another round of speedy-motorcycle riding, choppy-handed running, and look-Ma-no-CGI stuntwork in Mission: Impossible--Dead Reckoning Part One, the seventh and supposedly penultimate entry in the now 27-year-old action franchise. In the able hands of Christopher McQuarrie, who has directed the past three M:I movies in addition to writing or co-writing the past four, Dead Reckoning displays the serene if at times demented confidence of a series that's found its voice. Even at 163 minutes, it somehow moves with the no-nonsense briskness of a good airport thriller. To be clear, there is some nonsense involved: Dead Reckoning's plot hinges on an espionage-related MacGuffin so technologically advanced it might as well be magical. And there are several of the franchise's time-honored and much-memed "mask reveals," in which a character suddenly rips off their own face to reveal another cast member underneath.