A new form of variational autoencoder (VAE) is developed, in which the joint distribution of data and codes is considered in two (symmetric) forms: (i) from observed data fed through the encoder to yield codes, and (ii) from latent codes drawn from a simple prior and propagated through the decoder to manifest data. Lower bounds are learned for marginal log-likelihood fits observed data and latent codes. When learning with the variational bound, one seeks to minimize the symmetric Kullback-Leibler divergence of joint density functions from (i) and (ii), while simultaneously seeking to maximize the two marginal log-likelihoods. To facilitate learning, a new form of adversarial training is developed. An extensive set of experiments is performed, in which we demonstrate state-of-the-art data reconstruction and generation on several image benchmarks datasets.

This paper introduces Wasserstein variational inference, a new form of approximate Bayesian inference based on optimal transport theory. Wasserstein variational inference uses a new family of divergences that includes both f-divergences and the Wasserstein distance as special cases. The gradients of the Wasserstein variational loss are obtained by backpropagating through the Sinkhorn iterations. This technique results in a very stable likelihood-free training method that can be used with implicit distributions and probabilistic programs. Using the Wasserstein variational inference framework, we introduce several new forms of autoencoders and test their robustness and performance against existing variational autoencoding techniques.

A new form of variational autoencoder (VAE) is developed, in which the joint distribution of data and codes is considered in two (symmetric) forms: (i) from observed data fed through the encoder to yield codes, and (ii) from latent codes drawn from a simple prior and propagated through the decoder to manifest data. Lower bounds are learned for marginal log-likelihood fits observed data and latent codes. When learning with the variational bound, one seeks to minimize the symmetric Kullback-Leibler divergence of joint density functions from (i) and (ii), while simultaneously seeking to maximize the two marginal log-likelihoods. To facilitate learning, a new form of adversarial training is developed. An extensive set of experiments is performed, in which we demonstrate state-of-the-art data reconstruction and generation on several image benchmarks datasets.

This paper introduces Wasserstein variational inference, a new form of approximate Bayesian inference based on optimal transport theory. Wasserstein variational inference uses a new family of divergences that includes both f-divergences and the Wasserstein distance as special cases. The gradients of the Wasserstein variational loss are obtained by backpropagating through the Sinkhorn iterations. This technique results in a very stable likelihood-free training method that can be used with implicit distributions and probabilistic programs. Using the Wasserstein variational inference framework, we introduce several new forms of autoencoders and test their robustness and performance against existing variational autoencoding techniques.

This paper explores the integration of AI technologies into the artistic workflow through the creation of Studies for, a generative sound installation developed in collaboration with sound artist Evala (https://www.ntticc.or.jp/en/archive/works/studies-for/). The installation employs SpecMaskGIT, a lightweight yet high-quality sound generation AI model, to generate and playback eight-channel sound in real-time, creating an immersive auditory experience over the course of a three-month exhibition. The work is grounded in the concept of a "new form of archive," which aims to preserve the artistic style of an artist while expanding beyond artists' past artworks by continued generation of new sound elements. This speculative approach to archival preservation is facilitated by training the AI model on a dataset consisting of over 200 hours of Evala's past sound artworks. By addressing key requirements in the co-creation of art using AI, this study highlights the value of the following aspects: (1) the necessity of integrating artist feedback, (2) datasets derived from an artist's past works, and (3) ensuring the inclusion of unexpected, novel outputs. In Studies for, the model was designed to reflect the artist's artistic identity while generating new, previously unheard sounds, making it a fitting realization of the concept of "a new form of archive." We propose a Human-AI co-creation framework for effectively incorporating sound generation AI models into the sound art creation process and suggest new possibilities for creating and archiving sound art that extend an artist's work beyond their physical existence. Demo page: https://sony.github.io/studies-for/

Is AI the New Frontier of Women's Oppression? In her new book, feminist author Laura Bates explores how sexbots, AI assistants, and deepfakes are reinventing misogyny and harming women. After spending her early twenties as a nanny in the UK, Laura Bates noticed that the young girls she was caring for were preoccupied by their bodies, spurred on by the marketing they were receiving. In 2012, Bates, a London-based feminist author and activist, started The Everyday Sexism Project, a website dedicated to documenting and combatting sexism, misogyny, and gendered violence around the world by highlighting insidious instances of it such as invisible labor, referring to women as girls and commenting on their attire in professional settings. The site was turned into a book in 2014.

Police and federal agencies have found a controversial new way to skirt the growing patchwork of laws that curb how they use facial recognition: an AI model that can track people based on attributes like body size, gender, hair color and style, clothing, and accessories. The tool, called Track and built by the video analytics company Veritone, is used by 400 customers, including state and local police departments and universities all over the US. It is also expanding federally. The product has drawn criticism from the American Civil Liberties Union, which--after learning of the tool through MIT Technology Review--said it was the first instance they'd seen of a nonbiometric tracking system used at scale in the US. How the largest gathering of US police chiefs is talking about AI.

It feels like everything is slowly but surely being affected by the rise of artificial intelligence (AI). And like every other disruptive technology before it, AI is having both positive and negative outcomes for society. One of these negative outcomes is the very specific, yet very real cultural harm posed to Australia's Indigenous populations. The National Indigenous Times reports Adobe has come under fire for hosting AI-generated stock images that claim to depict "Indigenous Australians", but don't resemble Aboriginal and Torres Strait Islander peoples. Some of the figures in these generated images also have random body markings that are culturally meaningless.

This paper develops a critical theory of artificial intelligence, within a historical constellation where computational systems increasingly generate cultural content that destabilises traditional distinctions between human and machine production. Through this analysis, I introduce the concept of the algorithmic condition, a cultural moment when machine-generated work not only becomes indistinguishable from human creation but actively reshapes our understanding of ideas of authenticity. This transformation, I argue, moves beyond false consciousness towards what I call post-consciousness, where the boundaries between individual and synthetic consciousness become porous. Drawing on critical theory and extending recent work on computational ideology, I develop three key theoretical contributions, first, the concept of the Inversion to describe a new computational turn in algorithmic society; second, automimetric production as a framework for understanding emerging practices of automated value creation; and third, constellational analysis as a methodological approach for mapping the complex interplay of technical systems, cultural forms and political economic structures. Through these contributions, I argue that we need new critical methods capable of addressing both the technical specificity of AI systems and their role in restructuring forms of life under computational capitalism. The paper concludes by suggesting that critical reflexivity is needed to engage with the algorithmic condition without being subsumed by it and that it represents a growing challenge for contemporary critical theory.

Think about your most personal ritualistic event--walking your dog, grocery shopping, or a weekly meet-up with your friends. Most likely, it takes place in the same place, close to home, with the same people, and at a certain frequency. Rituals occur also on the social level; for example, Sunday Mass, birthday parties, and sports competitions. Ritualistic events are characterized by two dimensions: continuity and repetition, and emotional engagement. In this post, we discuss the links between rituals and data science, arguing that thanks to these two characteristics, machine learning algorithms can model rituals relatively easily.