This nimble biography examines the life of the legendary science-fiction writer Octavia Butler, whose works, such as "Parable of the Sower," often articulated unsettling visions of social collapse. Born in California in 1947 to a domestic worker and a veteran, Butler found escape in sci-fi books as a child. As Morris shows, Butler's stories, which reckoned with chattel slavery, climate catastrophe, and fascism, were as deeply attuned to West African culture and myth as they were to the American civil-rights movement. Yet Morris contends that Butler's stories "were not nihilistic predictions but a sort of love offering for readers to receive and be changed by." In this ambitious book, Vellend, a biologist, attempts to establish a "generalized evolutionary theory" to stand alongside physics as a crucial paradigm for understanding "how everything came to be."

Noise is ubiquitous during image acquisition. Sufficient denoising is often an important first step for image processing. In recent decades, deep neural networks (DNNs) have been widely used for image denoising. Most DNN-based image denoising methods require a large-scale dataset or focus on supervised settings, in which single/pairs of clean images or a set of noisy images are required. This poses a significant burden on the image acquisition process. Moreover, denoisers trained on datasets of limited scale may incur over-fitting. To mitigate these issues, we introduce a new self-supervised framework for image denoising based on the Tucker low-rank tensor approximation. With the proposed design, we are able to characterize our denoiser with fewer parameters and train it based on a single image, which considerably improves the model generalizability and reduces the cost of data acquisition. Extensive experiments on both synthetic and real-world noisy images have been conducted. Empirical results show that our proposed method outperforms existing non-learning-based methods (e.g., low-pass filter, non-local mean), single-image unsupervised denoisers (e.g., DIP, NN+BM3D) evaluated on both in-sample and out-sample datasets. The proposed method even achieves comparable performances with some supervised methods (e.g., DnCNN).

Timnit Gebru, a pioneering researcher on algorithmic bias, said Wednesday night that she had been abruptly let go by Google, where she was technical co-lead of the company's Ethical Artificial Intelligence Team, after she had privately threatened to resign. Gebru is known for her co-authorship with Joy Buolamwini of an influential 2018 paper on bias in facial recognition software, among other work. The study found that three leading facial recognition systems were far more likely to misidentify women and people of color than white men. The findings helped to fuel a backlash against facial recognition that has led some major companies and jurisdictions to stop developing or using the technology. OneZero's Dave Gershgorn wrote in June about the study's profound impact.

Unfolding across four decades via dozens of characters, this satirical novel takes aim at several targets. When, in 2012, Faye Andreson-Anderson pelts a right-wing Wyoming governor with rocks, in the lead-up to his Presidential bid, she achieves "meme status." She also becomes newly present for her son, Samuel, who tries to leverage the assault into a best-selling memoir. His research leads him deep into his mother's childhood and his own. Hill astutely skewers the media for the way it packages experience--Samuel's publisher claims to be an "interest maker" specializing in "multimodal cross-platform synergy."