Marietje Schaake is a former Dutch member of the European parliament. She is now the international policy director at Stanford University Cyber Policy Center and international policy fellow at Stanford's Institute for Human-Centred Artificial Intelligence. Her new book is entitled The Tech Coup: How to Save Democracy from Silicon Valley. In terms of power and political influence, what are the main differences between big tech and previous incarnations of big business? The difference is the role that these tech companies play in so many aspects of people's lives: in the state, the economy, geopolitics.

Recent work has aimed to capture nuances of human behavior by using LLMs to simulate responses from particular demographics in settings like social science experiments and public opinion surveys. However, there are currently no established ways to discuss or evaluate the quality of such LLM simulations. Moreover, there is growing concern that these LLM simulations are flattened caricatures of the personas that they aim to simulate, failing to capture the multidimensionality of people and perpetuating stereotypes. To bridge these gaps, we present CoMPosT, a framework to characterize LLM simulations using four dimensions: Context, Model, Persona, and Topic. We use this framework to measure open-ended LLM simulations' susceptibility to caricature, defined via two criteria: individuation and exaggeration. We evaluate the level of caricature in scenarios from existing work on LLM simulations. We find that for GPT-4, simulations of certain demographics (political and marginalized groups) and topics (general, uncontroversial) are highly susceptible to caricature.

Model visualizations provide information that outputs alone might miss. But can we trust that model visualizations reflect model behavior? For instance, can they diagnose abnormal behavior such as planted backdoors or overregularization? To evaluate visualization methods, we test whether they assign different visualizations to anomalously trained models and normal models. We find that while existing methods can detect models with starkly anomalous behavior, they struggle to identify more subtle anomalies. Moreover, they often fail to recognize the inputs that induce anomalous behavior, e.g. images containing a spurious cue. These results reveal blind spots and limitations of some popular model visualizations. By introducing a novel evaluation framework for visualizations, our work paves the way for developing more reliable model transparency methods in the future.

Only a few months ago, there was a brief window of time when many New Yorkers, among others, watched as the numbers of the vaccinated climbed and dared to hope that the year-long pandemic was finally coming to an end. Vacations were booked, weddings were scheduled, and parents began looking forward to getting their children out of the living room and back to attending school in person. But, as Barry Blitt captures in his new cover, the pandemic has not gone away, and, for students and their parents, the usual anxieties around returning to the classroom have been compounded by an increasing incidence of coronavirus infections in children, many of whom are too young to be vaccinated, and other related uncertainties. We recently spoke to Blitt about back-to-school blues and presenting his work at elementary schools. Were you a good student?

Facial recognition technology has advanced swiftly in the last five years. As University of Texas at Dallas researchers try to determine how computers have gotten as good as people at the task, they are also shedding light on how the human brain sorts information. UT Dallas scientists have analyzed the performance of the latest echelon of facial recognition algorithms, revealing the surprising way these programs--which are based on machine learning--work. Their study, published online Nov. 12 in Nature Machine Intelligence, shows that these sophisticated computer programs--called deep convolutional neural networks (DCNNs)--figured out how to identify faces differently than the researchers expected. "For the last 30 years, people have presumed that computer-based visual systems get rid of all the image-specific information--angle, lighting, expression and so on," said Dr. Alice O'Toole, senior author of the study and the Aage and Margareta Møller Professor in the School of Behavioral and Brain Sciences.

Facial recognition technology has advanced swiftly in the last five years. As University of Texas at Dallas researchers try to determine how computers have gotten as good as people at the task, they are also shedding light on how the human brain sorts information. UT Dallas scientists have analyzed the performance of the latest echelon of facial recognition algorithms, revealing the surprising way these programs -- which are based on machine learning -- work. Their study, published online Nov. 12 in Nature Machine Intelligence, shows that these sophisticated computer programs -- called deep convolutional neural networks (DCNNs) -- figured out how to identify faces differently than the researchers expected. "For the last 30 years, people have presumed that computer-based visual systems get rid of all the image-specific information -- angle, lighting, expression and so on," said Dr. Alice O'Toole, senior author of the study and the Aage and Margareta Møller Professor in the School of Behavioral and Brain Sciences.

A team of machine learning developers has created a system for making caricatures of people's faces. In a caricature, the artist creates a drawing of a face, with different parts of it greatly enlarged, or exaggerated, in other ways. The pictures or images are usually made to make the person look funny. Such drawings can be difficult for machines to produce. This is often because the human face is made up of complex shapes with a lot of extremely small details.

Stanford graduate student Kaidi Cao will join fellow AI researchers Jing Liao, of City University of Hong Kong, and Lu Yuan of Microsoft at SIGGRAPH Asia in Tokyo this December to present their incredible caricature-drawing neural network. That's not bad, considering Cao was only an intern at the Visual Computing Group at the Microsoft Research Lab in Beijing when he worked on the project. The AI, actually a pair of generative adversarial networks (GAN), is called CariGANs. The first of its neural networks, CariGeoGAN, determines the geometry of a face in a photograph and maps it to a caricature model. CariStyGAN, the other half of CariGANs, does the "style transfer," or applies the artistic look to the geometry map.

Facial caricature is an art form of drawing faces in an exaggerated way to convey humor or sarcasm. In this paper, we propose the first Generative Adversarial Network (GAN) for unpaired photo-to-caricature translation, which we call "CariGANs". It explicitly models geometric exaggeration and appearance stylization using two components: CariGeoGAN, which only models the geometry-to-geometry transformation from face photos to caricatures, and CariStyGAN, which transfers the style appearance from caricatures to face photos without any geometry deformation. In this way, a difficult cross-domain translation problem is decoupled into two easier tasks. The perceptual study shows that caricatures generated by our CariGANs are closer to the hand-drawn ones, and at the same time better persevere the identity, compared to state-of-the-art methods. Moreover, our CariGANs allow users to control the shape exaggeration degree and change the color/texture style by tuning the parameters or giving an example caricature.

Annalee Newitz is the Tech Culture Editor at Ars Technica. Her work focuses on cultural impact of science and technology. She founded the science and science fiction blog io9.com, and is the author of Scatter, Adapt, and Remember: How Humans Will Survive a Mass Extinction. Her first novel, Autonomous, comes out in September 2017. She has a Ph.D. in English and American Studies from UC Berkeley, and was the recipient of a Knight Science Journalism Fellowship at MIT.