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Missing Data Imputation by Reducing Mutual Information with Rectified Flows

Neural Information Processing Systems

This paper introduces a novel iterative method for missing data imputation that sequentially reduces the mutual information between data and the corresponding missingness mask. Inspired by GAN-based approaches that train generators to decrease the predictability of missingness patterns, our method explicitly targets this reduction in mutual information.


The Stories We Govern By: AI, Risk, and the Power of Imaginaries

arXiv.org Artificial Intelligence

This paper examines how competing sociotechnical imaginaries of artificial intelligence (AI) risk shape governance decisions and regulatory constraints. Drawing on concepts from science and technology studies, we analyse three dominant narrative groups: existential risk proponents, who emphasise catastrophic AGI scenarios; accelerationists, who portray AI as a transformative force to be unleashed; and critical AI scholars, who foreground present-day harms rooted in systemic inequality. Through an analysis of representative manifesto-style texts, we explore how these imaginaries differ across four dimensions: normative visions of the future, diagnoses of the present social order, views on science and technology, and perceived human agency in managing AI risks. Our findings reveal how these narratives embed distinct assumptions about risk and have the potential to progress into policy-making processes by narrowing the space for alternative governance approaches. We argue against speculative dogmatism and for moving beyond deterministic imaginaries toward regulatory strategies that are grounded in pragmatism.


Missing Data Imputation by Reducing Mutual Information with Rectified Flows

arXiv.org Machine Learning

This paper introduces a novel iterative method for missing data imputation that sequentially reduces the mutual information between data and their corresponding missing mask. Inspired by GAN-based approaches, which train generators to decrease the predictability of missingness patterns, our method explicitly targets the reduction of mutual information. Specifically, our algorithm iteratively minimizes the KL divergence between the joint distribution of the imputed data and missing mask, and the product of their marginals from the previous iteration. We show that the optimal imputation under this framework corresponds to solving an ODE, whose velocity field minimizes a rectified flow training objective. We further illustrate that some existing imputation techniques can be interpreted as approximate special cases of our mutual-information-reducing framework. Comprehensive experiments on synthetic and real-world datasets validate the efficacy of our proposed approach, demonstrating superior imputation performance.


Evaluating Large Language Models on the Spanish Medical Intern Resident (MIR) Examination 2024/2025:A Comparative Analysis of Clinical Reasoning and Knowledge Application

arXiv.org Artificial Intelligence

The MIR serves as a critical selection mechanism for medical graduates entering specialized training in Spain. A study is to be conducted on the ability of generative AI models to meet the challenges presented by MIR, with emphasis on clinical reasoning, image interpretation and epidemiological calculations. This research evaluates LLM performance in complex clinical scenarios and explores the extent to which LLMs demonstrate medical reasoning beyond mere information recall. Findings The results reveal key insights into the performance of 22 LLMs on the MIR 2024 and 2025 exams. The exam features 210 multiple-choice questions covering diverse medical domains and incorporates case-based scenarios, image interpretation (25 questions), and laboratory data analysis.


They wanted to save us from a dark AI future. Then six people were killed

The Guardian

Years before she became the peculiar central thread linking a double homicide in Pennsylvania, the fatal shooting of a federal agent in Vermont and the murder of an elderly landlord in California, a computer programmer bought a sailboat. The programmer was known to friends, foes and followers as Ziz. She had come to the San Francisco Bay Area in 2016 as part of an influx of young people arriving to study the dangers that artificial intelligence could pose to humanity. In one of the most expensive regions of the United States, however, it is difficult to save the world when you can't make rent. So she bought a boat for 600 and moored it next to a friend's vessel in a marina. For five years, she used it as an occasional, cramped bunk. In her waking hours, she worked on a blog of provocative and increasingly extreme ideas about confrontation and retaliation. At night, she fell asleep as the boat rocked back and forth, drifting with the flotsam of greater Silicon Valley. Then, on the night of 19 August 2022, her sister and a friend reported that they saw her fall overboard. The Coast Guard and local authorities scrambled boats and aircraft. After a nearly 30-hour search, neither Ziz nor her body could be found. A newspaper in Alaska, where she was born, published a short obituary referring to her by her birth name: "Jack Amadeus LaSota left our lives but not our hearts on Aug 19 after a boating accident. Loving adventure, friends and family, music, blueberries, biking, computer games and animals, you are missed." Ziz's ideas did not die in the waters of the California coast. She had faked her drowning and gone underground, before being arrested last month in western Maryland and charged with trespassing and illegal transportation of a firearm. The targets of Ziz's ire, who include some of Silicon Valley's most prominent intellectuals, have taken security precautions. "Ziz is not stupid," someone familiar with her, who asked to remain anonymous, told me. "This is a very smart person โ€“ both smart and crazy." Ziz's writing had polarized members of a niche but influential movement of AI theorists and tech bloggers who call themselves the "rationalists". The movement is less about specific ideas than it is about an ethos โ€“ applying rigorous, mathematically informed thinking to AI, philosophy, psychology and the big questions of our time. Rationalists are odd, though often charming, people. They tend to be fantasy and sci-fi geeks, use lots of jargon and think intensely about things other people barely think about at all.


The Delirious, Violent, Impossible True Story of the Zizians

WIRED

I know this is unconventional, but I'm going to start by telling you the ending. Or at least, the ending as it stands today. Most of the people involved in this story wind up either dead, maimed, spending months in a mental hospital, languishing in jail, or gone underground. It's a tragedy from almost any angle, especially because, at the outset, most of these people were idealists committed to doing as much good as possible in a world they saw as beset by existential threats. In spite of those aims, or perhaps in pursuit of them, over the course of this story their lives will devolve into senseless violence.


Visible Thoughts Project and Bounty Announcement - Machine Intelligence Research Institute

#artificialintelligence

We at MIRI are soliciting help with an AI-alignment project centered around building a dataset, described below. We have $200,000 in prizes for building the first fragments of the dataset, plus an additional $1M prize/budget for anyone who demonstrates the ability to build a larger dataset at scale. If this project goes well, then it may be the first of a series of prizes we offer for various projects. Below, I'll say more about the project, and about the payouts and interim support we're offering. Hypothesis: Language models can be made more understandable (and perhaps also more capable, though this is not the goal) by training them to produce visible thoughts.


Facial recognition creeps into everything at CES 2020 โ€“ Invest Records

#artificialintelligence

At Konami's headquarters in Las Vegas, its facial recognition powered cameras tracked me around the room. This story is part of CES 2020, our complete coverage of the showroom floor and the hottest new tech gadgets around. Konami Gaming, a slot machine maker, wants to weave facial recognition into its one-armed bandits. During a visit to its Las Vegas headquarters to hear more about its plans, I quickly discovered what the world would be like if facial recognition is everywhere. "Hello, Alfred," said a measured, robotic voice, startling me.


Producing Personhood

#artificialintelligence

During Final Jeopardy in the episode that aired on February 16, 2011, a computer screen sat between long-running stars Brad Rutter and Ken Jennings. On it, swirling green and blue lines represented the thought patterns of IBM's Watson--a question-answering computer system. Watson's hardware filled a neighboring room as he worked to process natural language and sift through 200 million pages of data to find the winning answer. The category was 19th Century novelists. Alex Trebek, the show's host, read the clue: "William Wilkinson's'An Account of the Principalities of Wallachia and Moldavia' inspired this author's most famous novel." Watson had thirty seconds to find the correct response.


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The Machine Intelligence Research Institute exists to ensure that the creation of smarter-than-human intelligence has a positive impact.