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Bryan Johnson is on a mission to not die. The 47-year-old multimillionaire has already applied his slogan “Don’t Die” to events, merchandise, and a Netflix documentary. Now he’s founding a Don’t Die religion. Johnson, who famously spends millions of dollars on scans, tests, supplements, and a lifestyle routine designed to slow or reverse the aging process, has enjoyed extensive media coverage, and a huge social media following. For many people, he has become the face of the longevity field. I sat down with Johnson at an event for people interested in longevity in Berkeley, California, in late April. We spoke on the sidelines after lunch (conference plastic-lidded container meal for me; what seemed to be a plastic-free, compostable box of chicken and vegetables for him), and he sat with an impeccable posture, his expression neutral.  Earlier that morning, Johnson, in worn trainers and the kind of hoodie that is almost certainly deceptively expensive, had told the audience about what he saw as the end of humanity. Specifically, he was worried about AI—that we face an “event horizon,” a point at which superintelligent AI escapes human understanding and control. He had come to Berkeley to persuade people who are interested in longevity to focus their efforts on AI.  It is this particular concern that ultimately underpins his Don’t Die mission. First, humans must embrace the Don’t Die ideology. Then we must ensure AI is aligned with preserving human existence. Were it not for AI, he says, he wouldn’t be doing any of his anti-death activities and regimens. “I am convinced that we are at an existential moment as a species,” says Johnson, who was raised Mormon but has since left the church. Solving aging will take decades, he says—we’ll survive that long only if we make sure that AI is aligned with human survival.  The following Q&A has been lightly edited for length and clarity. Why are you creating a new religion? We’re in this new phase where [because of advances in AI] we’re trying to reimagine what it means to be human. It requires imagination and creativity and open-mindedness, and that’s a big ask. Approaching that conversation as a community, or a lifestyle, doesn’t carry enough weight or power. Religions have proven, over the past several thousand years, to be the most efficacious form to organize human efforts. It’s just a tried-and-true methodology.  How do you go about founding a new religion? It’s a good question. If you look at historical [examples], Buddha went through his own self-exploratory process and came up with a framework. And Muhammad had a story. Jesus had an origin story … You might even say Satoshi [Nakamoto, the mysterious creator of bitcoin] is like [the founder of] a modern-day religion, [launched] with the white paper. Adam Smith launched capitalism with his book. The question is: What is a modern-day religion, and how does it convince? It’s an open question for me. I don’t know yet. Your goal is to align AI with Don’t Die—or, in other words, ensure that AI models prioritize and protect human life. How will you do that? I’m talking to a lot of AI researchers about this. Communities of AIs could be instilled with values of conflict resolution that do not end in the death of a human. Or an AI. Or the planet. Would you say that Don’t Die is “your” religion? No, I think it’s humanity’s religion. It’s different from other religions, which are very founder-centric. I think this is going to be decentralized, and it will be something that everybody can make their own. So there’s no God? We’re playing with the idea that the body is God. We’ve been experimenting with this format of a Don’t Die fam, where eight to 12 people get together on a weekly basis. It’s patterned off of other groups like Alcoholics Anonymous. We structure an opening ritual. We have a mantra. And then there’s a part where people apologize to their body for something they’ve done that has inflicted harm upon themselves.  It’s reframing our relationship to body and to mind. It is also a way for people to have deep friendships, to explore emotionally vulnerable topics, and to support each other in health practices. What we’re really trying to say is: Existence is the virtue. Existence is the objective. If someone believes in God, that’s fine. People can be Christian and do this; they can be Muslim and do this. Don’t Die is a “yes, and” to all groups. So it’s a different way of thinking about religion? Yeah. Right now, religion doesn’t hold the highest status in society. A lot of people look down on it in some way. I think as AI progresses, it’s going to create additional questions on who we are: What is our identity? What do we believe about our existence in the future? People are going to want some kind of framework that helps them make sense of the moment. So I think there’s going to be a shift toward religion in the coming years. People might say that [founding a religion now] is kind of a weird move, and that [religion] turns people off. But I think that’s fine. I think we’re ahead. Does the religion incorporate, or make reference to, AI in any way? Yeah. AI is going to be omnipresent. And this is why we’ve been contemplating “the body is God.” Over the past couple of years … I’ve been testing the hypothesis that if I get a whole bunch of data about my body, and I give it to an algorithm, and feed that algorithm updates with scientific evidence, then it would eventually do a better job than a doctor. So I gave myself over to an algorithm.  It really is in my best interest to let it tell me what to eat, tell me when to sleep and exercise, because it would do a better job of making me happy. Instead of my mind haphazardly deciding what it

arXiv:2504.21035v2 Announce Type: replace-cross Abstract: Sanitizing sensitive text data typically involves removing personally identifiable information (PII) or generating synthetic data under the assumption that these methods adequately protect privacy; however, their effectiveness is often only assessed by measuring the leakage of explicit identifiers but ignoring nuanced textual markers that can lead to re-identification. We challenge the above illusion of privacy by proposing a new framework that evaluates re-identification attacks to quantify individual privacy risks upon data release. Our approach shows that seemingly innocuous auxiliary information — such as routine social activities — can be used to infer sensitive attributes like age or substance use history from sanitized data. For instance, we demonstrate that Azure’s commercial PII removal tool fails to protect 74% of information in the MedQA dataset. Although differential privacy mitigates these risks to some extent, it significantly reduces the utility of the sanitized text for downstream tasks. Our findings indicate that current sanitization techniques offer a textit{false sense of privacy}, highlighting the need for more robust methods that protect against semantic-level information leakage.

arXiv:2505.00725v1 Announce Type: new Abstract: Motivated by the emerging demand in the financial industry for the automatic analysis of unstructured and structured data at scale, Question Answering (QA) systems can provide lucrative and competitive advantages to companies by facilitating the decision making of financial advisers. Consequently, we propose a novel financial QA system using the transformer-based pre-trained BERT language model to address the limitations of data scarcity and language specificity in the financial domain. Our system focuses on financial non-factoid answer selection, which retrieves a set of passage-level texts and selects the most relevant as the answer. To increase efficiency, we formulate the answer selection task as a re-ranking problem, in which our system consists of an Answer Retriever using BM25, a simple information retrieval approach, to first return a list of candidate answers, and an Answer Re-ranker built with variants of pre-trained BERT language models to re-rank and select the most relevant answers. We investigate various learning, further pre-training, and fine-tuning approaches for BERT. Our experiments suggest that FinBERT-QA, a model built from applying the Transfer and Adapt further fine-tuning and pointwise learning approach, is the most effective, improving the state-of-the-art results of task 2 of the FiQA dataset by 16% on MRR, 17% on NDCG, and 21% on Precision@1.

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