I’m trying to fund startups/projects helping with COVID-19, because it’s basically the one thing I know how to do that can help.  I think we will soon have enough testing capacity, so now I’d like to start funding more startups working on: Producing a lot of ventilators or masks/gowns very quickly.  This will require a lot of repurposing and creativity but thankfully is an engineering problem not a scientific ones. Screening existing drugs for effectiveness. Novel approaches to […]

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Although I still hope things will go differently, the experts I’ve spoken to think we are likely to face a global tragedy—hundreds of thousands of deaths from Covid-19. I hope that society views this as a warning for the future.  Covid-19 is bad, but only a warm-up.  I think it’s unlikely that this is the worst new pandemic (human-created or otherwise) we’ll see in our lifetimes.  We need to be ready to deal with it much better next […]

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The transition of Google towards efficient AI models has transformed image generation in information technology. Google Nano Banana was introduced as a means to show that it is possible to have an AI image generator that is powerful, fast, scalable, and cheap at the same time. It was created for optimization and accessibility, with the intention to bring image generation closer to real-world workflows, with on-device and low-latency use cases. What Is Google Nano Banana? Google Nano Banana […]

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Over the past two years, I’ve been asked repeatedly at conferences and talks about my general opinion on watermarking AI-generated content. This is usually coupled with various concrete concerns that I like to categorize into three problems: misinformation at internet scale through social media, impersonation and deep fakes, and copyright issues. Of course, there are many other concers that might not perfectly fit these three categories, but I feel these are three big problems that researchers but also […]

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arXiv:2512.20762v2 Announce Type: replace-cross Abstract: We study the problem of subgroup discovery for survival analysis, where the goal is to find an interpretable subset of the data on which a Cox model is highly accurate. Our work is the first to study this particular subgroup problem, for which we make several contributions. Subgroup discovery methods generally require a “quality function” in order to sift through and select the most advantageous subgroups. We first examine why existing natural choices […]

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Understanding customer purchasing behavior is essential for businesses to optimize marketing strategies and improve customer retention. This study employs machine learningbased clustering techniques to segment customers based on transactional data. By leveraging Recency, Frequency, and Monetary (RFM) analysis, the study compares multiple clustering algorithms to identify distinct customer groups. Experimental results demonstrate that the proposed approach effectively categorizes customers, enabling data-driven decision-making for targeted marketing. These findings highlight the potential of unsupervised learning methods in enhancing business intelligence […]

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arXiv:2601.02509v1 Announce Type: new Abstract: Following the initial publication of hdlib, a Python library for designing Vector-Symbolic Architectures (VSA), we introduce a major extension that significantly enhances its machine learning capabilities. VSA, also known as Hyperdimensional Computing, is a computing paradigm that represents and processes information using high-dimensional vectors. While the first version of hdlib established a robust foundation for creating and manipulating these vectors, this update addresses the growing need for more advanced, data-driven modeling within the […]

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Probabilistic models of the performance of computer systems are useful both for predicting system performance in new conditions, and for diagnosing past performance problems. The most popular performance models are networks of queues. However, no current methods exist for parameter estimation or inference in networks of queues with missing data. In this paper, we present a novel viewpoint that combines queueing networks and graphical models, allowing Markov chain Monte Carlo to be applied. We demonstrate the effectiveness of […]

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There is a lot of advice about how to be a good startup founder.  But there isn’t very much about how to be a good startup investor. Before going any further, I should point out that this is a particularly hard time to invest in startups—it’s easier right now to be a capital-taker than a capital-giver.  It seems that more people want to be investors than founders, and that there’s an apparent never-ending flow of capital looking for […]

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