arXiv:2601.08864v1 Announce Type: new Abstract: Artificial intelligence research faces a critical ethical paradox: determining whether AI systems are conscious requires experiments that may harm entities whose moral status remains uncertain. Recent work proposes avoiding consciousness-uncertain AI systems entirely, yet this faces practical limitations-we cannot guarantee such systems will not emerge. This paper addresses a gap in research ethics frameworks: how to conduct consciousness research on AI systems whose moral status cannot be definitively established. Existing graduated moral status […]

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arXiv:2603.05559v1 Announce Type: new Abstract: Decision makers exploiting photonic chaotic dynamics obtained by semiconductor lasers provide an ultrafast approach to solving multi-armed bandit problems by using a temporal optical signal as the driving source for sequential decisions. In such systems, the sampling interval of the chaotic waveform shapes the temporal correlation of the resulting time series, and experiments have reported that decision accuracy depends strongly on this autocorrelation property. However, it remains unclear whether the benefit of autocorrelation […]

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arXiv:2407.13731v3 Announce Type: replace Abstract: We study the problem of learning a partially observed matrix under the low rank assumption in the presence of fully observed side information that depends linearly on the true underlying matrix. This problem consists of an important generalization of the Matrix Completion problem, a central problem in Statistics, Operations Research and Machine Learning, that arises in applications such as recommendation systems, signal processing, system identification and image denoising. We formalize this problem as […]

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Sequential recommendation has become increasingly prominent in both academia and industry, particularly in e-commerce. The primary goal is to extract user preferences from historical interaction sequences and predict items a user is likely to engage with next. Recent advances have leveraged contrastive learning and graph neural networks to learn more expressive representations from interaction histories — graphs capture relational structure between nodes, while ID-based representations encode item-specific information. However, few studies have explored multi-view contrastive learning between ID […]

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Temporal causal representation learning methods assume that causal mechanisms switch instantaneously between discrete domains, yet real-world systems often exhibit continuous mechanism transitions. For example, a vehicle’s dynamics evolve gradually through a turning maneuver, and human gait shifts smoothly from walking to running. We formalize this setting by modeling transitional mechanisms as convex combinations of finitely many atomic mechanisms, governed by time-varying mixing coefficients. Our theoretical contributions establish that both the latent causal variables and the continuous mixing trajectory […]

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arXiv:2506.12818v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) has traditionally solved black-box problems where function evaluation is expensive and, therefore, observations are few. Recently, however, there has been growing interest in applying BO to problems where function evaluation is cheaper and observations are more plentiful. In this regime, scaling to many observations $N$ is impeded by Gaussian-process (GP) surrogates: GP hyperparameter fitting scales as $mathcal{O}(N^3)$ (reduced to roughly $mathcal{O}(N^2)$ in modern implementations), and it is repeated at every […]

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An AI is world-class at writing test-pleasing code. It knows exactly how to write the code that will result in the coveted green checkmark, but not necessarily solve for the edge cases. Code coverage measures what was executed. Mutation testing measures what your tests would detect if the code was wrong. And in the world of AI, that is the only thing that matters. You could think of it like this: You want to make sure all the […]

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Jolly Shah, an embedded software leader at FAANG, is making notable contributions to the field of scalable, power-efficient firmware and sustainable computing. With an engineering career spanning over 18 years, she has played key roles at some big semiconductor companies and other industry-defining organizations, specializing in the architecture and development of low-power firmware for embedded devices, multicore SoCs, and data center storage controllers.  Jolly’s expertise centers on building resilient firmware systems that manage performance, energy consumption, and reliability […]

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Effective user modeling requires distinguishing between short-term and long-term preference evolution. While item embeddings have become a key component of recommender systems, standard approaches like Item2Vec treat user histories as unordered sets (bag-of-items), implicitly assuming that interactions separated by minutes are as semantically related as those separated by months. This simplification flattens the rich temporal structure of user behavior, obscuring the distinction between coherent consumption sessions and gradual interest drifts. In this work, we introduce TAI2Vec (Time-Aware Item-to-Vector), […]

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This tutorial shows you how to build a real-time medical transcription system that captures patient conversations, separates speakers, and automatically generates SOAP notes. You’ll create a streaming application that processes audio as it happens, giving healthcare providers instant transcription with proper clinical documentation format. You’ll use AssemblyAI’s Python SDK for real-time speech-to-text with multichannel speaker separation, combined with OpenAI’s GPT-4 API to transform raw transcripts into structured clinical notes. The implementation includes microphone audio capture, streaming transcription processing, […]

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