arXiv:2601.17060v1 Announce Type: new Abstract: Artificially intelligent systems have become remarkably sophisticated. They hold conversations, write essays, and seem to understand context in ways that surprise even their creators. This raises a crucial question: Are we creating systems that are conscious? The Digital Consciousness Model (DCM) is a first attempt to assess the evidence for consciousness in AI systems in a systematic, probabilistic way. It provides a shared framework for comparing different AIs and biological organisms, and for […]

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How are you, hacker? 🪐 What’s happening in tech today, January 9, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Steve Jobs announced the iPhone at the Macworld convention in 2007, Connecticut became the fifth state to join the United States of America in 1788, The government of Tunisia fell following a month of protests and demonstrations in 2011, and we present you with these top quality stories. From How a […]

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In this article, we are going to understand how self-attention works from scratch. This means we will code it ourselves one step at a time. Since its introduction via the original transformer paper, self-attention has become a cornerstone of many state-of-the-art deep learning models, particularly in the field of Natural Language Processing. Since self-attention is now everywhere, it’s important to understand how it works.

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arXiv:2602.11206v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) offer energy-efficient, biologically plausible computation but suffer from non-differentiable spike generation, necessitating reliance on heuristic surrogate gradients. This paper introduces UltraLIF, a principled framework that replaces surrogate gradients with ultradiscretization, a mathematical formalism from tropical geometry providing continuous relaxations of discrete dynamics. The central insight is that the max-plus semiring underlying ultradiscretization naturally models neural threshold dynamics: the log-sum-exp function serves as a differentiable soft-maximum that converges to hard […]

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Big data scenarios, where massive, heterogeneous datasets are distributed across clients, demand scalable, privacy-preserving learning methods. Federated learning (FL) enables decentralized training of machine learning (ML) models across clients without data centralization. Decentralized training, however, introduces a computational burden on client devices. U-shaped federated split learning (UFSL) offloads a fraction of the client computation to the server while keeping both data and labels on the clients’ side. However, the intermediate representations (i.e., smashed data) shared by clients with […]

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The PyTorch team recently announced TorchData, a prototype library focused on implementing composable and reusable data loading utilities for PyTorch. In particular, the TorchData library is centered around DataPipes, which are meant to be a DataLoader-compatible replacement for the existing Dataset class.

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arXiv:2411.03384v2 Announce Type: replace Abstract: In this paper, we solve stochastic partial differential equations (SPDEs) numerically by using (possibly random) neural networks in the truncated Wiener chaos expansion of their corresponding solution. Moreover, we provide some approximation rates for learning the solution of SPDEs with additive and/or multiplicative noise. Finally, we apply our results in numerical examples to approximate the solution of three SPDEs: the stochastic heat equation, the Heath-Jarrow-Morton equation, and the Zakai equation.

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Fast matrix multiplication can be described as searching for low-rank decompositions of the matrix–multiplication tensor. We design a neural architecture, textsc{StrassenNet}, which reproduces the Strassen algorithm for $2times 2$ multiplication. Across many independent runs the network always converges to a rank-$7$ tensor, thus numerically recovering Strassen’s optimal algorithm. We then train the same architecture on $3times 3$ multiplication with rank $rin{19,dots,23}$. Our experiments reveal a clear numerical threshold: models with $r=23$ attain significantly lower validation error than those […]

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CollectivIQ looks to give users more accurate answers to their AI queries by showing them responses that pull information from ChatGPT, Gemini, Claude, Grok — and up to 10 other models — all at the same time.

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