Neural models get the hype. But that lightning-fast word suggestion as you type? That’s not a neural model. It’s a lookup table. And it’s beating transformers on the metric that matters. I’ve spent a lot of time thinking about prediction under pressure. When I was working on Indian-language typing for early Kindle devices, we had a problem that no amount of ML enthusiasm could solve: the hardware simply didn’t care about our ambitions. We had roughly 512 MB […]

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You might have heard Linux Torvalds’ name a dozen times, but you might not have heard of Richard Stallman. And that is a pity! (This is a longer story, so please stay to the end about the 2026 interview that almost happened.) In the sprawling, sterilized history of the digital age, Richard Matthew Stallman (RMS) exists as a singular, indigestible singularity. He is the ghost in the machine of modern computing, a figure whose code runs the internet […]

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Tree-based models are widely recognized for their interpretability and have proven effective in various application domains, particularly in high-stakes domains. However, learning decision trees (DTs) poses a significant challenge due to their combinatorial complexity and discrete, non-differentiable nature. As a result, traditional methods such as CART, which rely on greedy search procedures, remain the most widely used approaches. These methods make locally optimal decisions at each node, constraining the search space and often leading to suboptimal tree structures. […]

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Masked Autoencoders (MAEs) achieve impressive performance in image classification tasks, yet the internal representations they learn remain less understood. This work started as an attempt to understand the strong downstream classification performance of MAE. In this process we discover that representations learned with the pretraining and fine-tuning, are quite robust – demonstrating a good classification performance in the presence of degradations, such as blur and occlusions. Through layer-wise analysis of token embeddings, we show that pretrained MAE progressively […]

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In this work, we extend our systematic capacity region perspective to multi-junction traffic networks, focussing on the special case of an urban corridor network. In particular, we train and evaluate centralized, fully decentralized, and parameter-sharing decentralized RL controllers, and compare their capacity regions and ATTs together with a classical baseline MaxPressure controller. Further, we show how the parametersharing controller may be generalised to be deployed on a larger network than it was originally trained on. In this setting, […]

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arXiv:2601.02389v1 Announce Type: new Abstract: Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are revolutionizing network management systems, paving the way towards fully autonomous and self-optimizing communication systems. These models enable networks to address complex decision-making tasks across both short-term operational scenarios and long-term strategic planning. Through natural language understanding, LLMs can analyze customer inquiries, predict network congestion patterns, and automate troubleshooting processes, leading to more efficient customer support and network maintenance. GenAI can optimize content delivery […]

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:::info Astounding Stories of Super-Science February, 2026, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Moors and the Fens, volume 1 (of 3) – Chapter XIII: The Spider and the Fly Astounding Stories of Super-Science February 2026: The Moors and the Fens, volume 1 (of 3) – Chapter XIII The Spider and the Fly By J. H. Riddell ::: Malcolm Frazer was quite correct in […]

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arXiv:2601.04206v1 Announce Type: new Abstract: University admissions offices face the significant challenge of managing high volumes of inquiries efficiently while maintaining response quality, which critically impacts prospective students’ perceptions. This paper addresses the issues of response time and information accuracy by proposing an AI system integrating a fine-tuned language model with Retrieval-Augmented Generation (RAG). While RAG effectively retrieves relevant information from large datasets, its performance in narrow, complex domains like university admissions can be limited without adaptation, potentially […]

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