From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence

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arXiv:2601.03220v1 Announce Type: cross Abstract: Can we learn more from data than existed in the generating process itself? Can new and useful information be constructed from merely applying deterministic transformations to existing data? Can the learnable content in data be evaluated without considering a downstream task? On these questions, Shannon information and Kolmogorov complexity come up nearly empty-handed, in part because they assume observers with unlimited computational capacity and fail to target the useful information content. In this […]

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hdlib 2.0: Extending Machine Learning Capabilities of Vector-Symbolic Architectures

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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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The science of how (and when) we decide to self-censor

The science of how (and when) we decide to self-censor

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Freedom of speech is a foundational principle of healthy democracies and hence a primary target for aspiring authoritarians, who typically try to squash dissent. There is a point where the threat from authorities is sufficiently severe that a population will self-censor rather than risk punishment. Social media has complicated matters, blurring traditional boundaries between public and private speech, while new technologies such as facial recognition and moderation algorithms give authoritarians powerful new tools. Researchers explored the nuanced dynamics […]

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SOMA-DR: Decision Receipts for Explainable Recovery and Key Rotation in Post-Quantum IAM

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Identity and Access Management (IAM) increasingly relies on adaptive controls—step-up challenges, recovery verification, device and behavior signals, and continuous authorization—to reduce account takeover and misuse. At the same time, IAM systems must prepare for post-quantum cryptography (PQC) transitions that affect credentials, signing, and verification paths. These shifts create a practical governance problem: when an identity action is allowed, challenged, denied, or escalated (e.g., passwordless enrollment, recovery credential release, privileged step-up, or machine key rotation), teams must be able […]

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Is the ChatGPT API Refusing to Summarize Academic Papers? Not so fast.

Is the ChatGPT API Refusing to Summarize Academic Papers? Not so fast.

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Yesterday on X, I shared a post about some responses I was getting from the ChatGPT 3.5 API indicating that it was refusing to summarize arXiv papers: Seeing the ChatGPT 3.5 API respond with variations of this today when asking it to summarize an arXiv paper: “I’m sorry, but I can’t do that. However, you can use the information provided to craft your own summary about the paper. Good luck!” That’s… new… — Matt Mazur (@mhmazur) January 2, […]

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Learning Machine Learning

Learning Machine Learning

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Machine learning is one of the most interesting and advancing aspects of engineering and computer science. It shows you the potential of machines in our modern day world. Machine learning is essentially making a machine learn to perform different tasks by feeding enormous amounts of data. To accomplish this, there are multiple algorithms used for machines to learn and adapt to perform a task easier with new inputs. One of the algorithms is called neural networks. A Neural […]

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CropNeRF: A Neural Radiance Field-Based Framework for Crop Counting

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arXiv:2601.00207v1 Announce Type: new Abstract: Rigorous crop counting is crucial for effective agricultural management and informed intervention strategies. However, in outdoor field environments, partial occlusions combined with inherent ambiguity in distinguishing clustered crops from individual viewpoints poses an immense challenge for image-based segmentation methods. To address these problems, we introduce a novel crop counting framework designed for exact enumeration via 3D instance segmentation. Our approach utilizes 2D images captured from multiple viewpoints and associates independent instance masks for […]

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Feature Engineering for Machine Learning

Feature Engineering for Machine Learning

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Despite being vital to machine learning, feature engineering is not always given due attention. Feature engineering is a supporting step in machine learning modeling, but with a smart approach to data selection, it can increase a model’s efficiency and lead to more accurate results. It involves extracting meaningful features from raw data, sorting features, dismissing duplicate records, and modifying some data columns to obtain new features more relevant to your goals. From this article, you will learn what […]

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