The accelerating pace of artificial intelligence research and deployment makes both extraordinary opportunity and profound peril increasingly apparent. This paper discusses the innovative proposition that AI can be marsharded, paradoxically, as a proactive guardian of human cognition against the harmful applications of the very technology on which it relies. The heuristic of “brain hacking”—an intentional deployment of AI-driven interventions that systematically augment mental capacities while fortifying neural substrates against adversarial incursions—emerges as a promising trajectory for both theoretical […]

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In the first part, I talked about what Data Quality, Anomaly Detection and Outliers Detection are and what’s the difference between outliers detection and novelty detection. In this part, I will talk about a very known and easy method to detect outliers called Interquartile Range. Introduction The Interquartile Range method, also known as IQR, was developed by John Widler Turky, an American mathematician best known for development of the FFT algorithm and box plot.

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Hi everyone, I’ve recently finished re-engineering the Fuzzy-Pattern Tsetlin Machine (FPTM) from the ground up. My goal was to leverage low-level optimizations to see just how much throughput I could squeeze out of the architecture. The results are pretty wild. By focusing on cache locality and SIMD instructions, the new implementation is up to 10× faster in training and 34× faster in inference compared to the original FPTM. MNIST Benchmarks (Ryzen 7950X3D): ⚡ Throughput: 4 GB/s 🧠 Inference: […]

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Human-guided AI collaboration The post Probabilistic Multi-Variant Reasoning: Turning Fluent LLM Answers Into Weighted Options appeared first on Towards Data Science.

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arXiv:2601.00237v1 Announce Type: new Abstract: This paper addresses the critical bottleneck of infrared (IR) data scarcity in Printed Circuit Board (PCB) defect detection by proposing a cross-modal data augmentation framework integrating CycleGAN and YOLOv8. Unlike conventional methods relying on paired supervision, we leverage CycleGAN to perform unpaired image-to-image translation, mapping abundant visible-light PCB images into the infrared domain. This generative process synthesizes high-fidelity pseudo-IR samples that preserve the structural semantics of defects while accurately simulating thermal distribution patterns. […]

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arXiv:2601.02998v1 Announce Type: cross Abstract: In many fairness and distribution robustness problems, one has access to labeled data from multiple source distributions yet the test data may come from an arbitrary member or a mixture of them. We study the problem of constructing a conformal prediction set that is uniformly valid across multiple, heterogeneous distributions, in the sense that no matter which distribution the test point is from, the coverage of the prediction set is guaranteed to exceed […]

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The U.S. Patent and Trademark Office (USPTO) has proposed new rules that would effectively end the public’s ability to challenge improperly granted patents at their source—the Patent Office itself. If these rules take effect, they will hand patent trolls exactly what they’ve been chasing for years: a way to keep bad patents alive and out of reach. People targeted with troll lawsuits will be left with almost no realistic or affordable way to defend themselves. We need EFF […]

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How Structured Schemas Are Quietly Revolutionizing Human-AI Communication Protocols The discourse around prompt engineering has largely centered on linguistic creativity — the art of crafting persuasive natural language that coaxes better responses from large language models. But beneath this surface-level conversation, a more fundamental shift is occurring: the migration from probabilistic, free-form prompting to deterministic, schema-driven interaction patterns. JSON prompting represents not merely a formatting preference, but a paradigm shift in how we architect human-AI interfaces. The Interface Design Problem […]

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This work addresses correlation bias and causal effect confounding in advertising recommendation systems and presents a causal learning–based recommendation framework. We first examine the limitations of conventional recommendation algorithms in complex advertising environments, where confounding variables and exposure bias often prevent models from capturing users’ true preferences. To tackle these issues, we design a unified embedding architecture that jointly represents user, advertisement, and contextual features, and incorporates a structural causal graph to explicitly model dependencies among variables. During […]

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