February 2026

KDD vs Data Mining: 6 Differences Explained with Examples

KDD vs Data Mining: 6 Differences Explained with Examples

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KDD vs data mining often confuses beginners in data analytics courses. Knowledge Discovery in Databases (KDD) forms the full pipeline to turn raw data into actionable insights, while data mining acts as one step within it. Understanding this distinction helps analysts handle large datasets from e-commerce or finance effectively. This blog breaks down KDD vs data mining, highlighting their roles in data analytics. It defines each term, presents KDD vs data mining difference with a table, covers the […]

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Alignment Adapter to Improve the Performance of Compressed Deep Learning Models

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Compressed Deep Learning (DL) models are essential for deployment in resource-constrained environments. But their performance often lags behind their large-scale counterparts. To bridge this gap, we propose Alignment Adapter (AlAd): a lightweight, sliding-window-based adapter. It aligns the token-level embeddings of a compressed model with those of the original large model. AlAd preserves local contextual semantics, enables flexible alignment across differing dimensionalities or architectures, and is entirely agnostic to the underlying compression method. AlAd can be deployed in two […]

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Tabular Foundation Models Can Learn Association Rules

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Association Rule Mining (ARM) is a fundamental task for knowledge discovery in tabular data and is widely used in high-stakes decision-making. Classical ARM methods rely on frequent itemset mining, leading to rule explosion and poor scalability, while recent neural approaches mitigate these issues but suffer from degraded performance in low-data regimes. Tabular foundation models (TFMs), pretrained on diverse tabular data with strong in-context generalization, provide a basis for addressing these limitations. We introduce a model-agnostic association rule learning […]

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Tabular Foundation Models Can Learn Association Rules

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Association Rule Mining (ARM) is a fundamental task for knowledge discovery in tabular data and is widely used in high-stakes decision-making. Classical ARM methods rely on frequent itemset mining, leading to rule explosion and poor scalability, while recent neural approaches mitigate these issues but suffer from degraded performance in low-data regimes. Tabular foundation models (TFMs), pretrained on diverse tabular data with strong in-context generalization, provide a basis for addressing these limitations. We introduce a model-agnostic association rule learning […]

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AI's new physics discovery

AI’s new physics discovery

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Read Online | Sign Up | Advertise Good morning, {{ first_name | AI enthusiasts }}. OpenAI’s GPT-5.2 just discovered that a widely accepted answer in particle physics was wrong, proposed the correct one, and autonomously wrote the formal proof in 12 hours. The “can AI actually think?” debate from skeptics isn’t going away, but the real conversation is shifting from if AI can contribute to science to how fast it rewrites what we thought we already knew. In […]

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URBN tests agentic AI to automate retail reporting

URBN tests agentic AI to automate retail reporting

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Retail decisions often depend on weekly performance reports, but compiling those reports can take hours of manual work. Urban Outfitters Inc. (URBN) is testing a new approach by using agentic AI systems to generate those reports automatically, changing routine analysis from staff to software. The retailer runs brands like Urban Outfitters, Anthropologie, and Free People, and has deployed AI systems that analyse store-level data and produce weekly summaries for merchandising teams. Instead of reviewing multiple spreadsheets or dashboards, […]

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The Rise of Synthetic Labor

The Rise of Synthetic Labor

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Author(s): Sam Okoye Originally published on Towards AI. Abstract Advanced economies are entering a sustained structural labor deficit driven by demographic decline, aging populations, and persistent sector-specific shortages. Traditional automation, including robotic process automation and narrow task-based systems, has delivered productivity improvements but has proven insufficient to address this gap at scale. This paper introduces synthetic labor, defined as agentic artificial intelligence systems that perform economically productive work with context awareness, memory, planning, tool use, coordination, and governance. […]

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Decoupled Continuous-Time Reinforcement Learning via Hamiltonian Flow

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Many real-world control problems, ranging from finance to robotics, evolve in continuous time with non-uniform, event-driven decisions. Standard discrete-time reinforcement learning (RL), based on fixed-step Bellman updates, struggles in this setting: as time gaps shrink, the $Q$-function collapses to the value function $V$, eliminating action ranking. Existing continuous-time methods reintroduce action information via an advantage-rate function $q$. However, they enforce optimality through complicated martingale losses or orthogonality constraints, which are sensitive to the choice of test processes. These […]

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The Hard Truth About Machine Learning for Amazon FBA Sellers

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For the past few years, I have spent time trying to bend machine learning into something useful for Amazon FBA demand forecasting. On paper, it did sound straightforward to me with all these past sales in and future demand out. But in reality, Amazon’s data is messy, has so many missing pieces, and changes all the time, which makes it hard to build a reliable model. If you have ever wondered why your Prophet model looked great in […]

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