February 2026

Beyond the Chatbox: Why Native Multimodality is the New Enterprise Standard

Beyond the Chatbox: Why Native Multimodality is the New Enterprise Standard

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Running a business on text-only LLMs is the equivalent of navigating a 3D market through a 1D keyhole. Native Multimodality isn’t a “feature” — it is a fundamental shift from AI that merely reads your business to AI that perceives it. By integrating video, voice, and structured telemetry into a unified decision engine, enterprises can finally bridge the gap between fragmented data and high-velocity execution. If your system cannot correlate the sentiment of a customer’s voice with the technical error […]

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Evolutionary System Prompt Learning can Facilitate Reinforcement Learning for LLMs

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Building agentic systems that can autonomously self-improve from experience is a longstanding goal of AI. Large language models (LLMs) today primarily self-improve via two mechanisms: self-reflection for context updates, and reinforcement learning (RL) for weight updates. In this work, we propose Evolutionary System Prompt Learning (E-SPL), a method for jointly improving model contexts and model weights. In each RL iteration, E-SPL selects multiple system prompts and runs rollouts with each in parallel. It applies RL updates to model […]

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Banking AI in multiple business functions at NatWest

Banking AI in multiple business functions at NatWest

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NatWest Group has expanded the use of artificial intelligence in several areas of its operations, citing customer service, document management in its wealth management division, and software development. According to a blog post by its chief information officer, Scott Marcar, 2025 was the first year in which these systems were deployed at scale. The aim is to improve productivity and customer engagement. Generative AI in customer service In customer service, generative AI has been added to Cora, the […]

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Debenhams pilots agentic AI commerce via PayPal integration

Debenhams pilots agentic AI commerce via PayPal integration

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Debenhams is piloting agentic AI commerce via PayPal integration to reduce mobile friction and help solve a familiar problem for retailers. Mobile checkout abandonment remains a persistent revenue leak for digital retailers. Debenhams Group is attempting to close this gap by deploying an agentic AI interface within the PayPal app. The pilot makes Debenhams the first UK retailer to test an automated checkout flow that keeps the user entirely inside a payment provider’s ecosystem. Shoppers using PayPal can […]

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3 Game-Changing Tools for Modern Data Science

3 Game-Changing Tools for Modern Data Science

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Introduction The rise of LLMs facilitates “vibe coding,” making it fast to generate initial Python scripts. However, this ease creates a false sense of progress. Building professional-grade data products requires more than quick scripts; it demands a shift from local experimentation to scalable systems. Professional data science maturity is defined by strategic tool selection and architectural mastery that transforms raw code into business value, moving the individual from a “coder” to a “data professional.” The article’s three tools Table […]

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Sideways on the ice, in a supercar: Stability control is getting very good

Sideways on the ice, in a supercar: Stability control is getting very good

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McLaren provided flights from Washington, DC, to Ivalo, Finland, and accommodation so Ars could drive its car on a frozen lake. Ars does not accept paid editorial content. SAARISELKÄ, FINLAND—If you’re expecting it, the feeling in the pit of your stomach when the rear of your car breaks traction and begins to slide is rather pleasant. It’s the same exhilaration we get from roller coasters, but when you’re in the driver’s seat, you’re in charge of the ride. […]

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Scaling on Demand: How Serverless Architectures Empower Agile and High-Performing Platforms

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The transition to serverless architecture is not a buzzword to developers and software developers involved in developer work in macOS. It is a true revolution in the way apps are built, implemented, and scaled. Gone are the days of staring at terminal windows full of endless server logs and sleepless nights in order to keep the infrastructure from hiccuping. Serverless sets that mess with the work of someone. If you have been developing on macOS, then it is […]

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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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