Machine Learning

Opening — Why this matters now Everyone wants autonomous agents. Few seem willing to admit that most of them are still glorified retry machines. In production systems—from coding copilots to web automation agents—the dominant strategy is embarrassingly simple: try, fail, try again, and hope that one trajectory sticks. This works, but only if you can afford the latency, compute cost, and engineering complexity of massive sampling. ...

A practical comparison of large language models and classical machine learning, with guidance on when each approach fits a business problem.

Opening — Why this matters now Railway delays are one of those problems everyone experiences and almost no one truly understands. Passengers blame weather. Operators blame operations. Data scientists blame missing variables. Everyone is partially correct. What has quietly shifted in recent years is not the weather itself, but our ability to observe it alongside operations—continuously, spatially, and at scale. As rail systems push toward AI‑assisted scheduling, predictive maintenance, and real‑time disruption management, delay prediction without weather is no longer just incomplete—it is structurally misleading. ...

Opening — Why this matters now Mixed-Integer Linear Programming (MILP) sits quietly underneath a surprising amount of modern infrastructure: logistics routing, auctions, facility placement, chip layout, resource allocation. When it works, no one notices. When it doesn’t, the solver spins for hours, racks up nodes, and quietly burns money. At the center of this tension is branch-and-bound—an exact algorithm that is elegant in theory and painfully sensitive in practice. Its speed hinges less on raw compute than on where it looks first. For decades, that decision has been guided by human-designed heuristics: clever, brittle, and wildly inconsistent across problem families. ...

Opening — Why this matters now Machine learning has become science’s most productive employee—and its most awkward colleague. It delivers predictions at superhuman scale, spots patterns no graduate student could ever see, and does so without asking for coffee breaks or tenure. But as ML systems increasingly mediate discovery, a more uncomfortable question has resurfaced: who is actually in control of scientific knowledge production? ...

Opening — Why this matters now Large Language Models (LLMs) are often praised for what they generalize. Yet, beneath the surface, a less glamorous behavior quietly persists: they remember—sometimes too well. In an era where models are trained on ever-larger corpora under increasing regulatory scrutiny, understanding when memorization occurs, why it happens, and how it can be isolated is no longer an academic indulgence. It is an operational concern. ...

When biologists talk about ecosystems, they speak of inheritance, mutation, adaptation, and drift. In the open-source AI world, the same vocabulary fits surprisingly well. A new empirical study of 1.86 million Hugging Face models maps the family trees of machine learning (ML) development and finds that AI evolution follows its own rules — with implications for openness, specialization, and sustainability. The Ecosystem as a Living Organism Hugging Face isn’t just a repository — it’s a breeding ground for derivative models. Pretrained models are fine-tuned, quantized, adapted, and sometimes merged, producing sprawling “phylogenies” that resemble biological family trees. The authors’ dataset connects models to their parents, letting them trace “genetic” similarity via metadata and model cards. The result: sibling models often share more traits than parent–child pairs, a sign that fine-tuning mutations are fast, non-random, and directionally biased. ...

Deep learning has revolutionized many domains of finance, but when it comes to asset pricing, its power is often undercut by a familiar enemy: noise. Financial datasets are notoriously riddled with weak signals and irrelevant patterns, which easily mislead even the most sophisticated models. The result? Overfitting, poor generalization, and ultimately, bad bets. A recent paper by Che Sun proposes an elegant fix by drawing inspiration from information theory. Titled An Information Bottleneck Asset Pricing Model, the paper integrates information bottleneck (IB) regularization into an autoencoder-based asset pricing framework. The goal is simple yet profound: compress away the noise, and preserve only what matters for predicting asset returns. ...

The AI hype in pharma has mostly yielded faster failures. Despite generative models for molecules and AlphaFold for protein folding, the fundamental chasm remains: what works in silico or in vitro still too often flops in vivo. A new proposal — Programmable Virtual Humans (PVHs) — may finally aim high enough: modeling the entire cascade of drug action across human biology, not just optimizing isolated steps. 🧬 The Translational Gap Isn’t Just a Data Problem Most AI models in drug discovery focus on digitizing existing methods. Target-based models optimize binding affinity; phenotype-based approaches predict morphology changes in cell lines. But both ignore the reality that molecular behavior in humans is emergent — shaped by multiscale interactions between genes, proteins, tissues, and organs. ...

In a world where climate models span continents and economic simulators evolve across decades, it’s no longer enough to ask which variable affects the output the most. We must now ask: how does each input reshape the entire output distribution? The R package gsaot brings a mathematically rigorous answer, harnessing the power of Optimal Transport (OT) to provide a fresh take on sensitivity analysis. ...