Genome assembly is a computational pipeline designed to reconstruct chromosomes from small sequencing reads. Following their assembly, contiguous sequences (contigs) are arranged into chromosome-long sequences during scaffolding. Hi-C, a long-range linkage information between regions of the genome widely used in recent large sequencing projects, is often required to correctly order contigs. Several tools have been developed to automate this task following either statistical or deep-learning a... Read more ›
The rapid evolution of Quantum Development Kits (QDKs) introduces a specific form of technical debt that compromises code maintainability and hinders software reuse. In the specialized domain of Quantum Software Engineering (QSE), this challenge is intensified by the scarcity of high-quality training data and the high volatility of emerging frameworks, which often lead general-purpose Large Language Models (LLMs) to produce unreliable or halluci... Read more ›
When I think about learning — especially learning in the brain — I keep returning to a fundamental discrepancy between neuroscience and… Read more ›
A machine learning-powered simulation is giving researchers a new window into the processes that create some of the universe’s heaviest elements. Where do the gold in jewelry, the uranium in nuclear fuel, and many of the universe’s heaviest elements come from? Scientists believe they are forged in some of the most violent events in the [...] Read more ›
Teaching Software Engineering for AI-enabled systems entails addressing the integration of AI components within full-scale software architectures under realistic constraints. While machine learning courses emphasize model development, students often lack experience in architectural design, deployment, and monitoring of AI-enabled systems. Empirical evaluations of such system-oriented AI courses remain limited. This paper reflects on the design a... Read more ›
In this Journal Club, M. Jerome Beetz highlights a study published 2021 that examined hippocampal representation of large environments in flying bats. Read more ›
From pretraining to RLHF/GRPO — every algorithm hand-written in pure PyTorch. Read more ›
Engineering LLM-native software remains a challenging and immature field. Current practice is largely exploratory, relying on experimentation and heuristic techniques such as prompting and context engineering. These, however, are low-level and lack the principled structure needed to support design-level reasoning or analysis. In contrast, traditional software engineering leverages modularity and abstraction to communicate and analyze system beha... Read more ›
Separating the hype from what actually moves the needle Read more ›
Objective: To enhance the accuracy, interpretability, and robustness of large language models (LLMs) in medical question answering (MedQA). Method: We designed a multi-agent peer-reviewed reasoning method in which multiple LLM agents independently generate chain-of-thought reasoning with candidate answers, then act as peer reviewers to evaluate each other's reasoning for factual correctness and logical soundness. The highest-rated reasoning chai... Read more ›
Introduction to Deep Learning and Neural Networks: The Only Guide You Need to Start Read more ›
Neural scaling laws describe how model performance improves as a power law in compute, model size, and dataset size. While well-established for large language models, these relationships are emerging for large models in particle physics. As with language, empirical studies show that the performance scales as a power law. However, unlike natural language or image domains, fundamental physics has high-fidelity simulators that produce synthetic d... Read more ›
Researchers have developed a new "emotionally aware" AI-based model for classifying mental health conditions, which could help clinicians better diagnose patients' mental health conditions. The Emo-MHC model uses machine learning (ML) and deep learning (DL) techniques to analyze text from sources like doctors' notes, social media posts and online forums to help doctors classify patients' mental health conditions more accurately and quickly than existing models, which could help provide more e... Read more ›
If you told someone you were an “AI Engineer” a few years ago, they probably assumed you were elbow-deep in PyTorch, wrangling massive… Read more ›
The largest ongoing debate about AI is “Are Large Language Models (LLMs) intelligent?” That makes sense, at least: the evidence is ambiguous and the stakes a... Read more ›
For someone with a reputation as an AI skeptic, I actually have a lot of time for machine learning approaches, at least when pursued with due humility and attention to detail. Science is a vast sea of data, full of currents, seamounts, hidden reefs, archipelagos, and shorelines, and we need all the help we can get in navigating it. And successes in protein structure prediction are evidence enough that if you have a large enough data set, well-curated and covering a wide range of examples, tha... Read more ›
Two models on Hugging Face looked ordinary — but the instant you loaded one, it opened a backdoor to your machine. The trick: a file… Read more ›
IntroductionAn ambitious problem in mechanistic interpretability of neural networks is finding an input for a neural network that produces a certain output (), however this is shown to not have a general computationally tractable solution that works in cases such as when the network acts as a verifier for an NP hard problem. However, a general algorithm might not be necessary in practice, as "Eliciting bad contexts" suggests. We also know that finding the input that maximizes the output of an... Read more ›
Artificial Intelligence has transformed from a niche research field into a technology that influences daily life, powering applications… Read more ›