The latest AI news we announced in January
Google AI announcements from January
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The quiet work behind Citi’s 4,000-person internal AI rollout
For many large companies, artificial intelligence still lives in side projects. Small teams test tools, run pilots, and present results that struggle to spread beyond a few departments. Citi has taken a different path, where instead of keeping AI limited to specialists, the bank has spent the past two years pushing the technology into daily work in the organisation. That effort has resulted in an internal AI workforce of roughly 4,000 employees, drawn from roles that range from […]
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Explaining Machine Learning Predictive Models through Conditional Expectation Methods
The rapid adoption of complex Artificial Intelligence (AI) and Machine Learning (ML) models has led to their characterization as black boxes due to the difficulty of explaining their internal decision-making processes. This lack of transparency hinders users’ ability to understand, validate and trust model behavior, particularly in high-risk applications. Although explainable AI (XAI) has made significant progress, there remains a need for versatile and effective techniques to address increasingly complex models. This work introduces Multivariate Conditional Expectation (MUCE), […]
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Modular Deep Learning
This post gives a brief overview of modularity in deep learning. For a more in-depth review, refer to our survey. For modular fine-tuning for NLP, check out our EMNLP 2022 tutorial. For more resources, check out modulardeeplearning.com. Fuelled by scaling laws, state-of-the-art models in machine learning have been growing larger and larger. These models are monoliths. They are pre-trained from scratch in highly choreographed engineering endeavours. Due to their size, fine-tuning has become expensive while alternatives, such as […]
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Green LLM Techniques in Action: How Effective Are Existing Techniques for Improving the Energy Efficiency of LLM-Based Applications in Industry?
arXiv:2601.02512v1 Announce Type: new Abstract: The rapid adoption of large language models (LLMs) has raised concerns about their substantial energy consumption, especially when deployed at industry scale. While several techniques have been proposed to address this, limited empirical evidence exists regarding the effectiveness of applying them to LLM-based industry applications. To fill this gap, we analyzed a chatbot application in an industrial context at Schuberg Philis, a Dutch IT services company. We then selected four techniques, namely Small […]
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Uncertainty-Aware PCA for Arbitrarily Distributed Data Modeled by Gaussian Mixture Models
arXiv:2508.13990v2 Announce Type: replace Abstract: Multidimensional data is often associated with uncertainties that are not well-described by normal distributions. In this work, we describe how such distributions can be projected to a low-dimensional space using uncertainty-aware principal component analysis (UAPCA). We propose to model multidimensional distributions using Gaussian mixture models (GMMs) and derive the projection from a general formulation that allows projecting arbitrary probability density functions. The low-dimensional projections of the densities exhibit more details about the distributions […]
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What is RAG? A Beginner’s Guide to Retrieval-Augmented Generation
Not long ago, all large language models (LLMs) had what we called a knowledge cutoff. This meant they only knew information up until a certain date — anything that happened after that, they simply couldn’t help you. Today, that’s changed, at least for cloud-based LLMs like ChatGPT. These models can now access real-time or recent information. So if you ask something like “Who won the Australia 2025 elections?”, ChatGPT can give you an up-to-date answer: The Australian Labor […]
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A Systematic Review of Reinforcement Learning for Dynamic Risk Assessment
Traditional risk assessment méthodologies are often inadequate in dynamic environments due to their reliance on static historical data. A viable substitute for adaptive, sequential decision-making in the face of uncertainty is Reinforcement Learning (RL). However, the research landscape is fragmented, lacking a unified framework to guide the selection of RL paradigms, such as risk-sensitive, safe, and robust RL, for specific risk categories. To close this gap, this review examines RL’s use in risk assessment in a methodical manner. […]
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OSIRIS: Bridging Analog Circuit Design and Machine Learning with Scalable Dataset Generation
The automation of analog integrated circuit (IC) design remains a longstanding challenge, primarily due to the intricate interdependencies among physical layout, parasitic effects, and circuit-level performance. These interactions impose complex constraints that are difficult to accurately capture and optimize using conventional design methodologies. Although recent advances in machine learning (ML) have shown promise in automating specific stages of the analog design flow, the development of holistic, end-to-end frameworks that integrate these stages and iteratively refine layouts using post-layout, […]
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CORVUS: Red-Teaming Hallucination Detectors via Internal Signal Camouflage in Large Language Models
arXiv:2601.14310v1 Announce Type: new Abstract: Single-pass hallucination detectors rely on internal telemetry (e.g., uncertainty, hidden-state geometry, and attention) of large language models, implicitly assuming hallucinations leave separable traces in these signals. We study a white-box, model-side adversary that fine-tunes lightweight LoRA adapters on the model while keeping the detector fixed, and introduce CORVUS, an efficient red-teaming procedure that learns to camouflage detector-visible telemetry under teacher forcing, including an embedding-space FGSM attention stress test. Trained on 1,000 out-of-distribution Alpaca […]
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