Combating misinformation in the age of llms: Opportunities and challenges
Misinformation such as fake news and rumors is a serious threat for information ecosystems
and public trust. The emergence of large language models (LLMs) has great potential to …
and public trust. The emergence of large language models (LLMs) has great potential to …
Building machines that learn and think with people
What do we want from machine intelligence? We envision machines that are not just tools
for thought but partners in thought: reasonable, insightful, knowledgeable, reliable and …
for thought but partners in thought: reasonable, insightful, knowledgeable, reliable and …
Foundational challenges in assuring alignment and safety of large language models
This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …
language models (LLMs). These challenges are organized into three different categories …
Contrastive learning explains the emergence and function of visual category-selective regions
Modular and distributed coding theories of category selectivity along the human ventral
visual stream have long existed in tension. Here, we present a reconciling framework …
visual stream have long existed in tension. Here, we present a reconciling framework …
A large-scale examination of inductive biases shaping high-level visual representation in brains and machines
The rapid release of high-performing computer vision models offers new potential to study
the impact of different inductive biases on the emergent brain alignment of learned …
the impact of different inductive biases on the emergent brain alignment of learned …
Do large language models perform the way people expect? measuring the human generalization function
What makes large language models (LLMs) impressive is also what makes them hard to
evaluate: their diversity of uses. To evaluate these models, we must understand the …
evaluate: their diversity of uses. To evaluate these models, we must understand the …
[HTML][HTML] An explainable AI framework for robust and transparent data-driven wind turbine power curve models
In recent years, increasingly complex machine learning methods have become state-of-the-
art in modelling wind turbine power curves based on operational data. While these methods …
art in modelling wind turbine power curves based on operational data. While these methods …
Mechanistic Interpretability for AI Safety--A Review
Understanding AI systems' inner workings is critical for ensuring value alignment and safety.
This review explores mechanistic interpretability: reverse-engineering the computational …
This review explores mechanistic interpretability: reverse-engineering the computational …
When Does Perceptual Alignment Benefit Vision Representations?
Humans judge perceptual similarity according to diverse visual attributes, including scene
layout, subject location, and camera pose. Existing vision models understand a wide range …
layout, subject location, and camera pose. Existing vision models understand a wide range …
Towards symbolic XAI–explanation through human understandable logical relationships between features
Abstract Explainable Artificial Intelligence (XAI) plays a crucial role in fostering transparency
and trust in AI systems. Traditional XAI methods typically provide a single level of abstraction …
and trust in AI systems. Traditional XAI methods typically provide a single level of abstraction …