Communications of the ACM

Can We Understand How Large Language Models Reason?

Large language models can write essays, solve math problems, and generate computer code, but it’s not fully understood how ...
Times Higher Education

Design learning that works with the brain, not against it

Educating used to be about explaining concepts – now it’s about designing experiences to help the brain develop understanding. Find advice to tweak your teaching here ...
IEEE

Applying Cognitive Load Theory and the Split Attention Effect to Learning Data Structures

Abstract: Learning data structures is a hard task for computer science students, given the mental effort required to simultaneously understand abstract diagrams and the dynamic manipulation of nodes ...
WebMD

What Is Applied Behavior Analysis?

Applied Behavior Analysis (ABA) looks different for every child, with a unique set of goals. (Photo Credit: E+/Getty Images) ABA is a type of interpersonal therapy. Your child usually works with a ...
GitHub

AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning Large Language Models

Recent work on pruning large language models (LLMs) has shown that one can eliminate a large number of parameters without compromising performance, making pruning a promising strategy to reduce LLM ...

Understanding cognitive development: Different stages and Piaget’s theory

What is cognitive development? Expert Dr Samantha Durrant answers parents' questions around this tricky subject, and how it ...
Nature

Could machine learning help to build a unified theory of cognition?

Giosuè Baggio is in the Language Acquisition and Language Processing Lab, Norwegian University of Science and Technology (NTNU), Trondheim NO-7491, Norway. Binz et al. explore horizontal integration ...
PNAS

Do large language models have a theory of mind?

Michal Kosinski’s recent study on theory of mind (ToM) tasks given to different large language models (LLMs) (1) is fascinating and offers many insights into the continued evolution and development of ...
IEEE

Information Compression in the AI Era: Recent Advances and Future Challenges

Abstract: This survey article focuses on the emerging connections between machine learning and data compression. While the fundamental limits of classical (lossy) data compression are well-established ...
Frontiers

The cognitive paradox of AI in education: between enhancement and erosion

Artificial intelligence (AI) is rapidly transforming learning through unparalleled levels of personalization, efficiency, and scalability (Govea et al., 2023; Mahmoud and Sørensen, 2024). AI-based ...
GitHub

A Roadmap for Transfer Learning

This repo is a collection of AWESOME papers, code related with transfer learning, pre-training and domain adaptation etc. Feel free to star and fork. Feel free to let us know the missing papers (issue ...