There is a persistent belief in the ‘AI’ community that large language models (LLMs) have the ability to learn and self-improve by tweaking the weights in their vector space. Although ...

For decades, cognitive neuroscience relied on highly controlled, albeit artificial, experimental designs using isolated words or fragmented sentences to map ...

During an April 2 showcase event, Research Track students presented the work they developed over the two quarters. The 12 ...

However, a new study warns that the same capabilities driving their adoption are also creating a broad and evolving landscape of security, privacy, and ethical risks that existing safeguards are ...

Recent advances in large-scale AI models, including large language and vision-language-action models, have significantly expanded the capabilities of ...

A misconception is currently thriving in the industry that one can become a Generative AI expert without learning ...

MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced that they have developed a set of quantum algorithms for feedforward neural networks, breaking through the performance ...

This study presents valuable findings by reanalyzing previously published MEG and ECoG datasets to challenge the predictive nature of pre-onset neural encoding effects. The evidence supporting the ...

We introduced the LLM-Synergy framework, consisting of two ensemble methods: (1) a Boosting-based Weighted Majority Vote ensemble, refining decision-making by adaptively weighting each LLM, and (2) a ...

The Efficacy of Rule-Based Versus Large Language Model-Based Chatbots in Alleviating Symptoms of Depression and Anxiety: Systematic Review and Meta-Analysis J Med Internet Res 2025;27:e78186 ...

Abstract: Recently, model predictive control is widely applied in practice. To enhance the robustness of the controlled system, model-free predictive control is singled out as an alternative solution.

It also plays a key role in understanding how intelligent AI is, preventing the misallocation of resources, and guiding improvements in future model development. Despite its importance, rigorous ...