News

Neurosymbolic AI is the answer to large language models’ inability to stop hallucinating
Neurosymbolic AI combines the learning of LLMs with teaching the machine formal rules that should make them more reliable and ...
Stop guessing why your LLMs break: Anthropic’s new tool shows you exactly what goes wrong
Anthropic's open-source circuit tracing tool can help developers debug, optimize, and control AI for reliable and trustable ...
Communications of the ACM3d
A Knockout Blow for LLMs?
What the Apple paper shows, most fundamentally, regardless of how you define AGI, is that LLMs are no substitute for good well-specified conventional algorithms. (They also can’t play chess as well as ...
Study finds that LLM use leads to ‘likely decrease’ in learning skills
Scientists from the Massachusetts Institute of Technology have determined that using large language models leads to a “likely ...
Self-learning neural network cracks iconic black holes
A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions ...
Tech Xplore on MSN2d
Lost in the middle: How LLM architecture and training data shape AI's position bias
Research has shown that large language models (LLMs) tend to overemphasize information at the beginning and end of a document ...
Phys.org29d
Using a fermionic neural network to find the ground state of fractional quantum Hall liquids
"We wanted to demonstrate that this neural network based variational approach ... improved further and used to predict the quantum phase diagram of various 2D electron systems.
TechCrunch25d
From LLMs to hallucinations, here’s a simple guide to common AI terms
A subset of self-improving machine learning in which AI algorithms are designed with a multi-layered, artificial neural network (ANN ... When you prompt an LLM, the model generates the most ...