True agentic AI is years away - here's why and how we get there

Today's AI agents are a primitive approximation of what agents are meant to be. True agentic AI requires serious advances in reinforcement learning and complex memory.
IEEE

Gradient Descent Algorithm based on Extended State Observer for Loss Minimization of Ultralocal Model Predictive Controlled PMSM Drives

Abstract: Hybrid loss minimization algorithms in electrical drives combine the benefits of search-based and model-based approaches to deliver fast and robust dynamic responses. This article presents a ...
eLife

A differentiable model for optimizing the genetic drivers of synaptogenesis

This study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...
IEEE

Channel-Aware Gradient Descent Bit-Flipping Algorithm for LDPC Codes

Abstract: The gradient descent bit-flipping with momentum (GDBF-w/M) and probabilistic GDBF-w/M (PGDBF-w/M) algorithms significantly improve the decoding performance of the bit-flipping (BF) algorithm ...
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How one algorithm transformed science, math, and computing

In a world run by computers, there is one algorithm that stands above all the rest. It powers search engines, encrypts your data, guides rockets, runs simulations, and makes the modern digital ...