TMCnet

MicroAlgo Inc. Develops Optimal Precise Quantum Query Algorithm Based on Sum-of-Squares Representation Form of Boolean Functions

MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced the proposal of a new approach to solving the Boolean function query problem. This framework starts from the ...

New AI framework autonomously optimizes training data, architectures and algorithms — outperforming human baselines

EVOLVE, an agentic framework that autonomously optimizes AI training data, model architectures, and learning algorithms — ...
Hosted on MSN

How AI is reshaping student life and learning

AI is becoming a core part of higher education, transforming how students study, research, and engage with coursework. From AI tutors like UT Sage to advanced research assistants, these tools promise ...

WRD wants to do for language learning what GPS did for navigation — remove the thinking

WRD’s own research, published on the app’s website, shows that users following this approach reach the same levels of ...
TMCnet

MicroAlgo Inc. Develops Quantum Algorithm Technology for Feedforward Neural Networks to Drive Neural Network Revolution

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 ...
JD Supra

Can you patent your AI? New guidance in Canada and the US for software and AI inventors

Corporate investment in AI continues to accelerate globally and patent filings for AI-related inventions are rising across ...
IEEE

An improved teaching-learning-based optimization algorithm for Function Optimization

Abstract: Teaching-learning-based optimization (TLBO) algorithm, one of the representative intelligent optimization algorithms. TLBO imitates the teaching-learning process in the classroom. This paper ...
IEEE

A Centralized Reinforcement Learning-Based Method for Traffic Signal Optimization Using an Adaptive Sequential Decision

Abstract: Deep reinforcement learning (DRL) algorithms have been proven to be effective in traffic signal timing scheme optimization and helpful for urban traffic congestion alleviation. Recently, the ...