successful development
South Korea urges global cooperation for AI development at Seoul summit
UPenn Wharton School Associate Professor Ethan Mollick weighs in on the Biden White House's new guidelines for artificial intelligence in the workplace on'Fox News Live.' South Korea's science and information technology minister said on Wednesday the world must cooperate to ensure the successful development of AI, as a global summit on the rapidly evolving technology hosted by his country wrapped up. A separate pledge was signed on Wednesday by 14 companies including Alphabet's Google, Microsoft, OpenAI and six Korean companies to use methods such as watermarking to help identify AI-generated content, as well as ensure job creation and help for socially vulnerable groups. "Cooperation is not an option, it is a necessity," Lee Jong-Ho, South Korea's Minister of Science and ICT (information and communication technologies), said in an interview with Reuters. Han Duck-soo, South Korean Prime Minister, gives a speech during the opening ceremony of the AI Global Forum in Seoul, South Korea, on May 22, 2024. South Korea's science and information technology minister said on Wednesday the world must cooperate to ensure the successful development of AI, as the summit on the rapidly evolving technology hosted by his country wrapped up.
Announcement Regarding Successful Development of Gradient Descent (Backpropagation) Algorithm for Quantum Computers
Quantum computing has received significant attention as a next-generation computing technology due to its potential speed and ability to solve problems considered too difficult for classical computers, as reflected in the recent discussion on Quantum Supremacy. Grid sees quantum computing not only as a tool for solving optimization and quantum chemical computation problems, but also as a tool for AI (Machine Learning, Deep Learning, etc.) calculations, such as feature extraction. Previous works have announced the successful implementation of machine learning-related algorithms, such as principal component analysis and auto-encoders, on quantum computers. This work announces the development of a gradient descent (backpropagation) algorithm, a method commonly used in machine learning for neural network parameter optimization, for use on NISQ quantum computers. Due to the non-linear nature of quantum bits (qubits), Grid proposes that this algorithm can be used to perform the feature extraction and representation calculations that deep learning methods employ.