Machine Heart Report Editor: Wang Qiang What happens when neural networks reach 100%? What is the ultimate form of neural networks? What is a network superbody? The answers to these questions may be found in the movie “Lucy”. In the movie, as the protagonist Lucy gradually develops her brain power, she gains the following abilities: … Read more
Click the image for details↑ Human vision is an extraordinary ability. Although it has evolved over millions of years in specific environments, it can accomplish tasks that early visual systems have never experienced. Reading is a great example, such as recognizing cars, airplanes, road signs, and other man-made objects. However, the visual system also has … Read more
As the next generation of advanced computing technology, quantum computers are on the brink of practical application. A landmark milestone was Google’s demonstration of quantum supremacy in 2019 using a 54-qubit superconducting quantum processor [1]. Since then, how to utilize quantum computing devices to solve real-world problems and achieve performance surpassing classical computers under existing … Read more
Li Zi from A Fei Si Quantum Bit | WeChat Official Account QbitAI ”Master, can you help me Photoshop my face onto the model in the street photo? Oh right, not just that, it has to look like the model…” Extracting an element from one photo and seamlessly blending it into another seems like a … Read more
Selected from arXiv Compiled by Machine Heart Contributors: Jiang Siyuan, Smith, Li Yazhou Recently, a paper titled “Self-Normalizing Neural Networks” published on arXiv has garnered significant attention in the community. It introduces the Scaled Exponential Linear Unit (SELU), which brings in a self-normalizing property. This unit mainly uses a function g to map the mean … Read more
Selected from arXiv Compiled by Machine Heart Contributors: Jane W, Wu Pan Neuromorphic computing is considered an important direction for future artificial intelligence computing. Recently, several researchers from the Institute of Electrical and Electronics Engineers (IEEE) jointly published an 88-page overview paper that comprehensively reviews the development of neuromorphic computing over the past 35 years … Read more
Selected by OpenAI Author: JAKOB FOERSTER Translation by Machine Heart Using linear networks for nonlinear computation is an unconventional approach. Recently, OpenAI published a blog introducing their new research on deep linear networks, which do not use activation functions, yet achieve 99% training accuracy and 96.7% testing accuracy on MNIST. This new research has reignited … Read more
Abstract: This issue discusses the technological innovations in neural network compression and acceleration at different levels, mainly involving three aspects: (1) neural network compression and its hardware-software co-design methods; (2) neural network compression software frameworks and compilation optimization techniques; (3) automation of neural network compression and support for new architectures. It aims to help readers … Read more
Original link:https://medium.com/towards-artificial-intelligence/one-lego-at-a-time-explaining-the-math-of-how-neural-networks-learn-with-implementation-from-scratch-39144a1cf80 From:Yongyu Excerpted from Algorithm Notes https://github.com/omar-florez/scratch_mlp/ The author explains step by step the mathematical processes used in training a neural network from scratch. Neural networks are cleverly arranged linear and nonlinear modules. The above image describes some of the mathematical processes involved in training a neural network. We will explain this in the … Read more
Welcome to the special winter vacation premium column “High-Tech Lessons for Kids” launched by Science Popularization China! Artificial intelligence, as one of the most cutting-edge technologies today, is changing our lives at an astonishing pace. From smart voice assistants to self-driving cars, from AI painting to machine learning, it opens up a future full of … Read more