Posts

The first neural network was created in 1943

  The first mathematical model of a neural network was created in 1943 by Warren McCulloch and Walter Pitts, while the first physical, working neural network hardware (the Perceptron) was built in 1957–1958 by Frank Rosenblatt. [ 1 , 2 , 3 , 4 ] Key Milestones in Early Neural Network History 1943 (The First Model): Neurophysiologist Warren McCulloch and mathematician Walter Pitts published a paper proposing the first computational model of an artificial neuron using electrical circuits and threshold logic . [ 1 , 2 , 3 ] 1949 (Learning Rule): Psychologist Donald Hebb introduced Hebbian learning, suggesting that neural pathways strengthen when they fire together ("cells that fire together, wire together"). [ 1 , 2 ] 1951 (First Simulator): Marvin Minsky built the SNARC, considered the first neural network simulation machine. [ 1 ] 1957–1958 (First Physical Network): Frank Rosenblatt developed and built the Perceptron (the Mark 1 Perceptron), the first real-world, a...
Post a Comment
Read more

AI study from compiler to security issue - with GOOGLE AI

  The Architecture, History, and Geopolitics of Modern AI Hardware and Software The Silicon & Soul of AI An Engineering Journey from Microcontrollers to Superalignment To understand the artificial intelligence revolution, one must trace the physical wires where software meets silicon, alongside the high-stakes human drama that dictates who controls the future of computing. This comprehensive breakdown explores the underlying engineering of AI hardware, the historic milestones that unlocked its power, and the geopolitical philosophies dividing its greatest minds. 1. The Engineering: Rewriting Architecture for Massive Parallelism Traditional computing relies on Scalar Execution —handling one or two numbers at a time via a standard CPU. This layout functions like a couple of highly intelligent Ferrari sports cars driving down a narrow highway. In cont...
Post a Comment
Read more

AI study from compiler to security issue - with GOOGLE AI

Image
  AI Mode conversation: what is AI compiler work to do You said: what is AI compiler work to do what is AI compiler work to do 7:27 pm An AI compiler (or machine learning graph compiler) translates high-level deep learning models (built in frameworks like PyTorch or TensorFlow ) into low-level, high-performance machine code tailored for specific hardware ( CPUs , GPUs , TPUs , or custom accelerators) . [ 1 , 2 , 3 ] Unlike traditional software compilers (like GCC or LLVM ) that compile procedural source code, an AI compiler works on computational graphs of neural network operators (such as matrix multiplications, convolutions, and activations). [ 1 , 2 ] Key Core Responsibilities of an AI Compiler 1. Graph-Level Optimization (Upper Layer) Computational Graph Generation: Takes a trained model and turns it into a high-level Intermediate Representation (IR) graph. Operator Fusion: Combines multiple sequential operations (like a convolution followed by a bias add and an activatio...
Post a Comment
Read more