The Conflict Between AI Processor Structural Transformation and the Memory Wall Brought About by 'Dust', a Backpropagation-Independent Transformer Training Met…

Pretraining Transformers Without Backpropagation" is a proposal for a computational algorithm and training framework that ...
BIOENGINEER

Evolutionary Algorithm Designs and Trains Neural Networks Simultaneously, Beating Classic Methods

IMODE, an improved multi-operator differential evolution algorithm that simultaneously optimizes the architecture and parameters of feedforward neural networks, achieving ...
Opinion

AI that surpasses humans in intelligence and environmental impact—can it be an alternative for humanity?

Social Contract Theory in the Age of AI #13Last time, using Arrow's impossibility theorem and the cypherpunk and free software movements of the 1990s as clues, I discussed whether it is truly ...
finance.biggo

Alok: We'll Stop Using Backprop in 10 Years, and the Brain Is the Proof

The AI boom has pushed NVIDIA's market value to record heights, with Elon Musk planning to deploy over 1.2 million of its chips in a single ...
scmp.com

Geoffrey Hinton

The latest news and top stories on Geoffrey Hinton, a British-Canadian cognitive psychologist and computer scientist. Widely known as the “Godfather of AI“, he is University Professor Emeritus at the ...
quantumzeitgeist

Quantinuum’s Backpropagation Algorithm Rivals Sparse Pauli Simulation Speed

Quantinuum researchers have developed a new backpropagation algorithm that significantly reduces the memory demands of optimizing quantum circuits. The method achieves a reduction in memory cost by a ...
EditorialGE

How Neural Networks Learn: Backpropagation Without the Math

That cycle repeats thousands of times until the network gets really good at its job. Most people assume that understanding how neural networks learn requires a PhD in mathematics. It doesn’t. The core ...
nytimes.com

The Breakup Loop: Why the TikTok Algorithm May Keep You Heartbroken

As social media companies face scrutiny over addictive design features, mental health experts cite a stream of breakup content as an example of the failure to protect users. As social media companies ...
IEEE

Toward Backpropagation-Free On-Chip Training: Circuit-Algorithm Co-Design of a Goodness-Based Learning Tile

The proliferation of machine learning has led to widespread use of artificial neural networks (ANNs) across diverse computing platforms, from cloud servers to edge devices. In the dominant paradigm, ...
BIOENGINEER

On-Chip Backpropagation Empowers Photonic Neural Networks

In a groundbreaking advancement at the crossroads of photonics and artificial intelligence, researchers have unveiled a fully integrated photonic neural network system capable of performing ...
letsdatascience

Backpropagation: The Engine of Deep Learning

Every time you train a neural network, backpropagation is doing the heavy lifting. It's the algorithm that answers the most important question in optimization: how should each weight change to reduce ...