Analogies provide us the tools to explore, discover, and understand invention, and to communicate about the invention itself. Draft horses of old still stand fast as such an analogy.
In the 19th Century, James Watt estimated that a strong dray horse could lift 33,000 pounds by one foot in one minute. That provided a comparative measure for the steam engine, and it carries right through to the engines under the hoods of today’s F1 speedsters and NASCAR racers.
While a commonly accepted measure of AI performance is still developing for ChatGPT and other Generative AI systems, there is an analogy at work, and it lies in the neural firings of the brain.
Lift the hood on Generative AI and you are looking at the neural network, which is an equivalent electrical circuit model of workings of the brain. It is an equation or algorithm that is rendered in software. The software runs– these days – on a GPU (graphical processor unit).
The neural net analogy gained the sanction of the academy with the award of the Nobel Prize for Physics (2024). That went to John J. Hopfield and Geoffrey Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.” Both men formulated neural networks in the 1980s, that built on a near-half century of previous work.
The Nobelists would be first to admit the limits of analogies are always evident, although the rapid rise and hype of Generative AI –now called “Agentic AI” – obscures that from the general public. [Read more…] about March of Analogies and AI neural networks
