Jack Vaughan

Complexities in deploying Gen AI and LLMs dim the light on some initial hype. These are the days of engineering, coordination, and integration.

By Jack Vaughan

The early procession of ‘2025 Outlooks’ seems to start with a lot of looks back. It’s mostly about Generative AI, which has proved to be a stock market mover, stock art maker, and stock item in years in review.

The song remains the same but the tenor may change. ChatGPT is two years old, and its market shaking meteoric rise now feels a bit less meteoric, if only because changing the world requires effort.

[Read more…] about Brief exploration: Generative AI circa 2025

Updated – The Royal Swedish Academy of Sciences today announced that neural network science pioneers John Hopfield and Geoffrey Hinton will be awarded the Nobel Prize in Physics for 2024. The two researchers are cited for “foundational discoveries and inventions that enable machine learning with artificial neural networks.”

The Nobel award is a capstone of sorts for two premier researchers in neural networks. This computer technology has gained global attention in recent years as the underpinning engine for large-scale advances in computer vision, language processing, prediction and human-machine interaction.

John Hopfield is best known for efforts to re-create interconnect models of the human brain. At the California Institute of Technology in the 1980s, he developed foundational concepts for neural network models.

These efforts led to what became known as a Hopfield network architecture. In effect, these are simulations that work as associative memory models that can process, store and retrieve patterns based on partial and imprecise information inputs.

Geoffrey Hinton, now a professor of computer science at the University of Toronto, began studies in the 1970s on neural networks mimicking human brain activity. Such reinterpretations of the brain’s interconnectedness found gradually greater use in pattern recognition through many years, leading to extensive use today in small- and large-arrays of semiconductors.

In the 1980s, Hinton and fellow researchers focused efforts on backpropagation algorithms for training neural networks and, later, so-called deep learning networks which are at the heart of today’s generative AI models. As implemented on computer chips from Nvidia and others, these models are now anticipated to drive breakthrough innovations in a wide variety of scientific and business fields. Hinton has been vocal in concerns about the future course of AI, and how it may have detrimental effects. He recently left a consulting position at Google, so he could speak more freely about his concerns.

Control at issue

What’s now known as artificial intelligent in the form of neural networks began to take shape in the 1950s. Research in both neural networks and competitive expert system AI approaches saw a decline in the 1990s as disappointing results accompanied the end of the Soviet Union and the Cold War, which had been a big driver of funds.

This period was known as the “AI Winter.” Hinton’s and Hopfield’s work was key in carrying the neural efforts forward until advances in language and imaging processing inspired new interest in the neural approaches.

Drawbacks still track the neural movement – most notably the frequent difficulty researchers face in understanding the work going on within a “black box’ of neural networks.

Both Hinton and Hopfield addressed the issues of AI in society during Nobel Prize-related press conferences. The unknown limits of AI capabilities – particularly a possible future superintelligence – have been widely discussed, and were echoed in reporters’ questions and Nobelists’ replies.

“We have no experience of what it’s like to have things smarter than us,” Hinton told the presser assembled by the Nobel committee. It’s going to be wonderful in many respects, in areas like healthcare. But we also have to worry about a number of possible bad consequences. Particularly the threat of these things getting out of control.”

Hopfield, by Zoom, echoed those concerns, discussing neural-based AI at an event honoring his accomplishment at Princeton.

Taking a somewhat evolutionary perspective, he said: “I worry about anything which says ‘I’m big. I’m fast. I’m faster than you are. I’m bigger than you are, and I can also outrun you. Now can you peacefully inhabit with me?’ I don’t know.”

But is it physics?

As implemented on computer chips from Nvidia and others, the neural models are anticipated to drive breakthrough innovations in a wide variety of scientific and business fields.

Both Hinton’s and Hopfield’s work spanned fields like biology, physics, computer science, and neuroscience. The multidisciplinary nature of much of this work required Nobel judges to clamber and jimmy and ultimately fit the neural network accomplishments into the Physics category.

“The laureates’ work has already been of the greatest benefit. In physics we use artificial neural networks in a vast range of areas, such as developing new materials with specific properties,” said Ellen Moons, Chair of the Nobel Committee for physics in a statement. – JV

shown above Picture: TRW neural network based pattern recognition, 1990s