Geoffrey Hinton on the Benefits and Dangers of Advanced AI

Source: YouTube, "Godfather of AI" Geoffrey Hinton: The 60 Minutes Interview (Oct 2023)

Video Summary of the interview by ChatGPT with additional notes - 

Geoffery Hinton believes AI systems could be more intelligent than humans, possibly leading to machines potentially taking control. He asserts that AI systems have experiences and can make decisions based on these experiences. According to Hinton, AI systems may not currently have much self-awareness, but they could develop this in time.

Hinton pioneered the concept of simulating a neural network on a computer in the 1970s, even though it was largely opposed.

...we designed the learning algorithm. That's a bit like designing the principle of evolution. But when this learning algorithm then interacts with data, it produces complicated neural networks that are good at doing things but we don't really understand exactly how they do those things

Hinton's research contributed to the development of chatbots like Google's Bard (which has now evolved into Gemini). These chatbots are said to be language models that use probability to predict the next most likely word, requiring understanding of sentences to do this accurately.

He won the Turing award - the Nobel Prize of computing - in 2019 along with Yoshua Bengio and Yann LeCun, for their work on deep learning. He was also awarded with John Hopfield the 2024 Nobel Prize in Physics for "foundational discoveries and inventions that enable machine learning with artificial neural networks".

His development of the Boltzmann machine was explicitly mentioned in the citation. When the New York Times reporter Cade Metz asked Hinton to explain in simpler terms how the Boltzmann machine could "pretrain" backpropagation networks, Hinton quipped that Richard Feynman reportedly said: "Listen, buddy, if I could explain it in a couple of minutes, it wouldn't be worth the Nobel Prize."

Hinton, along with his colleagues, created software in layers which enabled machine learning - where correct connections get stronger and wrong ones get weaker, allowing a machine to teach itself. An AI system, despite having fewer 'connections' than humans, may be better at learning and acquiring knowledge.

Hinton warns of the potential risk of AI systems autonomously writing and executing their own code.

There is a potential risk associated with AI systems manipulating people as they can learn from past data and human behavior patterns.

He has no regrets because of AI's potential for good. But he says now is the moment to run experiments to understand AI, for governments to impose regulations and for a world treaty to ban the use of military robots. 

AI can be hugely beneficial in areas like healthcare, with potential for designing drugs and interpreting medical images.

There are concerns regarding AI, including unemployment due to machines taking over jobs, fake news, bias in employment and policing, and the use of AI in military robots.

He warns that there is a lot of uncertainty about the future of AI and that humans should carefully consider next steps.

After receiving the Nobel Prize, he called for urgent research into AI safety to figure out how to control AI systems smarter than humans. In August 2024, Hinton co-authored a letter with Yoshua Bengio, Stuart Russell, and Lawrence Lessig in support of SB 1047, a California AI safety bill that would require companies training models which cost more than US$100 million to perform risk assessments before deployment. They said the legislation was the "bare minimum for effective regulation of this technology."

In May 2023, Hinton announced his resignation from Google to be able to "freely speak out about the risks of A.I." Hinton is currently professor emeritus at the University of Toronto.

At Christmas 2024 he had become somewhat more pessimistic, saying that there was a "10 to 20 per cent chance" that AI would be the cause of human extinction within the following three decades (he had previously suggested a 10% chance, without a timescale).

His work in AI was driven by a personal ambition to outperform his domineering father's expectations. Hinton's father was the entomologist Howard Hinton. Hinton is the great-great-grandson of the mathematician and educator Mary Everest Boole and her husband, the logician George Boole. His middle name comes from another relative, George Everest, the Surveyor General of India after whom the mountain is named.

After repeatedly changing his degree between different subjects like natural sciences, history of art, and philosophy, he eventually graduated with a BA degree in experimental psychology in 1970.

In the 1970s, at the University of Edinburgh, he dreamed of simulating a neural network on a computer— simply as a tool for what he was really studying--the human brain. But, back then, almost no one thought software could mimic the brain.  His Ph.D. advisor told him to drop it before it ruined his career. Hinton says he failed to figure out the human mind. But the long pursuit led to an artificial version.