This is an AI-enabled summary of an interview with cognitive psychologist and computer scientist Geoffrey Hinton. He’s played a big role in the development of computer neural networks and was the guest of Brook Silva-Braga on the CBS Saturday morning show. The YouTube video can be seen at the end of this summary. I added a couple of salient quotes that touch on the “alignment” problem. The art is by Bing’s Image Creator.
Hinton’s Role in AI History
Hinton discusses the current state of artificial intelligence and machine learning. He explains that his core interest is understanding how the brain works and that the current technique used in big models, backpropagation, is not what the human brain is doing. He also discusses the history of AI and neural nets, which he was a proponent of, and how neural nets have proven to be successful despite skepticism from mainstream AI researchers.
The video describes how ChatGPT has vast knowledge compared to a single person due to its ability to absorb large amounts of data over time. The model was first proposed in 1986 and was later able to surpass traditional speech recognition methods thanks to advancements in deep learning and pre-training techniques. Hinton’s background in psychology originally led him to neural networks, and his students’ research resulted in significant developments in speech recognition and object recognition systems.
Trepidations
The interview touches on various topics related to computer science and AI, such as the potential impact on people’s lives, the power consumption differences between biological and digital computers, and the use of AI technology in areas like Google search. Hinton also discusses the challenges of regulating the use of big language models and the need to ensure that AI is developed and used in a way that is beneficial to society (a need he doesn’t feel is being well met).
Silva-Braga: What do you think the chances are of AI just wiping out humanity? Can we put a number on that?
Hinton: It’s somewhere between 1 and 100 percent (laughs). Okay, I think it’s not inconceivable. That’s all I’ll say. I think if we’re sensible, we’ll try and develop it so that it doesn’t, but what worries me is the political situation we’re where it needs everybody to be sensible. There’s a massive political challenge it seems to me, and there’s a massive economic challenge in that you can have a whole lot of individuals who pursue the right course and yet the profit motive of corporations may not be as cautious as the individuals who work for them.
Hinton addresses the common criticism that large language models like GPT-3 are simply autocomplete models. He argues that these models need to understand what is being said to predict the next word accurately. In addition, they discuss the potential for computers to come up with their own ideas to improve themselves and the need for control. Hinton also addresses concerns about job displacement caused by these models, arguing that while jobs will change, people will still need to do the more creative tasks that these models cannot do.
Silva-Braga: Are we close to the computers coming up with their own ideas for improving themselves?
Hinton: Um, yes we might be
Silva-Braga: And then it could just go fast
Hinton: That’s an issue we have to think hard about, how to control that
Silva-Braga: Yeah, can we?
Hinton: We don’t know. We haven’t been there yet, but we can try.
Silva-Braga: Okay, that seems kind of concerning
Hinton: Um, yes
Overall, the interview provides insights into the current state and future of AI and machine learning, as well as the challenges and opportunities that come with their widespread use. It highlights the need for careful consideration and regulation to ensure that these technologies are developed and used in a way that benefits society.
To read a full transcript of the interview, go the original YouTube page (click on the three horizontal dots and then select “Show transcript”)
