The Deep Language Revolution
From a long perspective of working in the trenches of machine learning, Terry Sejnowski has been an enthusiastic advocate for the positive impact of artificial intelligence (AI). In 2018, he wrote in the book The Deep Learning Revolution that "AI will make you smarter."
The Rise of Large Language Models
Things move fast in AI time. Since 2018, generative AI (Gen AI) has invaded our lives. In his latest book, ChatGPT and the Future of AI: The Deep Language Revolution, published last month by MIT Press, Sejnowski reviews the rise of large language models (LLMs) and concludes that "AI is indeed making us smarter."
Measuring Smarter
But how do we measure smarter? What exactly does that mean?
"What is intelligence? Intelligence is really about problem-solving," Sejnowski told ZDNET in an interview. With ChatGPT, and programs like it, "I am able to get up to speed faster, but, also, it leads me to things that I might never have even thought of or explored; it’s opening up doors."
The Tool-Using Stage
Like a shovel, argued Sejnowski, the large language tool is helping us do things better than we could with our bare hands. He said writers are getting better with ChatGPT because "it helps them through mental blocks."
The Book’s Focus
Written with the same engaging voice and authoritative knowledge of AI, the new book is very different from the previous one. In 2018, Sejnowski gave a history lesson. In the new Revolution, Sejnowski is interested in where these tools are headed and how they’re changing our notions of thought and how we regard ourselves.
The Virtuous Cycle
AI is, for example, revealing aspects of the brain to neuroscientists, and neuroscience is in turn opening up new possibilities for AI, he argues, in a kind of virtuous cycle.
The Book’s Weak Spot
One thing ChatGPT doesn’t do is write anywhere nearly as well as Sejnowski. Throughout the book, he offers ChatGPT-generated summaries of chapters, hoping they may be "easier to follow than the text." In fact, the summaries are banal, much like a lot of GPT-generated prose, and seem like mostly a gimmick. It is the book’s only weak spot and a small enough transgression to be forgiven in what is otherwise a masterly and thoroughly engrossing read.
The Book’s Strengths
Lest you think the book is a love letter to ChatGPT, the deeper element of the book, taking up most of its pages, is an analysis of how generative AI affects science, and vice versa.
The Cross-Pollination of Efforts
The book’s most fascinating aspect, highlighting how much is left to be understood in both camps. LLMs have an underlying structure that AI researchers are only just beginning to understand. Sejnowski predicts that unfolding that mystery could lead to new forms of mathematics, which, in turn, could dramatically advance AI.
Conclusion
The mirror effect leads to a tantalizing prospect: we are not going to achieve "artificial general intelligence", the holy grail of AI, in the cliche, sci-fi form of a life-like humanoid that walks and talks like us. Rather, we will shift our understanding of what we think we know about intelligence. It is beyond mere tool use, but we don’t yet have an expression for what that something else might be.
FAQs
Q: What is the main focus of the book?
A: The book focuses on the rise of large language models (LLMs) and their impact on our understanding of intelligence and thought.
Q: What does Sejnowski mean by "AI is making us smarter"?
A: Sejnowski believes that AI is helping us solve problems faster and opening up new doors of exploration and discovery.
Q: What is the virtuous cycle that Sejnowski discusses in the book?
A: The virtuous cycle refers to the way AI is revealing aspects of the brain to neuroscientists, and neuroscience is in turn opening up new possibilities for AI.
Q: What is the book’s weak spot?
A: The book’s weak spot is the use of ChatGPT-generated summaries, which are banal and seem like a gimmick.
Q: What is the book’s strength?
A: The book’s strength is its analysis of how generative AI affects science, and vice versa, and its exploration of the underlying structure of LLMs.
