Babbage: The science that built the AI revolution—part one
Mar 6, 2024
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Daniel Glaser, a neuroscientist at the Institute of Philosophy, discusses the fascinating evolution of artificial intelligence through the lens of neuroscience. He explores how neurally inspired systems simulate human brain functions and the milestones that led to today's sophisticated models like ChatGPT. The conversation delves into brain imaging research, the complexities of intelligence, and the biochemical foundations that contribute to cognitive functions. Glaser also examines the early attempts to mimic human intelligence, highlighting pivotal advancements in AI development.
The UK Biobank Imaging Study provides extensive imaging data that helps researchers correlate brain anatomy with cognitive abilities, shedding light on intelligence.
The evolution of AI from simple models to deep learning reflects significant technological advancements and a better understanding of human neural functions.
Deep dives
Insights from the UK Biobank Imaging Study
The UK Biobank Imaging Study aims to better understand human brain structure and function through extensive imaging data. Each participant contributes approximately 9,000 images, which provide detailed information on brain size, volume, and activity during specific tasks. This study incorporates cognitive tasks that allow researchers to observe brain activity, illuminating areas involved in decision-making. Such vast data sets are crucial for scientists seeking to correlate anatomical structures with cognitive abilities, revealing connections between physical brain properties and intelligence.
The Evolution and Definitions of Intelligence
Understanding the essence of intelligence is complex, as there isn't a single definition that encapsulates it. Intelligence encompasses various attributes, such as reasoning, abstract thought, and the ability to learn, yet these qualities can manifest differently across species. For instance, some animals can demonstrate planning capabilities, suggesting that intelligence may not be exclusive to humans. This ambiguity prompts ongoing research to explore what constitutes intelligence beyond human-centric assumptions.
Artificial Intelligence's Roots in Human Brain Function
The development of artificial intelligence (AI) has heavily relied on insights from human brain function, particularly in understanding how neurons operate. Early models of artificial neurons, such as perceptrons, aimed to mimic the brain's threshold-based firing mechanisms. While initially limited, the exploration of multiple layers of artificial neurons led to the capability to recognize more complex patterns and functions. This layering concept parallels the brain's architecture, facilitating advancements in machine learning and the capabilities of modern AI.
The Journey from Early AI to Modern Deep Learning
The passage from simplistic artificial intelligence systems to sophisticated deep learning models reflects significant technological advancements and increased understanding of neural functions. Pioneering work in the late 20th century laid the groundwork for AI systems to perform complex tasks, such as speech recognition and image classification. Research in deep learning emphasized the importance of network architecture, specifically the addition of hidden layers that enhanced computational capabilities. This evolution underscores the interplay between neuroscience and artificial intelligence, as insights from biological systems inform developments in AI technology.
What is intelligence? In the middle of the 20th century, the inner workings of the human brain inspired computer scientists to build the first “thinking machines”. But how does human intelligence actually relate to the artificial kind?
This is the first episode in a four-part series on the evolution of modern generative AI. What were the scientific and technological developments that took the very first, clunky artificial neurons and ended up with the astonishingly powerful large language models that power apps such as ChatGPT?
Host: Alok Jha, The Economist’s science and technology editor. Contributors: Ainslie Johnstone, The Economist’s data journalist and science correspondent; Dawood Dassu and Steve Garratt of UK Biobank; Daniel Glaser, a neuroscientist at London’s Institute of Philosophy; Daniela Rus, director of MIT’s Computer Science and Artificial Intelligence Laboratory; Yoshua Bengio of the University of Montréal, who is known as one of the “godfathers” of modern AI.
On Thursday April 4th, we’re hosting a live event where we’ll answer as many of your questions on AI as possible, following this Babbage series. If you’re a subscriber, you can submit your question and find out more at economist.com/aievent.
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