Insights from Maths and Neuroscience to inform AGI development

These two books while written by non-experts in their domain, offer profound insights into the progression of AI and what needs to be done for current AI models to achieve human level intelligence. Having spent the last few weeks reading them in my spare time, here are my reviews for each book.

A Brief History of Intelligence: Drawing on insights from neuroscience, anatomy and artificial intelligence, Max Bennett's?A Brief History of Intelligence?offers a groundbreaking perspective on the development of artificial intelligence by exploring five key "breakthroughs" that have shaped human intelligence. Bennett argues that understanding the evolution of the human brain, with its triumphs and setbacks, is crucial for unlocking AI's full potential and bridging the gap between AI's remarkable capabilities in specific domains and its struggles with seemingly simple tasks. By collaborating with eminent neuroscientists and adopting a novel perspective, Bennett presents this vast history in an accessible framework, emphasizing how comprehending the brain's evolutionary story can contribute to the next generation of AI advancements. The book draws intriguing parallels between human intelligence and AI progress, illustrating where current AI systems have matched or surpassed human capabilities and where they still fall short. Bennett posits that until AI systems can successfully emulate every aspect of the human brain's developmental journey, they will not exhibit human-like intelligence. Synthesizing relevant scientific knowledge and cutting-edge research, Bennett crafts an engaging narrative that traces the evolution of the human brain, offering valuable insights into the most intriguing enigmas of human intelligence and equipping readers with the tools to navigate our technological future successfully. This groundbreaking work has earned endorsements from numerous prominent neuroscientists, cementing its place as a crucial text for understanding the interplay between neuroscience and artificial intelligence.

Why Machines Learn: In this book, Ananthaswamy, elucidates the intricate mathematics and algorithms underpinning machine learning, a branch of artificial intelligence that enables machines to discern patterns in data without explicit programming. By referencing principles from math, computer science, physics, and neuroscience, the author provides readers with a comprehensive understanding of the AI's creation. Although a strong math background, particularly in statistics and calculus, would be beneficial, the author's clear and detailed illustrations make the content accessible to anyone willing to engage with the material. Ananthaswamy demonstrates the potential of AI through captivating case studies, exploring its capacity to generate new media such as text, images, video, and music, and its contribution to areas like drug development and gene expression dynamics. The author also presents a vivid picture of AI's transformative impact on everyday activities and its potential to revolutionize social and economic lives. Ananthaswamy emphasizes the need for understanding the inevitability of learning machines to tackle the future, where AI will be ubiquitous, bringing both benefits and risks.?