On origins of computational neuroscience and AI as scientific fields - with Terrence Sejnowski (vintage) - #9
Mar 16, 2024
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Delving into the origins of computational neuroscience and AI, the podcast explores the transition from rule-based to learning-based AI approaches. It highlights the unreasonable effectiveness of math in deep learning and the evolution of reinforcement learning in neural structures. The synergy of AI and neuroscience in medical diagnostics, advancements in self-driving technology, and the transformative impact of AI on society are also discussed.
The podcast explores the parallel development of computational neuroscience and AI from the 1960s, showcasing pioneers like Terry Sanovsky.
Insights into the evolution of learning algorithms, from early perceptron models to advanced structures like Boltzmann machines, are discussed.
The application of supervised and unsupervised learning in AI tasks like speech recognition and language translation is highlighted.
Temporal difference learning in reinforcement algorithms, combined with deep learning, has revolutionized industries like gaming and medical diagnostics.
Deep dives
Origin of Computational Neuroscience and AI Pioneers
The podcast episode delves into the early days of computational neuroscience and artificial intelligence (AI) with a focus on pioneers like Terry Sanovsky. Terry's contributions in establishing computational neuroscience at UCSD in the 1980s are highlighted, showcasing his role in merging neuroscience and AI fields. Significant work like the parallel development of computational neuroscience and AI from the 1960s to present is discussed, emphasizing Terry's insights shared in his book 'The Deep Learning Revolution'.
Evolution of Learning Algorithms and Neural Networks
The evolution of learning algorithms and neural networks is explored, starting from early models like the perceptron to more complex structures like Boltzmann machines developed by Terry and Jeff Hinton. The shift towards deep learning architectures and their scalability with increased computer power is emphasized. Insights into the capability of deep learning networks to handle high-dimensional spaces and the effectiveness of recurrent neural networks for time-dependent tasks are discussed.
Application of Supervised and Unsupervised Learning in AI
The podcast delves into the application of supervised and unsupervised learning in artificial intelligence (AI), highlighting their significant impact on tasks like speech recognition and language translation. Concepts like attention mechanisms and working memory are identified as critical additions to improve learning tasks. The incorporation of approaches like reinforcement learning for sequential decision making and goal-oriented actions is also explored.
Temporal Difference Learning and Reinforcement Learning Algorithms
The episode introduces the concept of temporal difference learning in reinforcement learning algorithms, demonstrating how algorithms like temporal difference learning solve the challenge of maximizing future rewards through prediction errors. The mechanism of computing reward prediction errors and adjusting synaptic strengths based on prediction accuracy is explained. The efficiency of temporal difference learning in dynamic programming and optimal decision-making processes is highlighted.
Enhancing Learning with AI
Combining temporal difference learning with deep learning, companies like DeepMind have transformed learning processes. By training AI on games like Go, it not only advances its own skills but also introduces unique strategies unparalleled by humans. This amalgamation of deep learning representation and cognitive functions enables the AI to creatively approach problems, setting the stage for novel frameworks and superhuman capabilities.
AI Revolutionizing Medicine
AI is revolutionizing medical diagnostics by processing vast image datasets to accurately diagnose skin lesions. With algorithms trained on extensive images, AI can achieve diagnostic accuracy on par with the best doctors. Collaborating with human expertise, AI augments diagnostic accuracy significantly, showcasing the potential for AI-human partnerships to elevate medical outcomes through combined knowledge and abilities.
Impact of AI on Education and Society
AI's role extends to transforming education, especially addressing individual learning needs. Smart speakers are poised to interact with students, assess their knowledge, and provide tailored assistance, ensuring efficient learning processes. Beyond education, AI's pervasive influence across all industries demands proactive workforce readiness, emphasizing the need for future-focused education and harnessing AI's potential for societal adaptation and advancement.
Today’s guest is a pioneer both in the fields of computational neuroscience and artificial intelligence (AI) and has had a front seat during their development.
His many contributions include, for example, the invention of the Boltzmann machine with Ackley and Hinton in the mid 1980s.
In this “vintage” episode recorded in late 2019 he describes the joint births of these adjacent scientific fields and outlines how they came about.
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