{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Crazy Wisdom","title":"Uncharted Territory: Unlocking the Full Potential of AI Through Neuroscience with Subutai Ahmad","html":"","width":"100%","height":180,"duration":3186,"description":"Show Notes for Crazy Wisdom Podcast Episode with Subutai Ahmad Introduction The episode features Subutai Ahmad, the CEO of Numenta and a pioneering figure in both neuroscience and artificial intelligence (AI). The discussion navigates the complex relationship between the human brain's architecture and contemporary AI models like deep learning systems. Topics range from the historical evolution of these disciplines to the cutting-edge research that could shape their future. Historical Perspective The initial inspiration for artificial neural networks came from our rudimentary understanding of how neurons and connections work, going back to the 1940s. Donald Hebb significantly influenced the back-propagation model developed in the 1980s. Hebb's work, combined with the discoveries of Hubel and Wiesel in the '50s, laid the groundwork for understanding how neurons learn features from the visual world, including edge detectors and higher-level shapes. State of Neural Networks Today Despite advancements, today’s neural networks still rely on a simplified model of what a neuron is, and they differ fundamentally from biological systems. One glaring difference is in power consumption; a human brain uses only about 20 watts, while running a deep learning network can require power equivalent to an entire city. Learning Modes and Algorithms Deep learning systems usually operate in two modes: inference and training. In contrast, the human brain doesn't distinguish between these states, learning continuously from environmental stimuli. Algorithms, particularly back propagation, are still part of the problem. They try to minimize error, unlike the brain, which adapts and learns contextually. The Numenta Angle Founded by Jeff Hawkins and Donna Dubinsky, Numenta has been researching to understand the principles underlying brain function. Recently, they have focused on applying this understanding to AI. Their approach comprises three main pillars: Efficiency: Using 'sparsity' to...","thumbnail_url":"https://img.transistorcdn.com/UZbrDrlO5VTfDNcq188THwbv0T09vcmLyzx3BcPI9bs/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS81Y2Rj/OGFiMTYyMGFkNTM5/N2NjOWI2MWM5YzQ1/YTc2Ny5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}