Melanie and I talk about the limitations of artificial intelligence in its current deep learning state (a la her New York Times Op-Ed), what AI needs to proceed toward general AI, what complexity is and how it relates to the fields of AI and neuroscience, and plenty more.
Matt and I discuss his neuroscience and AI research at DeepMind, including how AI benefits from neuroscience, his work on meta-reinforcement learning to create systems that learn more efficiently, how meta-reinforcement learning might be implemented in a neural circuit involving the prefrontal cortex and the dopamine system, how theory of mind might be implemented in machines to help them understand each other and to help us understand them, and a lot more.
Anna and I discuss home and DIY use of neurotechnology- specifically transcranial direct current stimulation (tDCS) and electroencephalography (EEG) products marketed to improve cognition. We talk about who uses these products and for what (enhancement or self-treatment), how they get marketed, and the possibilities for how they may get regulated.
Julie and I discuss her work using spintronic nano-devices to implement bio-inspired computing and neural networks in hardware. We talk about neuromorphic chips in general, their history, how they could solve the energy efficiency problem, where it’s all headed, some of the physics behind her nano-oscillators, and more.
Dean and I talk about how time and duration is encoded in the brain, how he implemented timing and sequences using short-term synaptic plasticity, in neuronal cultures, and in recurrent neural networks. We also discuss the subjective nature of time, consciousness, and how time might be implemented in future general AI.