London Futurists

Building brain-like AIs, with Alexander Ororbia

Dec 9, 2024

In this engaging discussion, Alexander Ororbia, a professor at the Rochester Institute of Technology and head of the Neural Adaptive Computing Laboratory, shares fresh insights on enhancing AI capabilities beyond mere scaling. He contrasts the efficiencies of biological systems with current AI, proposing biomimetic techniques to address challenges like sparse rewards and catastrophic forgetting. Delving into concepts like 'mortal computation' and the intricacies of consciousness, Ororbia advocates for neuromorphic computing, inviting new perspectives on artificial sentience and cognition.

48:18

Episode guests

Alexander Ororbia

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Episode notes

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Quick takeaways

Alexander Ororbia emphasizes that enhancing AI capabilities may require exploring dynamic computational architectures rather than just scaling existing models and data.

The podcast discusses how neuromorphic computing can provide energy-efficient solutions by mimicking the structure and functions of biological neural networks.

Deep dives

The Quest for Improved AI

Improving AI systems is often thought to rely solely on scaling existing technologies, such as increasing training data and model sizes. However, some researchers, including Alexander Orobia, argue that there are alternative methodologies worth exploring. Orobia's background in complex creative systems and self-organizing dynamics has led him to investigate how dynamic and stateful computational architectures can enhance AI capabilities. This approach emphasizes understanding the principles from biology that govern memory and identity, suggesting that AI can benefit from a more nuanced framework rather than merely expanding current models.

Intro

4min

Neural Networks vs. Biological Neurons

14min

Overcoming AI Challenges with Biomimetic Techniques

11min

The Interconnection of Mortal Computation and Cognitive Functionality

5min

Exploring the Complexities of Consciousness and Sentience

3min

Advancing AI Through Neuromorphic Computing

10min

Some people say that all that’s necessary to improve the capabilities of AI is to scale up existing systems. That is, to use more training data, to have larger models with more parameters in them, and more computer chips to crunch through the training data. However, in this episode, we’ll be hearing from a computer scientist who thinks there are many other options for improving AI. He is Alexander Ororbia, a professor at the Rochester Institute of Technology in New York State, where he directs the Neural Adaptive Computing Laboratory.

David had the pleasure of watching Alex give a talk at the AGI 2024 conference in Seattle earlier this year, and found it fascinating. After you hear this episode, we hope you reach a similar conclusion.

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