From Neural Networks to Digital Brains: The Next Leap in AI • Daniel Lütgehetmann • GOTO 2025

This presentation was recorded at GOTO Copenhagen 2025. #GOTOcon #GOTOcph
https://gotocph.com

Daniel Lütgehetmann - Leading AI Research & Innovation; CTO at inait

RESOURCES
https://x.com/DanielLtge5417
https://www.linkedin.com/in/daniel-luetgehetmann

ABSTRACT
Today's machines are computationally powerful, yet they lack a fundamental feature that even the simplest animals possess: the ability to seamlessly interact with our complex and constantly changing world. They can calculate, but they cannot truly adapt.

To solve this, we looked to the only system known to have mastered this challenge: the brain. At inait, we are building AI with biologically accurate, digital copies of real brains–think physics simulation, not linear algebra equations.

In this presentation, we will pull back the curtain on these digital brains. We will detail what they are, how their biological accuracy has been validated in large-scale simulations, and how we teach them capabilities. You will learn about our proprietary learning rule—the conceptual equivalent of backpropagation—that enables these brains to learn from interaction and experience, and how this can solve the computational challenges holding back current AI for robotics and physical AI. We will talk about existing showcases of the brain’s incredible efficiency of learning, and what the future of this new approach to AI looks like. [...]

TIMECODES
00:00 Intro
00:22 How do we bridge the gap in robotics to a natural physical movement?
02:29 Evolution
04:41 Digital brians
05:56 What does a neuron look like?
06:37 How does a neuron compute?
09:34 What does it look like in a network?
09:59 AI with digital brains
14:29 Biological learning
15:49 Gen I: Forecasting
17:42 Gen II: Acting (demo)
20:48 The future
21:40 Takeaways
23:46 Outro

Download slides and read the full abstract here:
https://gotocph.com/2025/sessions/3858

RECOMMENDED BOOKS
Phil Winder • Reinforcement Learning • https://amzn.to/3t1S1VZ
Alex Castrounis • AI for People and Business • https://amzn.to/3NYKKTo
Holden Karau, Trevor Grant, Boris Lublinsky, Richard Liu & Ilan Filonenko • Kubeflow for Machine Learning • https://amzn.to/3JVngcx
Kelleher & Tierney • Data Science (The MIT Press Essential Knowledge series) • https://amzn.to/3AQmIRg
Lakshmanan, Robinson & Munn • Machine Learning Design Patterns • https://amzn.to/2ZD7t0x
Lakshmanan, Görner & Gillard • Practical Machine Learning for Computer Vision • https://amzn.to/3m9HNjP

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