AF - Announcing ILIAD - Theoretical AI Alignment Conference by Nora Ammann

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Announcing ILIAD - Theoretical AI Alignment Conference, published by Nora Ammann on June 5, 2024 on The AI Alignment Forum.
We are pleased to announce ILIAD - a 5-day conference bringing together 100+ researchers to build strong scientific foundations for AI alignment.
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Apply to attend by June 30!***
When: Aug 28 - Sep 3, 2024
Where: @Lighthaven (Berkeley, US)
What: A mix of topic-specific tracks, and unconference style programming, 100+ attendees. Topics will include Singular Learning Theory, Agent Foundations, Causal Incentives, Computational Mechanics and more to be announced.
Who: Currently confirmed speakers include: Daniel Murfet, Jesse Hoogland, Adam Shai, Lucius Bushnaq, Tom Everitt, Paul Riechers, Scott Garrabrant, John Wentworth, Vanessa Kosoy, Fernando Rosas and James Crutchfield.
Costs: Tickets are free. Financial support is available on a needs basis.
See our website here. For any questions, email
iliadconference@gmail.com
About ILIAD
ILIAD is a 100+ person conference about alignment with a mathematical focus. The theme is ecumenical. If that excites you, do apply!
Program and Unconference Format
ILIAD will feature an unconference format - meaning that participants can propose and lead their own sessions. We believe that this is the best way to release the latent creative energies in everyone attending.
That said, freedom can be scary! If taking charge of your own learning sounds terrifying, rest assured there will be plenty of organized sessions as well. We will also run the topic-specific workshop tracks such as:
Computational Mechanics is a framework for understanding complex systems by focusing on their intrinsic computation and information processing capabilities. Pioneered by J. Crutchfield, it has recently found its way into AI safety. This workshop is led by Paul Riechers.
Singular learning theory, developed by S. Watanabe, is the modern theory of Bayesian learning. SLT studies the loss landscape of neural networks, using ideas from statistical mechanics, Bayesian statistics and algebraic geometry. The track lead is Jesse Hoogland.
Agent Foundations uses tools from theoretical economics, decision theory, Bayesian epistemology, logic, game theory and more to deeply understand agents: how they reason, cooperate, believe and desire. The track lead is Daniel Hermann.
Causal Incentives is a collection of researchers interested in using causal models to understand agents and their incentives. The track lead is Tom Everitt.
"Everything Else" is a track which will include an assortment of other sessions under the direction of John Wentworth. Details to be announced at a later time.
Financial Support
Financial support for accommodation & travel are available on a needs basis. Lighthaven has capacity to accommodate 60% of participants. Note that these rooms are shared.
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