The Glitchatorio
30-minute introductions to some of the trickiest issues around AI today, such as:
- The alignment problem
- Questions of LLM consciousness
- AI and animal welfare
- Scheming and hallucinations
The Glitchatorio is a podcast about the aspects of AI that don't fit neatly into marketing messages or notions of technology-as-destiny. We look into the failure modes, emergent mysteries and unexpected behaviors of artificial intelligence that baffle even the experts. You'll hear from technical researchers, data scientists and machine learning experts, as well as psychologists, philosophers and others whose work intersects with AI.
Most Glitchatorio episodes follow the standard podcast interview format. Sometimes these episodes alternate with fictional audio skits or personal voice notes.
The voices, music and audio effects you hear on The Glitchatorio are all recorded or composed by the Witch of Glitch; they are not AI-generated.
The Glitchatorio
You Be The Judge
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Can we trust AI to keep AI honest?
Having a human in the loop is already more illusion than reality, as the task of checking and overseeing LLM outputs is increasingly assigned to other LLMs. The problem is that these LLM judges tend to be biased in favor of the answers they generate themselves — even when the answers are wrong.
To understand why this is, and what we can do about it, listen to my conversation with AI safety researcher Taslim Mahbub. We'll talk about his research into self-preference bias, the surprising results of his experiments and some potential mitigation strategies, as outlined in this post on mitigating collusive self-preference: https://www.lesswrong.com/posts/nB7kAf8c4tvnvZ4u3/mitigating-collusive-self-preference-by-redaction-and-2
and this paper on mitigating self-preference through authorship obfuscation: https://arxiv.org/abs/2512.05379
As a bonus, if you're interested in Taslim's earlier research on using machine learning in service of biodiversity monitoring, here's the abstract of his paper on convolutional neural networks (CNN) for identifying bat species: https://ieeexplore.ieee.org/document/9311084