What If You Could Turn AI Hallucinations Down Like a Knob? @GadgetHacks
What If You Could Turn AI Hallucinations Down Like a Knob?  @GadgetHacks
Uploaded August 2026 | Updated September 2026, 1 week ago
What if fixing AI hallucinations eventually looked less like guesswork—and more like turning a control?

Goodfire CEO Eric Ho explains how AI interpretability could allow researchers to identify internal concepts inside neural networks and deliberately steer model behavior.

That could mean detecting confidence, reducing hallucinations, predicting what a model will learn from training data, preserving desirable behaviors, and designing AI systems with far more intention than today's trial-and-error approach.

Eric also explains predictive data debugging, feature rewards, model steering and Goodfire's vision for an “AI neuroscientist” that helps researchers understand what's happening inside a model while they train it.

The goal: make AI models more understandable, reliable and controllable.

Learn more about Goodfire and Silico: goodfire.ai

Sponsored by SAS AI Governance: sas.com/en_us/solutions/ai/governance.html?utm_source=other&utm_medium=cpm&utm_campaign=sas-vpl-gbc-gps-global#

More from The Neuron: theneuron.ai

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0:00 Democratizing AI Model Research and Training
2:02 Reducing Hallucinations Through Deep Analysis
3:38 Steering Models: Adjusting Internal Behaviors
5:21 Unlocking the Potential of Continual Learning
8:12 Business Strategies for Independent AI Ownership
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What If You Could Turn AI Hallucinations Down Like a Knob?

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