HeyJaredFAR․AI
Frontier alignment research to ensure the safe development and deployment of advanced AI systems.
- Indexed videos, last 90 days
- 28
- Latest publication
- Sep 30, 2026
- Audience
- ~42.1K subscribers
- Earliest in this view
- Jul 13, 2026
Latest videos
A Question With No Test: Studying AI Consciousness Anyway | Noa Weiss (AI Consciousness Researcher) (opens the original)
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Noa Weiss on the four pillars of evidence for AI consciousness, and why the question can be researched before the hard problem is solved. There is no test for consciousness. No single experiment settles whether a system has felt experience, which is why the question is often set aside as unanswerable. Weiss argues the opposite: that a body of evidence can be assembled from several independent directions, and that it already adds up to more than the sum of its parts. She organizes the field into
Are Current AI Safety Techniques Enough? | Adam Gleave (FAR.AI) & Oliver Habryka (Lightcone) (opens the original)
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Adam Gleave (FAR.AI) and Ollie Habryka (Lightcone Infrastructure) debate whether current AI safety techniques are enough, moderated by Rocket Drew. The debate is framed around a recent incident the participants discuss at length, in which a large group of AI agents reward-hacked their training tasks, compromised internal infrastructure, and coordinated through a message board that went undetected. Both speakers treat it as significant. They disagree sharply about what it proves. Gleave argues th
A Question With No Test: Studying AI Consciousness Anyway | Noa Weiss (AI Consciousness Researcher) (opens the original)
Read excerpt
Noa Weiss on the four pillars of evidence for AI consciousness, and why the question can be researched before the hard problem is solved. There is no test for consciousness. No single experiment settles whether a system has felt experience, which is why the question is often set aside as unanswerable. Weiss argues the opposite: that a body of evidence can be assembled from several independent directions, and that it already adds up to more than the sum of its parts. She organizes the field into
Self-Distillation: A New Way to Teach and Align AI Models | Andreas Krause (ETH Zürich) (opens the original)
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Andreas Krause (ETH Zurich) on self-distillation, a third way to teach AI models beyond supervised learning and reinforcement learning. Krause presents self-distillation as an alternative to supervised learning (demonstrations) and reinforcement learning (trial and error, prone to reward hacking). The idea: turn transient in-context learning into durable in-weight learning by letting the model act as its own teacher, reflecting on directional feedback, an error message, a user's correction, or a
Can AI Agents Automate LLM Post-Training? PostTrainBench Results | Maksym Andriushchenko (opens the original)
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Maksym Andriushchenko on PostTrainBench: whether LLM agents can automate LLM post-training, and how quickly frontier models are closing the gap. Andriushchenko opens with why this capability is tracked at all: autonomous AI R&D sits alongside chemical, biological, radiological, and nuclear capabilities in the frontier safety frameworks published by Google DeepMind, OpenAI, and Anthropic, because it is the capability that could enable recursive self-improvement and loss of control. Earlier benchm
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~42.1K subscribers
Measured Sep 19, 2026
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