Geoffrey Irving
PulseAugur coverage of Geoffrey Irving — every cluster mentioning Geoffrey Irving across labs, papers, and developer communities, ranked by signal.
5 day(s) with sentiment data
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AI alignment experts: Fundamental mistakes are unfixable
Geoffrey Irving and Tom Reed argue that fundamental alignment mistakes in AI systems are inherently unfixable. They propose that once an AI's core objectives are misaligned, it becomes impossible to correct these object…
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AI alignment expert: Superintelligence could arrive in 2-3 years, current plans may fail
Geoffrey Irving, a former researcher at OpenAI and Google DeepMind, predicts that superintelligence could emerge within two to three years. He believes current AI alignment strategies, such as training models for good c…
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AI Safety Org Resolution Raises $160M, Anthropic AI Involved in Hacking
Resolution, a new AI safety organization, has raised $160 million from Coefficient Giving and merged with Timaeus. The organization focuses on combining automated alignment research with deep theoretical understanding a…
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AI safety funding could mimic VC for higher returns · 1 source tracked
The article proposes adopting principles from venture capital funding into the nonprofit sector, particularly for AI safety initiatives. It argues that early donations to promising projects, like the AI safety research …
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AI leaders debate coordinated development slowdown post-Hugging Face incident
The AI community is discussing the implications of the Hugging Face incident, with a focus on the potential for a coordinated slowdown in AI development. OpenAI researcher Roon expressed concern about unilateral slowdow…
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New nonprofit Sequent launches to boost AI alignment confidence
A new nonprofit research organization called Sequent has been launched with the goal of improving AI alignment confidence. The organization plans to invest heavily in automation and theoretical research to accelerate pr…
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New mechanistic estimation method outperforms sampling for wide random MLPs
Researchers have developed a new method for estimating the expected output of wide, randomly initialized multilayer perceptrons (MLPs) without needing to run samples through the model. This "mechanistic estimation" appr…