arXiv:2608. 14426v1 Announce Type: new Abstract: AI is increasingly being used to help with AI R&D.
arXiv:2609.00137v1 Announce Type: new Abstract: AI is increasingly used in the R\&D process that produces future AI systems. We study the conditions under which this feedback becomes self-amplifying....
arXiv:2609.05894v1 Announce Type: new Abstract: The exponentiation of Artificial intelligence (AI) in the recent past has entered a transformative era that has been driven by the growth in large lang...
The article examines whether increasing automation of AI research and development could trigger an intelligence explosion, compressing years of progress into months. It reviews preliminary evidence suggesting such acceleration is possible, discusses potential benefits and extreme risks—including loss of control over superhuman AI and erosion of checks on power—and calls for urgent policy action. The authors argue that, despite uncertainties, the high stakes demand serious attention and proactive measures.
arXiv:2606. 20231v1 Announce Type: new Abstract: Can intelligence be measured?
arXiv:2602. 16065v2 Announce Type: replace-cross Abstract: As artificial intelligence (AI)-generated content proliferates, models are increasingly trained on their own outputs, risking progressive degradation or collapse.
The standard objection to full automation is demand-side: if humans earn nothing, who buys the output? This confuses an accounting role with a biological species.
arXiv:2608. 20231v1 Announce Type: cross Abstract: The standard objection to full automation is demand-side: if humans earn nothing, who buys the output?
Long-range learning is hard for recurrent networks trained with stochastic gradient descent, because the influence of a past input fades with the lag $\ell$, and if it fades too fast the dependence cannot be learned from finite data. This fade is captured by an envelope $f(\ell)$.
arXiv:2605. 05113v2 Announce Type: replace Abstract: We study signal propagation in linear recurrent models at finite width.
arXiv:2606. 12683v1 Announce Type: new Abstract: Over the last decade, building human-level artificial general intelligence has moved from far-fetched speculation to being a concrete next-decade target for many of the largest AI organisations.
arXiv:2606. 29519v1 Announce Type: new Abstract: Long-range learning is hard for recurrent networks trained with stochastic gradient descent, because the influence of a past input fades with the lag $\ell$, and if it fades too fast the dependence cannot be learned from finite data.