Pith. sign in

REVIEW 4 cited by

International Scientific Report on the Safety of Advanced AI (Interim Report)

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2412.05282 v2 pith:GNK3H7ZH submitted 2024-11-05 cs.CY cs.AI

International Scientific Report on the Safety of Advanced AI (Interim Report)

Yoshua Bengio , S\"oren Mindermann , Daniel Privitera , Tamay Besiroglu , Rishi Bommasani , Stephen Casper , Yejin Choi , Danielle Goldfarb
show 36 more authors
Hoda Heidari Leila Khalatbari Shayne Longpre Vasilios Mavroudis Mantas Mazeika Kwan Yee Ng Chinasa T. Okolo Deborah Raji Theodora Skeadas Florian Tram\`er Bayo Adekanmbi Paul Christiano David Dalrymple Thomas G. Dietterich Edward Felten Pascale Fung Pierre-Olivier Gourinchas Nick Jennings Andreas Krause Percy Liang Teresa Ludermir Vidushi Marda Helen Margetts John A. McDermid Arvind Narayanan Alondra Nelson Alice Oh Gopal Ramchurn Stuart Russell Marietje Schaake Dawn Song Alvaro Soto Lee Tiedrich Ga\"el Varoquaux Andrew Yao Ya-Qin Zhang
This is my paper
classification cs.CY cs.AI
keywords reportinternationalscientificadvancedexpertsinterimsafetyunderstanding
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

This is the interim publication of the first International Scientific Report on the Safety of Advanced AI. The report synthesises the scientific understanding of general-purpose AI -- AI that can perform a wide variety of tasks -- with a focus on understanding and managing its risks. A diverse group of 75 AI experts contributed to this report, including an international Expert Advisory Panel nominated by 30 countries, the EU, and the UN. Led by the Chair, these independent experts collectively had full discretion over the report's content. The final report is available at arXiv:2501.17805

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Scientific reasoning does not reliably translate into scientific forecasting in frontier AI

    cs.AI 2026-05 unverdicted novelty 7.0

    Introduces the CUSP benchmark across 4760 events and finds frontier AI models can pick plausible directions but fail to predict whether or when scientific advances will occur, with performance varying by domain and in...

  2. Proof-of-Learning with Incentive Security

    cs.CR 2024-04 unverdicted novelty 6.0

    The paper introduces an incentive-secure Proof-of-Learning protocol for blockchain consensus that claims provable security against two attacks, reduced computational overhead, and guarantees even with untrusted proble...

  3. Greedy Coordinate Diffusion: Effective and Semantically Coherent Adversarial Attacks via Diffusion Guidance

    cs.LG 2026-06 unverdicted novelty 5.0

    GCD uses diffusion model priors to guide suffix search, achieving higher attack success rates with better semantic adherence and lower detection than GCG-style methods.

  4. From monoliths to modules: Decomposing transducers for efficient world modelling

    cs.AI 2025-12 unverdicted novelty 5.0

    A framework for decomposing transducers into sub-transducers on distinct subspaces to enable parallel and interpretable world models.