Bibliographic record
Abstract
This essay is a revised transcription of Yoshua Bengio's July 2023 testimony in front of the US Senate Subcommittee on Privacy, Technology, and the Law meeting on the topic of oversight of AI. It argues for caution and government interventions in regulation and research investments to mitigate the potentially catastrophic outcomes from future advances in AI as the technology approaches human-level cognitive abilities. It summarizes the trends in advancing capabilities and the uncertain timeline to these future advances, as well as the different types of catastrophic scenarios that could follow, including both intentional and unintentional cases, misuse by bad actors and intentional as well as unintended loss of control of powerful AIs. It makes public policy recommendations that include national regulation, international agreements, public research investments in AI safety as well as classified research investments to design aligned AI systems that can safely protect us from bad actors and uncontrolled dangerous AI systems. It highlights the need for strong democratic governance processes to control the safety and ethical use of future powerful AI systems, whether they are in private hands or under government authority.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".