Bracing for Impact- The Artificial Intelligence Challenge Conference
Bibliographic record
Abstract
Friday, February 2, 2018 | 9:00am - 4:30pm | Law Society of Ontario, Osgoode Hall Register: aichallenge.osgoode.yorku.ca/\nCanada has positioned itself as a world leader and destination of choice for companies looking to invest in artificial intelligence and innovation. The “Bracing for Impact: The Artificial Intelligence Challenge” conference is a recognition that advancements in AI will have a huge impact on our social, moral and legal norms. It is therefore important to not only fund AI innovation, but we must also move quickly to ensure that robust and effective governance structures are in place.\nThe conference will be held at the Law Society of Ontario, Donald Lamont Centre, from 8:30 a.m. to 4:30 p.m. and will feature internationally renowned AI experts who will discuss some of the fundamental questions that arise when machines start to think for themselves.\nTopics include: • The impact and implications of AI for Industry; • Concerns about Intellectual Property and Commercialization; • Cybersecurity and Algorithmic Accountability; • Social Good.
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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.030 | 0.013 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.035 | 0.026 |
| Insufficient payload (model declined to judge) | 0.058 | 0.027 |
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".