Will artificial intelligence be a blessing or concern in assistive robots for play?
Bibliographic record
Abstract
Os avanços recentes e popularidade da Inteligência Artificial (IA) oferecem possibilidades animadoras para melhorar a tecnologia, mas, também, trazem preocupação. Neste artigo, usamos nossa pesquisa para apresentar os benefícios potenciais do uso da IA em tecnologia assistiva para crianças com deficiências brincarem e examinar possíveis preocupações éticas em torno dos dados exigidos pelos algoritmos de IA. Uma vez que o brincar é um fator chave no bem-estar infantil e no desenvolvimento cognitivo, as incapacidades secundárias podem surgir como consequência de deficiências motoras. Robôs assistivos para manipulação aumentativa podem ser fundamentais para proporcionar às crianças com deficiência física oportunidades de brincar, mas precisamos adotar uma abordagem baseada em princípios e centrada no usuário para inovações técnicas.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".