Deep Mind AI Safety Research
GPTKB entity
Statements (55)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Research_Institute
|
| gptkbp:bfsLayer |
3
|
| gptkbp:bfsParent |
gptkb:philosopher
|
| gptkbp:addresses |
AI alignment
|
| gptkbp:advocates_for |
gptkb:initiative
|
| gptkbp:aims_to |
foster public trust in AI
promote responsible AI use prevent harmful AI behavior ensure safe AI development enhance AI reliability |
| gptkbp:analyzes |
AI decision-making processes
AI governance models AI risks long-term impacts of AI societal impacts of AI ethical dilemmas in AI AI deployment scenarios failure modes in AI systems |
| gptkbp:collaborates_with |
government agencies
non-profit organizations academic institutions industry partners |
| gptkbp:conducts |
experiments
risk assessments |
| gptkbp:contributed_to |
policy discussions
AI safety literature |
| gptkbp:develops |
safety protocols
safety benchmarks AI safety frameworks evaluation metrics for AI safety tools for AI safety assessment training methodologies for safe AI |
| gptkbp:engages_in |
gptkb:public_outreach
community discussions policy advocacy |
| gptkbp:explores |
human-AI collaboration
robustness in AI systems value alignment in AI |
| gptkbp:focuses_on |
AI safety
transparency in AI systems safety in reinforcement learning |
| https://www.w3.org/2000/01/rdf-schema#label |
Deep Mind AI Safety Research
|
| gptkbp:part_of |
gptkb:philosopher
|
| gptkbp:participates_in |
workshops
conferences |
| gptkbp:publishes |
research papers
case studies on AI safety guidelines for AI safety |
| gptkbp:research |
adversarial robustness
interpretability in AI |
| gptkbp:supports |
cross-disciplinary research
AI ethics research open research initiatives |
| gptkbp:utilizes |
machine learning techniques
|
| gptkbp:works_on |
scalable oversight mechanisms
|