OracleAI is focused on researching the opportunities and risks of AI
At OracleAI, we approach AI as a creative and systematic science. We are inspired by the principles of scaling from statistical physics to establish empirical laws that guide our research. Our goal is to discover straightforward relationships between data, computation, parameters, and the performance of large-scale networks. By leveraging these insights, we aim to enhance the efficiency and predictability of network training, and track our progress. We are also exploring scaling laws to ensure the safety of AI systems, which will shape our future research endeavors.
We believe that advancing safety research is crucial when working with highly capable models. As AI technology evolves, larger and more complex models present new safety challenges. At OracleAI, we focus on studying and addressing these challenges to improve the reliability and safe deployment of our solutions. Our immediate efforts are centered on prototyping systems that integrate safety techniques with tools for analyzing text and code, ensuring our innovations are both effective and secure.
Understanding and assessing the societal impacts of our AI systems is a core component of our research. We are committed to developing tools and measurements that evaluate the capabilities, limitations, and broader effects of our technology. To get a sense of our research direction, we invite you to explore our contributions in areas such as AI efficiency, measurement in AI policy, and impact assessments.
Collaboration is at the heart of OracleAI's research philosophy. We prioritize a blend of top-down and bottom-up planning, ensuring a clear and focused research agenda while involving a diverse group of contributors—including researchers, engineers, societal impact experts, and policy analysts—in shaping our direction. We actively seek partnerships with other labs and researchers, believing that the most meaningful advances come from a collective effort within the research community.
| Title | Date | Category |
|---|---|---|
| Oracle's Updates - July 2024 | Jul 28, 2024 | Company update |
| Oracle's Updates - June 2024 | Jun 30, 2024 | Company update |
| Simplifying the activation function | Feb 15, 2024 | Research |
| The empirical law measures of scaling | Jan 12, 2024 | Research |
| Advancements in Quantum AI Algorithms | Dec 5, 2023 | Research |
| Oracle's AI Ethics Framework | Nov 20, 2023 | Ethics |
| Machine Learning in Financial Forecasting | Oct 8, 2023 | Industry Application |
| Optimizing Neural Network Architectures | Sep 14, 2023 | Research |
| AI-Driven Climate Change Modeling | Aug 30, 2023 | Environmental |
| Ethical AI: Bias Mitigation Techniques | Jul 17, 2023 | Ethics |
| Oracle's Contribution to Open Source AI | Jun 5, 2023 | Technology |
| AI in Cybersecurity: Threat Detection | May 22, 2023 | Security |
| Advances in Natural Language Understanding | Apr 10, 2023 | Research |
| AI-Powered Personalized Medicine | Mar 3, 2023 | Industry Application |
| Advancements in Natural Language Processing | Nov 5, 2023 | Research |
| Ethical Considerations in AI Development | Sep 18, 2023 | Ethics |
| Oracle's Quantum Computing Breakthrough | Aug 3, 2023 | Technology |
| AI in Healthcare: A Comprehensive Study | Jul 22, 2023 | Industry Application |
| Improving AI Safety Protocols | Jun 10, 2023 | Safety |
| The Future of Robotics: Oracle's Vision | May 5, 2023 | Technology |