Building Responsible AI Systems: The Gemini Project

TLDRGemini is the most capable AI system developed by Google DeepMind, prioritizing safety and responsibility. As AI systems become more capable, new questions arise regarding multimodality and contextual challenges. Google DeepMind takes proactive measures to address risks like cybersecurity, bias, and toxicity. External evaluations and collaborations with organizations like MLCommons and frameworks like SAIF contribute to continuous improvement and responsibility in AI.

Key insights

🤖Gemini is the most capable AI system developed by Google DeepMind.

🔒 Safety and responsibility are prioritized in building AI systems at Google DeepMind.

New questions arise as AI systems become more capable, especially regarding multimodality and contextual challenges.

🔬Proactive policies and evaluations help address risks like cybersecurity, bias, and toxicity.

🌍External evaluations and collaborations with organizations contribute to continuous improvement and responsibility in AI.

Q&A

What is Gemini?

Gemini is the most capable AI system developed by Google DeepMind.

How does Google DeepMind prioritize safety and responsibility?

Safety and responsibility are built into AI systems from the beginning.

What are the new challenges as AI systems become more capable?

New challenges include multimodality and contextual issues.

How does Google DeepMind address risks like cybersecurity, bias, and toxicity?

Proactive policies and evaluations are implemented.

How does Google DeepMind collaborate with other organizations?

Google DeepMind collaborates with organizations like MLCommons.

Timestamped Summary

00:00[gentle music]

00:02Gemini is the most capable AI system developed by Google DeepMind, prioritizing safety and responsibility.

00:07As AI systems become more capable, new questions arise regarding multimodality and contextual challenges.

00:11Proactive measures are taken to address risks like cybersecurity, bias, and toxicity.

00:19External evaluations and collaborations with organizations contribute to continuous improvement and responsibility in AI.