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      "text": "What is your perspective on improving reliability and safety of AI models? \"While humans are about 97% accurate in data-extraction tasks, AI is now approaching a similar level. However, users expect AI to be nearly infallible. Techniques such as retrieval-augmented generation (RAG) and controlled-context windows reduce hallucinations by anchoring model responses in verified data.\"",
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      "text": "What do you think about the growing importance of open-source models in the industry? \"The industry is moving toward a mixed landscape, where both open and closed models coexist. Although open-source models offer flexibility, they often come with significant operational overheads, including hardware setup, driver management, and ongoing maintenance, making them impractical for many organizations. As models become smaller and more efficient, on-device AI will grow, further supporting open-source adoption in specific contexts. However, for cloud-based workloads, proprietary models still dominate in performance, reliability, and total cost of ownership [TCO].\"",
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      "text": "What are your views on the adoption of AI agents and bridging the gap between experimentation and production? \"There are significant challenges in taking agentic AI to production but all major AI providers are converging on the development of reasoning-capable agents, signaling that the ecosystem is maturing. Google has released an Agent Development Kit, and there are major advancements in reasoning models and tool integration, such as Gemini 2.5 Pro and Flash, which now enable faster, more capable agents.\"",
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      "text": "Discussion with Jason Gelman\nDirector of Product Management – Vertex AI, Google Cloud",
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