Why modeling 'I don’t know' is AI's most urgent problem

AI systems almost always have an answer, even when they should say, “I don’t know.”

In this episode of The Deep View Conversations, senior reporter Sabrina Ortiz speaks with Ruchir Puri, chief scientist at IBM Research, about why uncertainty modeling may be AI’s most urgent technical challenge.

Puri explains why today’s models struggle to recognize the limits of their own knowledge, how that failure contributes to hallucinations, and what researchers must solve before AI can become more reliable. He also explores the need for self-improving models, the enormous energy gap between artificial and human intelligence, and why the future of AI depends on doing more with less compute.

The conversation also covers:

• Why Puri predicted in 2020 that AI would transform software development
• How big data, GPUs, and transformer architectures created the current AI boom
• Why intelligence involves more than IQ
• The roles of emotional and relationship intelligence
• Why language models cannot capture the full complexity of the physical world
• How AI could help redesign software, quantum computing, and chip development
• Why Puri prefers "artificial useful intelligence" over AGI

Rather than chasing abstract definitions of general intelligence, Puri argues that the industry should focus on building AI that is useful, efficient, adaptable, and honest about what it does not know.

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