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Behind the DeepSeek Hype, AI is Learning to Reason

Feb 20, 2025

Randy Fernando, co-founder of CHT and former NVIDIA employee, dives into the groundbreaking advancements in AI reasoning. He discusses how models like DeepSeek's R1 are reshaping the landscape, allowing AI to learn beyond mere pattern matching. Randy explores the potential for AI to innovate and develop new strategies, raising critical questions about ensuring this transformative technology benefits humanity. The conversation also addresses the security risks and the need for purposeful innovation as AI rapidly evolves.

31:34

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Randy Fernando

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Quick takeaways

The rise of cost-effective AI models like DeepSeek's R1 signifies a shift in competitive dynamics in the AI industry.

As AI transitions from pattern recognition to genuine reasoning, it presents both unprecedented problem-solving capabilities and ethical challenges for society.

Deep dives

Key Inflection Points in AI Technology

The emergence of advanced AI models like OpenAI's O3 and DeepSeq's R1 represents a significant shift in AI technology, making it apparent that the competitive landscape is evolving. Notably, the cost-effectiveness of these models challenges the dominance of larger labs, as a reported numerical figure suggests that a Chinese lab achieved comparable performance at a fraction of the expected cost. However, this perceived cost advantage has raised debates about its accuracy and implications. The operational efficiency and innovative methodologies applied in the development of these models underline that intelligent design and optimization play critical roles in their performance.

Intro

2min

Exploring AI Model Innovations and Their Limitations

2min

Patterns and Limitations of AI

7min

Enhancing AI Through Iterative Feedback and Learning

2min

AI Evolution: Creativity and Computation

16min

Envisioning Purposeful Innovation for a Better Future

3min

When Chinese AI company DeepSeek announced they had built a model that could compete with OpenAI at a fraction of the cost, it sent shockwaves through the industry and roiled global markets. But amid all the noise around DeepSeek, there was a clear signal: machine reasoning is here and it's transforming AI.

In this episode, Aza sits down with CHT co-founder Randy Fernando to explore what happens when AI moves beyond pattern matching to actual reasoning. They unpack how these new models can not only learn from human knowledge but discover entirely new strategies we've never seen before – bringing unprecedented problem-solving potential but also unpredictable risks.

These capabilities are a step toward a critical threshold - when AI can accelerate its own development. With major labs racing to build self-improving systems, the crucial question isn't how fast we can go, but where we're trying to get to. How do we ensure this transformative technology serves human flourishing rather than undermining it?

Your Undivided Attention is produced by the Center for Humane Technology. Follow us on Twitter: @HumaneTech_

Clarification: In making the point that reasoning models excel at tasks for which there is a right or wrong answer, Randy referred to Chess, Go, and Starcraft as examples of games where a reasoning model would do well. However, this is only true on the basis of individual decisions within those games. None of these games have been “solved” in the the game theory sense.

Correction: Aza mispronounced the name of the Go champion Lee Sedol, who was bested by Move 37.

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The study that found training AIs to code also made them better writers

More information on the AI coding company Cursor

Further reading on Eric Schmidt’s threshold to “pull the plug” on AI

Further reading on Move 37

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