Major breakthroughs in artificial intelligence research often reshape the design and utility of Al in both business and society. In this special rebroadcast episode of Smart Talks with IBM, Malcolm
Gladwell and Jacob Goldstein explore the conceptual underpinnings of modern Al with Dr. David Cox, VP of Al models at IBM Research. They talk foundation models, self-supervised machine learning, and the practical applications of Al and data platforms like watsonx in business and technology.
When we first aired this episode last year, the concept of foundation models was just beginning to capture our attention. Since then, this technology has evolved and redefined the boundaries of what's possible. Businesses are becoming more savvy about selecting the right models and understanding how they can drive revenue and efficiency.
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Visit us at ibm.com/smarttalks
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Major breakthroughs in artificial intelligence research often reshape the design and utility of AI in both business and society. In this episode of Smart Talks with IBM, Malcolm Gladwell and Jacob Goldstein explore the conceptual underpinnings of modern AI with Dr. David Cox, VP of AI Models at IBM Research. They talk foundation models, self-supervised machine learning, and the practical applications of AI and data platforms like watsonx in business and technology.
Visit us at: https://www.ibm.com/thought-leadership/smart/talks/
Learn more about watsonx: https://www.ibm.com/watsonx
This is a paid advertisement from IBM.
See omnystudio.com/listener for privacy information.
There has been a debate in the past few years between the symbolists and the connectionists about the future of artificial intelligence. The symbolists say that traditional, explainable, logic-based approaches still hold tremendous promise while the connectionists say that the power of deep learning, for all its current opacity and narrow application, holds the key to more general forms of machine intelligence. This week, I speak with David Cox, IBM Director of the MIT-IBM Watson AI Lab, which is blending the two traditions in what they call neuro-symbolic AI in hopes to move AI forward.