Our guest this week was Mohan Mahadevan, a senior VP at Onfido, a machine-learning powered identity verification platform. He has previously worked at Amazon heading up a computer vision team working on robotics applications as well as for many years at KLA, a leading semiconductor hardware company. He holds a doctorate in theoretical physics from Colorado State University.

Mohan had mentioned that he thought it might be interesting to discuss neurosymbolic AI, and the implications of a shift towards that as a core paradigm for production AI systems. In particular, we discuss the practical consequences of such a shift, both in terms of team composition as well as infrastructure requirements.

In this clip, Mohan explains some of the benefits of traditional end-to-end neural networks as compared to neurosymbolic approaches:

I didn’t know much about this approach and the tradeoffs before doing some reading ahead of this conversation, and I think it offers a useful overview of some of the tradeoffs to be considered when implementing a neurosymbolic solution to a problem. Check out out the full episode below or however you prefer to listen to podcasts!


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