In this episode, Iā€™m joined by Barak Turovsky, VP of AI at Cisco and former Director of Head of Product at Google for Google Languages AI. Barak shares his journey and insights into artificial intelligence, emphasizing the importance of understanding customer needs and aligning AI with business units. He discusses prioritizing use cases and the challenges of deploying AI at scale, introducing a framework for categorizing use cases based on fluency and accuracy.

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Key Takeaways:
00:00 to 01:20 – Introduction
04:13 Productizing AI: understanding customer needs.
09:35 Prioritizing use cases for AI deployment.
12:45 The challenges of deploying AI solutions at scale.
23:21 Augmenting data with synthetic data: challenges and considerations.
26:13 Reinforcement learning and the importance of human feedback.
28:06 Addressing data security and privacy concerns in AI.
29:26 The importance of domain-specific models.
31:12 Measuring the business impact and ROI of AI adoption.
37:03 Building a strong data science team for AI success.
39:47 Attracting top AI talent: motivation and excitement.
41:30 Fostering a culture of innovation and experimentation.
43:19 Upskilling the workforce for the AI era.
44:05 Establishing a chief AI officer role: empowerment and partnership.

Resources Mentioned:
Barak Turovsky | LinkedIn: https://www.linkedin.com/in/baraktur/
Cisco | LinkedIn: https://www.linkedin.com/company/cisco/
Google | LinkedIn: https://www.linkedin.com/company/google/
ā€œFramework for evaluating Generative AI use casesā€ article: https://www.linkedin.com/pulse/framework-evaluating-generative-ai-use-cases-barak-turovsky/

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