Practical AI Towards stability and robustness
Jul 20, 2021
Roey Mechrez, CTO at BeyondMinds, shares his expertise on building robust AI systems and highlights the gap between academic research and practical deployment. He discusses why 90% of AI projects fail to create value, emphasizing the importance of stability and specificity in model performance. Roey dives into crucial techniques like data filtration, out-of-distribution detection, and the need for human oversight to enhance reliability. His insights reveal the critical role of tailored solutions and continuous model retraining to navigate the complexities of real-world AI implementation.
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Bridging the Gap
- Roey Mechrez transitioned from academic computer vision research to practical AI due to a high failure rate in enterprise AI projects.
- He believes the gap between academic research and practical application needs bridging to create real-world value.
Academia vs. Industry
- While academic research focuses on state-of-the-art models, enterprise AI requires a different mindset.
- PhDs and data scientists excel at innovation but may lack the practical skills for production deployment.
Stay Current, But Practical
- Stay updated on AI advancements by regularly reviewing research papers, even briefly.
- Remember that state-of-the-art models are only a small part of a successful AI solution.