Tom’s Substack | Revenue & Trends

The hottest Substack posts of Tom’s Substack

And their main takeaways

Focus on solving the right problem at the right time, don't get blinded by AI hype.
Dive deep into evaluating AI model behavior, considering trade-offs and potential misuse.
Establish robust monitoring and error reporting processes post-deployment to improve AI systems over time.

Increased diversity in healthcare data for AI training leads to better performance for all patient demographics.
AI models may memorize training data for individual patients, potentially impacting future care.
Development of AI models in healthcare requires careful consideration to avoid biases and ensure accurate performance.

Evaluation of models should focus on selecting the best performing model, giving confidence in AI outputs, identifying safety and ethical issues, and providing actionable insights for improvement.
Standard evaluation approaches face challenges like broad performance metrics, data leakage from benchmarks, and lack of contextual understanding.
To improve evaluations, embrace human-centered evaluation methods and red-teaming to understand user perceptions, uncover vulnerabilities, and ensure models are safe and effective.