Machine Learning as a Software Engineering Enterprise with Charles Isbell - #441
Analysis
This article summarizes a podcast episode from Practical AI featuring Charles Isbell, discussing machine learning as a software engineering enterprise. The conversation covers Isbell's invited talk at NeurIPS 2020, the success of Georgia Tech's online Master's program in CS, and the importance of accessible education. It also touches upon the impact of machine learning, the need for diverse perspectives in the field, and the fallout from Timnit Gebru's departure. The episode emphasizes the shift from traditional compiler hacking to embracing the opportunities within machine learning.
Key Takeaways
- •The podcast discusses the intersection of machine learning and software engineering.
- •It highlights the importance of accessible education in the field of machine learning.
- •The episode touches upon the need for diversity and different perspectives in machine learning research and development.
“We spend quite a bit speaking about the impact machine learning is beginning to have on the world, and how we should move from thinking of ourselves as compiler hackers, and begin to see the possibilities and opportunities that have been ignored.”