Machine Learning Street Talk (MLST) #046 The Great ML Stagnation (Mark Saroufim and Dr. Mathew Salvaris)
16 snips
Mar 6, 2021 
Mark Saroufim, author of "Machine Learning: The Great Stagnation," joins Mathew Salvaris, a lead ML scientist at iRobot, to dissect the stagnation in machine learning. They discuss how academia’s incentive structures stifle innovation and the implications of 'state-of-the-art' chasing. They highlight the rise of the 'gentleman scientist,' the complexities of achieving measurable success, and the need for a user-focused approach in research. The duo emphasizes collaboration and the importance of embracing failures as part of the learning process.
AI Snips
Chapters
Books
Transcript
Episode notes
Building Mastery through Summarization
- Summarize and share other people's work to deepen your understanding and connect with peers.
- Use this as a springboard to identify potential contributions and build open-source projects.
From Code to Content
- Mark Saroufim left Microsoft to pursue a reinforcement learning service for game developers.
- He found more success by writing and sharing his learnings, leading to unexpected opportunities.
The Barbell Strategy for Research
- Use a "barbell strategy" in research.
- Dedicate time to both profitable, incremental work and riskier, passion projects.
