Machine Learning Street Talk (MLST) #046 The Great ML Stagnation (Mark Saroufim and Dr. Mathew Salvaris)
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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.
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SOTA Chasing vs. Breakthroughs
- Machine learning research often prioritizes short-term gains and hype over genuine scientific breakthroughs.
- This focus on "SOTA chasing" stifles innovation and leads to incremental improvements rather than groundbreaking discoveries.
Honesty and Risk-Taking
- Be honest about your motivations, whether it's scientific curiosity or financial gain.
- Take risks and pursue ambitious projects once you have financial security.
The Peer Review System and Individual Incentives
- SOTA chasing is driven by individual incentives within the current peer review system.
- Publishing innovative work outside of established venues requires accepting potential career downsides.
