Adding to the agreement and reaction to the useful Andreesen Horowitz post on The New Business of AI from last week ..
Reaching the sunlit uplands of AI is going to be rather harder than many of its investors and protagonists have predicted. https://a16z.com/2020/02/16/the-new-business-of-ai-and-how-its-different-from-traditional-software/
In particular, many AI companies have:
- Lower gross margins due to heavy cloud infrastructure usage and ongoing human support;
- Scaling challenges due to the thorny problem of edge cases;
- Weaker defensive moats due to the commoditization of AI models and challenges with data network effects.
Adding a couple of examples to illustrate some of the systems design issues :
Frank Denneman, from VMware, on paralellism used for scaling training models https://frankdenneman.nl/2020/02/19/multi-gpu-and-distributed-deep-learning/
Emily Potyraj, from Pure Storage, on optimizing ECG data layout to improve deep learning training performance. https://towardsdatascience.com/what-format-should-i-store-my-ecg-data-in-for-dl-training-bc808eb64981
All of this points to a continuing requirement for a high degree of skilled problem analysis and systems design in order to make best use of AI/ML . There’s an opportunity for existing services companies to dramatically improve with judicious use of ML/AI .