The more time you spend on marketing, the better.
We found that as our AI got worse, our product got better.
> We found that as our AI got worse, our product got better.
Interesting. Could you elaborate on this too?
So, specifically, our startup started with the idea that high-brow tech would be a key differentiator and give the best user experience. This is the common story trotted out by survivorship bias stories that make good tech news articles.
Whereas making cuts to our R&D time and focusing on UI/UX and working with simpler science ultimately led to better product.
From consulting, sales, and corporate work, one learns that the dirty secret of big-iron large tech companies is that all stuff sold as ML is just nicely packaged logistic regression. Or it was was five years ago. Nowadays I guess it would MAYBE be transformers, but the point being that off-the-shelf ML is what drives 99% of good products.
I guess one could take this lesson from science. What you learn from a good PhD advisor is: a) read the latest work b) note the simple baseline approach constantly trashed as scoring 2% worse than the sophisticated intricate new things proposed and c) implement the simple baseline. Achieve impact not by hillclimbing on a standard metric but define a new problem or arbitrage insights from adjacent fields, etc.
As in, with the pace of improvements from other AI startups and general availability of their APIs (e.g. GPT-4), is there a specific advantage (aside from maybe cost) to learning to build my own models? Or is the course more suitable for people wanting to become ML engineers (or similar) and to find a job as such? Thanks in advance