Gemini fast: „Walking: It will take you about 45 seconds. You will arrive refreshed and full of steps, but you will be standing next to a high-pressure hose with no car to spray.“
Foundational models can work where so far „needs human intuition“ was the state of things. I can picture a time series model with large enough Training corpus being able to deal quite well with typical quirks of seasonalities, shocks, outliers, etc.
I fully agree regarding how things have been so far, but I’m excited to see practitioners try out models such as the one presented here — it might just work.
- energy management (shifting loads to times when energy is cheap) for consumer/commercial/industrial use cases
- energy markets, especially power trading: often highly algorithmic, and driven by models that turn fundamentals data (weather, calendar, …) into supply/demand predictions, and from there into price predictions
The same applies within large companies. If you’re within a business team, and you’re requesting work from a design team/engineering team/data science team, you’ll face the same issues with scope creep + churn + competing priorities etc.
I wouldn’t blame agencies for being bad, this is people being people plus a bit of other things. Anticipating and steering around/against these dynamics has been one of my biggest career learnings over the last years. The author has some good suggestions for how to do it — if you work in a large company, take another look and ask yourself if they don’t also apply to your work!