Re:Lessons for Building AI-Driven Products
by alexbeloi on Friday March 23, 2018 @04:04PM
(#16662166)
>Always have a naive solution to compare against the AI solution. 'Naive' here may be a simple linear regression, RMSE, or multi armed bandit/Thompson sampling.
This cannot be stressed enough, optimism bias will always push the scientist towards the 'more interesting/complete/new' method and model, but a seasoned practitioner will have the discipline to always establish a baseline (<1 days work).
Re:Lessons for Building AI-Driven Products
by itronitron on Saturday March 24, 2018 @01:11AM
(#16664990)
In my experience, simplifying a solution over time is the best way to ensure its adoption and long-term success. That applies both to UX/UI and the supporting computation/ML.
1)Don't let your -brilliant- colleagues try to force their -brilliantly complex- solution of a problem - clearly define market problems, and don't let the team try to go the route of trying to force fit a solution to a market problem. Market problems come first.
2)Frame the market problems appropriately for your ML/AI teams, and practice trying to frame the problem from a variety of angles. Framing from different angles promotes the 'Ah-ha' moment in terms of the right way to solve the problem from the ML side.
3)Don't commit serious time to a model before having a naive solution to benchmark against. Always have a naive solution to compare against the AI solution. 'Naive' here may be a simple linear regression, RMSE, or multi armed bandit/Thompson sampling.