> The downside is it now similarly becomes challenging to formalize the machine's intuition Tooting my own horn here: I'm taking up this challenge professional

> The downside is it now similarly becomes challenging to formalize the machine's intuition Tooting my own horn here: I'm taking up this challenge professionally. There are new methods being discovered to extract interpretations for humans out of a model[1][2][3]; and I'll gladly work and provide advice and implementations on this, as an independent contractor. Having an interpretable/explicable ML pipeline helps in: * Knowing what drives your customers' buying patterns * Knowing where to look for signals closer to the root, helping go from correlation to causation * Orienting your marketing messages. You don't engage users the same way if they're a repeat buyer or a "hot now" lead * Concentrating on "would buy our product but won't because of X", which is where the market share battle lies; as opposed to "definitely would buy" and "definitely wouldn't buy", which are cases where you have little influence on. * Listing buying patterns exhaustively, enabling you to discover low noise niches * Providing auditable explanations of the decisions the model has taken, if it has been required legally Here is my email: pro@benoit.paris [1] https://distill.pub/2018/building-blocks/ https://distill.pub/2018/building-blocks/ [2] https://www.darpa.mil/program/explainable-artificial-intelligence https://www.darpa.mil/program/explainable-artificial-intelli... [3] http://interpretable.ml/ http://interpretable.ml/