> data: not enough of the right data.
> cost: the data, compute, storage and talent resources needed for ML allow mostly large companies to benefit from it.
> utility: most ML content you see online are gimmicky tasks that can't extend into valuable real world applications.
> trust: ML needs to be engineered reliably and robustly.
This is a very honest take on ML / AI. I work in blockchain and what drives me crazy is that crypto / blockchain has delivered huge, tangible successes and value (measured easily and accurately via real-time prices) but the HN cognoscenti orgasms over ML and considers blockchain a scam.
IMO the situation is flipped - crypto is proving its success in a massive, revolutionary way while ML / AI is pretty stagnant at delivering world-changing products. It's cool that bots are sorting and picking at the warehouse but the value has been overstated by many orders of magnitude (thus far).
ML is a tool in a box right now, one of many that can potentially solve a class of problems, but it isn't (yet) a paradigm shift in how businesses operate.