[YorkshireSeason]

   How was early Machine Learning 
   different from statistics?

I'd argue: in two ways.

First: ML's algorithmic focus. Just about anything in modern AI/ML works because it uses compute at extreme scale. For example neural nets seem to work well only when trained with huge amounts of data. Statisticians lacked the background to make this happen.

Second: most work in statistics assumed that data was generated by given stochastic data model. In contrast, ML has been using algorithmic models and the data given by an unknown mechanism. In most real-world situations, the mechanism is unknown.

It's not just hype. Statistics was stuck in a local optimum, and it was ML's focus on algorithms, data structures, GPUs/TPUs, big data, ... together with the jump into 'weird' data (e.g. the proverbial cat photos), that propelled ML ahead of statistics.