Difficulty level 1: The only ML in there was some 1-line language translation feature they were consuming off Watson REST services for a tangential feature on their app
Difficulty level 2: Self-trained CNN on a custom dataset using TensorFlow and changes to start/end layers on the NN
Difficulty level 3: Custom rolled 12 layer CNN trained with with novel hand labeled data.
How many people to you think know the difference between these and the fact that each step is a magnitude or more harder than the last? That's not even getting into trying to apply research grade stuff.
Why should people be able to tell the difference vs the outcome of those products, unless someone (investors?) is specifically just buying into the AI hype itself? (in which case, this is nothing different from when everything was "social")
I'm not saying people should be able to tell the difference, nor am I discounting the value of the final outcome. Just that pitching an app as ML-driven, when it is barely 1% ML-driven is deceptive and mis-informs the public.
Consider taking the side mirror from a Ferrari, putting it on a Ford Escort, and then pitching the Ford Escort as a race-car. The Ford Escort may be a great family car, but it is not a sports car. People who don't know the difference might come to think of it as a sports car, and might even question the wisdom of spending money on a Ferrari.
I get (and perhaps even agree) to the sentiment, logically at least, but realistically all marketing is deceptive. It's pragmatically more useful to understand the how's and why's of that and play along.
Most off the shelf ML tools are not that powerful - and they don't make a difference in how you differentiate as a business. If you have a ML team and know how to build models from scratch with novel data, then you can iterate on your model and expand, something you can't do if you don't own the model.
You should show your differentiation by differentiating. But in many cases the ML differentiation isn't the same as differentiating the value prop of the product.
Difficulty level 2: Self-trained CNN on a custom dataset using TensorFlow and changes to start/end layers on the NN
Difficulty level 3: Custom rolled 12 layer CNN trained with with novel hand labeled data.
How many people to you think know the difference between these and the fact that each step is a magnitude or more harder than the last? That's not even getting into trying to apply research grade stuff.