I believe NYT does this already, but using minimal oversight to prevent any edge case misses or false positives.
Presently there’s not much in the way of suitable options for large media that build their modules in house. At the same time media tends to prefer to not invest too heavily in hardware if they don’t have to. Convincing leadership of using a cloud service to train an AI/ML model sounds leaner and lets them tick off even more buzzwords for the executive, etc. That said, results from efforts in the aforementioned application sound promising.
On the retail side: Using computer vision to deliver alerts about shelf condition.
For farming: Using computer vision + ML to devise and track health monitoring for crops.
For manufacturing: Predictive maintenance of equipment has been a very popular area of focus.
There have been countless use cases on the finance side of things. For instance, anomaly detection techniques help with reconciling accounts and detecting fraud.
The energy industry seems to never run out of use cases for tracking commodities and/or helping predict load.
In HR, predicting turnover and education demands are some of the early use cases being approached but I expect a lot more over time.
Logistics is another area that will have a seemingly endless supply of use case. Things like loss tracking, warehouse optimization, raw material allocation and sourcing. I don't think I've ever been involved in a logistics/manufacturing project that couldn't have used some ML to add efficiency to the process.
We all see success stories for very refined and well defined problems with huge amount of training data, with models created by 1% top engineers, but for average business such conditions may not be achievable, to train model to recognize various shelf conditions in different situations, buildings, etc. you need nontrivial set of training data, and will have unclear expectations about model performance.
Don't startups want to win a big exit though? Google won't need to buy the startup for billions, because the TOS already grants them permission to use all the models and training data for free. Seems like a Faustian bargain to me.
1) AI startups usually don't have a lot of value to potential acquirers based on their data, but based on other things (e.g., talent, customers, business model, platform, brand). That's like saying you shouldn't use AWS because Amazon can just steal and commercialize all your data.
2) There are other companies than Google that acquire startups
Having said that, I highly doubt that Google can just use all the training data to on GCloud to launch their own products with that. They can surely look at it and maybe do stuff with them internally, but I am pretty sure that they can't use them commercially.
How would you ever know if they did? People who worked at Google have been accused, by Google, of stealing the entire self driving car program and taking it to a competitor.
It's also vastly different. Of course someone working at at google on a project has access to that project. It doesn't mean they have access to your stuff.
Hard to get employees to not steal from you if you are stealing from your customers.
I shiggy diggy.
Also, even Google's general consume terms of service really isn't what you think: https://www.google.com/policies/terms/
"You retain ownership of any intellectual property rights that you hold in that content. In short, what belongs to you stays yours."
The TOS you are quoting only refers to the information you provide in the survey. Here are the Google Cloud TOS: https://cloud.google.com/terms/ if you're interested in what Cloud does with customers data.
5.2 Use of Customer Data. Google will not access or use Customer Data, except as necessary to provide the Services to Customer.
Your training data and models are secure.