[joe_the_user]

Basically, the system is massively ad-hoc and driven by this large scale annotation, training and testing.

The big question here is, what happens when the world changes next year? You rebuild the application. I know there are companies that advertise doing continuous updating of deep learning models but it seems like calculating total costs and total benefits is going to be hard here.

[killjoywashere]

Sometimes the mine makes money, sometimes it doesn't make sense to run the mine.

[moandcompany]

To extend the mining metaphor, and relate back to the original articles:

People and organizations are chasing what they believe, or are told to believe, is pay dirt.

Many unfamiliar investors have rushed in, possibly fearing missing out, and fund many of the prospectors, yet many of the prospectors and investors aren't really aware of the costs of running a mine, nor the practices required to run them efficiently.

It turns out that there's more aspects to the value creation process than dig/refine/polish (data/train/predict), especially when usefulness in application matters and there are finite resources available for digging.

Companies selling shovels are some of the primary beneficiaries of this, by selling shovels (i.e. renting compute) funded by the malinvestment.

Additional beneficiaries are the refiners (training experts) that are able to charge steep labor premiums, however organizations are starting to figure out that their refiners are expensive to keep idle and often operate the mines poorly in terms of throughput/cost-effectiveness/repeatability/application (see the various threads on "Data Engineers")