For instance: quality control: abnormality detection (for instance: in medicine), agriculture (lots of movement there right now), parts inspection, assembly inspection, sorting and so on. There are more applications for this stuff than you might think at first glance, essentially if a toddler can do it and it is a job right now that's a good target.
Your statement reminds of 'all the good domains are taken', which I've been hearing since 1996 or so. Of course you'll need to do some work to identify a niche that doesn't have a major player in it yet. But the 'boring' niches are where a lot of money is to be made, the sexy stuff (cancer, fruit sorting) is well covered. But more obscure things are still wide open, I get decks with some regularity about new players in very interesting spaces using thinly wrapped ML to do very profitable things.
none of these is anything someone can run from their bedroom because they have very high quality and regulatory requirements and require constant work outside of the actual AI training.
This is actually reflected in the margins of "AI" companies, which are significantly lower than traditional SAAS businesses and require significantly more manpower to deal with the long tailed problems, which is where the AI fails but it's what actually matters.
In the long term, and to stay competitive you will always have to get out of bed and go to work. But the initial push can easily be just a very low number of people engaging an otherwise dormant niche.
Yes, medicine has regulatory requirements. But as long as you advise rather than diagnose the regulatory requirements drop to almost nil.
Do it for a couple publicly available docs and then contact the org saying you offer 'archive digitization' so their data ppl can mine for intelligence.
Most of the time and resources of 'Digital Transformation'/Data Science Depts goes to just manually extracting info from all kinds of old docs, pdfs, spreadsheets containing institutional knowledge.
The opportunity is present for a decentralized network that allows for training of models to be done from training sets at facilities.
Think of all the data sitting in silos from clinical trials. There is of course the painful process of authenticating researchers for access to data like that but it can be done. There just needs to be an economic reason to make that kind of effort.
I got pulled into a direction of using ML to predict costs of care in insurance so didn’t go further down the rabbit hole but I did author a patent for a novel approach to have a decentralized identity exchange data.
If any of this sounds exciting to you feel free to email me. hn (at) strapr (dot) com