I don't think thst it's self-evident that all of those individual behaviors can actually yield a truly unique behavioral pattern per user for each type of app. Maybe when combined, if your app involves a lot of deep user interaction, but since you haven't shared what your app is supposed to actually do, it's impossible to give an informed opinion on your probability of success a priori, so all I can say is I'm skeptical but I wish you good luck building a solution.

it depends on what your users are doing in your app. Some unique fingerprint has to come from some sort of behavioral or bio data that can reasonably be assumed to uniquely identify a user. Encoding data in some meaningful way (ml or otherwise) can only happen after you choose what you're encoding.

I would probably ask them the user to draw a particular shape or set of shapes with their finger and record where they start and how they deviate from the perfect lining of that shape, then (using a vector that represents those deviations over time, their speed, the total time to completion and the starting position), build a database to of users and loosely identify a user using a nearest neighbor algo, or using a deep classifier that has the users as its output layer. What's challenging is you need to start building the data before you can apply it to logins, unless you already have a good proxy task in that context of your app that doesn't require logins (or that you can get after authenticating users using different means).

Then either make it a 'stay logged in' experience or use bio info (facial recognition, fingerprints), depending on your security requirements.

Custom machine learning models are difficult to maintain and integrate compared to out of the box standard it solution and api integrations with external (ml) services. We should really only apply them when it makes sense (ie when we have an important, complex problem we can't navigate with simple heuristics and a large amount of relevant data).

OK I understand the what now, but not the 'why'. If you're processing personal information to recognize users, that requires their consent. If you have their consent, and we're talking about an installed app on an iPhone or Android, why not just use the user ID or device Id as the identifier? No ml required. Are you trying to identify different users of the same device?

... What? I read this twice and still had no idea what it is you're trying to achieve or why. Could you try to explain it as a user story?

I still do, but the points about complexity and roi remain the same. I get that you like this form of data and that's okay (actually, that's great!) , but not everybody has to adopt it because you find it exciting.

2 things.

1. there is still a ton of room for valuable innovation with structured data
2. the cost of processing is typically astronomical, and the return hard to quantify.

In short I see this tech as a very specific solution to a very specific set of problems.

"From what I understand is the repeated iteration will take random weights and at some point those weights will be kinda perfect for the given task (plz correct me if i'm wrong)"

You're at least somewhat wrong - it's not all random. The weights are indeed initialized randomly, but then adjusted to fit batches of training data. The weights are updated to more closely match the data. This is usually done through stochastic gradient descent and leverages the difference between your network's current predictions and the known ground truth as calculated using the chosen loss function (e.g. the mean square error or binary cross-entropy).

right on Balocre.