That sounds like a nit / premature optimization.
Electricity is cheap. If this is sufficiently or actually important for your org, you should measure it yourself. There are too many variables and factors subject to your org’s hardware.
Electricity is cheap. If this is sufficiently or actually important for your org, you should measure it yourself. There are too many variables and factors subject to your org’s hardware.
This entire use case seems to be one suited for servers handling user media.
Of course, it's arguably unlikely a virus scanner would opt for an ML-based approach, as they specifically need to be robust against adversarial inputs.
Several major players such as Norton, McAfee, and Symantec all at least claim to use AI/ML in their antivirus products.
Of course on Windows the convention is to use the file extension, but on other platforms the convention is to look at the file contents
MacOS (that is, Finder) also looks at the extension. That has also been the case with any file manager I've used on Linux distros that I can recall.
Since the data files can be large, this approach bypasses having to trnasfer the file twice, first to the server, and then to S3 after parsing.
Certainly you should do an audit of all the Android and iOS apps as well, to make sure they've been made in a efficient manner.
Block ads as well, they waste power.
This file identification is SUCH a small aspect of everything that is burning power in your laptop or phone as to be laughable.
What happens when we introduce more bespoke models for manipulating the data in that file?
This feels like it could slowly boil to the point of programs using magnitudes higher power, at which point it'll be hard to claw it back.
It's the equivalent to saying many people programming in Ruby is causing all future programs to be less efficient. Which is not true. In fact, many people programming in Ruby has caused Ruby to become more efficient because it gets optimised as it gets used more (or Python for that matter).
It's not as energy efficient as C, but it hasn't caused it to get worse and worse, and spiral out of control.
Likewise smart contracts are incredibly inefficient mechanisms of computation. The result is mostly that people don't use them for any meaningful amounts of computation, that all gets done "Off Chain".
Generative AI is definitely less efficient, but it's likely to improve over time, and indeed things like quantization has allowed models that would normally to require much more substantial hardware resources (and therefore, more energy intensive) to be run on smaller systems.
We're already there. Modern software is, by and large, profoundly inefficient.