Hacker News Reader

Comment by indigo945 | Hacker News Reader

One obvious use case is edge computing, such as in industrial applications that cannot tolerate the risk of a network link or cloud service going down. Even embedded use cases are possible, such as an image classifier model in a security camera.

View on HN

In fact any application where the task is stable and the model good enough to address that task. As you suggest, industrial applications where a robot must deal with variants of the same repetitive task. Or a military drone which needs to be jamming proof.

> Or a military drone which needs to be jamming proof.

That, if used in war, I would think, would need the ability to be updated frequently. For example, your enemy might find out (say by running tests on hardware they captured from you) that painting some red paint in a particular shape (a smiley might even work) on their hardware prevented your drones from attacking them because it confuses that pattern with the Red Cross logo.

You keep the "reasoning core" burned and play the cat-and-mouse game at the I/O edge. Enemy invents a smiley shield, your R&D figures out some filtering step that defeats this effect without compromising general image recognition. Then the enemy figures out a new trick, your R&D invents a countermeasure, and so on - point is, this can happen for a long time in layers on top of the core model. If the enemy invents some robust way to attack the core that cannot be filtered out, it's game over for that hardware, but that is a much more difficult task and might take longer than expected service time of a given batch of drones.

Sort of mirrors how biological organisms work. E.g. in a bird, the core functionality of knowing how to fly is burned in. Hunting food is probably a combination of experiential learning on top of instinctive behavior, and is somewhat adaptable to local conditions.

Those are really two different things. One is the computer vision that could be “hard coded” and the other is the image library, that would be updated regularly. Look at facial recognition. You can download and run a facial recognition LLM on your GPU that looks at a library of your personal photos. The LLM doesn’t change when it scans your photos for faces, it just writes the data associated with a “face” to whatever library. When you add a new picture, it adds that face data and compares it to the library for a match. The actual LLM never needs to change. It is the same as the one I downloaded and ran on my GPU for my photos. If it was written on chips we both bought and installed, it would work the same way.[1]

[1] Yes, this is a massive simplification

Reply on news.ycombinator.com