[CarbonCycles]

This paper and a recent post by Sebastian Raschka (where he decomposed a Forrester report about the uptake of technologies in industry) is alluding to something I have witnessed in system/control design and applied research.

Both LLMs and massive CV architectures are NOT the holistic solution. Rather, they are the sensors and edge devices that have now improved both the fidelity and reliability to a point where even more interesting things can happen.

I present a relevant use case regarding robotic arm manipulation. Before the latest SOTA CV algorithms were developed, the legacy technology couldn't provide the fidelity and feedback needed. Now, the embedded fusion of control systems, CV models, etc. we are seeing robotic arms that can manipulate and sort items previously deemed to be extremely difficult.

Research appears to follow the same pattern...observations and hypothesis that were once deemed too difficult or impossible at that time to validate are now common (e.g., Einstein's work with relativity).

My head is already spinning on how many companies and non-technical managers/executives are going to be sorely disappointed in the next year or two that Stable Diffusion, Chat GPT, etc. will deliver very little other than massive headaches for the legal, engineering, recruiting teams that will have to deal with this.