Innovation Sustainability: R&D 300 ≠250 Ai

Innovation Sustainability is almost an oxymoron as whether it’s Ai Vector databases, catalogued MetaData, or RAG pipelines, etc., today, 250 training repetitions can ≠ 300 Spartans at Thermopylae as R&D is still front and center for NPD success. How? Here’s How.

When the underlying foundation of R&D, science, engineering and experimentation based analysis is the consistent convergence of large sample size and affiliated scientific verification ( and not only detailed in  Persistent Pre-Training of LLMs but in Large Language Models Using Semantic Entropy and many more ) we see how any number of industry standard Ai training sets ( and even with the emergence of real-time Ai system ” last straw effect ” 250 data point inputs ( and from any location, and in data format on the internet ) means persistence across any inputs can cause butterfly effects in final Ai outputs. More, it’s  where these inputs can not only radically alter the ability of Ai systems to produce output that is not directly effected by ” last minute ” modalities ( like the last mile problem in logistics systems ) we also see how with a 300 ≠250 footprint, the length of the journey to the last inches can have severely unintended effects. Last second edge constraint changes are not conducive to sustained scientific, data driven artifacts in Ai systems. Where any number of minute changes and especially in last mile data scenarios can change whole gradient descent Ai system outputs even when unconstrained mathematical optimization models in first-order iterative algorithms used to minimize differentiable multivariate functions occur in any number of iterative methods for optimization be it search engines or Ai video or image output efforts, this is in fact happening in some of today’s Ai system architecture. 250 is 300 but not for long.

When companies, organizations, group and even individuals move beyond the expected norms of scientific validation of empirical evidence the question becomes at what point do Ai systems begin to alter their output regardless of expert and industry validated guidance and more when sentiment and meta data a bearing that it should not. It is in these cases, and as expertly described in Medical Multi-Agent Systems we see how even in the most high ” accurate ” Ai systems alone are not a sufficient measure of clinical accuracy and when it comes to effected accuracy, and there is none of higher importance than medical next steps suggestion systems.

Further detailed in Semantic and Generalized Entropy Loss Functions for Semi-Supervised Deep Learning we see how not only are the medical areas of discipline some of the 1st bastions of 300 ≠250 but also areas such as geological systems, nano-materials, optical chip fabrication, and environmental prediction systems ( areas with extremely high signal to noise ratio sensitivity ) and where Ai systems where interference from outside untested influences means edge constraints have larger chances of directly and negatively effecting tested and operational capabilities, this is the area for the largest concern. And, this is where specific new Ai system architectures are evolving to adjust for such discrepancies before output intended for next step actions is utilized and made available as usable and actionable.