Exponential Growth of Technology and the Impact on Economic Jobs and Teachings: Change by Assimilation
Bibliographic record
Abstract
Today’s computer programs, AI, and Robotics, are vastly superior, faster, more accurate, and some people would argue, easier to deal with than most economists, statisticians, and the currently used economic models. If that is the case, what are we to do, and what are the options? There are many. This paper will provide research information on what is working or not, has changed, and what other options might lurk in the future for this holy grail of science. The literature shows that Artificial Intelligence offers a way to amplify and go beyond the current capacity of capital and labor to drive economic growth. UMO is just one of the examples showing what a hive mentality can achieve and how accurate and predictable the outcome can be. We will discuss how to suspend assumptions and why we ought to stay open to different ideas. Organizations such as Accenture research report on the impact of AI in 12 developed economies, stating it “reveals that AI could double annual economic growth rates in 2035 by changing the nature of work and creating a new relationship between man and machine.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".