Application of artificial intelligence in automation
Bibliographic record
Abstract
The dramatically accelerating pace of development and adoption of new technologies in recent decades is likely to continue.Automation is not new.From the beginning, humans have constantly developed new and superior tools and technologies to produce greater economic output with less human effort.Some of these advances have been transformational, with broad impact across many sectors of the economy.Think of inventions like the steam engine, electricity, and information technologies.Other gains have been more specialized -for example, mechanized weaving looms, industrial robots, or automated teller machines.But now the IT era is transforming into an artificial intelligence (AI) era pervaded by more powerful digital technologies such as artificial intelligence.Which raises the question: What will the next phase of the automation look like?Will it be different?Automation and AI, in this vein, are increasingly looking like sources of the productivity gains badly needed to secure higher-quality economic growth in the country.As such, automation could well lift the national economy in the coming years and increase prosperity at a time of uncertainty [2].Regardless of its scope, automation fundamentally exists to substitute work activities undertaken by human labor with work done by machines, with the aim of increasing quality and quantity of output at a reduced unit cost.This ability to increase workers' productive capacity has historically enabled humans to transition out of physically difficult, mundane, or menial labor, and in so doing, raised the standard of living.Artificial intelligence now includes capabilities in image recognition, problem solving and logical reasoning that sometimes exceed those of humans.Artificial intelligence, particularly in combination with robotics, also has the potential to transform production processes and business, especially in manufacturing [3].The first national strategy on AI was launched by Canada in March 2017, followed soon after by technology leaders Japan and China.In Europe, the European Commission put forward a communication on AI, initiating the development of independent strategies by Member States.Asia has in many respects led the way in AI strategy, with Japan being the second country to
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".