Big Data and Artificial Intelligence: Revolutionizing Business Decision-Making
Bibliographic record
Abstract
The term artificial intelligence (AI) refers to the fourth industrial revolution. Globally, artificial intelligence and big data have revolutionized all sectors. Artificial intelligence has grown into a revolutionary technology with profound implications on many industries, included business, thanks to the usage of big data. Artificial intelligence (AI) and big data has transformed decision-making procedures in the past few years by giving businesses access to sophisticated analytical tools that let them glean insightful information from massive volumes of data. Artificial intelligence segments machine learning and deep learning have been extensively utilized to tackle and enhance a wide range of business issues, including marketing, credit card fraud detection, algorithmic trading, customer support, demonstrating products according to consumer preferences, and insurance underwriting. Using AI and big data into marketing techniques may help a business owner boost audience response and create a powerful online brand that can compete with competitors. Examining how big data and decision-making are used in artificial intelligence applications in business settings is the main objective of the research. In specifically, this research looks at how artificial intelligence is being used to improve decision-making processes and how that's affecting corporate structures. According to the report, artificial intelligence plays a revolutionary role in business decision-making, providing a host of benefits related to effectiveness, precision, and creativity.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".