Innovation in the Digital Age: Expanding the Boundaries of the Creative Process with Generative AI
Bibliographic record
Abstract
Until now, Artificial intelligence (AI) has primarily created economic value through applications of supervised learning, a process in which an algorithm is trained to categorize data into specific classes (e.g., label customers as churners or non-churners) or predict a sequence of labels (e.g., text translation). However, recent advancements in generative AI have enabled the generation of complex and diverse outputs such as images and written text. These systems evoke the impression of being capable of creative behavior. The introduction of large language models, such as ChatGPT, and large text-to-image engines, such as DALL-E, Midjourney, or Stable Diffusion, has created a lively debate on their potential use in organizations, schools, and the broader society. Given the rapidly growing need for a better understanding of generative AI and its implications on creative processes, this symposium aims to present the most recent perspectives and insights on this topic, as well as integrate across the different views and provide fruitful directions for future research. Specifically, in this symposium, we will discuss how new generative AI technologies impact organizational ideation and innovation in both desirable and undesirable ways and reflect on respective research avenues. In the session, we will have an introduction presenter, four paper presentations, and an integrative discussion. Combining generative AI and human creative processes Author: Jeffrey V. Nickerson; Stevens Institute of Technology AI-deation: The effect of AI-based search algorithms on idea creation Author: Ben Wolfson; New York U. Author: Moran Lazar; Coller School of Management, Tel Aviv U. Author: Hila Lifshitz-Assaf; Warwick Business School InnoVAE: Generative AI for Patents and Innovation Author: Zhaoqi Cheng; - Author: Dokyun Lee; Boston U. Questrom School of Business Author: Prasanna Tambe; The Wharton School, U. of Pennsylvania Predicting Idea Creativity with AI Language Models Author: Xubo Cao; Stanford Graduate School of Business
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 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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".