Hybrid Intelligence for Innovation: Augmenting NPD Teams with Artificial Intelligence and Machine Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Artificial intelligence (AI) and machine learning (ML) are perhaps the technologies with the most impact on industries and societies.But Cockburn et al. (2019) argue that AI's greatest economic impact is still to come: its potential as a new method of invention.New methods of invention that can reshape the nature of the innovation process are relatively rare, and AI could be one of these rare cases.Two opening case examples may serve as an illustration of this change.Choosy, a New York-based fashion brand, delivers algorithmically informed fashion items in as little as two weeks (Eldor 2020).Founded by Jessie Zeng in 2018, the company's core assets are a group of algorithms that basically do most of its NPD work.First, a predictive algorithm using natural language processing spots top-trending fashion on Instagram by creating a database of all posts from a large group of influencers and visually tracking not just their posts, but also all comments received.This allows Choosy to rank the popularity of specific items and their underlying design features.Once the team (and algorithm) is sure that they discovered a hot fashion trend not covered by mainstream fashion brands yet, they use a generative algorithm to actually design new fashion items incorporating the identified trends.At this stage, human
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it