Exploring the Use of Generative AI in the Search for Extraterrestrial Intelligence (SETI)
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
The search for extraterrestrial intelligence (SETI) is a field that has long been within the domain of traditional signal processing techniques.However, with the advent of powerful generative AI models, such as GPT-3, we are now able to explore new ways of analyzing SETI data and potentially uncover previously hidden signals.In this work, we present a novel approach for using generative AI to analyze SETI data, with focus on data processing and machine learning techniques.Our proposed method uses a combination of deep learning and generative models to analyze radio telescope data, with the goal of identifying potential signals from extraterrestrial civilizations.We also discuss the challenges and limitations of using generative AI in SETI, as well as potential future directions for this research.Our findings suggest that generative AI has the potential to significantly improve the efficiency and effectiveness of the search for extraterrestrial intelligence, and we encourage further exploration of this approach in the SETI community.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it