Human augmentation to deliver an enhanced and resilient people capability for Defence
Bibliographic record
Abstract
From first tools, to flight, to advances in medicine and biotechnology, enhancing our innate abilities has been a constant goal and militaries the world over have long sought to advance the limits of human performance in their warfighters. Human augmentation (HA) encompasses a wide range of technologies that straddle a diversity of scientific disciplines and maturity levels, including wearable assistive technologies such as exoskeletons, neurotechnology, pharmacology, telexistence and genetics. Recent and rapid advances in life sciences and biotechnology and the convergence of fields such as artificial intelligence, robotics and medicine present us with a radically different opportunity for optimising and enhancing human performance. HA can be considered a potentially important strategy underpinning our ability to fight and win wars, by making soldiers more lethal and better able to survive. This paper is based on the HA thematic session held at the 6th International Congress on Soldiers' Physical Performance (ICSPP) in London in 2023. It considers aspects of HA of interest to participating nations and provides a state-of-the-art review of HA from a military perspective by experts engaged in this area. It considers the development of capability requirements, ethical, legal and social aspects and candidate HA technologies, one with ancient roots but modern applications for Defence (pharmacological augmentation) and one emerging area (non-invasive brain stimulation). HA offers a number of benefits, opportunities and challenges to the Defence community. Deployment of these technologies must take place within the boundaries of a nation's core values and beliefs, the rules-based international order and the freedoms that underpin their militaries' moral and ethical foundations.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".