Every Bit Matters: A Hardware/Software Approach for Enabling More Powerful Machine Learning Models
Bibliographic record
Abstract
Machine Learning (ML) has empowered computing devices to perform tasks traditionally associated with human intelli-gence, such as “thinking,” “seeing,” “hearing,” “reading,” and “writing.” This capability allows systems to interact with the physical world and process information in ways that can rival human abilities, enhancing discovery, learning, and decision-making. Although the foundational techniques of ML have existed for decades, its recent surge in attention is partly due to advancements in computing hardware. Around 2009, computing hardware in the form of graphics processors had reached a level of performance and storage capacity that made implementing core ML methods practical in terms of cost and time. Despite these advances, the performance and storage capacities of current hardware still constrain ML applications. As ML applications are being deployed across the full spectrum of computing devices, from the data center to Internet-of- Things systems, enhancing hardware remains a critical factor for unlocking ML's full potential.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".