Blockchain Landscape and AI Renaissance
Bibliographic record
Abstract
Known for powering cryptocurrencies such as Bitcoin and Ethereum, blockchain is seen as a disruptive technology capable of revolutionizing a wide variety of domains, ranging from finance to governance, by offering superior security, reliability, and transparency founded upon a decentralized and democratic computational model. In this tutorial, we first present the original Bitcoin design, along with Ethereum and Hyperledger, and reflect on their design choices through the academic lens. We further provide an overview of potential applications and associated research challenges, as well as a survey of ongoing research directions related to byzantine fault-tolerance consensus protocols. We highlight the new opportunities blockchain creates for building the next generation of secure middleware platforms and explore the possible interplay between AI and blockchains, or more specifically, how blockchain technology can enable the notion of "decentralized intelligence." We conclude with a walkthrough demonstrating the process of developing a decentralized application using a popular Smart Contract language (Solidity) over the Ethereum platform
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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 | 0.000 | 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 | 0.000 | 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".