# Blockchain and Artificial Intelligence: Synergies and Conflicts

arXiv: https://arxiv.org/abs/2405.13462 · HTML: https://mlsanity.com/paper/2405.13462/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 73 references that are in the map.
- First posted on arXiv 22 May 2024.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018)
- [Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models](https://mlsanity.com/paper/1708.08296/) (2017)
- [IPFS - Content Addressed, Versioned, P2P File System](https://mlsanity.com/paper/1407.3561/) (2014)
- [Decentralized & Collaborative AI on Blockchain](https://mlsanity.com/paper/1907.07247/) (2019)
- [opML: Optimistic Machine Learning on Blockchain](https://mlsanity.com/paper/2401.17555/) (2024)
- [Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review](https://mlsanity.com/paper/2205.07855/) (2022)
- [Time-Optimal Interactive Proofs for Circuit Evaluation](https://mlsanity.com/paper/1304.3812/) (2013)
- [Defending Against Poisoning Attacks in Federated Learning with Blockchain](https://mlsanity.com/paper/2307.00543/) (2023)
- [BasedAI: A decentralized P2P network for Zero Knowledge Large Language Models (ZK-LLMs)](https://mlsanity.com/paper/2403.01008/) (2024)
- [Cryptocurrency Price Prediction using Twitter Sentiment Analysis](https://mlsanity.com/paper/2303.09397/) (2023)

## Built on by (most cited first, 2 of 2)

- [Blockchain for Large Language Model Security and Safety: A Holistic Survey](https://mlsanity.com/paper/2407.20181/) (2024)
- [Blockchain Data Analysis in the Era of Large-Language Models](https://mlsanity.com/paper/2412.09640/) (2024)

## Abstract (arXiv)

Blockchain technology and Artificial Intelligence (AI) have emerged as transformative forces in their respective domains. This paper explores synergies and challenges between these two technologies. Our research analyses the biggest projects combining blockchain and AI, based on market capitalization, and derives a novel framework to categorize contemporary and future use cases. Despite the theoretical compatibility, current real-world applications combining blockchain and AI remain in their infancy.