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| - [[random walk attention tokens]] |
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| - in this article i want to share mostly unedited output from chatgpt | ||
| - so you can judge for youself potential impact of [[cyber]] protocol | ||
| - ## intro | ||
| - introducing a [[random walk]]-based [[pagerank]] model | ||
| - weighted by cryptographic tokens of [[attention]] and [[will]] | ||
| - adds a new dimension to [[graph analysis]] | ||
| - especially in systems with decentralized [[consensus]] and content curation | ||
| - this kind of analysis aligns with the needs of modern ai industries | ||
| - especially in optimizing attention-based mechanisms | ||
| - and recommendation systems that involve collaborative filtering | ||
| - and personalized content distribution | ||
| - below is an expansion of the model incorporating these features | ||
| - and how this analysis can impact the modern ai industry | ||
| - short intro to [[truth machine]] mechanism | ||
| - pagerank in this context models the importance of [[particles]] made by [[neurons]] (nodes) based on their cryptographic token holdings (tokens of [[attention]] and [[will]]), their [[cyberlinks]] (edges), and the probability of random walks traversing these edges | ||
| - cryptographic tokens of [[attention]] and [[will]]: these tokens represent a form of stake (or voting power) that neurons possess. the greater the amount of attention a neuron holds, the more influence it exerts over the content and cyberlinks. the greater the amount of [[will]] the more cyberlinks [[neuron]] can do | ||
| - the weighted [[pagerank]] will update based on current token balances of neurons, with neurons possessing more tokens influencing the rankings of [[particles]] and [[cyberlinks]] more heavily | ||
| - ## groundbreaking vectors of graph analysis | ||
| - token-weighted centrality | ||
| - neurons connected to important ones, weighted by attention tokens | ||
| - gain higher centrality, similar to staking models | ||
| - impact: highlights key entities in content curation, influencing ai recommendations by prioritizing high-ranking nodes | ||
| - attention-driven content propagation | ||
| - random walker traverses the graph, biased by the cryptographic token distribution | ||
| - meaning content associated with high-stake neurons gets more attention | ||
| - this mechanism aligns with transformer models in ai | ||
| - e.g., attention heads in bert-like models | ||
| - where some tokens are given more weight or importance based on context | ||
| - impact: helps ai refine content discovery, with attention-rich neurons driving content propagation | ||
| - decay of token-based influence | ||
| - token influence decays over time, shifting neuron impact based on recency and relevance. | ||
| - impact: useful for ai models that prioritize recent trends, ensuring recommendations adapt dynamically. | ||
| - content distribution hotspots | ||
| - neurons with similar attention tokens form content-sharing communities, or “hotspots.” | ||
| - impact: helps ai identify key content creators and niche communities, improving collaborative filtering. | ||
| - token-driven authority and hubs | ||
| - hits algorithm differentiates content creators (hubs) from validators (authorities) based on token weight. | ||
| impact: aids ai models in distinguishing trusted content sources from general creators. | ||
| - temporal influence on learning | ||
| - time-series analysis of tokens and transactions predicts attention patterns | ||
| - and neuron behavior, similar to sequence prediction in ai | ||
| - impact: time-aware graph learning informs reinforcement learning and trend prediction in ai systems | ||
| - ## groundbreaking vectors in the modern ai industry | ||
| - decentralized ai learning | ||
| - by embedding attention-weighted pagerank in decentralized ai | ||
| - individual entities (neurons) could contribute to collaborative learning models | ||
| - the nodes with higher attention (more tokens) become more influential in shaping model training (akin to federated learning) | ||
| - this opens up possibilities for personalized ai models | ||
| - that reflect community-driven content recommendations | ||
| - based on decentralized token distribution | ||
| - improving the ai’s contextual relevance | ||
| - content recommendation systems | ||
| - token-weighted content propagation maps well to systems like netflix, youtube, or social media platforms | ||
| - where attention is the key driver of recommendation engines | ||
| - an ai-driven recommendation system based on token-weighted pagerank | ||
| - could dynamically learn from user behavior and engagement | ||
| - in ai, collaborative filtering models can be enhanced by taking into account not just the interaction frequency but also the weighted importance of each user or neuron, derived from their token balance and connections. | ||
| - explainable ai (xai) models | ||
| - understanding the weight of cryptographic tokens in determining pagerank and the influence of neurons on content can help make ai decisions more transparent | ||
| - the ai industry is moving toward explainable models | ||
| - and this analysis can reveal how much influence each neuron has on content curation | ||
| - token-weighted explanations of why certain content is recommended | ||
| - or ranked highly could be crucial in providing users with trustworthy ai recommendations | ||
| - ai in distributed systems and blockchain | ||
| - ai and blockchain convergence: with neurons representing public keys and attention-based tokens functioning as incentives, this model fits naturally within decentralized platforms | ||
| - ai models in such ecosystems can make better use of consensus mechanisms, [[ibc]] and reputation systems, similar to staking models in blockchain | ||
| - impact: ai systems running on blockchain can leverage these weighted graphs for predictive analytics, trust systems, and improving the efficiency of decentralized content curation or collaboration platforms | ||
| - ai for network security | ||
| - sybil attack detection: since attention tokens can be used to weight pagerank, | ||
| - neurons with disproportionately low or high tokens relative to their activity could be flagged for suspicious behavior | ||
| - this is crucial in ai systems focused on cybersecurity for decentralized platforms, where ensuring the authenticity of participants is critical | ||
| - ai models trained on such weighted graphs can automatically flag anomalies and potentially harmful nodes within the network | ||
| - conclusion | ||
| - by integrating token-weighted pagerank and random walks with cryptographic [[attention]] and [[will]] tokens | ||
| - the graph analysis derives new dimensions, especially for ai applications | ||
| - these groundbreaking vectors include attention-driven influence, community formation, content propagation, and the impact of weighted centrality | ||
| - this analysis fits modern ai industries, particularly in recommendation systems, decentralized learning, network security, and trust-based ai models |