Viewing a response to: @dana-edwards/how-tauchain-and-the-exocortex-can-give-anyone-a-conscience-and-make-anyone-more-law-abiding
Great to read more about your exocortex idea again. I'm curious about this TauChain tech. I had a look at what they currently have and the roadmap, and it's very early days still. I can see how you think it could apply to your project though. Just on your specific question: "what if you could have set a bot to search the Internet for opportunities that resemble Bitcoin in 2008?" and specifically this: > what if you could go beyond mere alerts and look for code on Github, and certain individuals involved with it, and certain growth patterns? This could potentially be done. As you say the definition is very important. Do you have any ideas towards a workable definition?
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|---|---|
| permlink | re-dana-edwards-how-tauchain-and-the-exocortex-can-give-anyone-a-conscience-and-make-anyone-more-law-abiding-20180905t135058402z |
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Semantic search would allow for greater precision in the definition because you can specify in a way you cannot do with keyword search. I do think Tauchain or something like it is necessary for decentralization of the web. I do not think we should be required to rely on big companies to do our computation if we can rely on more fundamental peer to peer infrastructure. So like how at one point in time we required FTP servers until Bittorent? The same can be true with something like Tauchain for search. - https://en.wikipedia.org/wiki/Semantic_search
| author | dana-edwards |
|---|---|
| permlink | re-fourth-re-dana-edwards-how-tauchain-and-the-exocortex-can-give-anyone-a-conscience-and-make-anyone-more-law-abiding-20180905t180805726z |
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It's definitely interesting. Looking forward to move developments from Tauchain.
| author | fourth |
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<b>Semantic search</b> Semantic search seeks to improve search accuracy by understanding the searcher's intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results. Semantic search systems consider various points including context of search, location, intent, variation of words, synonyms, generalized and specialized queries, concept matching and natural language queries to provide relevant search results. Major web search engines like Google and Bing incorporate some elements of semantic search. In vertical search, LinkedIn publishes their semantic search approach to job search by recognizing and standardizing entities in both queries and documents, e.g., companies, titles and skills, then constructing various entity-awared features based on the entities.Guha et al.
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