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Holistic generational offsets: Fostering a primitive online abstraction for human vs. machine cognition

dc.contributor.authorD'Souza, Shaun
dc.contributor.authorMudge, Trevor
dc.date.accessioned2024-06-21T18:33:17Z
dc.date.available2024-06-21T18:33:17Z
dc.date.issued2024-06-21
dc.identifier.urihttps://hdl.handle.net/2027.42/193904en
dc.description.abstractWe propose a unified architecture for next generation cognitive, low cost, mobile internet. The end user platform is able to scale as per the application and network requirements. It takes computing out of the data center and into end user platform. Internet enables open standards, accessible computing and applications programmability on a commodity platform. The architecture is a super-set to present day infrastructure web computing. The Java virtual machine (JVM) derives from the stack architecture. Applications can be developed and deployed on a multitude of host platforms. O(1) <-> O(N). Computing and the internet today are more accessible and available to the larger community. Machine learning has made extensive advances with the availability of modern computing. It is used widely in NLP, Computer Vision, Deep learning and AI. A prototype device for mobile could contain N compute and N MB of memory.en_US
dc.language.isoen_USen_US
dc.subjectMobileen_US
dc.subjectAIen_US
dc.subjectCognitiveen_US
dc.subjectServeren_US
dc.subjectInterneten_US
dc.titleHolistic generational offsets: Fostering a primitive online abstraction for human vs. machine cognitionen_US
dc.typeTechnical Reporten_US
dc.subject.hlbsecondlevelComputer Science
dc.subject.hlbsecondlevelElectrical Engineering
dc.subject.hlbtoplevelEngineering
dc.contributor.affiliationumElectrical Engineering and Computer Science, Department ofen_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/193904/3/Fostering_a_primitive_online_abstraction_for_human_vs._machine_cognition1.pdfen
dc.identifier.doihttps://dx.doi.org/10.7302/23386
dc.description.depositorSELFen_US
dc.working.doi10.7302/23386en_US
dc.owningcollnameElectrical Engineering and Computer Science, Department of (EECS)


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