Modern Machine and Deep Learning Systems as a way to achieve Man-Computer Symbiosis
Chirag Gupta

TL;DR
This paper evaluates how modern machine learning and deep learning systems align with J.C.R. Licklider's vision of Man-Computer Symbiosis, emphasizing their potential to fulfill the original criteria and achieve Artificial General Intelligence.
Contribution
It demonstrates that deep learning systems are the best candidates to realize true Man-Computer Symbiosis as envisioned by Licklider, fulfilling historical criteria with modern examples.
Findings
Deep learning systems meet key symbiosis criteria.
Deep neural networks show promise for Artificial General Intelligence.
Modern systems are closer to Licklider's vision than traditional algorithms.
Abstract
Man-Computer Symbiosis (MCS) was originally envisioned by the famous computer pioneer J.C.R. Licklider in 1960, as a logical evolution of the then inchoate relationship between computer and humans. In his paper, Licklider provided a set of criteria by which to judge if a Man-Computer System is a symbiotic one, and also provided some predictions about such systems in the near and far future. Since then, innovations in computer networks and the invention of the Internet were major developments towards that end. However, with most systems based on conventional logical algorithms, many aspects of Licklider's MCS remained unfulfilled. This paper explores the extent to which modern machine learning systems in general, and deep learning ones in particular best exemplify MCS systems, and why they are the prime contenders to achieve a true Man-Computer Symbiosis as described by Licklider in his…
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Taxonomy
TopicsEconomic and Technological Systems Analysis · Engineering Education and Technology · Scientific Research and Philosophical Inquiry
