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Pregled bibliografske jedinice broj: 1046349

AI-Completeness: Using Deep Learning to Eliminate the Human Factor


Šekrst, Kristina
AI-Completeness: Using Deep Learning to Eliminate the Human Factor // Guide to Deep Learning Basics / Skansi, Sandro (ur.).
Cham: Springer, 2020. str. 117-130 doi:10.1007/978-3-030-37591-1_11


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Naslov
AI-Completeness: Using Deep Learning to Eliminate the Human Factor

Autori
Šekrst, Kristina

Vrsta, podvrsta i kategorija rada
Poglavlja u knjigama, ostalo

Knjiga
Guide to Deep Learning Basics

Urednik/ci
Skansi, Sandro

Izdavač
Springer

Grad
Cham

Godina
2020

Raspon stranica
117-130

ISBN
978-3-030-37590-4

Ključne riječi
AI-completeness, computational complexity, AGI, artificial general intelligence, strong AI, deep learning, machine learning

Sažetak
Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A solution for the so-called NP-complete problems will also be a solution for any other such problems.Its artificial-intelligence analogue is the class of AI-complete problems, for which a complete mathematical formalization still does not exist.In this chapter we will focus on analysing computational classes to better understand possible formalizations of AI-complete problems, and to see whether a universal algorithm, such as a Turing test, could exist for all AI-complete problems. In order to better observe how modern computer science tries to deal with computational complexity issues, we present several different deep-learning strategies involving optimization methods to see that the inability to exactly solve a problem from a higher order computational class does not mean there is not a satisfactory solution using state-of-the-art machine-learning techniques. Such methods are compared to philosophical issues and psychological research regarding human abilities of solving analogous NP-complete problems, to fortify the claim that we do not need to have an exact and correct way of solving AI-complete problems to nevertheless possibly achieve the notion of strong AI.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Računarstvo, Filozofija



POVEZANOST RADA


Citiraj ovu publikaciju

Šekrst, Kristina
AI-Completeness: Using Deep Learning to Eliminate the Human Factor // Guide to Deep Learning Basics / Skansi, Sandro (ur.).
Cham: Springer, 2020. str. 117-130 doi:10.1007/978-3-030-37591-1_11
Šekrst, K. (2020) AI-Completeness: Using Deep Learning to Eliminate the Human Factor. U: Skansi, S. (ur.) Guide to Deep Learning Basics. Cham, Springer, str. 117-130 doi:10.1007/978-3-030-37591-1_11.
@inbook{inbook, author = {\v{S}ekrst, K.}, editor = {Skansi, S.}, year = {2020}, pages = {117-130}, DOI = {10.1007/978-3-030-37591-1\_11}, keywords = {AI-completeness, computational complexity, AGI, artificial general intelligence, strong AI, deep learning, machine learning}, doi = {10.1007/978-3-030-37591-1\_11}, isbn = {978-3-030-37590-4}, title = {AI-Completeness: Using Deep Learning to Eliminate the Human Factor}, keyword = {AI-completeness, computational complexity, AGI, artificial general intelligence, strong AI, deep learning, machine learning}, publisher = {Springer}, publisherplace = {Cham} }

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