Umjetna opća inteligencija

Osnovni cilj ovoga rada je prikazati glavne teoretske odrednice koje razlikuju umjetnu opću inteligenciju od ostatka tradicionalne umjetne inteligencije, a naglasak je na problemu definiranja pojma ljudske ili opće inteligencije. Također, u narednim poglavljima biti će prikazani osnovni pristupi u izradi takvih sustava s fokusom na njihovim prednostima i manama sa stajališta zahtjeva umjetne opće inteligencije. Bitno je napomenuti kako je suština ovoga rada usmjerena na razne konceptualne i praktične prepreke u ostvarenju ove ideje

Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials

© 2016 Taylor & FrancisWell-known analytical equations for predicting permeability are generally reported to overestimate this important property of porous media. In this work, more robust models developed from statistical (multivariable regression) and Artificial Neural Network (ANN) methods utilised additional particle characteristics [‘fines ratio’ (x50/x10) and particle shape] that are not found in traditional analytical equations. Using data from experiments and literature, model performance analyses with average absolute error (AAE) showed error of ~40% for the analytical models (Kozeny–Carman and Happel–Brenner). This error reduces to 9% with ANN model. This work establishes superiority of the new models, using experiments and mathematical techniques

Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials

Well-known analytical equations for predicting permeability are generally reported to overestimate this important property of porous media. In this work, more robust models developed from statistical (multivariable regression) and Artificial Neural Network (ANN) methods utilised additional particle characteristics [‘fines ratio’ (x50/x10) and particle shape] that are not found in traditional analytical equations. Using data from experiments and literature, model performance analyses with average absolute error (AAE) showed error of ~40% for the analytical models (Kozeny–Carman and Happel–Brenner). This error reduces to 9% with ANN model. This work establishes superiority of the new models, using experiments and mathematical techniques

Special Issue "On Defining Artificial Intelligence"—Commentaries and Author's Response

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