Analyzing Some Elements of Technological Singularity Using Regression Methods

Technological advancement has contributed immensely to human life and society. Technologies like industrial robots, artificial intelligence, and machine learning are advancing at a rapid pace. While the evolution of Artificial Intelligence has contributed significantly to the development of personal assistants, automated drones, smart home devices, etc., it has also raised questions about the much-anticipated point in the future where machines may develop intelligence that may be equal to or greater than humans, a term that is popularly known as Technological Singularity. Although technological singularity promises great benefits, past research works on Artificial Intelligence (AI) systems going rogue highlight the downside of Technological Singularity and assert that it may lead to catastrophic effects. Thus, there is a need to identify factors that contribute to technological advancement and may ultimately lead to Technological Singularity in the future. In this paper, we identify factors such as Number of scientific publications in Artificial Intelligence, Number of scientific publications in Machine Learning, Dynamic RAM (Random Access Memory) Price, Number of Transistors, and Speed of Computers’ Processors, and analyze their effects on Technological Singularity using Regression methods (Multiple Linear Regression and Simple Linear Regression). The predictive ability of the models has been validated using PRESS and k-fold cross-validation. Our study shows that academic advancement in AI and ML and Dynamic RAM prices contribute significantly to Technological Singularity. Investigating the factors would help researchers and industry experts comprehend what leads to Technological Singularity and, if needed, how to prevent undesirable outcomes.     

BISDBx: towards batch-incremental clustering for dynamic datasets using SNN-DBSCAN

Pattern Analysis and Applications - Many important applications such as recommender systems, e-commerce sites, web crawlers involve dynamic datasets. Dynamic datasets undergo frequent changes in...     

A new deep model for family and non-family photo identification

Multimedia Tools and Applications - Human trafficking is a global issue of the world and the problems related to human trafficking remain unsolved. This paper presents a new method for the...     

Bricks from Petroleum Effluent Treatment Plant Sludge: Properties and Environmental Characteristics

A voluminous and hazardous sludge containing a high amount of hydrocarbons and several trace metals is generated in petroleum oil field effluent treatment plants. The aim of this study was to utilize the sludge in preparing environmentally acceptable masonry bricks in a commercial brick plant. The effect of the sludge on the plasticity behavior of the brick raw mix was investigated. The addition of the sludge reduces the requirement of process water and fuel. The fired bricks meet all the requirements of the Indian Standard Specification. The bricks were subjected to toxicity characteristics leaching protocol leaching tests. Most of the toxic metals are fixed in the vitrification process and the leachates values meet the Environmental Protection Agency’s requirement for recycling of hazardous materials.     

Biosorptive uranium uptake by a Pseudomonas strain: characterization and equilibrium studies

The biosorptive uranium(VI) uptake capacity of live and lyophilized Pseudomonas cells was characterized in terms of equilibrium metal loading, effect of solution pH and possible interference by selected co‐ions. Uranium binding by the test biomass was rapid, achieving >90% sorption efficiency within 10 min of contact and the equilibrium was attained after 1 h. pH‐dependent uranium sorption was observed for both biomass types with the maximum being at pH 5.0. Metal uptake by live cells was not affected by culture age and the presence of an energy source or metabolic inhibitor. Sorption isotherm studies at a solution pH of either 3.5 or 5.0 indicated efficient and exceptionally high uranium loading by the test biomass, particularly at the higher pH level. At equilibrium, the lyophilized Pseudomonas exhibited a metal loading of 541 ± 34.21 mg g^?1 compared with a lower value by the live organisms (410 ± 25.93 mg g^?1). Experimental sorption data showing conformity to both Freundlich and Langmuir isotherm models indicate monolayered uranium binding by the test biomass. In bimetallic combinations a significant interference in uranium loading was offered by cations such as thorium(IV), iron(II and III), aluminium(III) and copper(II), while the anions tested, except carbonate, were ineffective. Uranium sorption studies in the presence of a range of Fe³⁺ and SO₄^2? concentrations indicate a strong inhibition (80%) by the former at an equimolar ratio while more than 70% adsorption efficiency was retained even at a high sulfate level (30 000 mg dm^?3). Overall data indicate the suitability of the Pseudomonas sp biomass in developing a biosorbent for uranium removal from aqueous waste streams. © 2001 Society of Chemical Industry