Intelligence Augmentation: Human Factors in AI and Future of Work

The availability of parallel and distributed processing at a reasonable cost and the diversity of data sources have contributed to advanced developments in artificial intelligence (AI). These developments in the AI computing environment are not concomitant with changes in the social, legal, and political environment. While considering deploying AI, the deployment context and the end goal of human intelligence augmentation for that specific context have surfaced as significant factors for professionals, organizations, and society. In this research commentary, we highlight some important socio-technical aspects associated with recent growth in AI systems. We elaborate on the intricacies of human-machine interaction that form the foundation of augmented intelligence. We also highlight the ethical considerations that relate to these interactions and explain how augmented intelligence can play a key role in shaping the future of human work

Bivariate genetic modelling of the response to an oral glucose tolerance challenge: A gene x environment interaction approach

AIMS/HYPOTHESIS: Twin and family studies have shown the importance of genetic factors influencing fasting and 2 h glucose and insulin levels. However, the genetics of the physiological response to a glucose load has not been thoroughly investigated. METHODS: We studied 580 monozygotic and 1,937 dizygotic British female twins from the Twins UK Registry. The effects of genetic and environmental factors on fasting and 2 h glucose and insulin levels were estimated using univariate genetic modelling. Bivariate model fitting was used to investigate the glucose and insulin responses to a glucose load, i.e. an OGTT. RESULTS: The genetic effect on fasting and 2 h glucose and insulin levels ranged between 40% and 56% after adjustment for age and BMI. Exposure to a glucose load resulted in the emergence of novel genetic effects on 2 h glucose independent of the fasting level, accounting for about 55% of its heritability. For 2 h insulin, the effect of the same genes that already influenced fasting insulin was amplified by about 30%. CONCLUSIONS/INTERPRETATION: Exposure to a glucose challenge uncovers new genetic variance for glucose and amplifies the effects of genes that already influence the fasting insulin level. Finding the genes acting on 2 h glucose independently of fasting glucose may offer new aetiological insight into the risk of cardiovascular events and death from all causes

Genetic, environmental and stochastic factors in monozygotic twin discordance with a focus on epigenetic differences

PMCID: PMC3566971This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

De Novo Genesis of Enhancers in Vertebrates

Whole genome duplication in teleost fish reveals that a few changes in non-regulatory genomic sequences are a source for generating new enhancers

Sex-specific genetic effects influence variation in body composition

Aims/hypothesis: Despite well-known sex differences in body composition it is not known whether sex-specific genetic or environmental effects contribute to these differences. Methods: We assessed body composition in 2,506 individuals, from a young Dutch genetic isolate participating in the Erasmus Rucphen Family study, by dual-energy X-ray absorptiometry and anthropometry. We used variance decomposition procedures to partition variation of body composition into genetic and environmental components common to both sexes and to men and women separately and calculated the correlation between genetic components in men and women. Results: After accounting for age

Anthropometry, carbohydrate and lipid metabolism in the East Flanders Prospective Twin Survey: heritabilities

AIMS/HYPOTHESIS: We determined the genetic contribution of 18 anthropometric and metabolic risk factors of type 2 diabetes using a young healthy twin population. METHODS: Traits were measured in 240 monozygotic (MZ) and 138 dizygotic (DZ) twin pairs aged 18 to 34 years. Twins were recruited from the Belgian population-based East Flanders Prospective Twin Survey, which is characterised by its accurate zygosity determination and extensive collection of perinatal and placental data, including information on chorionicity. Heritability was estimated using structural equation modelling implemented in the Mx software package. RESULTS: Intra-pair correlations of the anthropometric and metabolic characteristics did not differ between MZ monochorionic and MZ dichorionic pairs; consequently heritabilities were estimated using the classical twin approach. For body mass, BMI and fat mass, quantitative sex differences were observed; genetic variance explained 84, 85 and 81% of the total variation in men and 74, 75 and 70% in women, respectively. Heritability estimates of the waist-to-hip ratio, sum of four skinfold thicknesses and lean body mass were 70, 74 and 81%, respectively. The heritability estimates of fasting glucose, fasting insulin, homeostasis model assessment of insulin resistance and beta cell function, as well as insulin-like growth factor binding protein-1 levels were 67, 49, 48, 62 and 47%, in that order. Finally, for total cholesterol, LDL-cholesterol, HDL-cholesterol, total cholesterol:HDL-cholesterol ratio, triacylglycerol, NEFA and leptin levels, genetic factors explained 75, 78, 76, 79, 58, 37 and 53% of the total variation, respectively. CONCLUSIONS/INTERPRETATION: Genetic factors explain the greater part of the variation in traits related to obesity, glucose intolerance/insulin resistance and dyslipidaemia