Deepfakes: trick or treat?

Although manipulations of visual and auditory media are as old as the media themselves, the recent entrance of deepfakes has marked a turning point in the creation of fake content. Powered by latest technological advances in AI and machine learning, they offer automated procedures to create fake content that is harder and harder to detect to human observers. The possibilities to deceive are endless, including manipulated pictures, videos and audio, that will have large societal impact. Because of this, organizations need to understand the inner workings of the underlying techniques, as well as their strengths and limitations. This article provides a working definition of deepfakes together with an overview of the underlying technology. We classify different deepfake types: photo (face- and body-swapping), audio (voice-swapping, text to speech), video (face-swapping, face-morphing, full body puppetry) and audio and video (lip-synching), and identify risks and opportunities to help organizations think about the future of deepfakes. Finally, we propose the R.E.A.L. framework to manage deepfake risks: Record original content to assure deniability, Expose deepfakes early, Advocate for legal protection and Leverage trust to counter credulity. Following these principles, we hope that our society can be more prepared to counter the deepfake tricks as we appreciate its treats

Making sense of text: artificial intelligence-enabled content analysis

Purpose: The purpose of this paper is to introduce, apply and compare how artificial intelligence (AI), and specifically the IBM Watson system, can be used for content analysis in marketing research relative to manual and computer-aided (non-AI) approaches to content analysis. Design/methodology/approach: To illustrate the use of AI-enabled content analysis, this paper examines the text of leadership speeches, content related to organizational brand. The process and results of using AI are compared to manual and computer-aided approaches by using three performance factors for content analysis: reliability, validity and efficiency. Findings: Relative to manual and computer-aided approaches, AI-enabled content analysis provides clear advantages with high reliability, high validity and moderate efficiency. Research limitations/implications: This paper offers three contributions. First, it highlights the continued importance of the content analysis research method, particularly with the explosive growth of natural language-based user-generated content. Second, it provides a road map of how to use AI-enabled content analysis. Third, it applies and compares AI-enabled content analysis to manual and computer-aided, using leadership speeches. Practical implications: For each of the three approaches, nine steps are outlined and described to allow for replicability of this study. The advantages and disadvantages of using AI for content analysis are discussed. Together these are intended to motivate and guide researchers to apply and develop AI-enabled content analysis for research in marketing and other disciplines. Originality/value: To the best of the authors' knowledge, this paper is among the first to introduce, apply and compare how AI can be used for content analysis

From photos to sketches-how humans and deep neural networks process objects across different levels of visual abstraction

Line drawings convey meaning with just a few strokes. Despite strong simplifications, humans can recognize objects depicted in such abstracted images without effort. To what degree do deep convolutional neural networks (CNNs) mirror this human ability to generalize to abstracted object images? While CNNs trained on natural images have been shown to exhibit poor classification performance on drawings, other work has demonstrated highly similar latent representations in the networks for abstracted and natural images. Here, we address these seemingly conflicting findings by analyzing the activation patterns of a CNN trained on natural images across a set of photographs, drawings, and sketches of the same objects and comparing them to human behavior. We find a highly similar representational structure across levels of visual abstraction in early and intermediate layers of the network. This similarity, however, does not translate to later stages in the network, resulting in low classification performance for drawings and sketches. We identified that texture bias in CNNs contributes to the dissimilar representational structure in late layers and the poor performance on drawings. Finally, by fine-tuning late network layers with object drawings, we show that performance can be largely restored, demonstrating the general utility of features learned on natural images in early and intermediate layers for the recognition of drawings. In conclusion, generalization to abstracted images, such as drawings, seems to be an emergent property of CNNs trained on natural images, which is, however, suppressed by domain-related biases that arise during later processing stages in the network

Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision.

Deep feedforward neural network models of vision dominate in both computational neuroscience and engineering. The primate visual system, by contrast, contains abundant recurrent connections. Recurrent signal flow enables recycling of limited computational resources over time, and so might boost the performance of a physically finite brain or model. Here we show: (1) Recurrent convolutional neural network models outperform feedforward convolutional models matched in their number of parameters in large-scale visual recognition tasks on natural images. (2) Setting a confidence threshold, at which recurrent computations terminate and a decision is made, enables flexible trading of speed for accuracy. At a given confidence threshold, the model expends more time and energy on images that are harder to recognise, without requiring additional parameters for deeper computations. (3) The recurrent model's reaction time for an image predicts the human reaction time for the same image better than several parameter-matched and state-of-the-art feedforward models. (4) Across confidence thresholds, the recurrent model emulates the behaviour of feedforward control models in that it achieves the same accuracy at approximately the same computational cost (mean number of floating-point operations). However, the recurrent model can be run longer (higher confidence threshold) and then outperforms parameter-matched feedforward comparison models. These results suggest that recurrent connectivity, a hallmark of biological visual systems, may be essential for understanding the accuracy, flexibility, and dynamics of human visual recognition

Oxfordian microbial laminites from La Manga Formation, Neuquén Basin, Argentina: Remarkable nanobacteria preservation

Exceptionally preserved stromatolites have been found in the shallow marine carbonate facies of the Callovian-Oxfordian La Manga Formation, in the Neuquén Basin (Argentina). The stromatolites exhibit planar and crinkle lamination, often disrupted by sheet-cracks, mudcracks, and fenestral structures, which indicate periodic subaerial exposure. These and other evidences suggest that these stromatolites grew in low energy upper intertidal to lower supratidal environments. They consist of fine micrite/microsparite crystal fabrics (with a remarkable lack of allochems) that define submillimiter alternations of dense laminae. Extensive SEM examinations of polished samples of the stromatolites reveal exceptional preservation of rod-shaped bacteria, coccoid like microorganisms, and abundant aggregates of framboidal pyrite. The rod-like bacteria consists of a network of irregular distributed filaments, which range from 150 nm to an uncommon 640 nm in length; diameters range from 54 nm to 90 nm. Subspherical bodies range in size between 70 and 89 nm. The presence of abundant framboidal pyrites is interpreted as the result of the metabolic activity of sulfate–reducing bacteria and decay of organic matter.Se describen estromatolitos excepcionalmente preservados en facies carbonáticas marinas someras en la Formación La Manga, de edad Calloviense-Oxfordiense, en la Cuenca de Neuquén (Argentina). Los estromatolitos muestran una laminación tanto planar como ondulada, frecuentemente alterada por estructuras de tipo fenestral, y sheet y mud-crack, que indican etapas de exposición subaérea. Estas y otras características sugieren que estos estromatolitos crecieron en ambientes de baja energía, intermareales altos y supramareales. Están constituidos por una fábrica de micrita-microesparita (con una destacada ausencia de aloquímicos) que constituyen alternancias submilimétricas de laminación densa. Estudios detallados con SEM sobre muestras pulidas revelan una preservación excepcional con morfología alargada, microorganismos tipo cocoide, y abundantes agregados de pirita framboidal. Las bacterias con morfologías alargadas están constituyendo una red irregularmente distribuida de filamentos que oscilan en tamaño desde 150 nm hasta, excepcionalmente, 640 nm en longitud; el diámetro oscila entre 50 nm y 90 nm. Las morfologías subesféricas oscilan entre 70 nm y 89 nm. La presencia de abundantes piritas framboidales es interpretada como resultado de una actividad metabólica de bacterias sulfato-reductoras y la descomposición de materia orgánica

Palaeoenvironmental significance of middle Oxfordian deep marine deposits from La Manga Formation, Neuquén Basin, Argentina

The Callovian-Oxfordian of the Neuquén Basin (Argentina) is characterized by an extensive marine carbonate system (La Manga Formation) with a predominance of shallow and middle ramp deposits, although locally in tectonically controlled settings, deeper deposits also formed. These middle Oxfordian deeper deposits consist of mudstone-wackestone carbonates alternating with black shales and show marked centimetre-scale rhythmicity, probably controlled by the Earth’s orbit parameters, mainly precession and eccentricity. The present study was designed to examine these deposits in terms of their sedimentology, geochemistry, and small-scale stratigraphy in Arroyo Los Blancos, southern Mendoza Province, where they are exceptionally well exposed and preserved. Results indicate that the sediments were deposited in an anoxic to dysoxic, relatively deep, sea-floor environment as revealed by the presence of: a) organic-rich shales, b) undisrupted lamination in most facies, c) pyrite framboids, and d) absence or scarcity of trace fossils and benthic fauna. Thin beds of graded wackestone-packstone and accumulations of thin shells of Bositra bivalves indicate sporadic reworking of the sea floor by weak currents. Organic petrology, Rock-Eval pyrolysis and thermal maturity indicators were used to characterize TOC and different types of organic matter. Low pyrolysis S yields along with low hydrogen indices suggest poor kerogen convertibility. Vitrinite reflectance (%Ro) ranged from 2.39 to 2.97 with an average of 2.70. The thermal alteration index (TAI) was 4+(5), indicating overmaturity. A tectono-sedimentary model is proposed for these deposits. According to this model, the relatively deep, organic-matter rich facies of La Manga Formation would have been deposited in the deepest zones of topographic lows controlled by tilting and differential subsidence of tectonic blocks bounded by normal faults.These faults were probably inherited from previous extensional tectonics of Late Triassic - Early Jurassic age.Facultad de Ciencias Naturales y Muse

Recurrence is required to capture the representational dynamics of the human visual system.

The human visual system is an intricate network of brain regions that enables us to recognize the world around us. Despite its abundant lateral and feedback connections, object processing is commonly viewed and studied as a feedforward process. Here, we measure and model the rapid representational dynamics across multiple stages of the human ventral stream using time-resolved brain imaging and deep learning. We observe substantial representational transformations during the first 300 ms of processing within and across ventral-stream regions. Categorical divisions emerge in sequence, cascading forward and in reverse across regions, and Granger causality analysis suggests bidirectional information flow between regions. Finally, recurrent deep neural network models clearly outperform parameter-matched feedforward models in terms of their ability to capture the multiregion cortical dynamics. Targeted virtual cooling experiments on the recurrent deep network models further substantiate the importance of their lateral and top-down connections. These results establish that recurrent models are required to understand information processing in the human ventral stream

A systematic literature review of the use of social media for business process management

In today’s expansion of new technologies, innovation is found necessary for organizations to be up to date with the latest management trends. Although organizations are increasingly using new technologies, opportunities still exist to achieve the nowadays essential omnichannel management strategy. More precisely, social media are opening a path for benefiting more from an organization’s process orientation. However, social media strategies are still an under-investigated field, especially when it comes to the research of social media use for the management and improvement of business processes or the internal way of working in organizations. By classifying a variety of articles, this study explores the evolution of social media implementation within the BPM discipline. We also provide avenues for future research and strategic implications for practitioners to use social media more comprehensively

Palaeoenvironmental significance of middle Oxfordian deep marine deposits from La Manga Formation, Neuquén Basin, Argentina

[EN] The Callovian-Oxfordian of the Neuquén Basin (Argentina) is characterized by an extensive marine carbonate system (La Manga Formation) with a predominance of shallow and middle ramp deposits, although locally in tectonically controlled settings, deeper deposits also formed. These middle Oxfordian deeper deposits consist of mudstone-wackestone carbonates alternating with black shales and show marked centimetre-scale rhythmicity, probably controlled by the Earth’s orbit parameters, mainly precession and eccentricity. The present study was designed to examine these deposits in terms of their sedimentology, geochemistry, and small-scale stratigraphy in Arroyo Los Blancos, southern Mendoza Province, where they are exceptionally well exposed and preserved. Results indicate that the sediments were deposited in an anoxic to dysoxic, relatively deep, sea-floor environment as revealed by the presence of: a) organic-rich shales, b) undisrupted lamination in most facies, c) pyrite framboids, and d) absence or scarcity of trace fossils and benthic fauna. Thin beds of graded wackestone-packstone and accumulations of thin shells of Bositra bivalves indicate sporadic reworking of the sea floor by weak currents. Organic petrology, Rock-Eval pyrolysis and thermal maturity indicators were used to characterize TOC and different types of organic matter. Low pyrolysis S yields along with low hydrogen indices suggest poor kerogen convertibility. Vitrinite reflectance (%Ro) ranged from 2.39 to 2.97 with an average of 2.70. The thermal alteration index (TAI) was 4+(5), indicating overmaturity. A tectono-sedimentary model is proposed for these deposits. According to this model, the relatively deep, organic-matter rich facies of La Manga Formation would have been deposited in the deepest zones of topographic lows controlled by tilting and differential subsidence of tectonic blocks bounded by normal faults.These faults were probably inherited from previous extensional tectonics of Late Triassic - Early Jurassic age.[ES] El Calloviense-Oxfordiense en el norte de la Cuenca Neuquina (Argentina) estuvo caracterizado por el desarrollo de un amplio sistema de rampa carbonatada (Formación La Manga) en el que predominaron los depósitos de rampa media y somera aunque localmente, y debido a control tectónico, pudieron acumularse depósitos más profundos. Estos depósitos, de edad Oxfordiense medio, están expuestos y preservados excepcionalmente en la zona de Arroyo Los Blancos, al sur de la provincia de Mendoza, y su estudio sedimentológico, geoquímico y estratigráfico representan el objetivo de este trabajo. Consisten en carbonatos (mudstone-wackestone) con acumulaciones de finas conchas de bivalvos de tipo Bositra que indican retrabajamientos esporádicos del sustrato marino debido a corrientes de baja intensidad. Se han caracterizado diferentes tipos de materia orgánica y TOC mediante petrología orgánica, Rock-Eval Pyrolisis e indicadores de madurez térmica. Los bajos valores en el campo del S unido al también bajo índice de H sugieren una baja convertibilidad de Kerógeno. La reflectancia de la vitrinita (%Ro) tiene un rango que oscila entre 2,39 y 2,97, con una media de 2,70, mientras que el índice de alteración térmico (TAI) es de 4+(5) indicando sobremaduración. Se propone también un modelo tectono-sedimentario para estos depósitos. En este modelo, estas facies relativamente profundas y ricas en materia orgánica de la Formación La Manga estarían depositadas en la parte más deprimida de bajos topográficos controlados por una subsidencia diferencial y de pulsos en bloques marcados por fallas normales heredadas probablemente de una tectónica extensional durante el Triásico Superior-Jurásico Inferior.Peer reviewe

Individual differences among deep neural network models.

Deep neural networks (DNNs) excel at visual recognition tasks and are increasingly used as a modeling framework for neural computations in the primate brain. Just like individual brains, each DNN has a unique connectivity and representational profile. Here, we investigate individual differences among DNN instances that arise from varying only the random initialization of the network weights. Using tools typically employed in systems neuroscience, we show that this minimal change in initial conditions prior to training leads to substantial differences in intermediate and higher-level network representations despite similar network-level classification performance. We locate the origins of the effects in an under-constrained alignment of category exemplars, rather than misaligned category centroids. These results call into question the common practice of using single networks to derive insights into neural information processing and rather suggest that computational neuroscientists working with DNNs may need to base their inferences on groups of multiple network instances