scholarly journals Historical Thinking and the Human: Introduction

2020 ◽  
Vol 14 (3) ◽  
pp. 285-309
Author(s):  
Marek Tamm ◽  
Zoltán Boldizsár Simon

Abstract In recent years the age-old question “what is the human?” has acquired a new acuteness and novel dimensions. In introducing the special issue on “Historical Thinking and the Human”, this article argues that there are two main trends behind the contemporary “crisis of human”: ecological transformations (related to human-induced climate change and planetary environmental challenges), and technological ones (including advancements in human enhancement, biotechnology and artificial intelligence). After discussing the respective anthropocenic and technoscientific redefinitions of the human, the paper theorizes three elements in an emerging new historicity of the human: first, the move from a fixed category to a dynamic and indeterminate concept, considering the human as a lifeform in movement; second, the extent to which the human is conceived of in its relational dependence on various non-human agents, organic and non-organic; and third, the reconceptualization of the human not as one but as many, to comprehend that we cannot speak of human individuality in the classical biological sense. In the final part, the article addresses the consequences of the redefinition of the human for historical thinking. It makes the case for the need to elaborate a new notion of history – captured by the phrase “more-than-human history”, and attuned to an emerging planetary regime of historicity in which historical thinking becomes able to affirm multiple temporalities: digital, technoscientific, sociocultural, human, biological and anthropocenic. The article concludes by recognizing the necessity to venture into a new transdisciplinary knowledge economy, appropriate for making sense of the contemporary constellation of the entangled human, technological and natural worlds.

2020 ◽  
Author(s):  
Harmeet Singh Bakala ◽  
Gurjeet Singh ◽  
Puja Srivastava

Human society is at a turning point of its time as climate change is becoming more and more real and inevitable. From rising temperature, which undermines the food production, to melting glaciers, causing disastrous flooding and erosion, the global repercussions of climate change are unprecedented. Plant breeding has always played a pivotal role in human history by revolutionizing agriculture to feed the ever-growing population. It can rescue humankind from imminent threats to agriculture posed by weather fluctuations, rapidly evolving pests and limiting resources. Unlocking the repository of genetic diversity and extensive utilization of wild germplasm invariably is imperative to every crop improvement program. But recent advancements in genomics, high throughput phenomics, sequencing and breeding methodologies along with state-of-the-art genome-editing tools in integration with artificial intelligence open up new doors for accelerated climate-resilient crop improvement. Therefore, holistic smart breeding approaches can be promising way out to tackle climate change and develop better-adapted crop varieties.


Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.


2021 ◽  
pp. 1-3
Author(s):  
Anda David ◽  
Frédéric Docquier

How do weather shocks influence human mobility and poverty, and how will long-term climate change affect future migration over the course of the 21st century? These questions have gained unprecedented attention in public debates as global warming is already having severe impacts around the world, and prospects for the coming decades get worse. Low-latitude countries in general, and their agricultural areas in particular, have contributed the least to climate change but are the most adversely affected. The effect on people's voluntary and forced displacements is of major concern for both developed and developing countries. On 18 October 2019, Agence Française de Développement (AFD) and Luxembourg Institute of Socio-Economic Research (LISER) organized a workshop on Climate Migration with the aim of uncovering the mechanisms through which fast-onset variables (such as weather anomalies, storms, hurricanes, torrential rains, floods, landslides, etc.) and slow-onset variables (such as temperature trends, desertification, rising sea level, coastal erosion, etc.) influence both people's incentives to move and mobility constraints. This special issue gathers five papers prepared for this workshop, which shed light on (or predict) the effect of extreme weather shocks and long-term climate change on human mobility, and stress the implications for the development community.


2020 ◽  
Vol 54 (12) ◽  
pp. 942-947
Author(s):  
Pol Mac Aonghusa ◽  
Susan Michie

Abstract Background Artificial Intelligence (AI) is transforming the process of scientific research. AI, coupled with availability of large datasets and increasing computational power, is accelerating progress in areas such as genetics, climate change and astronomy [NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, Vancouver, Canada; Hausen R, Robertson BE. Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. Astrophys J Suppl Ser. 2020;248:20; Dias R, Torkamani A. AI in clinical and genomic diagnostics. Genome Med. 2019;11:70.]. The application of AI in behavioral science is still in its infancy and realizing the promise of AI requires adapting current practices. Purposes By using AI to synthesize and interpret behavior change intervention evaluation report findings at a scale beyond human capability, the HBCP seeks to improve the efficiency and effectiveness of research activities. We explore challenges facing AI adoption in behavioral science through the lens of lessons learned during the Human Behaviour-Change Project (HBCP). Methods The project used an iterative cycle of development and testing of AI algorithms. Using a corpus of published research reports of randomized controlled trials of behavioral interventions, behavioral science experts annotated occurrences of interventions and outcomes. AI algorithms were trained to recognize natural language patterns associated with interventions and outcomes from the expert human annotations. Once trained, the AI algorithms were used to predict outcomes for interventions that were checked by behavioral scientists. Results Intervention reports contain many items of information needing to be extracted and these are expressed in hugely variable and idiosyncratic language used in research reports to convey information makes developing algorithms to extract all the information with near perfect accuracy impractical. However, statistical matching algorithms combined with advanced machine learning approaches created reasonably accurate outcome predictions from incomplete data. Conclusions AI holds promise for achieving the goal of predicting outcomes of behavior change interventions, based on information that is automatically extracted from intervention evaluation reports. This information can be used to train knowledge systems using machine learning and reasoning algorithms.


2021 ◽  
Vol 13 (15) ◽  
pp. 2883
Author(s):  
Gwanggil Jeon

Remote sensing is a fundamental tool for comprehending the earth and supporting human–earth communications [...]


2021 ◽  
Vol 41 (1) ◽  
pp. 8-14
Author(s):  
Alexandra Luccioni ◽  
Victor Schmidt ◽  
Vahe Vardanyan ◽  
Yoshua Bengio ◽  
Theresa-Marie Rhyne

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