Labour, Energy, and Information as Historical Configurations

The essay contributes to the debate on the role of metrics in geoanthropology. It argues that the use of the energy metric in the study of the Anthropocene among other phenomena should be seen in its relation to the metrology of labour and productivity that originated in the industrial age. In order to clarify this genealogical question, the essay extends the method of ‘historical metrology’ (Kula) to the notion of energy and, in addition, to the notion of information, that can be understood in its own as a metric of knowledge, mental labour, communication and cooperation. In illuminating the nexus between the abstractions of political economy and technoscience, the essay stresses specifically the role of machines (such as the steam engine and telegraph) as ‘epistemic mediators’ (Wise). The essay concludes by advocating for the inclusion of political metrology in the necessary toolbox and ‘geopraxis’ (Omodeo) of the Anthropocene. Keywords: Anthropocene, Geoanthropology, History of Metrology, Political Econom

How a Machine Learns and Fails – A Grammar of Error for Artificial Intelligence.

Trabajando en la convergencia entre las humanidades y las ciencias de la computación, este texto pretende esbozar una gramática general del aprendizaje automático y proporcionar sistemáticamente una visión general de sus límites, aproximaciones, sesgos, errores, falacias y vulnerabilidades. Se conserva el término convencional de Inteligencia Artificial aunque técnicamente hablando, sería más preciso llamarla aprendizaje automático o estadística computacional, pero estos términos no serían atractivos para las empresas, las universidades y el mercado del arte. Se hace una revisión de las limitaciones que afectan a la IA como técnica matemática y cultural, destacando el papel del error en la definición de la inteligencia en general. Se describe al aprendizaje automático como compuesto por tres partes: conjunto de datos de entrenamiento, algoritmo estadístico y aplicación del modelo (como clasificación o predicción) y se distinguen tres tipos de sesgos: del mundo, de los datos y del algoritmo. Se sostiene que los límites lógicos de los modelos estadísticos producen o amplifican el sesgo (que a menudo ya está presente en los conjuntos de datos de entrenamiento) y provoca errores de clasificación y predicción. Por otro lado, el grado de compresión de la información por parte de los modelos estadísticos utilizados en el aprendizaje automático provoca una pérdida de información que se traduce en una pérdida de diversidad social y cultural. En definitiva, el principal efecto del aprendizaje automático en el conjunto de la sociedad es la normalización cultural y social. Existe un grado de mitificación y sesgo social en torno a sus construcciones matemáticas, donde la Inteligencia Artificial ha inaugurado la era de la ciencia ficción estadística.Working at the convergence between the humanities and computer science, this text aims to outline a general grammar of machine learning and systematically provide an overview of its limits, approaches, biases, errors, fallacies and vulnerabilities. The conventional term Artificial Intelligence is retained although technically speaking, it would be more accurate to call it machine learning or computational statistics, but these terms would not be attractive to companies, universities and the art market. A review is made of the limitations affecting AI as a mathematical and cultural technique, highlighting the role of error in the definition of intelligence in general. Machine learning is described as consisting of three parts: training data set, statistical algorithm and model application (as classification or prediction) and three types of biases are distinguished: world, data and algorithm. It is argued that the logical limits of statistical models produce or amplify bias (which is often already present in the training data sets) and cause classification and prediction errors. On the other hand, the degree of information compression by the statistical models used in machine learning causes a loss of information that results in a loss of social and cultural diversity. In short, the main effect of machine learning on society as a whole is cultural and social normalization. There is a degree of mythologizing and social bias around its mathematical constructs, where Artificial Intelligence has inaugurated the era of statistical science fiction.El presente artículo es una traducción de Pasquinelli, Matteo (2019). "How a Machine Learns and Fails – A Grammar of Error for Artificial Intelligence". Journal of Digital Cultures 5 (Spectres of AI). La traducción fue autorizada por el autor y realizada por parte del Equipo Editorial de Revista Hipertextos: Emilio Cafassi, Carolina Monti, Hernán Peckaitis y Graciana Zarauza.Facultad de Trabajo Socia

El Nooscopio de manifiesto: La inteligencia artificial como instrumento de extractivismo del conocimiento

El Nooscopio es una cartografía de los límites de la inteligencia artificial, que pretende ser una provocación tanto para la informática como para las humanidades. Cualquier mapa es una perspectiva parcial, una forma de provocar debate, y el propósito principal de este mapa es desafiar las mistificaciones de la inteligencia artificial

Como uma máquina aprende e falha – uma gramática de erro para inteligência artificial

Working at the convergence between the humanities and computer science, this text aims to outline a general grammar of machine learning and systematically provide an overview of its limits, approaches, biases, errors, fallacies and vulnerabilities. The conventional term Artificial Intelligence is retained although technically speaking, it would be more accurate to call it machine learning or computational statistics, but these terms would not be attractive to companies, universities and the art market. A review is made of the limitations affecting AI as a mathematical and cultural technique, highlighting the role of error in the definition of intelligence in general. Machine learning is described as consisting of three parts: training data set, statistical algorithm and model application (as classification or prediction) and three types of biases are distinguished: world, data and algorithm. It is argued that the logical limits of statistical models produce or amplify bias (which is often already present in the training data sets) and cause classification and prediction errors. On the other hand, the degree of information compression by the statistical models used in machine learning causes a loss of information that results in a loss of social and cultural diversity. In short, the main effect of machine learning on society as a whole is cultural and social normalization. There is a degree of mythologizing and social bias around its mathematical constructs, where Artificial Intelligence has inaugurated the era of statistical science fiction.Trabajando en la convergencia entre las humanidades y las ciencias de la computación, este texto pretende esbozar una gramática general del aprendizaje automático y proporcionar sistemáticamente una visión general de sus límites, aproximaciones, sesgos, errores, falacias y vulnerabilidades. Se conserva el término convencional de Inteligencia Artificial aunque técnicamente hablando, sería más preciso llamarla aprendizaje automático o estadística computacional, pero estos términos no serían atractivos para las empresas, las universidades y el mercado del arte. Se hace una revisión de las limitaciones que afectan a la IA como técnica matemática y cultural, destacando el papel del error en la definición de la inteligencia en general. Se describe al aprendizaje automático como compuesto por tres partes: conjunto de datos de entrenamiento, algoritmo estadístico y aplicación del modelo (como clasificación o predicción) y se distinguen tres tipos de sesgos: del mundo, de los datos y del algoritmo. Se sostiene que los límites lógicos de los modelos estadísticos producen o amplifican el sesgo (que a menudo ya está presente en los conjuntos de datos de entrenamiento) y provoca errores de clasificación y predicción. Por otro lado, el grado de compresión de la información por parte de los modelos estadísticos utilizados en el aprendizaje automático provoca una pérdida de información que se traduce en una pérdida de diversidad social y cultural. En definitiva, el principal efecto del aprendizaje automático en el conjunto de la sociedad es la normalización cultural y social. Existe un grado de mitificación y sesgo social en torno a sus construcciones matemáticas, donde la Inteligencia Artificial ha inaugurado la era de la ciencia ficción estadística.Trabalhando na convergência entre as ciências humanas e a informática, este texto visa delinear uma gramática geral de aprendizagem de máquinas e fornecer sistematicamente uma visão geral de seus limites, aproximações, enviesamentos, erros, falácias e vulnerabilidades. O termo convencional Inteligência Artificial é mantido, embora tecnicamente falando, seria mais preciso chamá-lo de aprendizagem mecânica ou estatística computacional, mas estes termos não seriam atraentes para as empresas, universidades e o mercado de arte. É feita uma revisão das limitações que afetam a IA como uma técnica matemática e cultural, destacando o papel do erro na definição da inteligência em geral. O aprendizado da máquina é descrito como consistindo de três partes: conjunto de dados de treinamento, algoritmo estatístico e aplicação do modelo (como classificação ou previsão) e três tipos de vieses são distinguidos: mundo, dados e algoritmo. Argumenta-se que os limites lógicos dos modelos estatísticos produzem ou amplificam o viés (que freqüentemente já está presente nos conjuntos de dados de treinamento) e levam a erros de classificação e previsão. Por outro lado, o grau de compressão da informação por modelos estatísticos utilizados na aprendizagem de máquinas causa uma perda de informação que resulta em uma perda de diversidade social e cultural. Em última análise, o principal efeito da aprendizagem mecânica na sociedade como um todo é a normalização cultural e social. Há um certo grau de mitologia e preconceito social em torno de suas construções matemáticas, onde a Inteligência Artificial deu início à era da ficção científica estatística

Histological findings of diabetic kidneys transplanted in non-diabetic recipients: a case series

Background Diabetic donors are recognized as a reliable source of organs, although the discard rate of kidneys is still high. Few data are available on the histological evolution of these organs especially on kidneys transplanted into non-diabetic patients who remain euglycemic. Methods We describe the histological evolution of ten kidney biopsies performed on non-diabetic recipients of diabetic donors. Results Mean donor age was 69 +/- 7 years, 60% were males. Two donors were treated with insulin, eight with oral antidiabetic drugs. Mean recipient age was 59.9 +/- 7 years, 70% were males. The pre-existing diabetic lesions identified in the pre-implantation biopsies, encompassed all histological classes, and were associated with mild IF/TA and vascular damages. The median follow-up was 59.5 [IQR 32.5-99.0] months; at follow-up, 40% of cases did not change histologic classification, two patients with class IIb downgraded to IIa or I and one with class III downgraded to IIb. Conversely, three cases showed a worsening, from class 0 to I, I to IIb or from IIa to IIb. We also observed a moderate evolution of IF/TA and vascular damages. At follow-up visit, estimated GFR was stable (50.7 mL/min vs. 54.8 at baseline) and proteinuria was mild (51.1 +/- 78.6 mg/day). Conclusions Kidneys from diabetic donors show variable evolution of the histologic features of diabetic nephropathy after transplant. This variability may be associated to recipients characteristics such as euglycemic milieu, in case of improvement, or obesity and hypertension, in case of worsening of histologic lesions

Carriers of ADAMTS13 Rare Variants Are at High Risk of Life-Threatening COVID-19

Thrombosis of small and large vessels is reported as a key player in COVID-19 severity. However, host genetic determinants of this susceptibility are still unclear. Congenital Thrombotic Thrombocytopenic Purpura is a severe autosomal recessive disorder characterized by uncleaved ultra-large vWF and thrombotic microangiopathy, frequently triggered by infections. Carriers are reported to be asymptomatic. Exome analysis of about 3000 SARS-CoV-2 infected subjects of different severities, belonging to the GEN-COVID cohort, revealed the specific role of vWF cleaving enzyme ADAMTS13 (A disintegrin-like and metalloprotease with thrombospondin type 1 motif, 13). We report here that ultra-rare variants in a heterozygous state lead to a rare form of COVID-19 characterized by hyper-inflammation signs, which segregates in families as an autosomal dominant disorder conditioned by SARS-CoV-2 infection, sex, and age. This has clinical relevance due to the availability of drugs such as Caplacizumab, which inhibits vWF-platelet interaction, and Crizanlizumab, which, by inhibiting P-selectin binding to its ligands, prevents leukocyte recruitment and platelet aggregation at the site of vascular damage

Gain- and Loss-of-Function CFTR Alleles Are Associated with COVID-19 Clinical Outcomes

Carriers of single pathogenic variants of the CFTR (cystic fibrosis transmembrane conductance regulator) gene have a higher risk of severe COVID-19 and 14-day death. The machine learning post-Mendelian model pinpointed CFTR as a bidirectional modulator of COVID-19 outcomes. Here, we demonstrate that the rare complex allele [G576V;R668C] is associated with a milder disease via a gain-of-function mechanism. Conversely, CFTR ultra-rare alleles with reduced function are associated with disease severity either alone (dominant disorder) or with another hypomorphic allele in the second chromosome (recessive disorder) with a global residual CFTR activity between 50 to 91%. Furthermore, we characterized novel CFTR complex alleles, including [A238V;F508del], [R74W;D1270N;V201M], [I1027T;F508del], [I506V;D1168G], and simple alleles, including R347C, F1052V, Y625N, I328V, K68E, A309D, A252T, G542*, V562I, R1066H, I506V, I807M, which lead to a reduced CFTR function and thus, to more severe COVID-19. In conclusion, CFTR genetic analysis is an important tool in identifying patients at risk of severe COVID-19