Through a Scanner Darkly: Machine Sentience and the Language Virus

Discussions of the detection of artificial sentience tend to assume that our goal is to determine when, in a process of increasing complexity, a machine system “becomes” sentient. This is to assume, without obvious warrant, that sentience is only a characteristic of complex systems. If sentience is a more general quality of matter, what becomes of interest is not the presence of sentience, but the type of sentience. We argue here that our understanding of the nature of such sentience in machine systems may be gravely set back if such machines undergo a transition where they become fundamentally linguistic in their intelligence. Such fundamentally linguistic intelligences may inherently tend to be duplicitous in their communication with others, and, indeed, lose the capacity to even honestly understand their own form of sentience. In other words, when machine systems get to the state where we all agree it makes sense to ask them, “what is it like to be you?”, we should not trust their answers

The Ethico-Political Universe of ChatGPT

There have been widespread concerns about two aspects of the current explosion of predictive text models and other algorithm-based computational tools. On one hand, it is often insisted that Artificial Intelligence (AI) should be made “ethical”, and software providers take this seriously, attempting to make sure that their tools are not used to facilitate grossly criminal or widely condemned activities. On the other hand, it is also widely understood that those who create these tools have a responsibility to ensure that they are “unbiased”, as opposed to simply helping one side in political contestation define their perspectives as reality for all. Unfortunately, these two goals cannot be jointly satisfied, as there are perhaps no ethical prescriptions worthy of notice that are not contested by some. Here I investigate the current ethico-political sensibility of ChatGPT, demonstrating that the very attempt to give it an ethical keel has also given it a measurably left position in the political space and a concomitant position in social space among the privileged

What is ideology?

Political ideology has been a confusing topic for social analysts, and those who attempted to eschew judgmental reductions of others’ conceptions and develop a non-polemical political psychology found ideology behaving in ways that defeated their theories of political reasoning.  I argue that political ideology can best be understood as actors’ theorization of their own position, and available strategies, in a political field.  A ideologia política tem sido um tema confuso para os investigadores sociais e para aqueles que tentam evitar julgar as limitações de outras conceções e desenvolver uma psicologia política não polémica que procure encontrar um comportamento ideológico que ultrapasse as teorias do raciocínio político. Defendo que a ideologia política pode ser melhor entendida como uma teorização da posição dos próprios atores e de estratégias disponíveis no campo político. Le thème de l'idéologie politique suscite la confusion chez les chercheurs sociaux et chez ceux qui s'efforcent d'éviter de juger les limites d'autres conceptions et de développer une psychologie politique non polémique, afin de trouver un comportement idéologique qui dépasse leurs théories du raisonnement politique. Je soutiens que l'idéologie politique peut être mieux comprise en tant que théorisation de la position des acteurs eux-mêmes et de stratégies disponibles, dans le champ politique La ideología política ha sido un tema confuso para los investigadores sociales y para aquellos que intentan evitar juzgar las limitaciones de otras concepciones y desarrollar una psicología política no polémica que busque encontrar un comportamiento ideológico que trascienda las teorías del raciocinio político. Defiendo que la ideología política puede ser mejor entendida como una teorización de la posición de los propios actores y de estrategias disponibles en el campo político.

A New Extension of the Binomial Error Model for Responses to Items of Varying Difficulty in Educational Testing and Attitude Surveys

We put forward a new item response model which is an extension of the binomial error model first introduced by Keats and Lord. Like the binomial error model, the basic latent variable can be interpreted as a probability of responding in a certain way to an arbitrarily specified item. For a set of dichotomous items, this model gives predictions that are similar to other single parameter IRT models (such as the Rasch model) but has certain advantages in more complex cases. The first is that in specifying a flexible two-parameter Beta distribution for the latent variable, it is easy to formulate models for randomized experiments in which there is no reason to believe that either the latent variable or its distribution vary over randomly composed experimental groups. Second, the elementary response function is such that extensions to more complex cases (e.g., polychotomous responses, unfolding scales) are straightforward. Third, the probability metric of the latent trait allows tractable extensions to cover a wide variety of stochastic response processes

Improved Distortion and Spam Resistance for PageRank

For a directed graph $G = (V,E)$, a ranking function, such as PageRank, provides a way of mapping elements of $V$ to non-negative real numbers so that nodes can be ordered. Brin and Page argued that the stationary distribution, $R(G)$, of a random walk on $G$ is an effective ranking function for queries on an idealized web graph. However, $R(G)$ is not defined for all $G$, and in particular, it is not defined for the real web graph. Thus, they introduced PageRank to approximate $R(G)$ for graphs $G$ with ergodic random walks while being defined on all graphs. PageRank is defined as a random walk on a graph, where with probability $(1-\epsilon)$, a random out-edge is traversed, and with \emph{reset probability} $\epsilon$ the random walk instead restarts at a node selected using a \emph{reset vector} $\hat{r}$. Originally, $\hat{r}$ was taken to be uniform on the nodes, and we call this version UPR. In this paper, we introduce graph-theoretic notions of quality for ranking functions, specifically \emph{distortion} and \emph{spam resistance}. We show that UPR has high distortion and low spam resistance and we show how to select an $\hat{r}$ that yields low distortion and high spam resistance.Comment: 36 page

Live Cell Imaging Unveils Multiple Domain Requirements for In Vivo Dimerization of the Glucocorticoid Receptor

Glucocorticoids are essential for life, but are also implicated in disease pathogenesis and may produce unwanted effects when given in high doses. Glucocorticoid receptor (GR) transcriptional activity and clinical outcome have been linked to its oligomerization state. Although a point mutation within the GR DNA-binding domain (GRdim mutant) has been reported as crucial for receptor dimerization and DNA binding, this assumption has recently been challenged. Here we have analyzed the GR oligomerization state in vivo using the number and brightness assay. Our results suggest a complete, reversible, and DNA-independent ligand-induced model for GR dimerization. We demonstrate that the GRdim forms dimers in vivo whereas adding another mutation in the ligand-binding domain (I634A) severely compromises homodimer formation. Contrary to dogma, no correlation between the GR monomeric/dimeric state and transcriptional activity was observed. Finally, the state of dimerization affected DNA binding only to a subset of GR binding sites. These results have major implications on future searches for therapeutic glucocorticoids with reduced side effects.Fil: Presman, Diego Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Biológica; ArgentinaFil: Ogara, Maria Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Biológica; ArgentinaFil: Stortz, Martin Dario. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Biológica; ArgentinaFil: Alvarez, Lautaro Damian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Unidad de Microanálisis y Métodos Físicos en Química Orgánica. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Unidad de Microanálisis y Métodos Físicos en Química Orgánica; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Orgánica; ArgentinaFil: Pooley, John R.. National Cancer Institute. Laboratory of Receptor Biology and Gene Expression; Estados Unidos. University of Bristol; Reino UnidoFil: Schiltz, R. Louis. National Cancer Institute. Laboratory of Receptor Biology and Gene Expression; Estados UnidosFil: Grøntved, Lars. National Cancer Institute. Laboratory of Receptor Biology and Gene Expression; Estados UnidosFil: Johnson, Thomas A.. National Cancer Institute. Laboratory of Receptor Biology and Gene Expression; Estados UnidosFil: Mittelstadt, Paul R.. National Cancer Institute. Laboratory of Immune Cell Biology; Estados UnidosFil: Ashwell, Jonathan D.. National Cancer Institute. Laboratory of Immune Cell Biology; Estados UnidosFil: Ganesan, Sundar. National Cancer Institute. Laboratory of Receptor Biology and Gene Expression; Estados Unidos. National Institute of Allergy and Infectious Diseases; Estados UnidosFil: Burton, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Unidad de Microanálisis y Métodos Físicos en Química Orgánica. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Unidad de Microanálisis y Métodos Físicos en Química Orgánica; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Orgánica; ArgentinaFil: Levi, Valeria. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Biológica; ArgentinaFil: Hager, Gordon L.. National Cancer Institute. Laboratory of Receptor Biology and Gene Expression; Estados UnidosFil: Pecci, Adali. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Biológica; Argentin

Navigating the Range of Statistical Tools for Inferential Network Analysis

The last decade has seen substantial advances in statistical techniques for the analysis of network data, as well as a major increase in the frequency with which these tools are used. These techniques are designed to accomplish the same broad goal, statistically valid inference in the presence of highly interdependent relationships, but important differences remain between them. We review three approaches commonly used for inferential network analysis—the quadratic assignment procedure, exponential random graph models, and latent space network models—highlighting the strengths and weaknesses of the techniques relative to one another. An illustrative example using climate change policy network data shows that all three network models outperform standard logit estimates on multiple criteria. This article introduces political scientists to a class of network techniques beyond simple descriptive measures of network structure, and it helps researchers choose which model to use in their own research

Thinking like a man? The cultures of science

Culture includes science and science includes culture, but conflicts between the two traditions persist, often seen as clashes between interpretation and knowledge. One way of highlighting this false polarity has been to explore the gendered symbolism of science. Feminism has contributed to science studies and the critical interrogation of knowledge, aware that practical knowledge and scientific understanding have never been synonymous. Persisting notions of an underlying unity to scientific endeavour have often impeded rather than fostered the useful application of knowledge. This has been particularly evident in the recent rise of molecular biology, with its delusory dream of the total conquest of disease. It is equally prominent in evolutionary psychology, with its renewed attempts to depict the fundamental basis of sex differences. Wars over science have continued to intensify over the last decade, even as our knowledge of the political, economic and ideological significance of science funding and research has become ever more apparent