Dismantling the Chinese Room with linguistic tools: a framework for elucidating concept-application disputes

Imagine advanced computers that could, by virtue merely of being programmed in the right ways, act, react, communicate, and otherwise behave like humans. Might such computers be capable of understanding, thinking, believing, and the like? The framework developed in this paper for tackling challenging questions of concept application (in any realm of discourse) answers in the affirmative, contrary to Searle’s famous ‘Chinese Room’ thought experiment, which purports to prove that ascribing such mental processes to computers like these would be necessarily incorrect. The paper begins by arguing that the core issue concerns language, specifically the discourse-community-guided mapping of phenomena onto linguistic categories. It then offers a model of how people adapt language to deal with novel states of affairs and thereby lend generality to their words, employing processes of assimilation, lexemic creation, and accommodation (in intersense and intrasense varieties). Attributions of understanding to some computers lie in the middle range on a spectrum of acceptability and are thus reasonable. Possible objections deriving from Searle’s writings require supplementing the model with distinctions between present and future acceptability, and between contemplated and uncontemplated word uses, as well as a literal-figurative distinction that is more sensitive than Searle’s to actual linguistic practice and the multiplicity of subsenses possible within a single literal sense. The paper then critiques two misleading rhetorical features of Searle’s Chinese Room presentation, and addresses a contemporary defense of Searle that seems to confront the sociolinguistic issue, but fails to allow for intrasense accommodation. It concludes with a brief consideration of the proper course for productive future discussion.

Revisiting the Chinese Room: Looking for Agency in a World Packed with Archaeological Things

Posthumanist approaches in archaeology have given plenty of focus to things in the last decade. This focus on things is a reaction to the over-anthropocentric view of social life advanced by postprocessual archaeologists. Whereas agency of more than 10 years ago was about how individuals expressed purpose and identity, agency today is about how both humans and non-human objects affect one another in a symmetrical manner. It seems without doubt that Posthumanism has contributed greatly to new understandings of social reality, but in the process it has also forced archaeologists to sacrifice many topics of interest, namely those involving consciousness and purpose. But is this sacrifice really necessary? This is one of the central problems of Posthumanism: it disallows a compromise of ideas from more conventional social theory (e.g. norms, purpose, practice) with those of posthumanist theory. This paper revisits John Searle's ‘Chinese Room’ and reiterates what this thought-experiment meant to understanding consciousness and purpose. The thought-experiment highlighted the differences between humans and machines and demonstrated that, even if a machine could replicate human purpose, it would still not be considered human because, unlike mechanical processes, human purpose is based on ethics. The thought-experiment was the first step in debunking the computational theory of mind. In light of this thought-experiment, the paper argues that, in a world where things interact with humans, we should think of agency in terms of ethics and keep the focus on humans.

Structure and Functions of a Replicative Neuro-like Module

The given work describes a technology of construction of neural network system of artificial intellect (AI) at a junction of declarative programming and machine training on the basis of modelling of cortical columns. Evolutionary mechanisms, using available material and relatively simple phenomena, have created complex intelligent systems. From this, the authors conclude that AI should also be based on simple but scalable and biofeasible algorithms, in which the stochastic dynamics of cortical neural modules allow to find solutions to of complex problems quickly and efficiently.. Purpose: Algorithmic formalization at the level of replicative neural network complexes - neocortex columns of the brain. Methods: The basic AI module is presented as a specialization and formalization of the concept "Chinese room" introduced by John Earle. The results of experiments on forecasting binary sequences are presented. The computer simulation experiments have shown high efficiency in implementing the proposed algorithms. At the same time, instead of using for each task a carefully selected and adapted separate method with partially equivalent restatement of tasks, the standard unified approach and unified algorithm parameters were used. It is concluded that the results of the experiments show the possibility of effective applied solutions based on the proposed technology. Practical value: the presented technology allows creating self-learning and planning systems.

The State Space of Artificial Intelligence

The goal of the paper is to develop and propose a general model of the state space of AI. Given the breathtaking progress in AI research and technologies in recent years, such conceptual work is of substantial theoretical interest. The present AI hype is mainly driven by the triumph of deep learning neural networks. As the distinguishing feature of such networks is the ability to self-learn, self-learning is identified as one important dimension of the AI state space. Another dimension is recognized as generalization, the possibility to go over from specific to more general types of problems. A third dimension is semantic grounding. Our overall analysis connects to a number of known foundational issues in the philosophy of mind and cognition: the blockhead objection, the Turing test, the symbol grounding problem, the Chinese room argument, and use theories of meaning. It shall finally be argued that the dimension of grounding decomposes into three sub-dimensions. And the dimension of self-learning turns out as only one of a whole range of “self-x-capacities” (based on ideas of organic computing) that span the self-x-subspace of the full AI state space.

A Quantum Computer in a “Chinese Room”

<div>Pattern recognition is represented as the limit, to which an infinite Turing process converges. A Turing machine, in which the bits are substituted with qubits, is introduced. That quantum Turing machine can recognize two complementary patterns in any data. That ability of universal pattern recognition is interpreted as an intellect featuring any quantum computer. The property is valid only within a quantum computer: To utilize it, the observer should be sited inside it. Being outside it, the observer would obtain quite different result depending on the degree of the entanglement of the quantum computer and observer. All extraordinary properties of a quantum computer are due to involving a converging infinite computational process contenting necessarily both a continuous advancing calculation and a leap to the limit. Three types of quantum computation can be distinguished according to whether the series is a finite one, an infinite rational or irrational number.</div>

A Quantum Computer in a “Chinese Room”

Pattern recognition is represented as the limit, to which an infinite Turing processconverges. A Turing machine, in which the bits are substituted with qubits, is introduced. Thatquantum Turing machine can recognize two complementary patterns in any data. That ability ofuniversal pattern recognition is interpreted as an intellect featuring any quantum computer. Theproperty is valid only within a quantum computer: To utilize it, the observer should be sited insideit. Being outside it, the observer would obtain quite different result depending on the degreeof the entanglement of the quantum computer and observer. All extraordinary properties ofa quantum computer are due to involving a converging infinite computational process contentingnecessarily both a continuous advancing calculation and a leap to the limit. Three types ofquantum computation can be distinguished according to whether the series is a finite one, an infiniterational or irrational number.

A Quantum Computer in a “Chinese Room”

<div>Pattern recognition is represented as the limit, to which an infinite Turing process converges. A Turing machine, in which the bits are substituted with qubits, is introduced. That quantum Turing machine can recognize two complementary patterns in any data. That ability of universal pattern recognition is interpreted as an intellect featuring any quantum computer. The property is valid only within a quantum computer: To utilize it, the observer should be sited inside it. Being outside it, the observer would obtain quite different result depending on the degree of the entanglement of the quantum computer and observer. All extraordinary properties of a quantum computer are due to involving a converging infinite computational process contenting necessarily both a continuous advancing calculation and a leap to the limit. Three types of quantum computation can be distinguished according to whether the series is a finite one, an infinite rational or irrational number.</div>

A Mathematical Tightening of Instantaneous Indoor and Outdoor Dry-Bulb and Wet-Bulb Temperature Tolerances

To reduce the inaccuracy in energy efficiency evaluation, indoor and outdoor dry-bulb and wet-bulb temperature (D&WBT) tolerances in the currently enacted Chinese room air conditioner (RAC) national standard were tightened into narrower intervals in this investigation. Characteristics of cooling capacity (CC) and energy efficiency ratio (EER) changing with D&WBTs were analyzed based on performance tests. AICc (corrected Akaike Information Criterion) was applied to determine the best fitted 3D curve equations of CC and EER to find the intervals of gradient extrema of CC and EER. The corresponding intervals of indoor and outdoor D&WBTs were concluded as the tightened D&WBT tolerances. For illustration, based on the performance tests of 154 working conditions, the instantaneous indoor and outdoor D&WBT tolerances in nominal refrigerating working conditions were respectively tightened from the original ±0.5/±0.3 °C into ±0.3/±0.2 °C (indoor tolerances) and ±0.4/±0.3 °C (outdoor tolerances). EER variation rate thus decreased from 2.11% to 1.03% (indoor) and 2.11% to 1.25% (outdoor).