scholarly journals In Defense of Strong AI

2017 ◽  
Vol 10 (1) ◽  
pp. 38-49
Author(s):  
Corey Baron
Keyword(s):  
Machine Learning ◽  
Thought Experiment ◽  
Second Order ◽  
Order System ◽  
Semantic Meaning ◽  
John Searle ◽  
Chinese Room ◽  
Chinese Room Argument ◽  
Strong Ai

This paper argues against John Searle in defense of the potential for computers to understand language (“Strong AI”) by showing that semantic meaning is itself a second-order system of rules that connects symbols and syntax with extralinguistic facts. Searle’s Chinese Room Argument is contested on theoretical and practical grounds by identifying two problems in the thought experiment, and evidence about “machine learning” is used to demonstrate that computers are already capable of learning to form true observation sentences in the same way humans do. Finally, sarcasm is used as an example to extend the argument to more complex uses of language

Chinese room argument

2018 ◽  
Author(s):  
Robert Van Gulick
Keyword(s):  
Artificial Intelligence ◽  
Computer Program ◽  
Mental States ◽  
Chinese Room ◽  
Chinese Room Argument ◽  
Strong Ai

John Searle’s ‘Chinese room’ argument aims to refute ‘strong AI’ (artificial intelligence), the view that instantiating a computer program is sufficient for having contentful mental states. Imagine a program that produces conversationally appropriate Chinese responses to Chinese utterances. Suppose Searle, who understands no Chinese, sits in a room and is passed slips of paper bearing strings of shapes which, unbeknown to him, are Chinese sentences. Searle performs the formal manipulations of the program and passes back slips bearing conversationally appropriate Chinese responses. Searle seems to instantiate the program, but understands no Chinese. So, Searle concludes, strong AI is false.

scholarly journals Chinese room and program identity

2017 ◽  
Vol 60 (1) ◽  
pp. 28-39
Author(s):  
Nenad Filipovic
Keyword(s):  
Artificial Intelligence ◽  
The Other ◽  
John Searle ◽  
Chinese Room ◽  
Chinese Room Argument ◽  
Strong Conclusion ◽  
Famous Argument ◽  
Present Argument

The Chinese room argument is famous argument introduced by John Searle, in which Searle presented various problems with the claim that it is possible for the artificial intelligence to have understanding of a language in a way in which intelligent beings such as humans have that capacity. The argument was influential enough to, in decades following it, sparke numerous responses and critiques, along with a few alleged improvements to it from Searle. In this article, I will analyze one atypical critique of Searle?s argument, made by Mark Sprevak. Sprevak, unlike the other critics of the argument, agrees with Searle that understanding does not exist in Chinese room in any way, but he claims that Chinese room cannot execute every possible program, like Searle claims. Because of that, Searle cannot conclude the strong conclusion he wants from The Chinese room argument. In this article, I will analyze Searle?s argument, I will give a brief overview of typical responses to it, and I will analyze Sprevak?s response. In the last section, I will present argument that shows that Sprevak, if he wants to keep his conclusions, must either give up one part of his response, or accept one of the typical responses to Searle?s argument, thus making his own response dependent on the response from others.

Artificial Life and the Chinese Room Argument

2002 ◽  
Vol 8 (4) ◽  
pp. 371-378
Author(s):  
David Anderson ◽  
B. Jack Copeland
Keyword(s):  
Computer Simulation ◽  
Artificial Life ◽  
Life Form ◽  
Chinese Room ◽  
Chinese Room Argument ◽  
Strong Ai ◽  
Do So

“Strong artificial life” refers to the thesis that a sufficiently sophisticated computer simulation of a life form is a life form in its own right. Can John Searle's Chinese room argument [12]—originally intended by him to show that the thesis he dubs “strong AI” is false—be deployed against strong ALife? We have often encountered the suggestion that it can be (even in print; see Harnad [8]). We do our best to transfer the argument from the domain of AI to that of ALife. We do so in order to show once and for all that the Chinese room argument proves nothing about ALife. There may indeed be powerful philosophical objections to the thesis of strong ALife, but the Chinese room argument is not among them.

scholarly journals A Fair Version of the Chinese Room

Problemos ◽  
2019 ◽  
Vol 96 ◽  
pp. 121-133
Author(s):  
Hasan Çağatay
Keyword(s):  
Pattern Recognition ◽  
Learning Process ◽  
Thought Experiment ◽  
Thought Experiments ◽  
Human Beings ◽  
John Searle ◽  
Chinese Room

By the Chinese room thought experiment, John Searle (1980) advocates the thesis that it is impossible for computers to think in the same way that human beings do. This article intends firstly to show that the Chinese room does not justify or even test this thesis and secondly to describe exactly how the person in the Chinese room can learn Chinese. Regarding this learning process, Searle ignores the relevance of an individual’s pattern recognition capacity for understanding. To counter Searle’s claim, this paper, via examining a series of thought experiments inspired by the Chinese room, aims to underline the importance of pattern recognition for understanding to emerge.

Intelligence et intentionalité. Critique de l'argument de Searle contre l'intelligence artificielle

Dialogue ◽  
1991 ◽  
Vol 30 (1-2) ◽  
pp. 85-102 ◽  
Author(s):  
Maryvonne Longeart
Keyword(s):  
John Searle ◽  
Chinese Room ◽  
Chinese Room Argument ◽  
Intelligence Artificielle ◽  
Alan Turing ◽  
Computing Machinery

Dans son célèbre article de 1950 «Computing Machinery and Intelligence», Alan Turing suggère qu'un critère d'intelligence pour une machine serait qu'elle puisse se faire passer pour un interlocuteur humain. Ce test, appelé «Test de Turing», a été critiqué par John Searle, qui a remis en cause sa pertinence en s'appuyant sur une expérience mentale connue sous le nom de l'argument de la chambre chinoise (Chinese Room Argument). L'expérience tend à montrer que l'intelligence artificielle au sens propre du terme est impossible, parce que les symboles manipulés par les programmes de prétendue «intelligence artificielle» (IA) sont dépourvus de signification. L'intentionalité propre à certains êtres vivants peut seule faire naître le sens. L'imitation d'un comportement intentionnel ne le peut pas. Le test de Turing serait done insatisfaisant, parce qu'il ne permettrait pas de distinguer les systèmes intentionnels de systèmes qui ne le sont pas, et que par conséquent il ne permettrait pas de distinguer l'intelligence authentique (intentionnelle) de la simulation de l'intelligence (non intentionnelle).

scholarly journals Searle, Syntax, and Observer Relativity

1996 ◽  
Vol 26 (1) ◽  
pp. 101-122 ◽  
Author(s):  
Ronald P. Endicott
Keyword(s):  
Physical Properties ◽  
Physical System ◽  
John Searle ◽  
Chinese Room ◽  
Chinese Room Argument ◽  
Syntactic Property ◽  
Computational Psychology ◽  
Syntactic Properties ◽  
The Mind ◽  
Digital Computers

In his bookThe Rediscovery of the Mind(hereafter RM), John Searle attacks computational psychology with a number of new and boldly provocative claims. Specifically, in the penultimate chapter entitled ‘The Critique of Cognitive Reason,’ Searle targets what he calls ‘cognitivism,’ according to which our brains are digital computers that process a mental syntax. And Searle denies this view on grounds thatthe attribution of syntaxisobserver relative.A syntactic property is arbitrarily assigned to a physical system, he thinks, with the result that syntactic states ‘do not even exist except in the eyes of the beholder’(RM,215). This unabashed anti-realism differs significantly from Searle's earlier work. The Chinese room argument, for example, was intended to show that syntactic properties will not suffice for semantics, where the syntax was realistically construed. But now Searle claims that physical properties will not suffice to determine a system's syntactic properties.

scholarly journals Second order Kalman filtering channel estimation and machine learning methods for spectrum sensing in cognitive radio networks

2021 ◽  
Author(s):  
Olusegun Peter Awe ◽  
Daniel Adebowale Babatunde ◽  
Sangarapillai Lambotharan ◽  
Basil AsSadhan
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Clustering Algorithm ◽  
Polynomial Regression ◽  
Primary User ◽  
Radio Networks ◽  
Second Order

AbstractWe address the problem of spectrum sensing in decentralized cognitive radio networks using a parametric machine learning method. In particular, to mitigate sensing performance degradation due to the mobility of the secondary users (SUs) in the presence of scatterers, we propose and investigate a classifier that uses a pilot based second order Kalman filter tracker for estimating the slowly varying channel gain between the primary user (PU) transmitter and the mobile SUs. Using the energy measurements at SU terminals as feature vectors, the algorithm is initialized by a K-means clustering algorithm with two centroids corresponding to the active and inactive status of PU transmitter. Under mobility, the centroid corresponding to the active PU status is adapted according to the estimates of the channels given by the Kalman filter and an adaptive K-means clustering technique is used to make classification decisions on the PU activity. Furthermore, to address the possibility that the SU receiver might experience location dependent co-channel interference, we have proposed a quadratic polynomial regression algorithm for estimating the noise plus interference power in the presence of mobility which can be used for adapting the centroid corresponding to inactive PU status. Simulation results demonstrate the efficacy of the proposed algorithm.

scholarly journals Search Patterns Based on Trajectories Extracted from the Response of Second-Order Systems

2021 ◽  
Vol 11 (8) ◽  
pp. 3430
Author(s):  
Erik Cuevas ◽  
Héctor Becerra ◽  
Héctor Escobar ◽  
Alberto Luque-Chang ◽  
Marco Pérez ◽  
...  
Keyword(s):  
Convergence Rates ◽  
Optimization Problems ◽  
Search Space ◽  
Second Order ◽  
Order System ◽  
Search Patterns ◽  
Multiple Behaviors ◽  
Complex Optimization ◽  
Second Order Systems ◽  
Order Algorithm

Recently, several new metaheuristic schemes have been introduced in the literature. Although all these approaches consider very different phenomena as metaphors, the search patterns used to explore the search space are very similar. On the other hand, second-order systems are models that present different temporal behaviors depending on the value of their parameters. Such temporal behaviors can be conceived as search patterns with multiple behaviors and simple configurations. In this paper, a set of new search patterns are introduced to explore the search space efficiently. They emulate the response of a second-order system. The proposed set of search patterns have been integrated as a complete search strategy, called Second-Order Algorithm (SOA), to obtain the global solution of complex optimization problems. To analyze the performance of the proposed scheme, it has been compared in a set of representative optimization problems, including multimodal, unimodal, and hybrid benchmark formulations. Numerical results demonstrate that the proposed SOA method exhibits remarkable performance in terms of accuracy and high convergence rates.

scholarly journals Radiomics: a new tool to differentiate adrenocortical adenoma from carcinoma

BJS Open ◽  
2021 ◽  
Vol 5 (1) ◽  
Author(s):  
F Torresan ◽  
F Crimì ◽  
F Ceccato ◽  
F Zavan ◽  
M Barbot ◽  
...  
Keyword(s):  
Machine Learning ◽  
Imaging Techniques ◽  
Second Order ◽  
Learning Approach ◽  
Adrenocortical Adenoma ◽  
Unsupervised Machine Learning ◽  
First Order ◽  
Main Challenge ◽  
Machine Learning Approach ◽  
Adrenocortical Tumours

Abstract Background The main challenge in the management of indeterminate incidentally discovered adrenal tumours is to differentiate benign from malignant lesions. In the absence of clear signs of invasion or metastases, imaging techniques do not always precisely define the nature of the mass. The present pilot study aimed to determine whether radiomics may predict malignancy in adrenocortical tumours. Methods CT images in unenhanced, arterial, and venous phases from 19 patients who had undergone resection of adrenocortical tumours and a cohort who had undergone surveillance for at least 5 years for incidentalomas were reviewed. A volume of interest was drawn for each lesion using dedicated software, and, for each phase, first-order (histogram) and second-order (grey-level colour matrix and run-length matrix) radiological features were extracted. Data were revised by an unsupervised machine learning approach using the K-means clustering technique. Results Of operated patients, nine had non-functional adenoma and 10 carcinoma. There were 11 patients in the surveillance group. Two first-order features in unenhanced CT and one in arterial CT, and 14 second-order parameters in unenhanced and venous CT and 10 second-order features in arterial CT, were able to differentiate adrenocortical carcinoma from adenoma (P < 0.050). After excluding two malignant outliers, the unsupervised machine learning approach correctly predicted malignancy in seven of eight adrenocortical carcinomas in all phases. Conclusion Radiomics with CT texture analysis was able to discriminate malignant from benign adrenocortical tumours, even by an unsupervised machine learning approach, in nearly all patients.

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