Exponential Contingency Explosion: Implications for Artificial General Intelligence

Haug, Samuel; Marks, Robert J.; Dembski, William A.

doi:10.1109/tsmc.2021.3056669

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…Additionally, in () we assume some sample error rates for the combinations (). We emphasize that the actual values of () are “known unknowns” to the practitioner, 21 and it is precisely their effect what needs to be corrected.

\begin{matrix} alignleft \\ align-1 \begin{array}{lllll} boldComb. \end{array} \end{matrix}

…”

Section: Data From the Israeli Ministry Of Healthmentioning

confidence: 99%

Correcting prevalence estimation for biased sampling with testing errors

Zhou,

Díaz‐Pachón,

Zhao

et al. 2023

Statistics in Medicine

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Sampling for prevalence estimation of infection is subject to bias by both oversampling of symptomatic individuals and error‐prone tests. This results in naïve estimators of prevalence (ie, proportion of observed infected individuals in the sample) that can be very far from the true proportion of infected. In this work, we present a method of prevalence estimation that reduces both the effect of bias due to testing errors and oversampling of symptomatic individuals, eliminating it altogether in some scenarios. Moreover, this procedure considers stratified errors in which tests have different error rate profiles for symptomatic and asymptomatic individuals. This results in easily implementable algorithms, for which code is provided, that produce better prevalence estimates than other methods (in terms of reducing and/or removing bias), as demonstrated by formal results, simulations, and on COVID‐19 data from the Israeli Ministry of Health.

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\begin{matrix} alignleft \\ align-1 \begin{array}{lllll} boldComb. \end{array} \end{matrix}

…”

Section: Data From the Israeli Ministry Of Healthmentioning

confidence: 99%

Correcting prevalence estimation for biased sampling with testing errors

Zhou,

Díaz‐Pachón,

Zhao

et al. 2023

Statistics in Medicine

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“…Maximum entropy distributions keep fixed what is known -the constraints in Step 2while randomizing as much as possible all that is not known. In this sense, the distribution function of interest represents the known unknowns (see, e.g., [50]). 10 There are three possibilities here: 1.…”

Section: Find the Family Of Maximum Entropy Distributionsmentioning

confidence: 99%

Sometimes size does not matter

Díaz–Pachón¹,

Hössjer²,

Marks³

2022

Preprint

Self Cite

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Cosmological fine-tuning has traditionally been associated with the narrowness of the intervals in which the parameters of the physical models must be located to make life possible. A more thorough approach focuses on the probability of the interval, not on its size. Most attempts to measure the probability of the life-permitting interval for a given parameter rely on a Bayesian statistical approach for which the prior distribution of the parameter is uniform. However, the parameters in these models often take values in spaces of infinite size, so that a uniformity assumption is not possible. This is known as the normalization problem. This paper explains a framework to measure tuning that, among others, deals with normalization, assuming that the prior distribution belongs to a class of maximum entropy (maxent) distributions. By analyzing an upper bound of the tuning probability for this class of distributions the method solves the so-called weak anthropic principle, and offer a solution, at least in this context, to the well-known lack of invariance of maxent distributions. The implication of this approach is that, since all mathematical models need parameters, tuning is not only a question of natural science, but also a problem of mathematical modeling. Cosmological tuning is thus a particular instantiation of a more general scenario. Therefore, whenever a mathematical model is used to describe nature, not only in physics but Size does not matter in all of science, tuning is present. And the question of whether the tuning is fine or coarse for a given parameter -if the interval in which the parameter is located has low or high probability, respectively -depends crucially not only on the interval but also on the assumed class of prior distributions. Novel upper bounds for tuning probabilities are presented.

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“…AI is an important driving force in the new round of technological revolution and industrial transformation [2]. Here, AI refers to technology that simulates and possesses human-like cognitive functions on various machine carriers, and the related computing technologies include natural language processing, cloud computing, big data, and deep learning, such as intelligent robots, virtual personal assistants, autonomous driving, and voice recognition [3][4][5][6][7]. In the late 1990s, with the improvement of computer technology, AI technology also achieved rapid development.…”

Section: Introductionmentioning

confidence: 99%

Research on the Collaborative Innovation Relationship of Artificial Intelligence Technology in Yangtze River Delta of China: A Complex Network Perspective

Dong

Meng

2022

Sustainability

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Artificial intelligence (AI), as a rapidly developing interdisciplinary field, is a key driver of future economic development. The Yangtze River Delta (YRD) is one of the most significant economic regions of China, which also has a leading role in the AI industry. In this study, based on the patent cooperation data of YRD in the past decade, we focus on studying the collaborative innovation relationship in the AI field of the YRD from the perspective of complex networks. In order to investigate the interprovincial, intra-city and inter-city collaborative innovation relationships, we construct the Yangtze River Delta AI collaborative innovation (YRD-AICI) network. Subsequently, to analyze the development status and collaborative innovation relationship of innovation bodies in the AI field of YRD, we construct the Yangtze River Delta AI patent cooperation (YRD-AIPC) network. Next, the basic characteristics and spatio-temporal evolution of these two networks are explored, and the research results are presented that: (1) Shanghai, Jiangsu Province, and Zhejiang Province have obvious leading advantages in the AI field of the YRD, and the development gap between cities is significant; (2) the pioneering innovation bodies in the AI industry of the YRD are identified using centrality measures, and their cooperative innovation relationship is revealed; (3) based on link prediction methods, future partnerships between cities and innovation bodies are predicted to provide the future development trend of the YRD. The results provide theoretical support for exploring the cooperation mechanism of collaborative innovation in the AI field of YRD and inspire future development planning.

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Exponential Contingency Explosion: Implications for Artificial General Intelligence

Cited by 7 publications

References 37 publications

Correcting prevalence estimation for biased sampling with testing errors

Correcting prevalence estimation for biased sampling with testing errors

Sometimes size does not matter

Research on the Collaborative Innovation Relationship of Artificial Intelligence Technology in Yangtze River Delta of China: A Complex Network Perspective

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