scholarly journals Imperfect Automata: Effects of “mutation” on the evolution of automaton 01101110 (Rule 110)

2019 ◽  
Author(s):  
Andrei Popa
Keyword(s):  
Artificial Intelligence ◽  
Initial Conditions ◽  
Biological Systems ◽  
Mutation Rates ◽  
Small Probability ◽  
Deterministic Systems ◽  
Simple Rules ◽  
Computational Work ◽  
A Cell ◽  
Key Topics

Cellular automata are discreet mathematical models. They consist of cells; each cell can exist in a limited number of mutually exclusive states, like 0 or 1. The state of each cell at time t is determined by simple rules, based on the states of its neighboring cells. While exploring their relevance to behavioral sciences (McDowell & Popa, 2009) one aspect caught my attention: it seemed that all emerging structures and patterns could be traced back to the first few generations; patterns were evolving, colliding, changing, and disappearing, but no new patterns or structures were emerging from non-patterns (i.e., "uniformity"). In biological systems, novelty is made possible by mutation (Thomas, 1974) – a concept central to my computational work on learning (Popa & McDowell, 2016) and on the emergence of psychological objects and phenomena from changes in neuronal activation states (Popa, 2019). Unlike biological systems, automata are deterministic systems, governed by precise rules. The question examined here was: what if these rules weren’t precise? What if every time a cell is created, there’s a small probability to make a mistake, to write 0 instead of 1 and vice-versa? I explored this idea in the context of Rule 110 (01101110), an elementary CA notorious for its fascinating properties (Wolfram, 2002). A small amount of mutation – e.g., 0.00005 probability to make mistakes – facilitated the emergence of new patterns and structures, disconnected from the initial conditions. Mutation rates of 0.0001 – 0.0005 produced an abundance of irregular, interacting structures. Mutation rates higher than 1% prevented the emergence of discernible patterns, producing instead an amalgam of ill-defined, organic-looking structures. These results suggested that imperfect automata may provide useful insight on the evolution of non-deterministic systems and on the emergence of novelty – two key topics in machine learning and artificial intelligence.

Artificial Planning Experience by Means of a Heuristic Cell-Space Model: Simulating International Migration in the Urban Process

1995 ◽  
Vol 27 (10) ◽  
pp. 1647-1665 ◽  
Author(s):  
J Portugali ◽  
I Benenson
Keyword(s):  
Urban Policy ◽  
Initial Conditions ◽  
Point Of View ◽  
Town Planning ◽  
Space Model ◽  
Self Organized ◽  
A Cell ◽  
Policy And Planning ◽  
The City ◽  
Theory And Model

We suggest considering the city as a complex, open, and thus self-organized system, and describing it by means of a cell-space model. A central property of self-organizing systems is that they are not controllable—not by individuals, nor by economic, political, and planning institutions. The city, in this respect, is complex and untamable. Inability to recognize and accept this property is one of the reasons for the difficulties and problems of modernist town planning. The theory and model we present are built to describe the urban process as a historical one in which, given identical initial conditions, each simulation run is unique and never fully repeats itself. From the point of view of urban policy and planning, our heuristic model can guide decisionmakers by answering the following question: ‘given the initial conditions of an inflow of new immigrants (that is, from the ex-USSR), what possible urban scenarios can result, and what are their global structural properties?’.

Leveraging Twitter Using Artificial Intelligence to Explore Mental Health Insights in the UK During the COVID-19 Pandemic (Preprint)

2021 ◽  
Author(s):  
Christopher Marshall ◽  
Kate Lanyi ◽  
Rhiannon Green ◽  
Georgie Wilkins ◽  
Fiona Pearson ◽  
...  
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Language Processing ◽  
Mental Health Problems ◽  
Well Being ◽  
Research Activity ◽  
Health Crisis ◽  
Key Topics ◽  
The Uk ◽  
The Impact

BACKGROUND There is increasing need to explore the value of soft-intelligence, leveraged using the latest artificial intelligence (AI) and natural language processing (NLP) techniques, as a source of analysed evidence to support public health research activity and decision-making. OBJECTIVE The aim of this study was to further explore the value of soft-intelligence analysed using AI through a case study, which examined a large collection of UK tweets relating to mental health during the COVID-19 pandemic. METHODS A search strategy comprising a list of terms related to mental health, COVID-19, and lockdown restrictions was developed to prospectively collate relevant tweets via Twitter’s advanced search application programming interface over a 24-week period. We deployed a specialist NLP platform to explore tweet frequency and sentiment across the UK and identify key topics of discussion. A series of keyword filters were used to clean the initial data retrieved and also set up to track specific mental health problems. Qualitative document analysis was carried out to further explore and expand upon the results generated by the NLP platform. All collated tweets were anonymised RESULTS We identified and analysed 286,902 tweets posted from UK user accounts from 23 July 2020 to 6 January 2021. The average sentiment score was 50%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume and sentiment appeared to coincide with key changes to any local and/or national social-distancing measures. Tweets around mental health were polarising, discussed with both positive and negative sentiment. Key topics of consistent discussion over the study period included the impact of the pandemic on people’s mental health (both positively and negatively), fear and anxiety over lockdowns, and anger and mistrust toward the government. CONCLUSIONS Through the primary use of an AI-based NLP platform, we were able to rapidly mine and analyse emerging health-related insights from UK tweets into how the pandemic may be impacting people’s mental health and well-being. This type of real-time analysed evidence could act as a useful intelligence source that agencies, local leaders, and health care decision makers can potentially draw from, particularly during a health crisis.

scholarly journals Geometric Effect for Biological Reactors and Biological Fluids

2018 ◽  
Vol 5 (4) ◽  
pp. 110 ◽  
Author(s):  
Kazusa Beppu ◽  
Ziane Izri ◽  
Yusuke Maeda ◽  
Ryota Sakamoto
Keyword(s):  
Biological Fluids ◽  
Biological Systems ◽  
Self Organization ◽  
Active Matter ◽  
Confined Space ◽  
Self Organized ◽  
Motile Cells ◽  
Recent Developments ◽  
A Cell

As expressed “God made the bulk; the surface was invented by the devil” by W. Pauli, the surface has remarkable properties because broken symmetry in surface alters the material properties. In biological systems, the smallest functional and structural unit, which has a functional bulk space enclosed by a thin interface, is a cell. Cells contain inner cytosolic soup in which genetic information stored in DNA can be expressed through transcription (TX) and translation (TL). The exploration of cell-sized confinement has been recently investigated by using micron-scale droplets and microfluidic devices. In the first part of this review article, we describe recent developments of cell-free bioreactors where bacterial TX-TL machinery and DNA are encapsulated in these cell-sized compartments. Since synthetic biology and microfluidics meet toward the bottom-up assembly of cell-free bioreactors, the interplay between cellular geometry and TX-TL advances better control of biological structure and dynamics in vitro system. Furthermore, biological systems that show self-organization in confined space are not limited to a single cell, but are also involved in the collective behavior of motile cells, named active matter. In the second part, we describe recent studies where collectively ordered patterns of active matter, from bacterial suspensions to active cytoskeleton, are self-organized. Since geometry and topology are vital concepts to understand the ordered phase of active matter, a microfluidic device with designed compartments allows one to explore geometric principles behind self-organization across the molecular scale to cellular scale. Finally, we discuss the future perspectives of a microfluidic approach to explore the further understanding of biological systems from geometric and topological aspects.

The Engineer

Rebel Genius ◽  
2016 ◽  
Author(s):  
Tara H. Abraham
Keyword(s):  
Artificial Intelligence ◽  
Reticular Formation ◽  
Research Laboratory ◽  
Biological Systems ◽  
Theoretical Modelling ◽  
Research Culture ◽  
Cognitive Sciences ◽  
New Research ◽  
Scientific Identity ◽  
The Brain

This chapter examines the ways that McCulloch’s new research culture at MIT’s Research Laboratory of Electronics shaped the evolution of his scientific identity into that of an engineer. This was an open, fluid, multidisciplinary culture that allowed McCulloch to shift his focus more squarely onto understanding the brain from the perspective of theoretical modelling, and to promote the cybernetic vision to diverse audiences. McCulloch’s practices, performed with a new set of student-collaborators, involved modeling the neurophysiology of perception, understanding reliability in biological systems, and pursuing knowledge of the reticular formation of the brain. The chapter provides a nuanced account of the relations between McCulloch’s work and the emerging fields of artificial intelligence and the cognitive sciences. It also highlights McCulloch’s identities as sage-collaborator and polymath, two roles that in part were the result of his students’ observations and in part products of his own self-fashioning.

Computational Foundations of the Anticipatory Artificial Autopoietic Cellular Automata

2021 ◽  
pp. 289-307
Author(s):  
Daniel M. Dubois ◽  
Stig C. Holmberg
Keyword(s):  
Artificial Intelligence ◽  
Cellular Automata ◽  
Living Cell ◽  
Artificial Life ◽  
Initial Conditions ◽  
Living Cells ◽  
Asymmetric Membrane

A survey of the Varela automata of autopoiesis is presented. The computation of the Varela program, with initial conditions given by a living cell, is not able to self-maintain the membrane of the living cell. In this chapter, the concept of anticipatory artificial autopoiesis (AAA) is introduced. In this chapter, the authors present a new algorithm of the anticipatory artificial autopoiesis, which extend the Varela automata. The main enhancement consists in defining an asymmetric membrane of the artificial lining cell. The simulations show the anticipatory generation of artificial living cells starting with any initial conditions. The new concept of anticipatory artificial autopoiesis is related to artificial life (Alife) and artificial intelligence (AI). This is a breakthrough in the computational foundation of autopoiesis.

scholarly journals Advancing from Convection-Allowing NWP to Warn-on-Forecast: Evidence of Progress

2018 ◽  
Vol 33 (2) ◽  
pp. 599-607 ◽  
Author(s):  
John R. Lawson ◽  
John S. Kain ◽  
Nusrat Yussouf ◽  
David C. Dowell ◽  
Dustan M. Wheatley ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Initial Conditions ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Radar Data ◽  
Model Systems ◽  
Deterministic Systems ◽  
Forecast Lead Time ◽  
The Impact

Abstract The Warn-on-Forecast (WoF) program, driven by advanced data assimilation and ensemble design of numerical weather prediction (NWP) systems, seeks to advance 0–3-h NWP to aid National Weather Service warnings for thunderstorm-induced hazards. An early prototype of the WoF prediction system is the National Severe Storms Laboratory (NSSL) Experimental WoF System for ensembles (NEWSe), which comprises 36 ensemble members with varied initial conditions and parameterization suites. In the present study, real-time 3-h quantitative precipitation forecasts (QPFs) during spring 2016 from NEWSe members are compared against those from two real-time deterministic systems: the operational High Resolution Rapid Refresh (HRRR, version 1) and an upgraded, experimental configuration of the HRRR. All three model systems were run at 3-km horizontal grid spacing and differ in initialization, particularly in the radar data assimilation methods. It is the impact of this difference that is evaluated herein using both traditional and scale-aware verification schemes. NEWSe, evaluated deterministically for each member, shows marked improvement over the two HRRR versions for 0–3-h QPFs, especially at higher thresholds and smaller spatial scales. This improvement diminishes with forecast lead time. The experimental HRRR model, which became operational as HRRR version 2 in August 2016, also provides added skill over HRRR version 1.

A Cell Mapping Method for Nonlinear Deterministic and Stochastic Systems—Part II: Examples of Application

1986 ◽  
Vol 53 (3) ◽  
pp. 702-710 ◽  
Author(s):  
H. M. Chiu ◽  
C. S. Hsu
Keyword(s):  
Present Method ◽  
Stochastic Systems ◽  
Random Excitation ◽  
Mapping Method ◽  
Cell Mapping ◽  
Generalized Cell Mapping ◽  
Deterministic Systems ◽  
Strongly Nonlinear ◽  
Stochastic Problems ◽  
A Cell

In this second part of the two-part paper we demonstrate the viability of the compatible simple and generalized cell mapping method by applying it to various deterministic and stochastic problems. First we consider deterministic problems with non-chaotic responses. For this class of problems we show how system responses and domains of attraction can be obtained by a refining procedure of the present method. Then, we consider stochastic problems with stochasticity lying in system parameters or excitation. Next, deterministic systems with chaotic responses are considered. By the present method, finding the statistical responses of such systems under random excitation also presents no difficulties. Some of the systems studied here are well-known. New results are, however, also obtained. These are results on Duffing systems with a stochastic coefficient, the global results of a Duffing system shown in Section 4, the results on strongly nonlinear Duffing systems under random excitations reported in Section 7.2, and the strange attractor results for systems subjected to random excitations.

NATURE–INSPIRED INTELLIGENCE: A REVIEW OF SELECTED METHODS AND APPLICATIONS

2009 ◽  
Vol 18 (04) ◽  
pp. 487-516 ◽  
Author(s):  
VASSILIOS VASSILIADIS ◽  
GEORGIOS DOUNIAS
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Optimization Problems ◽  
Biological Systems ◽  
Artificial Immune Systems ◽  
Rapid Evolution ◽  
Successful Implementation ◽  
Human Immune System ◽  
Survey Paper ◽  
The Way

The successful handling of numerous real–world complex problems has increased the popularity of nature–inspired intelligent (NII) algorithms and techniques. Their successful implementation primarily on difficult and complicated optimization problems, stresses their upcoming importance in the broader area of artificial intelligence. NII techniques take advantage of the way that biological systems deal with real–world situations. Specifically, they simulate the way real biological systems, such as the human brain, ant colonies and human immune system work, when solving complex real–world situations. In this survey paper, we briefly present a number of selected NII approaches and we point particular suitable areas of application for each of them. Specifically, five major categories of nature inspired approaches are presented, namely, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), DNA computing, artificial immune systems and membrane computing. Applications include problems related to optimization (financial, industrial and medical), task scheduling, system design (optimization of the system's parameters), image processing and data processing (feature selection and classification). We also refer to collaboration between NII techniques and classical AI methodologies, such as neural networks, genetic algorithms, fuzzy logic, etc. The current survey states that NII techniques are likely to become the next step in the rapid evolution of artificial intelligence tools.

Liability for Artificial Intelligence and the Internet of Things

2019 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Autonomous Systems ◽  
Product Liability ◽  
Single Market ◽  
The Internet ◽  
Legislative Process ◽  
Key Topics ◽  
Digital Law ◽  
The Internet Of Things

2018 witnessed several key developments which have shaped the digital single market. Whereas some issues are now in the final stages of the legislative process, other key topics are in their infancy and therefore require in-depth discussion as to how EU law should react to the challenges and needs of the digital economy. This volume focuses on an issue central to the digital single market: the ‘Liability for Artificial Intelligence and the Internet of Things’. European legislators face the challenge of deciding between adapting existing product liability rules or the creation of a new concept of objective liability for autonomous systems. The 2018 Münster colloquium provided a forum for intense discussion of these questions between renowned experts on digital law and representatives from EU institutions and industry. With contributions by Cristina Amato, Georg Borges, Jean-Sébastien Borghetti, Giovanni Comandé, Ernst Karner, Bernhard Koch, Sebastian Lohsse, Eva Lux, Miquel Martín-Casals, Reiner Schulze, Gerald Spindler, Dirk Staudenmayer, Gerhard Wagner, Herbert Zech

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