84 research outputs found
Population Curation in Swarms: Predicting Top Performers
In recent years, new Artificial Intelligence technologies have mimicked examples of collective intelligence occurring in the natural world including flocks of birds, schools of fish, and swarms of bees. One company in particular, Unanimous AI, built a platform (UNU Swarm) that enables a group of humans to make decisions as a single mind by forming a real-time closed-loop feedback system for individuals. This platform has proven the ability to amplify the predictive ability of groups of humans in realms including sports, medicine, politics, finance, and entertainment. Previous research has demonstrated it is possible to further enhance knowledge accumulation within a crowd through curation and bias methods applied to individuals in the crowd.\newline
This study explores the efficacy of applying a machine learning pipeline to identify the top performing individuals in the crowd based on a structural profile of survey responses. The ultimate goal is to select these users as Swarm participants to improve the accuracy of the overall system. Unanimous AI provided 24 weeks of survey data collection consisting of 1,139 users from the NHL 2017-2018 season. By applying a machine learning pipeline, this study able to curate a crowd consisting of users that had an average z-score 0.309 and Wisdom of the Crowd prediction accuracy of 61.5%, which is 4.1% higher than a randomly selected crowd and 1.4% lower than Vegas favorite picks
Amplifying the Collective Intelligence of Teams with Swarm AI
Group decision-making is strengthened by the varied knowledge and perspectives that each member brings, yet teams often fail to capitalize on their diversity. This paper describes how Swarm AI, a novel collaborative intelligence technology modeled on the decision-making process of honey bee swarms, enables networked human groups to more effectively leverage their combined insights. Through an empirical study conducted on 60 small teams, each of 3 to 6 members, we demonstrate the capacity of Swarm AI to significantly amplify the collective intelligence of human groups. A well-known testing instrumentâthe Reading the Mind in the Eyes (RME) test âwas used to measure the social intelligence of each teamâa key indicator of collective intelligence. The study compares the RME performance of (i) individuals, (ii) teams working by majority vote, and (iii) teams using an interactive software platform that employs Swarm AI technology
Amplifying the Social Intelligence of Teams Through Human Swarming
Artificial Swarm Intelligence (ASI) is a method for amplifying the collective intelligence of human groups by connecting networked participants into real-time systems modeled after natural swarms and moderated by AI algorithms. ASI has been shown to amplify performance in a wide range of tasks, from forecasting financial markets to prioritizing conflicting objectives. This study explores the ability of ASI systems to amplify the social intelligence of small teams. A set of 61 teams, each of 3 to 6 members, was administered a standard social sensitivity test â Reading the Mind in the Eyesâ or RME. Subjects took the test both as individuals and as ASI systems (i.e. âswarmsâ). The average individual scored 24 of 35 correct (32% error) on the RME test, while the average ASI swarm scored 30 of 35 correct (15% error). Statistical analysis found that the groups working as ASI swarms had significantly higher social sensitivity than individuals working alone or groups working together by plurality vote (p\u3c0.001). This suggests that when groups reach decisions as real-time ASI swarms, they make better use of their social intelligence than when working alone or by traditional group vote
Measuring Group Personality with Swarm AI
The aggregation of individual personality tests to predict team performance is widely accepted in management theory but has significant limitations: the isolated nature of individual personality surveys fails to capture much of the team dynamics that drive real-world team performance. Artificial Swarm Intelligence (ASI), a technology that enables networked teams to think together in real-time and answer questions as a unified system, promises a solution to these limitations by enabling teams to take personality tests together and converge upon answers that best represent the groupâs disposition. In the present study, the group personality of 94 small teams was assessed by having teams take a standard Big Five Inventory (BFI) test both as individuals, and as a real-time system enabled by an ASI technology known as Swarm AI. The predictive accuracy of each personality assessment method was assessed by correlating the BFI personality traits to a range of real-world performance metrics. The results showed that assessments of personality generated using Swarm AI were far more predictive of team performance than the traditional survey-based method, showing a significant improvement in correlation with at least 25% of performance metrics, and in no case showing a significant decrease in predictive performance. This suggests that Swarm AI technology may be used as a highly effective team personality assessment tool that more accurately predicts future team performance than traditional survey approaches
Architecting system of systems: artificial life analysis of financial market behavior
This research study focuses on developing a framework that can be utilized by system architects to understand the emergent behavior of system architectures. The objective is to design a framework that is modular and flexible in providing different ways of modeling sub-systems of System of Systems. At the same time, the framework should capture the adaptive behavior of the system since evolution is one of the key characteristics of System of Systems. Another objective is to design the framework so that humans can be incorporated into the analysis. The framework should help system architects understand the behavior as well as promoters or inhibitors of change in human systems. Computational intelligence tools have been successfully used in analysis of Complex Adaptive Systems. Since a System of Systems is a collection of Complex Adaptive Systems, a framework utilizing combination of these tools can be developed. Financial markets are selected to demonstrate the various architectures developed from the analysis framework --Introduction, page 3
A Field Guide to Genetic Programming
xiv, 233 p. : il. ; 23 cm.Libro ElectrĂłnicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction --
Representation, initialisation and operators in Tree-based GP --
Getting ready to run genetic programming --
Example genetic programming run --
Alternative initialisations and operators in Tree-based GP --
Modular, grammatical and developmental Tree-based GP --
Linear and graph genetic programming --
Probalistic genetic programming --
Multi-objective genetic programming --
Fast and distributed genetic programming --
GP theory and its applications --
Applications --
Troubleshooting GP --
Conclusions.Contents
xi
1 Introduction
1.1 Genetic Programming in a Nutshell
1.2 Getting Started
1.3 Prerequisites
1.4 Overview of this Field Guide I
Basics
2 Representation, Initialisation and GP
2.1 Representation
2.2 Initialising the Population
2.3 Selection
2.4 Recombination and Mutation Operators in Tree-based
3 Getting Ready to Run Genetic Programming 19
3.1 Step 1: Terminal Set 19
3.2 Step 2: Function Set 20
3.2.1 Closure 21
3.2.2 Sufficiency 23
3.2.3 Evolving Structures other than Programs 23
3.3 Step 3: Fitness Function 24
3.4 Step 4: GP Parameters 26
3.5 Step 5: Termination and solution designation 27
4 Example Genetic Programming Run
4.1 Preparatory Steps 29
4.2 Step-by-Step Sample Run 31
4.2.1 Initialisation 31
4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming
5 Alternative Initialisations and Operators in
5.1 Constructing the Initial Population
5.1.1 Uniform Initialisation
5.1.2 Initialisation may Affect Bloat
5.1.3 Seeding
5.2 GP Mutation
5.2.1 Is Mutation Necessary?
5.2.2 Mutation Cookbook
5.3 GP Crossover
5.4 Other Techniques 32
5.5 Tree-based GP 39
6 Modular, Grammatical and Developmental Tree-based GP 47
6.1 Evolving Modular and Hierarchical Structures 47
6.1.1 Automatically Defined Functions 48
6.1.2 Program Architecture and Architecture-Altering 50
6.2 Constraining Structures 51
6.2.1 Enforcing Particular Structures 52
6.2.2 Strongly Typed GP 52
6.2.3 Grammar-based Constraints 53
6.2.4 Constraints and Bias 55
6.3 Developmental Genetic Programming 57
6.4 Strongly Typed Autoconstructive GP with PushGP 59
7 Linear and Graph Genetic Programming 61
7.1 Linear Genetic Programming 61
7.1.1 Motivations 61
7.1.2 Linear GP Representations 62
7.1.3 Linear GP Operators 64
7.2 Graph-Based Genetic Programming 65
7.2.1 Parallel Distributed GP (PDGP) 65
7.2.2 PADO 67
7.2.3 Cartesian GP 67
7.2.4 Evolving Parallel Programs using Indirect Encodings 68
8 Probabilistic Genetic Programming
8.1 Estimation of Distribution Algorithms 69
8.2 Pure EDA GP 71
8.3 Mixing Grammars and Probabilities 74
9 Multi-objective Genetic Programming 75
9.1 Combining Multiple Objectives into a Scalar Fitness Function 75
9.2 Keeping the Objectives Separate 76
9.2.1 Multi-objective Bloat and Complexity Control 77
9.2.2 Other Objectives 78
9.2.3 Non-Pareto Criteria 80
9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80
9.4 Multi-objective Optimisation via Operator Bias 81
10 Fast and Distributed Genetic Programming 83
10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83
10.2 Reducing Cost of Fitness with Caches 86
10.3 Parallel and Distributed GP are Not Equivalent 88
10.4 Running GP on Parallel Hardware 89
10.4.1 Masterâslave GP 89
10.4.2 GP Running on GPUs 90
10.4.3 GP on FPGAs 92
10.4.4 Sub-machine-code GP 93
10.5 Geographically Distributed GP 93
11 GP Theory and its Applications 97
11.1 Mathematical Models 98
11.2 Search Spaces 99
11.3 Bloat 101
11.3.1 Bloat in Theory 101
11.3.2 Bloat Control in Practice 104
III
Practical Genetic Programming
12 Applications
12.1 Where GP has Done Well
12.2 Curve Fitting, Data Modelling and Symbolic Regression
12.3 Human Competitive Results â the Humies
12.4 Image and Signal Processing
12.5 Financial Trading, Time Series, and Economic Modelling
12.6 Industrial Process Control
12.7 Medicine, Biology and Bioinformatics
12.8 GP to Create Searchers and Solvers â Hyper-heuristics xiii
12.9 Entertainment and Computer Games 127
12.10The Arts 127
12.11Compression 128
13 Troubleshooting GP
13.1 Is there a Bug in the Code?
13.2 Can you Trust your Results?
13.3 There are No Silver Bullets
13.4 Small Changes can have Big Effects
13.5 Big Changes can have No Effect
13.6 Study your Populations
13.7 Encourage Diversity
13.8 Embrace Approximation
13.9 Control Bloat
13.10 Checkpoint Results
13.11 Report Well
13.12 Convince your Customers
14 Conclusions
Tricks of the Trade
A Resources
A.1 Key Books
A.2 Key Journals
A.3 Key International Meetings
A.4 GP Implementations
A.5 On-Line Resources 145
B TinyGP 151
B.1 Overview of TinyGP 151
B.2 Input Data Files for TinyGP 153
B.3 Source Code 154
B.4 Compiling and Running TinyGP 162
Bibliography 167
Inde
Computational Optimizations for Machine Learning
The present book contains the 10 articles finally accepted for publication in the Special Issue âComputational Optimizations for Machine Learningâ of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity
The relevance of prediction markets for corporate forecasting
Prediction markets (PMs) are virtual stock markets on which shares are traded taking
advantage of the wisdom-of-crowds principle to access collective intelligence. It is
claimed that the accumulation of information by groups leads to joint group decisions
often better than individual participantsâ approaches to solutions. A PM share represents
a future event or a market condition (e.g. expected sales figures of a product for a specific
month) and provides forecasts via its price which is interpreted as the probability of the
event occurring. PMs can be used in competition with other forecasting tools; when
applied for forecasting purposes within a company they are called corporate prediction
markets (CPMs). Despite great praise in the (academic) literature for the use of PMs as
an efficient instrument for bringing together scattered information and opinions,
corporate usage and applications are limited.
This research was directed towards an examination of this discrepancy by means of
focusing on the barriers to adoption within enterprises. Literature and reality diverged
and neglected the important aspect of corporate culture. Screening existing research and
interviews with business executives and corporate planners revealed challenges of
company hierarchy as an inhibitor to the acceptance of CPM outcomes.
Findings from 55 interviews and a thematic analysis of the literature exposed that CPMs
are useful but rarely used. Their lack of use arises from senior executivesâ perception of
the organisational hierarchy being taxed and fear of losing power as CPMs (can) include
lower rungs of the corporate ladder in decision-making processes. If these challenges can
be overcome the potential of CPMs can be released. It emerged â buttressed by ten
additional interviews â that CPMs would be worthwhile for company forecasting,
particularly supporting innovation management which would allow idea markets (as an
embodiment of CPMs) to excel.
A contribution of this research lies in its additions to the PM literature, explaining the
lack of adoption of CPMs despite their apparent benefits and making a case for the
incorporation of CPMs as a forecasting instrument to facilitate innovation management.
Furthermore, a framework to understand decision-making in the adoption of strategic
tools is provided. This framework permits tools to be accepted on a more rational base
and curb the emotional and political influences which can act against the adoption of good
and effective tools
Unconsciousness by Design: Addictive Technologies and the Escape from Freedom
How does human-centred design lead to addiction? This normative research project explores how designers of technology are complicit in the co-production of addictive user behaviour, unconsciously shifting the burden of responsibility by deferring to the desirability of âwhat people want.â These unconscious designers are themselves ideologically âaddictedâ to the promises of the technological fix, creating surface solutions that reinforce a status quo that commoditizes users of technology for profit. By foregrounding the technocratic intolerance of uncertainty and the need for existential responsibility, this research proposes how designers must consciously take an ethical stance to their practice in order to manifest empowering technologies that are respectful of the human condition
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