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Hernández-Orallo, José

doi:10.1023/a:1008367325700

Cited by 65 publications

(4 citation statements)

References 46 publications

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“…[280], [281], [282], [283], [284], [285], [286], [287], [288], [289], [290], [291], [292] Others 19. [293], [294], [295], [296], [297], [298], [299], [300], [301], [302], [303], [304], [305] 20. [306], [307], [308], [309], [310], [311], [312], [313], [314], [315], [316], [317], [318] 21.…”

Section: Discussionunclassified

Governance of a DAO for Facilitating Dialogue on Human-Algorithm Interaction and the Impact of Emerging Technologies on Society

Braga¹,

Regateiro²,

Stiubiener³

et al. 2023

Preprint

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Human-algorithm interaction is a crucial issue for humanity in light of the impacts of the recent release of ChatGPT3 and 4, among others. These advanced chatbots provoked a worldwide debate in March/2023, when a manifesto signed by several stakeholders was published and widely discussed in the media and academia. This work assumes that human-algorithm interactions are influenced by a context of diverse interests and perspectives, which adds high complexity to the problem. Therefore, this work proposes a solution to enable the effective participation of stakeholders from different domains and society in a constructive dialogue, using digital platforms as a medium. Inspired by the successful governance of the Internet infrastructure ecosystem, the proposal involves the creation of an Autonomous Decentralized Organization (DAO) implemented in the blockchain environment of the Ethereum network. However, before implementing the DAO, it is necessary to build a knowledge base, that is, an ontology, which guides its development in a safe and adequate way. A preliminary version of this knowledge base was manually built using Protégé with over 4,000 axioms.

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Section: Discussionunclassified

Governance of a DAO for Facilitating Dialogue on Human-Algorithm Interaction and the Impact of Emerging Technologies on Society

Braga¹,

Regateiro²,

Stiubiener³

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…What program are we looking for? If we are looking for AI according to the definition given in the articles [7][8][9][10][11][12], then we are looking for a program that is smart enough, and we measure how smart a program is by how it performs in arbitrary world. For a program to be successful, we must have a goal, and the achievement of that goal will be the criterion by which we will evaluate the success of the program.…”

Section: Reinforcement Learning Without Reinforcementmentioning

confidence: 99%

Before We Can Find a Model, We Must Forget about Perfection

Dobrev

2022

SJC

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With Reinforcement Learning we assume that a model of the world does exist. We assume furthermore that the model in question is perfect (i.e. it describes the world completely and unambiguously). This article will demonstrate that it does not make sense to search for the perfect model because this model is too complicated and practically impossible to find. We will show that we should abandon the pursuit of perfection and pursue Event-Driven (ED) models instead. These models are generalization of Markov Decision Process (MDP) models. This generalization is essential because nothing can be found without it. Rather than a single MDP, we will aim to find a raft of neat simple ED models each one describing a simple dependency or property. In other words, we will replace the search for a singular and complex perfect model with a search for a large number of simple models.

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“…In 2007 [39], method for measuring intelligence based on the capacity to compare alternatives with different levels of complexity was proposed. In 2010, Hernández-Orallo [40] presented an interesting study related to the Turing Test. A universal anytime intelligence test was proposed by Hernández-Orallo and Dowe in 2010 [41].…”

Section: State-of-the-art Metrics Designed To Measure Machine Intellimentioning

confidence: 99%

ExtrIntDetect—A New Universal Method for the Identification of Intelligent Cooperative Multiagent Systems with Extreme Intelligence

2019

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In this research, we define a specific type of performance of the intelligent agent-based systems (IABSs) in terms of a difficult problem-solving intelligence measure. Many studies present the successful application of intelligent cooperative multiagent systems (ICMASs) for efficient, flexible and robust solving of difficult real-life problems. Based on a comprehensive study of the scientific literature, we conclude that there is no unanimous view in the scientific literature on machine intelligence, or on what an intelligence metric must measure. Metrics presented in the scientific literature are based on diverse paradigms. In our approach, we assume that the measurement of intelligence is based on the ability to solve difficult problems. In our opinion, the measurement of intelligence in this context is important, as it allows the differentiation between ICMASs based on the degree of intelligence in problem-solving. The recent OutIntSys method presented in the scientific literature can identify systems with outlier high and outlier low intelligence from a set of studied ICMASs. In this paper, a novel universal method called ExtrIntDetect, defined on the basis of a specific series of computing processes and analyses, is proposed for the detection of the ICMASs with statistical outlier low and high problem-solving intelligence from a given set of studied ICMASs. ExtrIntDetect eliminates the disadvantage of the OutIntSys method with respect to its limited robustness. The recent symmetric MetrIntSimil metric presented in the literature is capable of measuring and comparing the intelligence of large numbers of ICMASs and based on their respective problem-solving intelligences in order to classify them into intelligence classes. Systems whose intelligence does not statistically differ are classified as belonging to the same class of intelligent systems. Systems classified in the same intelligence class are therefore able to solve difficult problems using similar levels of intelligence. One disadvantage of the symmetric MetrIntSimil lies in the fact that it is not able to detect outlier intelligence. Based on this fact, the ExtrIntDetect method could be used as an extension of the MetrIntSimil metric. To validate and evaluate the ExtrIntDetect method, an experimental evaluation study on six ICMASs is presented and discussed.

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Untitled

Cited by 65 publications

References 46 publications

Governance of a DAO for Facilitating Dialogue on Human-Algorithm Interaction and the Impact of Emerging Technologies on Society

Governance of a DAO for Facilitating Dialogue on Human-Algorithm Interaction and the Impact of Emerging Technologies on Society

Before We Can Find a Model, We Must Forget about Perfection

ExtrIntDetect—A New Universal Method for the Identification of Intelligent Cooperative Multiagent Systems with Extreme Intelligence

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