The Social Embedding of Intelligence

Edmonds, Bruce

doi:10.1007/978-1-4020-6710-5_14

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…For instance, Bruce Edmonds [Edm09], building on the "No Free Lunch" results [WM95], demonstrates that there is no such thing as a universal intelligence: no intelligence that outperforms others in every circumstance. Initially this seems to rule out AI entirely; but when one analyses what this means empirically, one realises there is far less to it.…”

Section: Extracting Falsifiable Predictionsmentioning

confidence: 99%

The errors, insights and lessons of famous AI predictions – and what they mean for the future

Armstrong

Sotala

hÉigeartaigh

2014

Journal of Experimental & Theoretical Artificial Intelligen

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Predicting the development of artificial intelligence (AI) is a difficult project -but a vital one, according to some analysts. AI predictions already abound: but are they reliable? This paper will start by proposing a decomposition schema for classifying them. Then it constructs a variety of theoretical tools for analysing, judging and improving them. These tools are demonstrated by careful analysis of five famous AI predictions: the initial Dartmouth conference, Dreyfus's criticism of AI, Searle's Chinese Room paper, Kurzweil's predictions in the 'Age of Spiritual Machines', and Omohundro's 'AI Drives' paper. These case studies illustrate several important principles, such as the general overconfidence of experts, the superiority of models over expert judgement, and the need for greater uncertainty in all types of predictions. The general reliability of expert judgement in AI timeline predictions is shown to be poor, a result that fits in with previous studies of expert competence.//

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Section: Extracting Falsifiable Predictionsmentioning

confidence: 99%

The errors, insights and lessons of famous AI predictions – and what they mean for the future

Armstrong

Sotala

hÉigeartaigh

2014

Journal of Experimental & Theoretical Artificial Intelligen

View full text Add to dashboard Cite

show abstract

“…Under this assumption, we have the conditions for the so-called no-free-lunch theorems (Wolpert & Macready, 1995;Wolpert, 2012), leading to the conclusion that, on average, no method can be better than any other. According to this, a general-purpose system and, indeed, the very concept of 'general intelligence' would be impossible (Edmonds, 2009). On the other hand, one can consider problems as programs.…”

Section: Introductionmentioning

confidence: 99%

AI Generality and Spearman’s Law of Diminishing Returns

Hernández-Orallo¹

2019

jair

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Many areas of AI today use benchmarks and competitions with larger and wider sets of tasks. This tries to deter AI systems (and research effort) from specialising to a single task, and encourage them to be prepared to solve previously unseen tasks. It is unclear, however, whether the methods with best performance are actually those that are most general and, in perspective, whether the trend moves towards more general AI systems. This question has a striking similarity with the analysis of the so-called positive manifold and general factors in the area of human intelligence. In this paper, we first show how the existence of a manifold (positive average pairwise task correlation) can also be analysed in AI, and how this relates to the notion of agent generality, from the individual and the populational points of view. From the populational perspective, we analyse the following question: is this manifold correlation higher for the most or for the least able group of agents? We contrast this analysis with one of the most controversial issues in human intelligence research, the so-called Spearman's Law of Diminishing Returns (SLODR), which basically states that the relevance of a general factor diminishes for most able human groups. We perform two empirical studies on these issues in AI. We analyse the results of the 2015 general video game AI (GVGAI) competition, with games as tasks and "controllers" as agents, and the results of a synthetic setting, with modified elementary cellular automata (ECA) rules as tasks and simple interactive programs as agents. In both cases, we see that SLODR doesnot appear. The data, and the use of just two scenarios, does not clearly support the reverse either, a Universal Law of Augmenting Returns (ULOAR), but calls for more experiments on this question.

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How We’re Predicting AI – or Failing to

Armstrong

Sotala²

2015

Topics in Intelligent Engineering and Informatics

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This paper will look at the various predictions that have been made about AI and propose decomposition schemas for analyzing them. It will propose a variety of theoretical tools for analyzing, judging, and improving these predictions. Focusing specifically on timeline predictions (dates given by which we should expect the creation of AI), it will show that there are strong theoretical grounds to expect predictions to be quite poor in this area. Using a database of 95 AI timeline predictions, it will show that these expectations are borne out in practice: expert predictions contradict each other considerably, and are indistinguishable from non-expert predictions and past failed predictions. Predictions that AI lie 15 to 25 years in the future are the most common, from experts and non-experts alike.

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The Social Embedding of Intelligence

Cited by 5 publications

References 12 publications

The errors, insights and lessons of famous AI predictions – and what they mean for the future

The errors, insights and lessons of famous AI predictions – and what they mean for the future

AI Generality and Spearman’s Law of Diminishing Returns

How We’re Predicting AI – or Failing to

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