Software psychology: Plugging into human performance in computer systems

1981 ◽  
Author(s):  
Greg Kearsley
Keyword(s):  
Human Performance ◽  
Computer Systems ◽  
Software Psychology

scholarly journals The Winograd Schemas from Hell

2020 ◽  
Author(s):  
Fábio Cozman ◽  
Hugo Munhoz
Keyword(s):  
Human Performance ◽  
Human Subjects ◽  
Computer Systems ◽  
Difficulty Level ◽  
Commonsense Reasoning

The Winograd Challenge has been advocated as a test of computer understanding with respect to commonsense reasoning. The challenge is based on Winograd Schemas: sentences that contain correferential ambiguities. Most Winograd Schemas are relatively easy for human subjects, and today the best computer systems for the Winograd Challenge can work close to human performance. In this paper, we examine the assumptions behind the Winograd Challenge, and investigate how far we can push the difficulty level of Winograd Schemas, proposing various strategies to build really challenging schemas.

scholarly journals Artificial Intelligence Is Stupid and Causal Reasoning Will Not Fix It

2021 ◽  
Vol 11 ◽  
Author(s):  
J. Mark Bishop
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Causal Reasoning ◽  
Imperfect Information ◽  
Human Performance ◽  
Autonomous Vehicle ◽  
Credit Scoring ◽  
Computer Systems ◽  
New York Times ◽  
Time Space

Artificial Neural Networks have reached “grandmaster” and even “super-human” performance across a variety of games, from those involving perfect information, such as Go, to those involving imperfect information, such as “Starcraft”. Such technological developments from artificial intelligence (AI) labs have ushered concomitant applications across the world of business, where an “AI” brand-tag is quickly becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong—an autonomous vehicle crashes, a chatbot exhibits “racist” behavior, automated credit-scoring processes “discriminate” on gender, etc.—there are often significant financial, legal, and brand consequences, and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that “... all the impressive achievements of deep learning amount to just curve fitting.” The key, as Pearl suggests, is to replace “reasoning by association” with “causal reasoning” —the ability to infer causes from observed phenomena. It is a point that was echoed by Gary Marcus and Ernest Davis in a recent piece for the New York Times: “we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets—often using an approach known as ‘Deep Learning’—and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space, and causality.” In this paper, foregrounding what in 1949 Gilbert Ryle termed “a category mistake”, I will offer an alternative explanation for AI errors; it is not so much that AI machinery cannot “grasp” causality, but that AI machinery (qua computation) cannot understand anything at all.

scholarly journals Computer Models Solving Intelligence Test Problems: Progress and Implications (Extended Abstract)

2017 ◽  
Author(s):  
José Hernández-Orallo ◽  
Fernando Martínez-Plumed ◽  
Ute Schmid ◽  
Michael Siebers ◽  
David Dowe
Keyword(s):  
Computational Models ◽  
Human Performance ◽  
Item Difficulty ◽  
Computer Systems ◽  
Computer Models ◽  
Intelligence Test ◽  
Test Problems ◽  
Xxth Century ◽  
Increasing Trend ◽  
Xxist Century

While some computational models of intelligence test problems were proposed throughout the second half of the XXth century, in the first years of the XXIst century we have seen an increasing number of computer systems being able to score well on particular intelligence test tasks. However, despitethis increasing trend there has been no general account of all these works in terms of how theyrelate to each other and what their real achievements are. In this paper, we provide some insighton these issues by giving a comprehensive account of about thirty computer models, from the 1960sto nowadays, and their relationships, focussing on the range of intelligence test tasks they address, thepurpose of the models, how general or specialised these models are, the AI techniques they use in eachcase, their comparison with human performance, and their evaluation of item difficulty.

The limits of human performance

2008 ◽  
Vol 44 ◽  
pp. 11-26 ◽  
Author(s):  
Ralph Beneke ◽  
Dieter Böning
Keyword(s):  
Human Performance ◽  
Safety Margin ◽  
Afferent Input ◽  
Metabolic Energy ◽  
Human Organism ◽  
Mechanical Resistance ◽  
As Doping ◽  
Gene And Protein Expression ◽  
Underlying Mechanisms ◽  
Biochemical Research

Human performance, defined by mechanical resistance and distance per time, includes human, task and environmental factors, all interrelated. It requires metabolic energy provided by anaerobic and aerobic metabolic energy sources. These sources have specific limitations in the capacity and rate to provide re-phosphorylation energy, which determines individual ratios of aerobic and anaerobic metabolic power and their sustainability. In healthy athletes, limits to provide and utilize metabolic energy are multifactorial, carefully matched and include a safety margin imposed in order to protect the integrity of the human organism under maximal effort. Perception of afferent input associated with effort leads to conscious or unconscious decisions to modulate or terminate performance; however, the underlying mechanisms of cerebral control are not fully understood. The idea to move borders of performance with the help of biochemicals is two millennia old. Biochemical findings resulted in highly effective substances widely used to increase performance in daily life, during preparation for sport events and during competition, but many of them must be considered as doping and therefore illegal. Supplements and food have ergogenic potential; however, numerous concepts are controversially discussed with respect to legality and particularly evidence in terms of usefulness and risks. The effect of evidence-based nutritional strategies on adaptations in terms of gene and protein expression that occur in skeletal muscle during and after exercise training sessions is widely unknown. Biochemical research is essential for better understanding of the basic mechanisms causing fatigue and the regulation of the dynamic adaptation to physical and mental training.

Sign in / Sign up

Export Citation Format

Share Document