Detecting Deceit – Guessing or Assessing? Study on the Applicability of Veracity Assessment Methods in Human Intelligence

Intelligence from a human source, that is falsely thought to be true, is potentially more harmful than a total lack of it. The veracity assessment of the gathered intelligence is one of the most important phases of the intelligence process. Lie detection and veracity assessment methods have been studied widely but a comprehensive analysis of these methods’ applicability is lacking. There are some problems related to the efficacy of lie detection and veracity assessment. According to a conventional belief an almighty lie detection method, that is almost 100% accurate and suitable for any social encounter, exists. However, scientific studies have shown that this is not the case, and popular approaches are often over simplified. The main research question of this study was: What is the applicability of veracity assessment methods, which are reliable and are based on scientific proof, in terms of the following criteria? o Accuracy, i.e. probability of detecting deception successfully o Ease of Use, i.e. easiness to apply the method correctly o Time Required to apply the method reliably o No Need for Special Equipment o Unobtrusiveness of the method In order to get an answer to the main research question, the following supporting research questions were answered first: What kinds of interviewing and interrogation techniques exist and how could they be used in the intelligence interview context, what kinds of lie detection and veracity assessment methods exist that are reliable and are based on scientific proof and what kind of uncertainty and other limitations are included in these methods? Two major databases, Google Scholar and Science Direct, were used to search and collect existing topic related studies and other papers. After the search phase, the understanding of the existing lie detection and veracity assessment methods was established through a meta-analysis. Multi Criteria Analysis utilizing Analytic Hierarchy Process was conducted to compare scientifically valid lie detection and veracity assessment methods in terms of the assessment criteria. In addition, a field study was arranged to get a firsthand experience of the applicability of different lie detection and veracity assessment methods. The Studied Features of Discourse and the Studied Features of Nonverbal Communication gained the highest ranking in overall applicability. They were assessed to be the easiest and fastest to apply, and to have required temporal and contextual sensitivity. The Plausibility and Inner Logic of the Statement, the Method for Assessing the Credibility of Evidence and the Criteria Based Content Analysis were also found to be useful, but with some limitations. The Discourse Analysis and the Polygraph were assessed to be the least applicable. Results from the field study support these findings. However, it was also discovered that the most applicable methods are not entirely troublefree either. In addition, this study highlighted that three channels of information, Content, Discourse and Nonverbal Communication, can be subjected to veracity assessment methods that are scientifically defensible. There is at least one reliable and applicable veracity assessment method for each of the three channels. All of the methods require disciplined application and a scientific working approach. There are no quick gains if high accuracy and reliability is desired. Since most of the current lie detection studies are concentrated around a scenario, where roughly half of the assessed people are totally truthful and the other half are liars who present a well prepared cover story, it is proposed that in future studies lie detection and veracity assessment methods are tested against partially truthful human sources. This kind of test setup would highlight new challenges and opportunities for the use of existing and widely studied lie detection methods, as well as for the modern ones that are still under development.

Building human intelligence ...

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Semantic linking through spaces for cyber-physical-socio intelligence:a methodology

Humans consciously and subconsciously establish various links, emerge semantic images and reason in mind, learn linking effect and rules, select linked individuals to interact, and form closed loops through links while co-experiencing in multiple spaces in lifetime. Machines are limited in these abilities although various graph-based models have been used to link resources in the cyber space. The following are fundamental limitations of machine intelligence: (1) machines know few links and rules in the physical space, physiological space, psychological space, socio space and mental space, so it is not realistic to expect machines to discover laws and solve problems in these spaces; and, (2) machines can only process pre-designed algorithms and data structures in the cyber space. They are limited in ability to go beyond the cyber space, to learn linking rules, to know the effect of linking, and to explain computing results according to physical, physiological, psychological and socio laws. Linking various spaces will create a complex space — the Cyber-Physical-Physiological-Psychological-Socio-Mental Environment CP3SME. Diverse spaces will emerge, evolve, compete and cooperate with each other to extend machine intelligence and human intelligence. From multi-disciplinary perspective, this paper reviews previous ideas on various links, introduces the concept of cyber-physical society, proposes the ideal of the CP3SME including its definition, characteristics, and multi-disciplinary revolution, and explores the methodology of linking through spaces for cyber-physical-socio intelligence. The methodology includes new models, principles, mechanisms, scientific issues, and philosophical explanation. The CP3SME aims at an ideal environment for humans to live and work. Exploration will go beyond previous ideals on intelligence and computing.

O jogo de futebol : investigação de sua estrutura, de seus modelos e da inteligencia de jogo, do ponto de vista da complexidade

Universidade Estadual de Campinas . Faculdade de Educação Física

Possibilidades corporais como expressão da inteligencia humana no processo de ensino-aprendizagem

Universidade Estadual de Campinas . Faculdade de Educação Física

A inteligencia corporal : cinestesica como manifestação da inteligencia humana no comportamento de crianças

Universidade Estadual de Campinas . Faculdade de Educação Física

Intel·ligència emocional : una aproximació crítica

L'objectiu d'aquest treball és intentar reflexionar sobre tots aquests interrogants i redefinir el concepte d'intel·ligència al començament del segle XXI, analitzant alhora l'impacte real que la IE ha tingut i té en el món de l'empresa espanyola. En conjunt, el treball proposa una relectura del fenomen de la intel·ligència humana i recull tant les opinions de més relleu dels autors més significatius com dades empíriques que ajuden a comprendre de manera pràctica si realment hi ha hagut, o s'està produint, una autèntica mutació social en la comprensió de la intel·ligència.

A tabela periódica dos elementos químicos prevista por redes neurais artificiais de Kohonen

Although several chemical elements were not known by end of the 18th century, Mendeleyev came up with an astonishing achievement: the periodic table of elements. He was not only able to predict the existence of (then) new elements but also to provide accurate estimates of their chemical and physical properties. This is certainly a relevant example of the human intelligence. Here, we intend to shed some light on the following question: Can an artificial intelligence system yield a classification of the elements that resembles, in some sense, the periodic table? To achieve our goal, we have fed a self-organized map (SOM) with information available at Mendeleyev's time. Our results show that similar elements tend to form individual clusters. Thus, SOM generates clusters of halogens, alkaline metals and transition metals that show a similarity with the periodic table of elements.

Toimintakykyinen ja oppiva henkilötiedusteluoperaattori

Henkilötiedustelua aiheena ei ole suomalaisessa sotatieteellisessä tutkimuksessa aiemmin tutkittu. Henkilötiedustelu tai HUMINT = Human Intelligence on nykyään yhä käytetympi termi sotilaiden keskuudessa, vaikka vain harva tietää, mitä työskentely todellisuudessa pitää sisällään. Tutkimuksessa tutkittiin henkilötiedustelijan toimintakykyä, kehyksenään sotilaspedagoginen viitekehys sotilaan toimintakyvylle. Lisäksi tutkimuksessa pyrittiin selvittämään asiakokonaisuuksia, joita HUMINT- operaattorin koulutuksessa tulisi ottaa huomioon, pyrkimyksenä henkilötiedustelukoulutuksen mahdollinen kehittyminen tulevaisuudessa. Tämän lisäksi tässä tutkimuksessa luotiin lukijalle pintapuolinen kuva henkilötiedustelutyöstä ennen toimintakykyä ja oppimista käsitteleviä kappaleita. Tutkimus toteutettiin kvalitatiivisena tutkimuksena, tutkimusmetodina oli hermeneuttinen sisällönanalyysi. Aineiston analyysitapana käytettiin teoriasidonnaista analyysiä. Tutkimuskysymykset laadittiin selvittämään henkilötiedustelijan toimintakykyä sekä oppimista 1) miten sotilaan toimintakyvyn nelikenttämalli toteutuu henkilötiedustelijan työssä ja 2) mitkä ovat keskeisimmät kehittämisen kohteet suomalaisessa henkilötiedustelukoulutuksessa? Tutkimuksessa havaittiin, että henkilötiedustelijan toimintakyvyn tärkeimmiksi osa-alueiksi nousivat psyykkinen ja sosiaalinen toimintakyky. Lisäksi tutkimustulokset osoittivat, että koulutuksen painopistettä tulisi kehittää nimenomaan lähteen käsittelyyn sekä puhuttamiseen.

Réseaux de neurones à relaxation entraînés par critère d'autoencodeur débruitant

L’apprentissage machine est un vaste domaine où l’on cherche à apprendre les paramètres de modèles à partir de données concrètes. Ce sera pour effectuer des tâches demandant des aptitudes attribuées à l’intelligence humaine, comme la capacité à traiter des don- nées de haute dimensionnalité présentant beaucoup de variations. Les réseaux de neu- rones artificiels sont un exemple de tels modèles. Dans certains réseaux de neurones dits profonds, des concepts "abstraits" sont appris automatiquement. Les travaux présentés ici prennent leur inspiration de réseaux de neurones profonds, de réseaux récurrents et de neuroscience du système visuel. Nos tâches de test sont la classification et le débruitement d’images quasi binaires. On permettra une rétroac- tion où des représentations de haut niveau (plus "abstraites") influencent des représentations à bas niveau. Cette influence s’effectuera au cours de ce qu’on nomme relaxation, des itérations où les différents niveaux (ou couches) du modèle s’interinfluencent. Nous présentons deux familles d’architectures, l’une, l’architecture complètement connectée, pouvant en principe traiter des données générales et une autre, l’architecture convolutionnelle, plus spécifiquement adaptée aux images. Dans tous les cas, les données utilisées sont des images, principalement des images de chiffres manuscrits. Dans un type d’expérience, nous cherchons à reconstruire des données qui ont été corrompues. On a pu y observer le phénomène d’influence décrit précédemment en comparant le résultat avec et sans la relaxation. On note aussi certains gains numériques et visuels en terme de performance de reconstruction en ajoutant l’influence des couches supérieures. Dans un autre type de tâche, la classification, peu de gains ont été observés. On a tout de même pu constater que dans certains cas la relaxation aiderait à apprendre des représentations utiles pour classifier des images corrompues. L’architecture convolutionnelle développée, plus incertaine au départ, permet malgré tout d’obtenir des reconstructions numériquement et visuellement semblables à celles obtenues avec l’autre architecture, même si sa connectivité est contrainte.

Generating Communications Systems Through Shared Context

In a distributed model of intelligence, peer components need to communicate with one another. I present a system which enables two agents connected by a thick twisted bundle of wires to bootstrap a simple communication system from observations of a shared environment. The agents learn a large vocabulary of symbols, as well as inflections on those symbols which allow thematic role-frames to be transmitted. Language acquisition time is rapid and linear in the number of symbols and inflections. The final communication system is robust and performance degrades gradually in the face of problems.