Latest Experiments with GDV Technique in Agronomy

We have recorded coronas of ripe apples as a follow up to last year’s study [6]. The results indicate that we are unable to detect differences between organically and conventionally grown apples of very similar standard quality. We are, however, able to pick up differences between plants grown using different fertilization schemes

Search versus Knowledge: An Empirical Study of Minimax on KRK

This article presents the results of an empirical experiment designed to gain insight into what is the effect of the minimax algorithm on the evaluation function. The experiment’s simulations were performed upon the KRK chess endgame. Our results show that dependencies between evaluations of sibling nodes in a game tree and an abundance of possibilities to commit blunders present in the KRK endgame are not sufficient to explain the success of the minimax principle in practical game-playing as was previously believed. The article shows that minimax in combination with a noisy evaluation function introduces a bias into the backed-up evaluations and argues that this bias is what masked the effectiveness of the minimax in previous studies

Bewusstseinskreierung bei virtuellen Datenverarbeitungsgeräten. Funktionalismus und Phänomenologie

This paper describes the efforts of those who work with informational machines and with informational analyses to provide a basis for understanding consciousness and for speculating on what it would take to make a conscious machine. Some of the origins of these considerations are covered and the contributions of several researchers are reviewed. A distinction is drawn between functional and phenomenological approaches showing how the former lead to algorithmic methods based on conventional programming, while the latter lead to neural network analyses. Attention is drawn to the many open questions that this approach generates and some speculation on what future work might bring is included.L’article décrit les efforts employés par ceux qui travaillent sur des machines et des analyses informationnelles afin de fournir des clés de compréhension de la conscience et des hypothèses sur les moyens nécessaires à la fabrication d’une machine consciente. Le texte mentionne certaines origines de ces considérations et fait le compte rendu des contributions de plusieurs chercheurs. Une distinction est établie entre les approches fonctionnelles et phénoménologiques. Les premières mènent aux méthodes algorithmiques fondées sur la programmation conventionnelle, tandis que les secondes mènent aux analyses du réseau neural. Le texte attire l’attention sur de nombreuses questions ouvertes suscitées par cette approche et comporte des réflexions sur des travaux futurs.In diesem Beitrag werden die Anstrengungen von Forschern beschrieben, die sich mit Datenverarbeitungsgeräten und Informationsanalysen beschäftigen, um die Grundvoraussetzungen zu schaffen für ein adäquates Verständnis von Bewusstsein sowie Spekulationen darüber, welche Schritte erforderlich sind, um eine mit einem Bewusstsein ausgestattete Maschine herzustellen. Während die Beiträge einiger Forscher im Einzelnen vorgestellt werden, bleiben andere Urheber erwähnter Spekulationen unerwähnt. Der Verfasser unterscheidet zwischen einem funktionalen und einem phänomenologischen Ansatz. Er zeigt auf, dass der funktionale Ansatz in algorithmischen, auf konventionellen Programmierungsmethoden gründenden Methoden resultiert, der phänomenologische Ansatz wiederum in neuralen Netzwerkanalysen. Sodann widmet sich der Verfasser zahlreichen offenen Fragen, die aus diesem Ansatz hervorgehen, und stellt Überlegungen über die möglichen Ergebnisse zukünftiger Forschungen an

Bewusstseinskreierung bei virtuellen Datenverarbeitungsgeräten. Funktionalismus und Phänomenologie

This paper describes the efforts of those who work with informational machines and with informational analyses to provide a basis for understanding consciousness and for speculating on what it would take to make a conscious machine. Some of the origins of these considerations are covered and the contributions of several researchers are reviewed. A distinction is drawn between functional and phenomenological approaches showing how the former lead to algorithmic methods based on conventional programming, while the latter lead to neural network analyses. Attention is drawn to the many open questions that this approach generates and some speculation on what future work might bring is included.L’article décrit les efforts employés par ceux qui travaillent sur des machines et des analyses informationnelles afin de fournir des clés de compréhension de la conscience et des hypothèses sur les moyens nécessaires à la fabrication d’une machine consciente. Le texte mentionne certaines origines de ces considérations et fait le compte rendu des contributions de plusieurs chercheurs. Une distinction est établie entre les approches fonctionnelles et phénoménologiques. Les premières mènent aux méthodes algorithmiques fondées sur la programmation conventionnelle, tandis que les secondes mènent aux analyses du réseau neural. Le texte attire l’attention sur de nombreuses questions ouvertes suscitées par cette approche et comporte des réflexions sur des travaux futurs.In diesem Beitrag werden die Anstrengungen von Forschern beschrieben, die sich mit Datenverarbeitungsgeräten und Informationsanalysen beschäftigen, um die Grundvoraussetzungen zu schaffen für ein adäquates Verständnis von Bewusstsein sowie Spekulationen darüber, welche Schritte erforderlich sind, um eine mit einem Bewusstsein ausgestattete Maschine herzustellen. Während die Beiträge einiger Forscher im Einzelnen vorgestellt werden, bleiben andere Urheber erwähnter Spekulationen unerwähnt. Der Verfasser unterscheidet zwischen einem funktionalen und einem phänomenologischen Ansatz. Er zeigt auf, dass der funktionale Ansatz in algorithmischen, auf konventionellen Programmierungsmethoden gründenden Methoden resultiert, der phänomenologische Ansatz wiederum in neuralen Netzwerkanalysen. Sodann widmet sich der Verfasser zahlreichen offenen Fragen, die aus diesem Ansatz hervorgehen, und stellt Überlegungen über die möglichen Ergebnisse zukünftiger Forschungen an

Machine learning from coronas using parametrization of images

We were interested to develop an algorithm for detection of coronas of people in altered states of consciousness (two-classes problem). Such coronas are known to have rings (double coronas), special branch-like structure of streamers and/or curious spots. We used several approaches to parametrization of images and various machine learning algorithms. We compared results of computer algorithms with the human expert’s accuracy. Results show that computer algorithms can achieve the same or even better accuracy than that of human experts

GDV images: Current research and results

We use statistical analysis and machine learning to interpret the GDV coronas of fruits and human’s fingers in order to verify two hypotheses: (A) the GDV images contain useful information about the object/patient and (B) the human bioelectromagnetic field can be influenced by some outside factors. We performed several independent studies, three of which we here briefly describe: (a) recording coronas of berries of different grapevines, (b) detecting the influence of drinking the tap water from ordinary glass and energetic glass K2000, and (c) detecting the influence of natural energy source in Tunjice near Kamnik, Slovenia on the human bioelectromagnetic field. All three studies, as well as some other studies described elsewhere, gave significant results and therefore support both hypotheses

Information Stored in Coronas of Fruits and Leaves

We recorded coronas of apple tree leaves and fruits in order to monitor and compare their state under different conditions. The results of our study show that coronas of leaves and fruits give useful information about the health status of plants and about the sort. At the same time we have to conclude that for time being we were not able to extract any useful information for differentiation between organically and conventionally grown plants and for assessing vitality of apple trees grown from various rootstocks

Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification

Re-identification is generally carried out by encoding the appearance of a subject in terms of outfit, suggesting scenarios where people do not change their attire. In this paper we overcome this restriction, by proposing a framework based on a deep convolutional neural network, SOMAnet, that additionally models other discriminative aspects, namely, structural attributes of the human figure (e.g. height, obesity, gender). Our method is unique in many respects. First, SOMAnet is based on the Inception architecture, departing from the usual siamese framework. This spares expensive data preparation (pairing images across cameras) and allows the understanding of what the network learned. Second, and most notably, the training data consists of a synthetic 100K instance dataset, SOMAset, created by photorealistic human body generation software. Synthetic data represents a good compromise between realistic imagery, usually not required in re-identification since surveillance cameras capture low-resolution silhouettes, and complete control of the samples, which is useful in order to customize the data w.r.t. the surveillance scenario at-hand, e.g. ethnicity. SOMAnet, trained on SOMAset and fine-tuned on recent re-identification benchmarks, outperforms all competitors, matching subjects even with different apparel. The combination of synthetic data with Inception architectures opens up new research avenues in re-identification.Comment: 14 page