The seminar in cognitive science and artificial intelligence is (starting from winter semester 2015-2016) a continuation of the joint seminar in artificial intelligence, organized by the (former) Institute of Applied Informatics at FIIT STU (prof. Kvasnička) and Department of Applied Informatics FMFI UK (doc. Šefránek, and later prof. Farkaš). In winter semester the seminar is oriented mostly for the students of cognitive science, to provide insight into the current research in our area, in summer semester it is focused more on artificial intelligence. The seminar is open, everybody is welcome to attend offered lectures.
The seminar is organized by Igor Farkaš.
Time and place: Tuesday 16:30-17:45 in room I-9 (building of informatics) at FMFI UK.
In this talk I'll present several computational models of phenomena related to language acquisition and knowledge representation in memory, which I have developed in the past years. I'll briefly show the application of this work in hyperrealistic embodied graphical simulations - digital personas.
In order to interact intelligently with the world, the embodied robots must acquire a number of abilities, one of them being their own body schema. We will present several examples of simple models along this direction, that are based on artificial neural networks, taking advantage of known paradigms such as supervised, unsupervised and reinforcement learning. The ideas will be presented via selected tasks of motor learning, learning touch and proprioception and mapping between reference frames in a simulated humanoid robot. These examples will serve as motivation for potential research projects - theoretical, or based on computational modelling.
The focus of the talk will be the theory of natural pedagogy, which provides a framework for the understanding of cognitive mechanisms that enable the process of cumulative culture. Proponents of this theory argue that a specific human adaptation supports efficient social learning and transmission of cultural knowledge. The presentation will attempt to outline the character of cognitive mechanisms and representations that are involved in pedagogical interactions and it will try to link the ability to understand pedagogical intentions to some other domains of social cognition. On the basis of experimental evidence, we will evaluate arguments for this theory but also the main objections that have been raised by its critics. Finally, we will discuss the relevance of this research not just for the understanding of cultural transmission, but also in relation to a design of human-robot interactions that involve teaching and learning as well as in relation to some therapeutic strategies.
The presentation provides a short introduction to transcranial electrical stimulation (tES) as a neuromodulation technique for cognitive enhancement. In the seminar, we will introduce several theoretical and methodological aspects of tES in behavioral research as well as our recent empirical findings indicating that tES of the left (pre)frontal and perisylvian brain regions can be used to modulate and enhance lexical-semantic cognition in healthy adults.
Research in the heuristics and biases paradigm has a long tradition of studying rational thinking using reasoning tasks specifically constructed to demonstrate that people systematically violate the rules of logic and probability when making judgments and decisions. A different domain of psychological research which addresses questions pertaining to human rationality is the study of epistemically suspect beliefs, i.e. the endorsement of paranormal claims, conspiracy theories, and pseudoscience. Importance of exploring such beliefs should be stressed both because of their surprisingly high prevalence and due to their relationship with various adverse real-life outcomes in the domain of health, political behavior, and rejection of science. It may seem at the first glance that acceptance of paranormal, conspiracy, and pseudoscientific beliefs has little to do with the type of rational thinking failure studied under heuristics and biases paradigm. And yet, recent research seems to suggest that similar underlying cognitive mechanisms may be implicated in these two domains of psychological research. In my talk, I will present selected findings of the research on cognitive biases and epistemically suspect beliefs that is taking place at Institute of Experimental Psychology of Slovak Academy of Sciences and will attempt to link the findings to the most recent trends in the two aforementioned domains of psychological study.
Frequency spectra and spatial sources of EEG oscillations are highly specific to individuals and may be manifested differently as functions of different interventions. However, most analyses of intervention effects on EEG oscillations use an approach based on standard frequency band powers measured at single electrodes. We have developed and applied a method that accurately and efficiently models individual EEG oscillations and tracks their activation over time or treatment conditions. In the talk, I will focus on basic principles of the approach and I will in detail discuss recent results which compare two specific models for EEG atomic decomposition, mainly the three-way parallel factor analysis (PARAFAC) model and its more flexible version represented by the Tucker model. These results are new and encouraging.
Autism spectrum disorder (ASD) is a neurodevelopmental disability characterized by impairments in communication, social interaction, restricted interests and repetitive behaviour. The aetiology of autism is poorly understood. ASD is diagnosed four times more frequently in males than in females. Previous studies have revealed that autism may arise as a result of exposure to high concentrations of prenatal testosterone. Ratio of the second and the fourth digits (2D:4D) is usually used as a proxy for prenatal testosterone. Our research findings on children with autism diagnosed at Academic Research Centre for Autism are discussed with the reference to the “extreme male - brain” theory of autism.
The phenomenon of artificial intelligence as the complex offspring of cognitive science and information technology (and inherently associated with "internetization", "digitalization", "virtualization" of reality, "robotization", etc.), is generally recognized as the key phenomenon of the contemporary human world having the potential not only to change the very nature of humanity itself but also to bring about one of its most serious existential risks (Open Letter on AI, 2015). For understanding the nature and value of AI, it is crucial to understand such concepts as human agency, intelligence (natural, general, weak, strong), consciousness, mind, thinking, free will, life, machine, technology, culture, etc., which fall under the competence of philosophy. Philosophy is the key for further development of AI (D. Deutsch). The area of the philosophy of AI has been developing (in parallels with cognitive science) since the historic 1956 DARPA conference, but the 1st academic synthesis has been provided by a textbook some 15 years ago (Russell & Norvig, 2002). Today the discussion of two groups of philosophical issues of AI can be distinguished: "internal" (stemming from Turing's problem of thinking machines and ending up with Bostrom's concept of super-intelligence) and "external" (starting from Minsky's "society of mind" and ending up with the ethical question of "good AI").
Humans are perfect at object handling. For a very long time, industrial robots were doing only simple, hard-coded manipulation. High-quality 3D scanning enabled us to localize objects precisely, and plan their picking and movement to avoid collisions. Localization and picking are tasks that we are currently solving with both analytical and machine learning approach. In the talk, we discuss both approaches and compare their advantages and disadvantages.
Relations stand for the links between entities (is unlocked by (COMPUTER PASSWORD)). On the one hand, the same relation can be held between different entities (“is an instrument of” works for "artist brush", "tailor needle", "hairdresser scissors", and "fisherman fishing", etc.) and in turn, may be accessed from different instances (Popov and Hristova, 2015), even when it is irrelevant and can disrupt the task at hand (Hristova, 2009). Furthermore, an active relation may mislead memory as suggested by the Relational luring effect (RLE) (Popov, Hristova, Anders, 2017): 1) word pairs were falsely recognised as studied if they were instances of the learned relations ("snail shell" instead of "bear den") and 2) the correct rejection RTs significantly slowed down with the number of the trials since previous instances of the same relation. On the other hand, since every two entities can be linked through different relations ("artist brush" can be meaningfully related via:”is an instrument of”, “broke”, “hide”, “toss” etc.), the winning ones may be the most contextually or goal relevant, but also the most typical ones. This encoding priority may indicate that some relations are more contextually relevant (Hristova, 2009), but also more typical for the perceived entities. Indeed, it turned out that the typicality of the instance, but not of the role-fillers predicts the RLE (Popov, Hristova, Pavlova, in prep). Hence, the relational, rather than the semantic or the role similarity may explain the RLE and respectively, the heightened readiness of the Long-Term relational representation it witnesses for.
Speech data mining (that will be represented by transcription in this talk) is an important discipline of machine learning. In addition to classical ML and computer science components, it also sources other sciences such as physiology, lexicography, and phonetics, making it a funny inter-disciplinary domain. Similarly to other ML sub-fields, it has been turned upside-down in the recent years by the massive use of neural networks. Although their use in speech dates back to 2000, it is only around 2010 they took the ground and started to dominate the field. Brno speech group has been through all these changes, sometimes following the others, sometimes defining the history. The talk will cover these developments as well as some new research trends.
Customers expect a lot from an online store: that it won't waste their time, that they will find products that fit them, that the experience will be personalized for them, etc. This is exactly what Artificial Intelligence can achieve if it has relevant data that is often hard to get. Fashion retail is in special position because most relevant data is contained in images of products. During the talk we will look at few technologies based on neural networks that can help online fashion retailers to meet customer expectations.
The past decade has witnessed enormous growth in research and applications of machine learning techniques. Modern deep learning techniques have beaten humans in several important benchmarks, yet it is clear that intelligence available in today's systems lags behind even the simplest animals. Part of the lag is theoretical, since there is little agreement on how to build connectionist systems. We would like to draw inspiration from biology, but there is still only fragmentary understanding of learning mechanisms in animal nervous systems. The other critical hurdle that needs to be cleared is what hardware should be used to implement future artificial intelligence systems. In our talk we will present our work on memristors, a promising nanotechnology device that may serve to emulate synapses in the next generation of artificial neural networks. Our results will be framed within the context of competition between deep learning and neuromorphic approaches to artificial intelligence.
In a narrow sense, religion is a unique feature of the human species. According to archaeological findings as well as research in anthropology, this ability is one of the oldest cultural features of a human kind. It has stood up with the rise of gene-cultural coevolution. Religion, in the meaning of religious thinking and religious behaviour, began to form a competitive advantage for human groups and individuals quickly. For groups, it was an additional strengthening mechanism, affecting closer cooperation, higher levels of intra-group altruism, better division of labour, and greater efficiency in resource gathering. Even it is not possible to name with certainty, which attributes of early human affective thinking emerge into religious, reflection of both, cultural and genetic evolution, may show us a way of evolution of religion and cognitive mechanisms, which allowed it and in a feedback loop, stimulating it further.