It is a sad fact of life that as we age many of us have difficulties retrieving memories. But now researchers think they know why -- it's because older people's brains allocate more space to knowledge built up over the years, meaning there is more material to navigate when trying to access memories. The study found that the older we get the more difficulty we have suppressing information that is no longer relevant. It means that when searching for a specific memory, older people often retrieve other, irrelevant memories along with it. It is a sad fact of life that as we age many of us have difficult retrieving memories.
Digitization is penetrating more and more areas of life. Tasks are increasingly being completed digitally, and are therefore not only fulfilled faster, more efficiently but also more purposefully and successfully. The rapid developments in the field of artificial intelligence in recent years have played a major role in this, as they brought up many helpful approaches to build on. At the same time, the eyes, their movements, and the meaning of these movements are being progressively researched. The combination of these developments has led to exciting approaches. In this dissertation, I present some of these approaches which I worked on during my Ph.D. First, I provide insight into the development of models that use artificial intelligence to connect eye movements with visual expertise. This is demonstrated for two domains or rather groups of people: athletes in decision-making actions and surgeons in arthroscopic procedures. The resulting models can be considered as digital diagnostic models for automatic expertise recognition. Furthermore, I show approaches that investigate the transferability of eye movement patterns to different expertise domains and subsequently, important aspects of techniques for generalization. Finally, I address the temporal detection of confusion based on eye movement data. The results suggest the use of the resulting model as a clock signal for possible digital assistance options in the training of young professionals. An interesting aspect of my research is that I was able to draw on very valuable data from DFB youth elite athletes as well as on long-standing experts in arthroscopy. In particular, the work with the DFB data attracted the interest of radio and print media, namely DeutschlandFunk Nova and SWR DasDing. All resulting articles presented here have been published in internationally renowned journals or at conferences.
Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.
If you're irritated by the mere sight of people fidgeting, a new scientific study suggests you're not alone. Researchers in Canada recruited 4,100 participants who were asked to self-report whether they have sensitivities to seeing people fidget. They found that almost one in three people experienced the psychological phenomenon known as'misokinesia, or a'hatred of movements'. Misokinesia is psychological response to the sight of someone else's small but repetitive movements, the experts say, and it can seriously affect daily living. Misokinesia - the'hatred of movements' - is a psychological response to the sight of someone else's small and repetitive movements (concept image) Misokinesia - or the'hatred of movements' - is a psychological phenomenon that is defined as a strong negative affective or emotional response to the sight of someone else's small and repetitive movements.
Artificial neural networks modeled on real brains can perform cognitive tasks. A new study shows that artificial intelligence networks based on human brain connectivity can perform cognitive tasks efficiently. By examining MRI data from a large Open Science repository, researchers reconstructed a brain connectivity pattern, and applied it to an artificial neural network (ANN). An ANN is a computing system consisting of multiple input and output units, much like the biological brain. A team of researchers from The Neuro (Montreal Neurological Institute-Hospital) and the Quebec Artificial Intelligence Institute trained the ANN to perform a cognitive memory task and observed how it worked to complete the assignment.
Artificial neural networks modeled on the real brain can perform cognitive tasks. New research shows that artificial intelligence networks based on the connectivity of the human brain can perform cognitive tasks efficiently. By examining MRI data from large open science repositories, researchers reconstructed brain connection patterns and applied them to artificial neural networks (ANNs). ANN, like the biological brain, is a computing system consisting of multiple input and output units. A team of researchers at the Neuro (Montreal Institute for Neurology-Hospital) and the Quebec Institute for Artificial Intelligence train ANNs to perform cognitive memory tasks and observe how they work to complete assignments.
Possibly a new breakthrough has been achieved in the domain of artificial intelligence. According to a new study, by a team of researchers from The Neuro (Montreal Neurological Institute-Hospital) and the Quebec Artificial Intelligence Institute, the artificial intelligence networks modelled on human brain connectivity are equipped to perform cognitive tasks efficiently and effectively. The study has been done via a sizable Open Science Repository by which the researchers tried to replicate and reconstruct the brain's connectivity pattern. This was then applied to an artificial neural network (ANN) to achieve cognitive abilities like the human brain. The Artificial Neural Network (ANN) is a system that has both input and output units in abundance in similarity to the human brain.
Summary: Artificial neural networks modeled on human brain connectivity can effectively perform complex cognitive tasks. A new study shows that artificial intelligence networks based on human brain connectivity can perform cognitive tasks efficiently. By examining MRI data from a large Open Science repository, researchers reconstructed a brain connectivity pattern, and applied it to an artificial neural network (ANN). An ANN is a computing system consisting of multiple input and output units, much like the biological brain. A team of researchers from The Neuro (Montreal Neurological Institute-Hospital) and the Quebec Artificial Intelligence Institute trained the ANN to perform a cognitive memory task and observed how it worked to complete the assignment.
Artificial intelligence (AI) networks designed to mimic the human brain are able to perform tasks more efficiently than other systems, a new study has found. Researchers from The Neuro (Montreal Neurological Institute-Hospital) and the Quebec Artificial Intelligence Institute developed an artificial neural network (ANN) computing system based on a biological brain's connectivity. Training the system to perform a memory task resulted in the ANN completing it more flexibly and efficiently than more traditional systems that do not reflect the organisation of real brain networks, they found. The study, published in the journal Nature Machine Intelligence, could help the team to learn more about how the way the human brain is wired helps to support specific cognitive skills. "The project unifies two vibrant and fast-paced scientific disciplines," said Bratislav Misic, a researcher at The Neuro and the paper's senior author.
Machine learning methods, such as deep learning, show promising results in the medical domain. However, the lack of interpretability of these algorithms may hinder their applicability to medical decision support systems. This paper studies an interpretable deep learning technique, called SincNet. SincNet is a convolutional neural network that efficiently learns customized band-pass filters through trainable sinc-functions. In this study, we use SincNet to analyze the neural activity of individuals with Autism Spectrum Disorder (ASD), who experience characteristic differences in neural oscillatory activity. In particular, we propose a novel SincNet-based neural network for detecting emotions in ASD patients using EEG signals. The learned filters can be easily inspected to detect which part of the EEG spectrum is used for predicting emotions. We found that our system automatically learns the high-$\alpha$ (9-13 Hz) and $\beta$ (13-30 Hz) band suppression often present in individuals with ASD. This result is consistent with recent neuroscience studies on emotion recognition, which found an association between these band suppressions and the behavioral deficits observed in individuals with ASD. The improved interpretability of SincNet is achieved without sacrificing performance in emotion recognition.