FAC2E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition
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Abstract In this paper, an object recognition method and a pose estimation approach using stereo vision is presented. The proposed approach was used for position based visual servoing of a 6 DoF manipulator. The object detection and recognition method was designed with the purpose of increasing robustness. A RGB color-based object descriptor and an online correction method is proposed for object detection and recognition. Pose was estimated by using the depth information derived from stereo vision camera and an SVD based method. Transformation between the desired pose and object pose was calculated and later used for position based visual servoing. Experiments were carried out to verify the proposed approach for object recognition. The stereo camera was also tested to see whether the depth accuracy is adequate. The proposed object recognition method is invariant to scale, orientation and lighting condition which increases the level of robustness. The accuracy of stereo vision camera can reach 1 mm. The accuracy is adequate for tasks such as grasping and manipulation.
Robustness
Visual Servoing
RGB color model
Computer stereo vision
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N-gram language models are frequently used by the speech recognition systems to constrain and guide the search. N-gram models use only the last N-1 words to predict the next word. Typical values of N that are used range from 2-4. N-gram language models thus lack the long-term context information. We show that the predictive power of the N-gram language models can be improved by using long-term context information about the topic of discussion. We use information retrieval techniques to generalize the available context information for topic-dependent language modeling. We demonstrate the effectiveness of this technique by performing experiments on the Wall Street Journal text corpus, which is a relatively difficult task for topic-dependent language modeling since the text is relatively homogeneous. The proposed method can reduce the perplexity of the baseline language model by 37%, indicating the predictive power of the topic-dependent language model.
Perplexity
n-gram
Cache language model
Predictive power
Context model
Language identification
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How can approaching the topic of 4E cognition from an evolutionary perspective shed light on aspects of cultural and biological evolution, the nature of cognition in general, and of human nature in particular? The four papers in this section offer a temporal analysis that puts cognition into a context of larger processes that span historical and indeed evolutionary time, and they embed the dynamics of cognition beyond brains and individuals, into groups and even species. This analysis does not deny the importance of abstract representations for human cognition. However, all four contributions suggest that in focusing too narrowly on representational cognition we lose sight of the basic mechanisms supporting and driving cognition. Furthermore, to understand these, we need to understand not only how these processes are embodied, embedded, enactive, and extended, but also how they are shaped, transmitted, and diversified in processes of group formation.
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Connectionism
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Natural language processing (NLP) is an area of machine learning that has garnered a lot of attention in recent days due to the revolution in artificial intelligence, robotics, and smart devices. NLP focuses on training machines to understand and analyze various languages, extract meaningful information from those, translate from one language to another, correct grammar, predict the next word, complete a sentence, or even generate a completely new sentence from an existing corpus. A major challenge in NLP lies in training the model for obtaining high prediction accuracy since training needs a vast dataset. For widely used languages like English, there are many datasets available that can be used for NLP tasks like training a model and summarization but for languages like Bengali, which is only spoken primarily in South Asia, there is a dearth of big datasets which can be used to build a robust machine learning model. Therefore, NLP researchers who mainly work with the Bengali language will find an extensive, robust dataset incredibly useful for their NLP tasks involving the Bengali language. With this pressing issue in mind, this research work has prepared a dataset whose content is curated from social media, blogs, newspapers, wiki pages, and other similar resources. The amount of samples in this dataset is 19132010, and the length varies from 3 to 512 words. This dataset can easily be used to build any unsupervised machine learning model with an aim to performing necessary NLP tasks involving the Bengali language. Also, this research work is releasing two preprocessed version of this dataset that is especially suited for training both core machine learning-based and statistical-based model. As very few attempts have been made in this domain, keeping Bengali language researchers in mind, it is believed that the proposed dataset will significantly contribute to the Bengali machine learning and NLP community.
Sentiment Analysis
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Traditional views separate cognitive processes from sensory-motor processes, seeing cognition as amodal, propositional, and compositional, and thus fundamentally different from the processes that underlie perceiving and acting. These were the ideas on which cognitive science was founded 30 years ago. However, advancing discoveries in neuroscience, cognitive neuroscience, and psychology suggests that cognition may be inseparable from processes of perceiving and acting. From this perspective, this study considers the future of cognitive science with respect to the study of cognitive development.
Animal cognition
LIDA
Motor Cognition
Developmental cognitive neuroscience
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Motor Cognition
Animal cognition
Cognitive robotics
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Robotics has been playing a vital role in our daily lives with a wide range of applications to improve the quality of life.With a variety of usable applications in the medical, manufacturing, and transportation industries, there is a continuous need for improving the performance of robotics for the importance of precision in executing commands and tasks.The implementation of precise commands has led to intense research on approaches to improve the performance of robotics.Machine Learning (ML) and Deep Learning (DL) have been drawing attention to applying architectures and algorithms to robotics which imposed a positive impact on the field of robotics.ML and DL applications in robotics include areas of computer vision, imitation learning, self-supervised learning, assistive and medical technologies, multi-agent learning, and manufacturing.This paper provides a comprehensive review of autonomous vs automatic robotics, robotic applications, extreme learning machine methods, and ML for soft robotics applications, in addition, to discussing the challenges, and future trends for AI applications in robotics applications.
Soft Robotics
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As the days pass, the amount of work to do increases day by day, so there is a considerable need to automate the work. This necessity has led to the invention of a new technology named Artificial Intelligence. Artificial Intelligence or Machine Intelligence is a branch of science that deals with a machine'ss ability to learn, understand, think, and act like humans. The main goal of Artificial Intelligence is making machines to learn from the environment and make them capable of doing the given tasks successfully, that helps in maximizing their goal achievements. Artificial Intelligence is interdisciplinary, which has subfields such as Machine learning, Deep Learning, and others. Machine learning makes the machines automatically learn from their experience; it is done with computer programs that access the given data and uses it for learning for themselves. Deep Learning is a subfield of Machine Learning that processes or filters information in the same way as the human brain. Here, it uses a computer model that takes the input and filters it through different layers to predict and classify the information. These three fields Artificial Intelligence, Machine Learning, and Deep Learning, made many advancements in technology in every sector that transformed the world into a new dimension.
Hyper-heuristic
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