233 research outputs found

    Learning What To Say And What To Do: A Model For Grounding Language And Actions

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    Automation is becoming increasingly important in nowadays society, with robots performing a lot of repetitive tasks in industry and even entering our households in the form of vacuum cleaners and lawn mowers. When considering regular tasks outside of the controlled environments of industry, robots tend to perform poorly. In particular, in situations where robots have to interact with humans, a problem arises: how can a robot understand what the human means? While a lot of work has been made in the past towards visual perception and classification of objects, but understanding what action a verb translates into has still been an unexplored area. In solving this challenge, we would enable robots to execute commands given in natural language, and also to verbalise what actions they are performing when prompted. This work studies how a robot can learn the meaning behind the sentences humans use, how it translates into its perception and the real world, but also how to translate its actions into sentences humans understand. To achieve this we propose a novel Bidirectional machine learning model, along with a data collection module that can be used by non-technical users. The main idea behind this model is the ability to generalise to novel concepts, being able to compose new sentences and actions from what it learned previously. Humans show this ability to generalise from a young age, and it is a desirable feature for this model. By using humans natural teaching instincts to teach the robot together with this generalisation ability we hope to obtain a model that allows people everywhere to teach the robot to perform the actions we desire. We validate the model in a number of tasks, using an iCub and Pepper robots physically interacting with objects in order to complete a natural language command. We test different actions, including motor actions and emotional displays, while using both transitive and intransitive verbs in the natural language commands. The main contribution of this thesis is the development of a Bidirectional Learning Algorithm, applied to a Multiple Timescale Recurrent Neural Network enabling these models to link action and language in a bidirectional way. A second contribution sees the extension of Multiple Timescale architectures to Long Short-Term Memory models, increasing the capabilities of these models. Finally the third contribution is in the form of data collection modules, with the development of an easy-to-use module based on physical interaction and speech to provide the iCub and Pepper robots with the data to be learned

    AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

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    This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

    Cortical-hippocampal processing: prefrontal-hippocampal contributions to the spatiotemporal relationship of events

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    The hippocampus and prefrontal cortex play distinct roles in the generation and retrieval of episodic memory. The hippocampus is crucial for binding inputs across behavioral timescales, whereas the prefrontal cortex is found to influence retrieval. Spiking of hippocampal principal neurons contains environmental information, including information about the presence of specific objects and their spatial or temporal position relative to environmental and behavioral cues. Neural activity in the prefrontal cortex is found to map behavioral sequences that share commonalities in sensory input, movement, and reward valence. Here I conducted a series of four experiments to test the hypothesis that external inputs from cortex update cell assemblies that are organized within the hippocampus. I propose that cortical inputs coordinate with CA3 to rapidly integrate information at fine timescales. Extracellular tetrode recordings of neurons in the orbitofrontal cortex were performed in rats during a task where object valences were dictated by the spatial context in which they were located. Orbitofrontal ensembles, during object sampling, were found to organize all measured task elements in inverse rank relative to the rank previously observed in the hippocampus, whereby orbitofrontal ensembles displayed greater differentiation for object valence and its contextual identity than spatial position. Using the same task, a follow-up experiment assessed coordination between prefrontal and hippocampal networks by simultaneously recording medial prefrontal and hippocampal activity. The circuit was found to coordinate at theta frequencies, whereby hippocampal theta engaged prefrontal signals during contextual sampling, and the order of engagement reversed during object sampling. Two additional experiments investigated hippocampal temporal representations. First, hippocampal patterns were found to represent conjunctions of time and odor during a head-fixed delayed match-to-sample task. Lastly, I assessed the dependence of hippocampal firing patterns on intrinsic connectivity during the delay period versus active navigation of spatial routes, as rats performed a delayed-alternation T-maze. Stimulation of the ventral hippocampal commissure induced remapping of hippocampal activity during the delay period selectively. Despite temporal reorganization, different hippocampal populations emerged to predict temporal position. These results show hippocampal representations are guided by stable cortical signals, but also, coordination between cortical and intrinsic circuitry stabilizes flexible CA1 temporal representations

    A functional cortical network for sensorimotor sequence generation

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    The brain generates complex sequences of movements that can be flexibly reconfigured in real-time based on sensory feedback, but how this occurs is not fully understood. We developed a novel ‘sequence licking’ task in which mice directed their tongue to a target that moved through a series of locations. Mice could rapidly reconfigure the sequence online based on tactile feedback. Closed-loop optogenetics and electrophysiology revealed that tongue/jaw regions of somatosensory (S1TJ) and motor (M1TJ) cortex encoded and controlled tongue kinematics at the level of individual licks. Tongue premotor (anterolateral motor, ALM) cortex encoded intended tongue angle in a smooth manner that spanned individual licks and even whole sequences, and progress toward the reward that marked successful sequence execution. ALM activity regulated sequence initiation, but multiple cortical areas collectively controlled termination of licking. Our results define a functional cortical network for hierarchical control of sensory- and reward-guided orofacial sequence generation

    A new class of neural architectures to model episodic memory : computational studies of distal reward learning

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    A computational cognitive neuroscience model is proposed, which models episodic memory based on the mammalian brain. A computational neural architecture instantiates the proposed model and is tested on a particular task of distal reward learning. Categorical Neural Semantic Theory informs the architecture design. To experiment upon the computational brain model, embodiment and an environment in which the embodiment exists are simulated. This simulated environment realizes the Morris Water Maze task, a well established biological experimental test of distal reward learning. The embodied neural architecture is treated as a virtual rat and the environment it acts in as a virtual water tank. Performance levels of the neural architectures are evaluated through analysis of embodied behavior in the distal reward learning task. Comparison is made to biological rat experimental data, as well as comparison to other published models. In addition, differences in performance are compared between the normal and categorically informed versions of the architecture

    On Differentiable Interpreters

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    Neural networks have transformed the fields of Machine Learning and Artificial Intelligence with the ability to model complex features and behaviours from raw data. They quickly became instrumental models, achieving numerous state-of-the-art performances across many tasks and domains. Yet the successes of these models often rely on large amounts of data. When data is scarce, resourceful ways of using background knowledge often help. However, though different types of background knowledge can be used to bias the model, it is not clear how one can use algorithmic knowledge to that extent. In this thesis, we present differentiable interpreters as an effective framework for utilising algorithmic background knowledge as architectural inductive biases of neural networks. By continuously approximating discrete elements of traditional program interpreters, we create differentiable interpreters that, due to the continuous nature of their execution, are amenable to optimisation with gradient descent methods. This enables us to write code mixed with parametric functions, where the code strongly biases the behaviour of the model while enabling the training of parameters and/or input representations from data. We investigate two such differentiable interpreters and their use cases in this thesis. First, we present a detailed construction of ∂4, a differentiable interpreter for the programming language FORTH. We demonstrate the ability of ∂4 to strongly bias neural models with incomplete programs of variable complexity while learning missing pieces of the program with parametrised neural networks. Such models can learn to solve tasks and strongly generalise to out-of-distribution data from small datasets. Second, we present greedy Neural Theorem Provers (gNTPs), a significant improvement of a differentiable Datalog interpreter NTP. gNTPs ameliorate the large computational cost of recursive differentiable interpretation, achieving drastic time and memory speedups while introducing soft reasoning over logic knowledge and natural language

    xxAI - Beyond Explainable AI

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    This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science.https://digitalcommons.unomaha.edu/isqafacbooks/1000/thumbnail.jp

    xxAI - Beyond Explainable AI

    Get PDF
    This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science

    Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

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    Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence
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