454 research outputs found

    An Intelligent Time and Performance Efficient Algorithm for Aircraft Design Optimization

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    Die Optimierung des Flugzeugentwurfs erfordert die Beherrschung der komplexen Zusammenhänge mehrerer Disziplinen. Trotz seiner Abhängigkeit von einer Vielzahl unabhängiger Variablen zeichnet sich dieses komplexe Entwurfsproblem durch starke indirekte Verbindungen und eine daraus resultierende geringe Anzahl lokaler Minima aus. Kürzlich entwickelte intelligente Methoden, die auf selbstlernenden Algorithmen basieren, ermutigten die Suche nach einer diesem Bereich zugeordneten neuen Methode. Tatsächlich wird der in dieser Arbeit entwickelte Hybrid-Algorithmus (Cavus) auf zwei Hauptdesignfälle im Luft- und Raumfahrtbereich angewendet: Flugzeugentwurf- und Flugbahnoptimierung. Der implementierte neue Ansatz ist in der Lage, die Anzahl der Versuchspunkte ohne große Kompromisse zu reduzieren. Die Trendanalyse zeigt, dass der Cavus-Algorithmus für die komplexen Designprobleme, mit einer proportionalen Anzahl von Prüfpunkten konservativer ist, um die erfolgreichen Muster zu finden. Aircraft Design Optimization requires mastering of the complex interrelationships of multiple disciplines. Despite its dependency on a diverse number of independent variables, this complex design problem has favourable nature as having strong indirect links and as a result a low number of local minimums. Recently developed intelligent methods that are based on self-learning algorithms encouraged finding a new method dedicated to this area. Indeed, the hybrid (Cavus) algorithm developed in this thesis is applied two main design cases in aerospace area: aircraft design optimization and trajectory optimization. The implemented new approach is capable of reducing the number of trial points without much compromise. The trend analysis shows that, for the complex design problems the Cavus algorithm is more conservative with a proportional number of trial points in finding the successful patterns

    Long Sequence Hopfield Memory

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    Sequence memory is an essential attribute of natural and artificial intelligence that enables agents to encode, store, and retrieve complex sequences of stimuli and actions. Computational models of sequence memory have been proposed where recurrent Hopfield-like neural networks are trained with temporally asymmetric Hebbian rules. However, these networks suffer from limited sequence capacity (maximal length of the stored sequence) due to interference between the memories. Inspired by recent work on Dense Associative Memories, we expand the sequence capacity of these models by introducing a nonlinear interaction term, enhancing separation between the patterns. We derive novel scaling laws for sequence capacity with respect to network size, significantly outperforming existing scaling laws for models based on traditional Hopfield networks, and verify these theoretical results with numerical simulation. Moreover, we introduce a generalized pseudoinverse rule to recall sequences of highly correlated patterns. Finally, we extend this model to store sequences with variable timing between states' transitions and describe a biologically-plausible implementation, with connections to motor neuroscience.Comment: NeurIPS 2023 Camera-Ready, 41 page

    Analog Photonics Computing for Information Processing, Inference and Optimisation

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    This review presents an overview of the current state-of-the-art in photonics computing, which leverages photons, photons coupled with matter, and optics-related technologies for effective and efficient computational purposes. It covers the history and development of photonics computing and modern analogue computing platforms and architectures, focusing on optimization tasks and neural network implementations. The authors examine special-purpose optimizers, mathematical descriptions of photonics optimizers, and their various interconnections. Disparate applications are discussed, including direct encoding, logistics, finance, phase retrieval, machine learning, neural networks, probabilistic graphical models, and image processing, among many others. The main directions of technological advancement and associated challenges in photonics computing are explored, along with an assessment of its efficiency. Finally, the paper discusses prospects and the field of optical quantum computing, providing insights into the potential applications of this technology.Comment: Invited submission by Journal of Advanced Quantum Technologies; accepted version 5/06/202

    A Semantic Framework for Neural-Symbolic Computing

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    Two approaches to AI, neural networks and symbolic systems, have been proven very successful for an array of AI problems. However, neither has been able to achieve the general reasoning ability required for human-like intelligence. It has been argued that this is due to inherent weaknesses in each approach. Luckily, these weaknesses appear to be complementary, with symbolic systems being adept at the kinds of things neural networks have trouble with and vice-versa. The field of neural-symbolic AI attempts to exploit this asymmetry by combining neural networks and symbolic AI into integrated systems. Often this has been done by encoding symbolic knowledge into neural networks. Unfortunately, although many different methods for this have been proposed, there is no common definition of an encoding to compare them. We seek to rectify this problem by introducing a semantic framework for neural-symbolic AI, which is then shown to be general enough to account for a large family of neural-symbolic systems. We provide a number of examples and proofs of the application of the framework to the neural encoding of various forms of knowledge representation and neural network. These, at first sight disparate approaches, are all shown to fall within the framework's formal definition of what we call semantic encoding for neural-symbolic AI

    Sequential Memory with Temporal Predictive Coding

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    Forming accurate memory of sequential stimuli is a fundamental function of biological agents. However, the computational mechanism underlying sequential memory in the brain remains unclear. Inspired by neuroscience theories and recent successes in applying predictive coding (PC) to \emph{static} memory tasks, in this work we propose a novel PC-based model for \emph{sequential} memory, called \emph{temporal predictive coding} (tPC). We show that our tPC models can memorize and retrieve sequential inputs accurately with a biologically plausible neural implementation. Importantly, our analytical study reveals that tPC can be viewed as a classical Asymmetric Hopfield Network (AHN) with an implicit statistical whitening process, which leads to more stable performance in sequential memory tasks of structured inputs. Moreover, we find that tPC exhibits properties consistent with behavioral observations and theories in neuroscience, thereby strengthening its biological relevance. Our work establishes a possible computational mechanism underlying sequential memory in the brain that can also be theoretically interpreted using existing memory model frameworks.Comment: 37th Conference on Neural Information Processing Systems (NeurIPS 2023

    Flexible Phase Dynamics for Bio-Plausible Contrastive Learning

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    Many learning algorithms used as normative models in neuroscience or as candidate approaches for learning on neuromorphic chips learn by contrasting one set of network states with another. These Contrastive Learning (CL) algorithms are traditionally implemented with rigid, temporally non-local, and periodic learning dynamics that could limit the range of physical systems capable of harnessing CL. In this study, we build on recent work exploring how CL might be implemented by biological or neurmorphic systems and show that this form of learning can be made temporally local, and can still function even if many of the dynamical requirements of standard training procedures are relaxed. Thanks to a set of general theorems corroborated by numerical experiments across several CL models, our results provide theoretical foundations for the study and development of CL methods for biological and neuromorphic neural networks.Comment: 23 pages, 4 figures. Paper accepted to ICML and update includes changes made based on reviewer feedbac

    Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

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    The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop

    Theory of Mind Might Have Spontaneously Emerged in Large Language Models

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    We explore the intriguing possibility that theory of mind (ToM), or the uniquely human ability to impute unobservable mental states to others, might have spontaneously emerged in large language models (LLMs). We designed 40 false-belief tasks, considered a gold standard in testing ToM in humans, and administered them to several LLMs. Each task included a false-belief scenario, three closely matched true-belief controls, and the reversed versions of all four. Smaller and older models solved no tasks; GPT-3-davinci-001 (from May 2020) and GPT-3-davinci-002 (from January 2022) solved 10%; and GPT-3-davinci-003 (from November 2022) and ChatGPT-3.5-turbo (from March 2023) solved 35% of the tasks, mirroring the performance of three-year-old children. ChatGPT-4 (from June 2023) solved 90% of the tasks, matching the performance of seven-year-old children. These findings suggest the intriguing possibility that ToM, previously considered exclusive to humans, may have spontaneously emerged as a byproduct of LLMs' improving language skills.Comment: TRY RUNNING ToM EXPERIMENTS ON YOUR OWN: The code and tasks used in this study are available at Colab (https://colab.research.google.com/drive/1ZRtmw87CdA4xp24DNS_Ik_uA2ypaRnoU). Don't worry if you are not an expert coder, you should be able to run this code with no-to-minimum Python skills. Or copy-paste the tasks to ChatGPT's web interfac
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