Guide Automatic Learning Techniques in Power Systems

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  1. Automatic Learning Techniques in Power Systems - Semantic Scholar
  2. AI in depth: monitoring home appliances from power readings with ML
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Automatic Learning Techniques in Power Systems - Semantic Scholar

In , Covariant. As of , [] researchers at The University of Texas at Austin UT developed a machine learning framework called Training an Agent Manually via Evaluative Reinforcement, or TAMER, which proposed new methods for robots or computer programs to learn how to perform tasks by interacting with a human instructor.

Deep learning has attracted both criticism and comment, in some cases from outside the field of computer science. A main criticism concerns the lack of theory surrounding some methods. However, the theory surrounding other algorithms, such as contrastive divergence is less clear. If so, how fast? What is it approximating? Deep learning methods are often looked at as a black box , with most confirmations done empirically, rather than theoretically. Others point out that deep learning should be looked at as a step towards realizing strong AI, not as an all-encompassing solution.

Despite the power of deep learning methods, they still lack much of the functionality needed for realizing this goal entirely. Research psychologist Gary Marcus noted:. Such techniques lack ways of representing causal relationships The most powerful A.

As an alternative to this emphasis on the limits of deep learning, one author speculated that it might be possible to train a machine vision stack to perform the sophisticated task of discriminating between "old master" and amateur figure drawings, and hypothesized that such a sensitivity might represent the rudiments of a non-trivial machine empathy. In further reference to the idea that artistic sensitivity might inhere within relatively low levels of the cognitive hierarchy, a published series of graphic representations of the internal states of deep layers neural networks attempting to discern within essentially random data the images on which they were trained [] demonstrate a visual appeal: the original research notice received well over 1, comments, and was the subject of what was for a time the most frequently accessed article on The Guardian 's [] web site.

Some deep learning architectures display problematic behaviors, [] such as confidently classifying unrecognizable images as belonging to a familiar category of ordinary images [] and misclassifying minuscule perturbations of correctly classified images. As deep learning moves from the lab into the world, research and experience shows that artificial neural networks are vulnerable to hacks and deception.

By identifying patterns that these systems use to function, attackers can modify inputs to ANNs in such a way that the ANN finds a match that human observers would not recognize. For example, an attacker can make subtle changes to an image such that the ANN finds a match even though the image looks to a human nothing like the search target. The modified images looked no different to human eyes.

Car Electrics Training Animation, Automotive Appreciation-Part 4

Another group showed that printouts of doctored images then photographed successfully tricked an image classification system. A refinement is to search using only parts of the image, to identify images from which that piece may have been taken. Another group showed that certain psychedelic spectacles could fool a facial recognition system into thinking ordinary people were celebrities, potentially allowing one person to impersonate another.

In researchers added stickers to stop signs and caused an ANN to misclassify them.

ANNs can however be further trained to detect attempts at deception, potentially leading attackers and defenders into an arms race similar to the kind that already defines the malware defense industry. ANNs have been trained to defeat ANN-based anti-malware software by repeatedly attacking a defense with malware that was continually altered by a genetic algorithm until it tricked the anti-malware while retaining its ability to damage the target. Another group demonstrated that certain sounds could make the Google Now voice command system open a particular web address that would download malware.

From Wikipedia, the free encyclopedia. For deep versus shallow learning in educational psychology, see Student approaches to learning. For more information, see Artificial neural network. Branch of machine learning. Dimensionality reduction. Structured prediction. Graphical models Bayes net Conditional random field Hidden Markov. Anomaly detection. Artificial neural networks. Reinforcement learning.

Machine-learning venues. Glossary of artificial intelligence. Related articles. List of datasets for machine-learning research Outline of machine learning. Main article: Artificial neural network. This section may be too technical for most readers to understand. Please help improve it to make it understandable to non-experts , without removing the technical details.

July Learn how and when to remove this template message. Main article: Speech recognition. Main article: Computer vision. Main article: Natural language processing. For more information, see Drug discovery and Toxicology.

AI in depth: monitoring home appliances from power readings with ML

Main article: Customer relationship management. Main article: Recommender system. Main article: Bioinformatics. See also: Explainable AI. Neural Networks. Bibcode : Natur. June Frontiers in Computational Neuroscience. Foundations and Trends in Signal Processing. Foundations and Trends in Machine Learning. Archived from the original PDF on Retrieved Deep Learning. Scholarpedia, 10 11 Bibcode : SchpJ Mathematics of Control, Signals, and Systems.


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Neural Networks: A Comprehensive Foundation. Prentice Hall. Fundamentals of Artificial Neural Networks. MIT Press. Neural Information Processing Systems, Machine Learning: A Probabilistic Perspective. Advances in Neural Information Processing Systems. Learning while searching in constraint-satisfaction problems. Aizenberg, Joos P. Vandewalle Cybernetic Predicting Devices.

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CCM Information Corporation. The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors. Master's Thesis in Finnish , Univ. Helsinki, Harvard University. Retrieved 12 June System modeling and optimization. Weng, N. Ahuja and T.