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Recent developments generalize word embedding to sentence embedding. Google Translate GT uses a large end-to-end long short-term memory network. Google Translate supports over one hundred languages. A large percentage of candidate drugs fail to win regulatory approval. These failures are caused by insufficient efficacy on-target effect , undesired interactions off-target effects , or unanticipated toxic effects.

AtomNet is a deep learning system for structure-based rational drug design. In generative neural networks were used to produce molecules that were validated experimentally all the way into mice [] , []. Deep reinforcement learning has been used to approximate the value of possible direct marketing actions, defined in terms of RFM variables.

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The estimated value function was shown to have a natural interpretation as customer lifetime value. Recommendation systems have used deep learning to extract meaningful features for a latent factor model for content-based music and journal recommendations. An autoencoder ANN was used in bioinformatics , to predict gene ontology annotations and gene-function relationships.

In medical informatics, deep learning was used to predict sleep quality based on data from wearables [] and predictions of health complications from electronic health record data. Deep learning has been shown to produce competitive results in medical application such as cancer cell classification, lesion detection, organ segmentation and image enhancement [] []. Finding the appropriate mobile audience for mobile advertising is always challenging, since many data points must be considered and assimilated before a target segment can be created and used in ad serving by any ad server.

This information can form the basis of machine learning to improve ad selection.

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Deep learning has been successfully applied to inverse problems such as denoising , super-resolution , inpainting , and film colorization. Deep learning is being successfully applied to financial fraud detection and anti-money laundering. The solution leverages both supervised learning techniques, such as the classification of suspicious transactions, and unsupervised learning, e. The United States Department of Defense applied deep learning to train robots in new tasks through observation. Deep learning is closely related to a class of theories of brain development specifically, neocortical development proposed by cognitive neuroscientists in the early s.

These developmental models share the property that various proposed learning dynamics in the brain e. Like the neocortex , neural networks employ a hierarchy of layered filters in which each layer considers information from a prior layer or the operating environment , and then passes its output and possibly the original input , to other layers. This process yields a self-organizing stack of transducers , well-tuned to their operating environment.

A description stated, " A variety of approaches have been used to investigate the plausibility of deep learning models from a neurobiological perspective. On the one hand, several variants of the backpropagation algorithm have been proposed in order to increase its processing realism. Although a systematic comparison between the human brain organization and the neuronal encoding in deep networks has not yet been established, several analogies have been reported.

For example, the computations performed by deep learning units could be similar to those of actual neurons [] [] and neural populations. Facebook 's AI lab performs tasks such as automatically tagging uploaded pictures with the names of the people in them. Google's DeepMind Technologies developed a system capable of learning how to play Atari video games using only pixels as data input. In they demonstrated their AlphaGo system, which learned the game of Go well enough to beat a professional Go player. In , Blippar demonstrated a mobile augmented reality application that uses deep learning to recognize objects in real time.

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.

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.

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The modified images looked no different to human eyes. 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.


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It has been argued in media philosophy that not only low-payed clickwork e. For this purpose Facebook introduced the feature that once a user is automatically recognized in an image, they receive a notification.

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They can choose whether of not they like to be publicly labeled on the image, or tell Facebook that it is not them in the picture. 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.

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Graphical models Bayes net Conditional random field Hidden Markov. Anomaly detection. Artificial neural network. 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.

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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.