Deep learning is the subset of machine learning methods which are based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, convolutional neural networks and transformers have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.

Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains. Specifically, artificial neural networks tend to be static and symbolic, while the biological brain of most living organisms is dynamic (plastic) and analog.

Definition
Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.

From another angle to view deep learning, deep learning refers to "computer-simulate" or "automate" human learning processes from a source (e.g., an image of dogs) to a learned object (dogs). Therefore, a notion coined as "deeper" learning or "deepest" learning makes sense. The deepest learning refers to the fully automatic learning from a source to a final learned object. A deeper learning thus refers to a mixed learning process: a human learning process from a source to a learned semi-object, followed by a computer learning process from the human learned semi-object to a final learned object.

Overview
Most modern deep learning models are based on multi-layered artificial neural networks such as convolutional neural networks and transformers, although they can also include propositional formulas or latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep Boltzmann machines.

In deep learning, each level learns to transform its input data into a slightly more abstract and composite representation. In an image recognition application, the raw input may be a matrix of pixels; the first representational layer may abstract the pixels and encode edges; the second layer may compose and encode arrangements of edges; the third layer may encode a nose and eyes; and the fourth layer may recognize that the image contains a face. Importantly, a deep learning process can learn which features to optimally place in which level on its own. This does not eliminate the need for hand-tuning; for example, varying numbers of layers and layer sizes can provide different degrees of abstraction.

The word "deep" in "deep learning" refers to the number of layers through which the data is transformed. More precisely, deep learning systems have a substantial credit assignment path (CAP) depth. The CAP is the chain of transformations from input to output. CAPs describe potentially causal connections between input and output. For a feedforward neural network, the depth of the CAPs is that of the network and is the number of hidden layers plus one (as the output layer is also parameterized). For recurrent neural networks, in which a signal may propagate through a layer more than once, the CAP depth is potentially unlimited. No universally agreed-upon threshold of depth divides shallow learning from deep learning, but most researchers agree that deep learning involves CAP depth higher than 2. CAP of depth 2 has been shown to be a universal approximator in the sense that it can emulate any function. Beyond that, more layers do not add to the function approximator ability of the network. Deep models (CAP > 2) are able to extract better features than shallow models and hence, extra layers help in learning the features effectively.

Deep learning architectures can be constructed with a greedy layer-by-layer method. Deep learning helps to disentangle these abstractions and pick out which features improve performance.

For supervised learning tasks, deep learning methods eliminate feature engineering, by translating the data into compact intermediate representations akin to principal components, and derive layered structures that remove redundancy in representation.

Deep learning algorithms can be applied to unsupervised learning tasks. This is an important benefit because unlabeled data are more abundant than the labeled data. Examples of deep structures that can be trained in an unsupervised manner are deep belief networks.

Interpretations
Deep neural networks are generally interpreted in terms of the universal approximation theorem or probabilistic inference.

The classic universal approximation theorem concerns the capacity of feedforward neural networks with a single hidden layer of finite size to approximate continuous functions. In 1989, the first proof was published by George Cybenko for sigmoid activation functions and was generalised to feed-forward multi-layer architectures in 1991 by Kurt Hornik. Recent work also showed that universal approximation also holds for non-bounded activation functions such as Kunihiko Fukushima's rectified linear unit.

The universal approximation theorem for deep neural networks concerns the capacity of networks with bounded width but the depth is allowed to grow. Lu et al. proved that if the width of a deep neural network with ReLU activation is strictly larger than the input dimension, then the network can approximate any Lebesgue integrable function; if the width is smaller or equal to the input dimension, then a deep neural network is not a universal approximator.

The probabilistic interpretation derives from the field of machine learning. It features inference, as well as the optimization concepts of training and testing, related to fitting and generalization, respectively. More specifically, the probabilistic interpretation considers the activation nonlinearity as a cumulative distribution function. The probabilistic interpretation led to the introduction of dropout as regularizer in neural networks. The probabilistic interpretation was introduced by researchers including Hopfield, Widrow and Narendra and popularized in surveys such as the one by Bishop.

History

There are two types of neural networks: feedforward neural networks (FNNs) and recurrent neural networks (RNNs). RNNs have cycles in their connectivity structure, FNNs don't. In the 1920s, Wilhelm Lenz and Ernst Ising created and analyzed the Ising model which is essentially a non-learning RNN architecture consisting of neuron-like threshold elements. In 1972, Shun'ichi Amari made this architecture adaptive. His learning RNN was popularised by John Hopfield in 1982. RNNs have become central for speech recognition and language processing.

Charles Tappert writes that Frank Rosenblatt developed and explored all of the basic ingredients of the deep learning systems of today, referring to Rosenblatt's 1962 book which introduced a multilayer perceptron (MLP) with 3 layers: an input layer, a hidden layer with randomized weights that did not learn, and an output layer. However, since only the output layer had learning connections, this was not yet deep learning. It was what later was called an extreme learning machine.

The first general, working learning algorithm for supervised, deep, feedforward, multilayer perceptrons was published by Alexey Ivakhnenko and Lapa in 1967. A 1971 paper described a deep network with eight layers trained by the group method of data handling.

The first deep learning multilayer perceptron trained by stochastic gradient descent was published in 1967 by Shun'ichi Amari. In computer experiments conducted by Amari's student Saito, a five layer MLP with two modifiable layers learned  internal representations to classify non-linearily separable pattern classes. In 1987 Matthew Brand reported that wide 12-layer nonlinear perceptrons could be fully end-to-end trained to reproduce logic functions of nontrivial circuit depth via gradient descent on small batches of random input/output samples, but concluded that training time on contemporary hardware (sub-megaflop computers) made the technique impractical, and proposed using fixed random early layers as an input hash for a single modifiable layer.  Instead, subsequent developments in hardware and hyperparameter tunings have made end-to-end stochastic gradient descent the currently dominant training technique.

In 1970, Seppo Linnainmaa published the reverse mode of automatic differentiation of discrete connected networks of nested differentiable functions. This became known as backpropagation. It is an efficient application of the chain rule derived by Gottfried Wilhelm Leibniz in 1673 to networks of differentiable nodes.
The terminology "back-propagating errors" was actually introduced in 1962 by Rosenblatt, but he did not know how to implement this, although Henry J. Kelley had a continuous precursor of backpropagation already in 1960 in the context of control theory. In 1982, Paul Werbos applied backpropagation to MLPs in the way that has become standard. In 1985, David E. Rumelhart et al. published an experimental analysis of the technique.

Deep learning architectures for convolutional neural networks (CNNs) with convolutional layers and downsampling layers began with the Neocognitron introduced by Kunihiko Fukushima in 1980. In 1969, he also introduced the ReLU (rectified linear unit) activation function. The rectifier has become the most popular activation function for CNNs and deep learning in general. CNNs have become an essential tool for computer vision.

The term Deep Learning was introduced to the machine learning community by Rina Dechter in 1986, and to artificial neural networks by Igor Aizenberg and colleagues in 2000, in the context of Boolean threshold neurons.

In 1988, Wei Zhang et al. applied the backpropagation algorithm
to a convolutional neural network (a simplified Neocognitron with convolutional interconnections between the image feature layers and the last fully connected layer) for alphabet recognition. They also proposed an implementation of the CNN with an optical computing system.
In 1989, Yann LeCun et al. applied backpropagation to a CNN with the purpose of recognizing handwritten ZIP codes on mail. While the algorithm worked, training required 3 days. Subsequently, Wei Zhang, et al. modified their model by removing the last fully connected layer and applied it for medical image object segmentation in 1991 and breast cancer detection in mammograms in 1994. LeNet-5 (1998), a 7-level CNN by Yann LeCun et al., that classifies digits, was applied by several banks to recognize hand-written numbers on checks  digitized in 32x32 pixel images.

In the 1980s, backpropagation did not work well for deep learning with long credit assignment paths. To overcome this problem, Jürgen Schmidhuber (1992) proposed a hierarchy of RNNs pre-trained one level at a time by self-supervised learning. It uses predictive coding  to learn internal representations at multiple self-organizing time scales. This can substantially facilitate downstream deep learning. The RNN hierarchy can be collapsed into a single RNN, by distilling a higher level chunker network into a lower level automatizer network. In 1993, a chunker solved a deep learning task whose depth exceeded 1000.

In 1992, Jürgen Schmidhuber also published an alternative to RNNs which is now called a linear Transformer or a  Transformer with linearized self-attention (save for a normalization operator). It learns internal spotlights of attention: a slow feedforward neural network learns by gradient descent to control the fast weights of another neural network through outer products of self-generated activation patterns FROM and TO (which are now called key and value for self-attention). This fast weight attention mapping is applied to a query pattern.

The modern Transformer was introduced by Ashish Vaswani et. al. in their 2017 paper "Attention Is All You Need".
It combines this with a softmax operator and a projection matrix.
Transformers have increasingly become the model of choice for natural language processing. Many modern large language models such as ChatGPT, GPT-4, and BERT use it. Transformers are also increasingly being used in computer vision.

In 1991, Jürgen Schmidhuber also published adversarial neural networks that contest with each other in the form of a zero-sum game, where one network's gain is the other network's loss. The first network is a generative model that models a probability distribution over output patterns. The second network learns by gradient descent to predict the reactions of the environment to these patterns. This was called "artificial curiosity". In 2014, this principle was used in a generative adversarial network (GAN) by Ian Goodfellow et al. Here the environmental reaction is 1 or 0 depending on whether the first network's output is in a given set. This can be used to create realistic deepfakes. Excellent image quality is achieved by Nvidia's StyleGAN (2018) based on the Progressive GAN by Tero Karras et. al. Here the GAN generator is grown from small to large scale in a pyramidal fashion.

Sepp Hochreiter's diploma thesis (1991) was called "one of the most important documents in the history of machine learning" by his supervisor Schmidhuber. It not only tested the neural history compressor, but also identified and analyzed the vanishing gradient problem. Hochreiter proposed recurrent residual connections to solve this problem. This led to the deep learning method called long short-term memory (LSTM), published in 1997. LSTM recurrent neural networks can learn "very deep learning" tasks with long credit assignment paths that require memories of events that happened thousands of discrete time steps before. The "vanilla LSTM" with forget gate was introduced in 1999 by Felix Gers, Schmidhuber and Fred Cummins. LSTM has become the  most cited neural network of the 20th century.
In 2015, Rupesh Kumar Srivastava, Klaus Greff, and Schmidhuber used LSTM principles to create the Highway network, a feedforward neural network with hundreds of layers, much deeper than previous networks. 7 months later, Kaiming He, Xiangyu Zhang;  Shaoqing Ren, and Jian Sun won the ImageNet 2015 competition with an open-gated or gateless Highway network variant called Residual neural network. This has become the most cited neural network of the 21st century.

In 1994, André de Carvalho, together with Mike Fairhurst and David Bisset, published experimental results of a multi-layer boolean neural network, also known as a weightless neural network, composed of a 3-layers self-organising feature extraction neural network module (SOFT) followed by a multi-layer classification neural network module (GSN), which were independently trained. Each layer in the feature extraction module extracted features with growing complexity regarding the previous layer.

In 1995, Brendan Frey demonstrated that it was possible to train (over two days) a network containing six fully connected layers and several hundred hidden units using the wake-sleep algorithm, co-developed with Peter Dayan and Hinton.

Since 1997, Sven Behnke extended the feed-forward hierarchical convolutional approach in the Neural Abstraction Pyramid by lateral and backward connections in order to flexibly incorporate context into decisions and iteratively resolve local ambiguities.

Simpler models that use task-specific handcrafted features such as Gabor filters and support vector machines (SVMs) were a popular choice in the 1990s and 2000s, because of artificial neural network's (ANN) computational cost and a lack of understanding of how the brain wires its biological networks.

Both shallow and deep learning (e.g., recurrent nets) of ANNs for speech recognition have been explored for many years. These methods never outperformed non-uniform internal-handcrafting Gaussian mixture model/Hidden Markov model (GMM-HMM) technology based on generative models of speech trained discriminatively. Key difficulties have been analyzed, including gradient diminishing and weak temporal correlation structure in neural predictive models. Additional difficulties were the lack of training data and limited computing power. Most speech recognition researchers moved away from neural nets to pursue generative modeling. An exception was at SRI International in the late 1990s. Funded by the US government's NSA and DARPA, SRI studied deep neural networks in speech and speaker recognition. The speaker recognition team led by Larry Heck reported significant success with deep neural networks in speech processing in the 1998 National Institute of Standards and Technology Speaker Recognition evaluation. The SRI deep neural network was then deployed in the Nuance Verifier, representing the first major industrial application of deep learning. The principle of elevating "raw" features over hand-crafted optimization was first explored successfully in the architecture of deep autoencoder on the "raw" spectrogram or linear filter-bank features in the late 1990s, showing its superiority over the Mel-Cepstral features that contain stages of fixed transformation from spectrograms. The raw features of speech, waveforms, later produced excellent larger-scale results.

Speech recognition was taken over by LSTM. In 2003, LSTM started to become competitive with traditional speech recognizers on certain tasks. In 2006, Alex Graves, Santiago Fernández, Faustino Gomez, and Schmidhuber combined it with connectionist temporal classification (CTC) in stacks of LSTM RNNs. In 2015, Google's speech recognition reportedly experienced a dramatic performance jump of 49% through CTC-trained LSTM, which they made available through Google Voice Search.

The impact of deep learning in industry began in the early 2000s, when CNNs already processed an estimated 10% to 20% of all the checks written in the US, according to Yann LeCun. Industrial applications of deep learning to large-scale speech recognition started around 2010.

In 2006, publications by Geoff Hinton, Ruslan Salakhutdinov, Osindero and Teh showed how a many-layered feedforward neural network could be effectively pre-trained one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then fine-tuning it using supervised backpropagation. The papers referred to learning for deep belief nets.

The 2009 NIPS Workshop on Deep Learning for Speech Recognition was motivated by the limitations of deep generative models of speech, and the possibility that given more capable hardware and large-scale data sets that deep neural nets (DNN) might become practical. It was believed that pre-training DNNs using generative models of deep belief nets (DBN) would overcome the main difficulties of neural nets. However, it was discovered that replacing pre-training with large amounts of training data for straightforward backpropagation when using DNNs with large, context-dependent output layers produced error rates dramatically lower than then-state-of-the-art Gaussian mixture model (GMM)/Hidden Markov Model (HMM) and also than more-advanced generative model-based systems. The nature of the recognition errors produced by the two types of systems was characteristically different, offering technical insights into how to integrate deep learning into the existing highly efficient, run-time speech decoding system deployed by all major speech recognition systems. Analysis around 2009–2010, contrasting the GMM (and other generative speech models) vs. DNN models, stimulated early industrial investment in deep learning for speech recognition.  That analysis was done with comparable performance (less than 1.5% in error rate) between discriminative DNNs and generative models.
In 2010, researchers extended deep learning from TIMIT to large vocabulary speech recognition, by adopting large output layers of the DNN based on context-dependent HMM states constructed by decision trees.

Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision and automatic speech recognition (ASR). Results on commonly used evaluation sets such as TIMIT (ASR) and MNIST (image classification), as well as a range of large-vocabulary speech recognition tasks have steadily improved. Convolutional neural networks (CNNs) were superseded for ASR by CTC for LSTM. but are more successful in computer vision.

Advances in hardware have driven renewed interest in deep learning. In 2009, Nvidia was involved in what was called the "big bang" of deep learning, "as deep-learning neural networks were trained with Nvidia graphics processing units (GPUs)". That year, Andrew Ng determined that GPUs could increase the speed of deep-learning systems by about 100 times. In particular, GPUs are well-suited for the matrix/vector computations involved in machine learning. GPUs speed up training algorithms by orders of magnitude, reducing running times from weeks to days. Further, specialized hardware and algorithm optimizations can be used for efficient processing of deep learning models.

Deep learning revolution

In the late 2000s, deep learning started to outperform other methods in machine learning competitions.
In 2009, a long short-term memory trained by connectionist temporal classification (Alex Graves, Santiago Fernández, Faustino Gomez, and Jürgen Schmidhuber, 2006) was the first RNN to win pattern recognition contests, winning three competitions in connected handwriting recognition. Google later used CTC-trained LSTM for speech recognition on the smartphone.

Significant impacts in image or object recognition were felt from 2011 to 2012. Although CNNs trained by backpropagation had been around for decades, and GPU implementations of NNs for years, including CNNs, faster implementations of CNNs on GPUs were needed to progress on computer vision. In 2011, the DanNet by Dan Ciresan, Ueli Meier, Jonathan Masci, Luca Maria Gambardella, and Jürgen Schmidhuber achieved for the first time superhuman performance in a visual pattern recognition contest, outperforming traditional methods by a factor of 3. Also in 2011, DanNet won the ICDAR Chinese handwriting contest, and in May 2012, it won the ISBI image segmentation contest. Until 2011, CNNs did not play a major role at computer vision conferences, but in June 2012, a paper by Ciresan et al. at the leading conference CVPR showed how max-pooling CNNs on GPU can dramatically improve many vision benchmark records.  In September 2012, DanNet also won the ICPR contest on analysis of large medical images for cancer detection, and in the following year also the MICCAI Grand Challenge on the same topic. In October 2012, the similar AlexNet by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton won the large-scale ImageNet competition by a significant margin over shallow machine learning methods.
The VGG-16 network by Karen Simonyan and Andrew Zisserman further reduced the error rate and
won the ImageNet 2014 competition, following a similar trend in large-scale speech recognition.

Image classification was then extended to the more challenging task of generating descriptions (captions) for images, often as a combination of CNNs and LSTMs.

In 2012, a team led by George E. Dahl won the "Merck Molecular Activity Challenge" using multi-task deep neural networks to predict the biomolecular target of one drug. In 2014, Sepp Hochreiter's group used deep learning to detect off-target and toxic effects of environmental chemicals in nutrients, household products and drugs and won the "Tox21 Data Challenge" of NIH, FDA and NCATS.

In 2016, Roger Parloff mentioned a "deep learning revolution" that has transformed the AI industry.

In March 2019, Yoshua Bengio, Geoffrey Hinton and Yann LeCun were awarded the Turing Award for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.

Neural networks

Artificial neural networks (ANNs) or connectionist systems are computing systems inspired by the biological neural networks that constitute animal brains. Such systems learn (progressively improve their ability) to do tasks by considering examples, generally without task-specific programming. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the analytic results to identify cats in other images. They have found most use in applications difficult to express with a traditional computer algorithm using rule-based programming.

An ANN is based on a collection of connected units called artificial neurons, (analogous to biological neurons in a biological brain). Each connection (synapse) between neurons can transmit a signal to another neuron. The receiving (postsynaptic) neuron can process the signal(s) and then signal downstream neurons connected to it. Neurons may have state, generally represented by real numbers, typically between 0 and 1. Neurons and synapses may also have a weight that varies as learning proceeds, which can increase or decrease the strength of the signal that it sends downstream.

Typically, neurons are organized in layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first (input), to the last (output) layer, possibly after traversing the layers multiple times.

The original goal of the neural network approach was to solve problems in the same way that a human brain would. Over time, attention focused on matching specific mental abilities, leading to deviations from biology such as backpropagation, or passing information in the reverse direction and adjusting the network to reflect that information.

Neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis.

As of 2017, neural networks typically have a few thousand to a few million units and millions of connections. Despite this number being several order of magnitude less than the number of neurons on a human brain, these networks can perform many tasks at a level beyond that of humans (e.g., recognizing faces, or playing "Go").

Deep neural networks
A deep neural network (DNN) is an artificial neural network (ANN) with multiple layers between the input and output layers. There are different types of neural networks but they always consist of the same components: neurons, synapses, weights, biases, and functions. These components as a whole function similarly to a human brain, and can be trained like any other ML algorithm.

For example, a DNN that is trained to recognize dog breeds will go over the given image and calculate the probability that the dog in the image is a certain breed. The user can review the results and select which probabilities the network should display (above a certain threshold, etc.) and return the proposed label. Each mathematical manipulation as such is considered a layer, and complex DNN have many layers, hence the name "deep" networks.

DNNs can model complex non-linear relationships. DNN architectures generate compositional models where the object is expressed as a layered composition of primitives. The extra layers enable composition of features from lower layers, potentially modeling complex data with fewer units than a similarly performing shallow network. For instance, it was proved that sparse multivariate polynomials are exponentially easier to approximate with DNNs than with shallow networks.

Deep architectures include many variants of a few basic approaches. Each architecture has found success in specific domains. It is not always possible to compare the performance of multiple architectures, unless they have been evaluated on the same data sets.

DNNs are typically feedforward networks in which data flows from the input layer to the output layer without looping back. At first, the DNN creates a map of virtual neurons and assigns random numerical values, or "weights", to connections between them. The weights and inputs are multiplied and return an output between 0 and 1. If the network did not accurately recognize a particular pattern, an algorithm would adjust the weights. That way the algorithm can make certain parameters more influential, until it determines the correct mathematical manipulation to fully process the data.

Recurrent neural networks (RNNs), in which data can flow in any direction, are used for applications such as language modeling. Long short-term memory is particularly effective for this use.

Convolutional deep neural networks (CNNs) are used in computer vision. CNNs also have been applied to acoustic modeling for automatic speech recognition (ASR).

Challenges
As with ANNs, many issues can arise with naively trained DNNs. Two common issues are overfitting and computation time.

DNNs are prone to overfitting because of the added layers of abstraction, which allow them to model rare dependencies in the training data. Regularization methods such as Ivakhnenko's unit pruning or weight decay (-regularization) or sparsity (-regularization) can be applied during training to combat overfitting. Alternatively dropout regularization randomly omits units from the hidden layers during training. This helps to exclude rare dependencies. Finally, data can be augmented via methods such as cropping and rotating such that smaller training sets can be increased in size to reduce the chances of overfitting.

DNNs must consider many training parameters, such as the size (number of layers and number of units per layer), the learning rate, and initial weights. Sweeping through the parameter space for optimal parameters may not be feasible due to the cost in time and computational resources. Various tricks, such as batching (computing the gradient on several training examples at once rather than individual examples) speed up computation. Large processing capabilities of many-core architectures (such as GPUs or the Intel Xeon Phi) have produced significant speedups in training, because of the suitability of such processing architectures for the matrix and vector computations.

Alternatively, engineers may look for other types of neural networks with more straightforward and convergent training algorithms. CMAC (cerebellar model articulation controller) is one such kind of neural network. It doesn't require learning rates or randomized initial weights. The training process can be guaranteed to converge in one step with a new batch of data, and the computational complexity of the training algorithm is linear with respect to the number of neurons involved.

Hardware
Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer. By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI. OpenAI estimated the hardware computation used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of computation required, with a doubling-time trendline of 3.4 months.

Special electronic circuits called deep learning processors were designed to speed up deep learning algorithms. Deep learning processors include neural processing units (NPUs) in Huawei cellphones and cloud computing servers such as tensor processing units (TPU) in the Google Cloud Platform. Cerebras Systems has also built a dedicated system to handle large deep learning models, the CS-2, based on the largest processor in the industry, the second-generation Wafer Scale Engine (WSE-2).

Atomically thin semiconductors are considered promising for energy-efficient deep learning hardware where the same basic device structure is used for both logic operations and data storage.
In 2020, Marega et al. published experiments with a large-area active channel material for developing logic-in-memory devices and circuits based on floating-gate field-effect transistors (FGFETs).

In 2021, J. Feldmann et al. proposed an integrated photonic hardware accelerator for parallel convolutional processing. The authors identify two key advantages of integrated photonics over its electronic counterparts: (1) massively parallel data transfer through wavelength division multiplexing in conjunction with frequency combs, and (2) extremely high data modulation speeds. Their system can execute trillions of multiply-accumulate operations per second, indicating the potential of integrated photonics in data-heavy AI applications.

Applications

Automatic speech recognition

Large-scale automatic speech recognition is the first and most convincing successful case of deep learning. LSTM RNNs can learn "Very Deep Learning" tasks that involve multi-second intervals containing speech events separated by thousands of discrete time steps, where one time step corresponds to about 10 ms. LSTM with forget gates is competitive with traditional speech recognizers on certain tasks.

The initial success in speech recognition was based on small-scale recognition tasks based on TIMIT. The data set contains 630 speakers from eight major dialects of American English, where each speaker reads 10 sentences. Its small size lets many configurations be tried. More importantly, the TIMIT task concerns phone-sequence recognition, which, unlike word-sequence recognition, allows weak phone bigram language models. This lets the strength of the acoustic modeling aspects of speech recognition be more easily analyzed. The error rates listed below, including these early results and measured as percent phone error rates (PER), have been summarized since 1991.

The debut of DNNs for speaker recognition in the late 1990s and speech recognition around 2009-2011 and of LSTM around 2003–2007, accelerated progress in eight major areas:

 Scale-up/out and accelerated DNN training and decoding
 Sequence discriminative training
 Feature processing by deep models with solid understanding of the underlying mechanisms
 Adaptation of DNNs and related deep models
 Multi-task and transfer learning by DNNs and related deep models
 CNNs and how to design them to best exploit domain knowledge of speech
 RNN and its rich LSTM variants
 Other types of deep models including tensor-based models and integrated deep generative/discriminative models.

All major commercial speech recognition systems (e.g., Microsoft Cortana, Xbox, Skype Translator, Amazon Alexa, Google Now, Apple Siri, Baidu and iFlyTek voice search, and a range of Nuance speech products, etc.) are based on deep learning.

Image recognition

A common evaluation set for image classification is the MNIST database data set. MNIST is composed of handwritten digits and includes 60,000 training examples and 10,000 test examples. As with TIMIT, its small size lets users test multiple configurations. A comprehensive list of results on this set is available.

Deep learning-based image recognition has become "superhuman", producing more accurate results than human contestants. This first occurred in 2011 in recognition of traffic signs, and in 2014, with recognition of human faces.

Deep learning-trained vehicles now interpret 360° camera views. Another example is Facial Dysmorphology Novel Analysis (FDNA) used to analyze cases of human malformation connected to a large database of genetic syndromes.

Visual art processing

Closely related to the progress that has been made in image recognition is the increasing application of deep learning techniques to various visual art tasks. DNNs have proven themselves capable, for example, of
identifying the style period of a given painting
Neural Style Transfer capturing the style of a given artwork and applying it in a visually pleasing manner to an arbitrary photograph or video
generating striking imagery based on random visual input fields.

Natural language processing

Neural networks have been used for implementing language models since the early 2000s. LSTM helped to improve machine translation and language modeling.

Other key techniques in this field are negative sampling and word embedding. Word embedding, such as word2vec, can be thought of as a representational layer in a deep learning architecture that transforms an atomic word into a positional representation of the word relative to other words in the dataset; the position is represented as a point in a vector space. Using word embedding as an RNN input layer allows the network to parse sentences and phrases using an effective compositional vector grammar. A compositional vector grammar can be thought of as probabilistic context free grammar (PCFG) implemented by an RNN. Recursive auto-encoders built atop word embeddings can assess sentence similarity and detect paraphrasing. Deep neural architectures provide the best results for constituency parsing, sentiment analysis, information retrieval, spoken language understanding, machine translation, contextual entity linking, writing style recognition, named-entity recognition (token classification), text classification, and others.

Recent developments generalize word embedding to sentence embedding.

Google Translate (GT) uses a large end-to-end long short-term memory (LSTM) network. Google Neural Machine Translation (GNMT) uses an example-based machine translation method in which the system "learns from millions of examples". It translates "whole sentences at a time, rather than pieces". Google Translate supports over one hundred languages. The network encodes the "semantics of the sentence rather than simply memorizing phrase-to-phrase translations". GT uses English as an intermediate between most language pairs.

Drug discovery and toxicology

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. Research has explored use of deep learning to predict the biomolecular targets, off-targets, and toxic effects of environmental chemicals in nutrients, household products and drugs.

AtomNet is a deep learning system for structure-based rational drug design. AtomNet was used to predict novel candidate biomolecules for disease targets such as the Ebola virus and multiple sclerosis.

In 2017 graph neural networks were used for the first time to predict various properties of molecules in a large toxicology data set. In 2019, generative neural networks were used to produce molecules that were validated experimentally all the way into mice.

Customer relationship management

Deep reinforcement learning has been used to approximate the value of possible direct marketing actions, defined in terms of RFM variables. The estimated value function was shown to have a natural interpretation as customer lifetime value.

Recommendation systems

Recommendation systems have used deep learning to extract meaningful features for a latent factor model for content-based music and journal recommendations. Multi-view deep learning has been applied for learning user preferences from multiple domains. The model uses a hybrid collaborative and content-based approach and enhances recommendations in multiple tasks.

Bioinformatics

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.

Medical image analysis
Deep learning has been shown to produce competitive results in medical application such as cancer cell classification, lesion detection, organ segmentation and image enhancement. Modern deep learning tools demonstrate the high accuracy of detecting various diseases and the helpfulness of their use by specialists to improve the diagnosis efficiency.

Mobile advertising
Finding the appropriate mobile audience for mobile advertising is always challenging, since many data points must be considered and analyzed before a target segment can be created and used in ad serving by any ad server. Deep learning has been used to interpret large, many-dimensioned advertising datasets. Many data points are collected during the request/serve/click internet advertising cycle. This information can form the basis of machine learning to improve ad selection.

Image restoration
Deep learning has been successfully applied to inverse problems such as denoising, super-resolution, inpainting, and film colorization. These applications include learning methods such as "Shrinkage Fields for Effective Image Restoration" which trains on an image dataset, and Deep Image Prior, which trains on the image that needs restoration.

Financial fraud detection
Deep learning is being successfully applied to financial fraud detection, tax evasion detection, and anti-money laundering.

Military

The United States Department of Defense applied deep learning to train robots in new tasks through observation.

Partial differential equations
Physics informed neural networks have been used to solve partial differential equations in both forward and inverse problems in a data driven manner. One example is the reconstructing fluid flow governed by the Navier-Stokes equations. Using physics informed neural networks does not require the often expensive mesh generation that conventional CFD methods relies on.

Image Reconstruction
Image reconstruction is the reconstruction of the underlying images from the image-related measurements. Several works showed the better and superior performance of the deep learning methods compared to analytical methods for various applications, e.g., spectral imaging  and ultrasound imaging.

Epigenetic clock

An epigenetic clock is a biochemical test that can be used to measure age. Galkin et al. used deep neural networks to train an epigenetic aging clock of unprecedented accuracy using >6,000 blood samples. The clock uses information from 1000 CpG sites and predicts people with certain conditions older than healthy controls: IBD, frontotemporal dementia, ovarian cancer, obesity. The aging clock was planned to be released for public use in 2021 by an Insilico Medicine spinoff company Deep Longevity.

Relation to human cognitive and brain development
Deep learning is closely related to a class of theories of brain development (specifically, neocortical development) proposed by cognitive neuroscientists in the early 1990s. These developmental theories were instantiated in computational models, making them predecessors of deep learning systems. These developmental models share the property that various proposed learning dynamics in the brain (e.g., a wave of nerve growth factor) support the self-organization somewhat analogous to the neural networks utilized in deep learning models. 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 1995 description stated, "...the infant's brain seems to organize itself under the influence of waves of so-called trophic-factors ... different regions of the brain become connected sequentially, with one layer of tissue maturing before another and so on until the whole brain is mature".

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. Other researchers have argued that unsupervised forms of deep learning, such as those based on hierarchical generative models and deep belief networks, may be closer to biological reality. In this respect, generative neural network models have been related to neurobiological evidence about sampling-based processing in the cerebral cortex.

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. Similarly, the representations developed by deep learning models are similar to those measured in the primate visual system both at the single-unit and at the population levels.

Commercial activity
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 2015 they demonstrated their AlphaGo system, which learned the game of Go well enough to beat a professional Go player. Google Translate uses a neural network to translate between more than 100 languages.

In 2017, Covariant.ai was launched, which focuses on integrating deep learning into factories.

As of 2008, 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. First developed as TAMER, a new algorithm called Deep TAMER was later introduced in 2018 during a collaboration between U.S. Army Research Laboratory (ARL) and UT researchers. Deep TAMER used deep learning to provide a robot the ability to learn new tasks through observation. Using Deep TAMER, a robot learned a task with a human trainer, watching video streams or observing a human perform a task in-person. The robot later practiced the task with the help of some coaching from the trainer, who provided feedback such as "good job" and "bad job".

Criticism and comment
Deep learning has attracted both criticism and comment, in some cases from outside the field of computer science.

Theory

A main criticism concerns the lack of theory surrounding some methods. Learning in the most common deep architectures is implemented using well-understood gradient descent. However, the theory surrounding other algorithms, such as contrastive divergence is less clear. (e.g., Does it converge? 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:
Realistically, deep learning is only part of the larger challenge of building intelligent machines. Such techniques lack ways of representing causal relationships (...) have no obvious ways of performing logical inferences, and they are also still a long way from integrating abstract knowledge, such as information about what objects are, what they are for, and how they are typically used. The most powerful A.I. systems, like Watson (...) use techniques like deep learning as just one element in a very complicated ensemble of techniques, ranging from the statistical technique of Bayesian inference to deductive reasoning.

In further reference to the idea that artistic sensitivity might be inherent in relatively low levels of the cognitive hierarchy, a published series of graphic representations of the internal states of deep (20-30 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 1,000 comments, and was the subject of what was for a time the most frequently accessed article on The Guardian's website.

Errors
Some deep learning architectures display problematic behaviors, such as confidently classifying unrecognizable images as belonging to a familiar category of ordinary images (2014) and misclassifying minuscule perturbations of correctly classified images (2013). Goertzel hypothesized that these behaviors are due to limitations in their internal representations and that these limitations would inhibit integration into heterogeneous multi-component artificial general intelligence (AGI) architectures. These issues may possibly be addressed by deep learning architectures that internally form states homologous to image-grammar decompositions of observed entities and events. Learning a grammar (visual or linguistic) from training data would be equivalent to restricting the system to commonsense reasoning that operates on concepts in terms of grammatical production rules and is a basic goal of both human language acquisition and artificial intelligence (AI).

Cyber threat
As deep learning moves from the lab into the world, research and experience show 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. Such manipulation is termed an "adversarial attack".

In 2016 researchers used one ANN to doctor images in trial and error fashion, identify another's focal points and thereby generate images that deceived it. 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. One defense is reverse image search, in which a possible fake image is submitted to a site such as TinEye that can then find other instances of it. 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 2017 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.

In 2016, another group demonstrated that certain sounds could make the Google Now voice command system open a particular web address, and hypothesized that this could "serve as a stepping stone for further attacks (e.g., opening a web page hosting drive-by malware)".

In "data poisoning", false data is continually smuggled into a machine learning system's training set to prevent it from achieving mastery.

Data collection ethics

Most Deep Learning systems rely on training and verification data that is generated and/or annotated by humans. It has been argued in media philosophy that not only low-paid clickwork (e.g. on Amazon Mechanical Turk) is regularly deployed for this purpose, but also implicit forms of human microwork that are often not recognized as such. The philosopher Rainer Mühlhoff distinguishes five types of "machinic capture" of human microwork to generate training data: (1) gamification (the embedding of annotation or computation tasks in the flow of a game), (2) "trapping and tracking" (e.g. CAPTCHAs for image recognition or click-tracking on Google search results pages), (3) exploitation of social motivations (e.g. tagging faces on Facebook to obtain labeled facial images), (4) information mining (e.g. by leveraging quantified-self devices such as activity trackers) and (5) clickwork.

Mühlhoff argues that in most commercial end-user applications of Deep Learning such as Facebook's face recognition system, the need for training data does not stop once an ANN is trained. Rather, there is a continued demand for human-generated verification data to constantly calibrate and update the ANN. For this purpose Facebook introduced the feature that once a user is automatically recognized in an image, they receive a notification. 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. This user interface is a mechanism to generate "a constant stream of verification data" to further train the network in real-time. As Mühlhoff argues, involvement of human users to generate training and verification data is so typical for most commercial end-user applications of Deep Learning that such systems may be referred to as "human-aided artificial intelligence".

See also
 Applications of artificial intelligence
 Comparison of deep learning software
 Compressed sensing
 Differentiable programming
 Echo state network
 List of artificial intelligence projects
 Liquid state machine
 List of datasets for machine-learning research
 Reservoir computing
 Scale space and deep learning
 Sparse coding
 Stochastic parrot

References

Further reading

Artificial neural networks
Emerging technologies

Deep learning


Pat Carey is a politeecian in the Republic o Ireland, an a member o the Irish pairlament in Dublin, representin the pairty Fianna Fáil.  He represents the North-wast Dublin consteetuency in Dáil Éireann.

1947 births
Livin fowk
Alumni o St Patrick's College o Eddication, Drumcondra
Alumni o Varsity College Dublin
Alumni o Trinity College, Dublin
Fianna Fáil politeecians
Erse schuilteachers
Local cooncillors in Dublin (ceety)
Members o the 28t Dáil
Members o the 29t Dáil
Members o the 30t Dáil
Politeecians frae Coonty Kerry
Teachtaí Dála
Meenisters for Transport (Ireland)
Meenisters o State o the 30t Dáil

Pat Carey


A central processing unit (CPU) is an important part of every computer. The CPU sends signals to control the other parts of the computer, almost like how a brain controls a body.

The CPU is an electronic machine that works on a list of computer things to do, called instructions. It reads the list of instructions and runs (executes) each one in order. A list of instructions that a CPU can run is a computer program.

The clock rate, or speed of a CPU's internal parts, is measured in hertz (Hz). Modern processors often run so fast that gigahertz (GHz) is used instead. One GHz is 1,000,000,000 cycles per second.

Most CPUs used in desktop (home) computers are microprocessors made by either Intel or Advanced Micro Devices (usually shortened to AMD). Some other companies that make CPUs are ARM (recently bought by Nvidia), and IBM. Most of their CPUs are used in embedded systems for more specialized things, like in mobile phones, cars, video game consoles, or in the military.

Types of CPUs
In the 20th century engineers invented many different computer architectures. Nowadays most desktop computers use either 32-bit CPUs or 64-bit CPUs. The instructions in a 32-bit CPU are good at handling data that is 32 bits in size (most instructions "think" in 32 bits in a 32-bit CPU). Likewise, a 64-bit CPU is good at handling data that is 64 bits in size (and often good at handling 32-bit data too). The size of data that a CPU handles best is often called the word size of the CPU. Many old CPUs from the 70s, 80s and early 90s (and many modern embedded systems) have an 8-bit or 16-bit word size. When CPUs were invented in the middle 20th century they had many different word sizes. Some had different word sizes for instructions and data. The less popular word sizes later stopped being used.

Most CPUs are microprocessors. This means that the CPU is just a single chip. Some chips with microprocessors inside them also contain other components, and are complete single-chip "computers". This is called a microcontroller.

Registers
When the CPU runs a computer program, it needs somewhere to store the data that the instructions operate on (the data that they read and write). This storage is called a register. A CPU usually has many registers. Registers must be very fast to access (to read and write). Therefore, they are part of the CPU chip itself.

Memory
Storing all data in registers would make most CPUs too complicated (and very expensive). Therefore, registers usually only store the data that the CPU is working on "right now". The rest of the data used by the program is stored in RAM (Random Access Memory). Except in microcontrollers, RAM is usually stored outside the CPU in separate chips.

When the CPU wants to read or write data in RAM, it outputs an address to that data. Each byte in RAM has a memory address. The size of addresses is often the same as the word size: A 32-bit CPU uses 32-bit addresses, etc. However, smaller CPUs, like 8-bit CPUs, often use addresses that are larger than the word size. Otherwise the maximum program length would be too short.

Because the size of addresses is limited, the maximum amount of memory is also limited. 32-bit processors can usually only handle up to 4 GB of RAM. This is the number of different bytes that can be selected using a 32-bit address (each bit can have two values—0 and 1—and 232 bytes is 4 GB). A 64-bit processor might be able to handle up to 16 EB of RAM (16 exabytes, around 16 billion GB, or 16 billion billion bytes). The operating system may limit it to using smaller amounts.

The information that is stored in RAM is usually volatile. This means that it will disappear if the computer is turned off.

Memory management units (MMUs) and virtual memory
Modern CPUs often use a memory management unit (MMU). An MMU is a component that translates addresses from the CPU to (usually) different RAM addresses. When using an MMU, the addresses used in a program are (usually) not the "real" addresses where the data is stored. This is called virtual (the opposite of "real") memory. A few of the reasons why it is good to have an MMU are listed here:
 An MMU can "hide" the memory of other programs from a program. This is done by not translating any addresses to the "hidden" addresses while the program is running. This is good because it means that programs can't read and modify the memory of other programs, which improves security and stability. (Programs can't "spy" on each other, or "step on each other's toes".)
 Many MMUs can make some parts of memory non-writeable, non-readable, or non-executable (meaning code stored in that part of memory can't be run). This can be good for stability reasons and security reasons, as well as for other reasons.
 MMUs allow different programs to have different "views" of memory. This is handy in many different situations. For example, it will always be possible to have the "main" code of a program at the same (virtual) address without colliding with other programs. It is also handy when there are many different pieces of code (from libraries) that are shared between programs.
 MMUs allow code from libraries to appear at different addresses every time a program is run. This is good because not knowing where things are in memory often makes it harder for hackers to make programs do bad things. This is called address space randomization.
 Advanced programs and operating systems can use tricks with MMUs to avoid having to copy data between different places in memory.

Cache
On modern computers, RAM is much slower than registers, so accessing RAM slows down programs. To speed up memory accesses, a faster type of memory called a cache is often put between the RAM and the main parts of the CPU. The cache is usually a part of the CPU chip itself, and is much more expensive per byte than RAM. The cache stores the same data as RAM, but is usually much smaller. Therefore, all the data used by the program might not fit in the cache. The cache tries to store data that is likely to be used a lot. Examples include recently used data and data close in memory to recently used data.

Often it makes sense to have a "cache for the cache", just as it makes sense to have a cache for RAM. In multi-level caching, there are many caches, called the L1 cache, the L2 cache, and so on. The L1 cache is the fastest (and most expensive per byte) cache and is "closest" to the CPU. The L2 cache is one step away and is slower than the L1 cache, etc. The L1 cache can often be viewed as a cache for the L2 cache, etc.

Buses
Computer buses are the wires used by the CPU to communicate with RAM and other components in the computer. Almost all CPUs have at least a data bus - used to read and write data - and an address bus - used to output addresses. Other buses inside the CPU carry data to different parts of the CPU.

Instruction sets
An instruction set (also called an ISA - Instruction Set Architecture) is a language understood directly by a particular CPU. These languages are also called machine code or binary. They say how you tell the CPU to do different things, like loading data from memory into a register, or adding the values from two registers. Each instruction in an instruction set has an encoding, which is how the instruction is written as a sequence of bits.

Programs written in programming languages like C and C++ can't be run directly by the CPU. They must be translated into machine code before the CPU can run them. A compiler is a computer program that does this translation.

Machine code is just a sequence of 0s and 1s, which makes it difficult for humans to read it. To make it more readable, machine code programs are usually written in assembly language. Assembly language uses text instead of 0s and 1s: You might write "LD A,0" to load the value 0 into register A for example. A program that translates assembly language into machine code is called an assembler.

Functionality
Here are some of the basic things a CPU can do:

  Read data from memory and write data to memory.
 Add one number to another number.
 Test to see if one number is bigger than another number.
 Move a number from one place to another (for example, from one register to another, or between a register and memory).
  Jump to another place in the instruction list, but only if some test is true (for example, only if one number is bigger than another).

Even very complicated programs can be made by combining many simple instructions like these. This is possible because each instruction takes a very short time to happen. Many CPUs today can do more than 1 billion (1,000,000,000) instructions in a single second. In general, the more a CPU can do in a given time, the faster it is. One way to measure a processor's speed is MIPS (Million Instructions Per Second). Flops (Floating-point operations per second) and CPU clock speed (usually measured in gigahertz) are also ways to measure how much work a processor can do in a certain time.

A CPU is built out of logic gates; it has no moving parts. The CPU of a computer is connected electronically to other parts of the computer, like the video card, or the BIOS. A computer program can control these peripherals by reading or writing numbers to special places in the computer's memory.

Instruction pipelines
Each instruction executed by a CPU is usually done in many steps. For example, the steps to run an instruction "INC A" (increase the value stored in register A by one) on a simple CPU could be this:
 Read the instruction from memory,
 decode the instruction (figure out what the instruction does), and
 add one to register A.

Different parts of the CPU do these different things. Often it is possible to run some steps from different instructions at the same time, which makes the CPU faster. For example, we can read an instruction from memory at the same time that we decode another instruction, since those steps use different modules. This can be thought of as having many instructions "inside the pipeline" at once. In the best case, all of the modules are working on different instructions at once, but this is not always possible.

Multiple cores
Multi-core processors became much more common in the early 21st century.  This means that they have many processors built on to the same chip so that they can run many instructions at once.  Some processors may have up to sixty-four cores, like the upcoming AMD Epyc "Milan" series. Even processors for consumers have many cores, such as the 16-core AMD Ryzen 9 5950x.

Multithreading
Some processors have a technology known as multithreading. This is the task of running more than one "thread" of instructions in an operating system. Many modern processors use this to boost performance with heavy multi-threaded programs, such as benchmark programs.

Manufacturers
The following companies make computer CPUs:
 ARM
 Intel
 Advanced Micro Devices
 MCST
 SRISA
 Sun Microsystems

Further information
 Microprocessor
 ALU
 Execution unit
 Floating point unit
 Intel
 AMD

References

Other websites
 Central processing unit at Citizendium

Microprocessor design

Central processing unit


A globe is a scale model with the shape of a sphere. Globes can be scale models of the Earth (terrestrial globes) or of other planets. Globes can also be models of the celestial sphere (celestial globes). The English word "globe" comes from the , meaning round mass or sphere.

Most globes are maps of the Earth. They have the advantage of avoiding the distortions of flat map projections. The most common types are political and physical. Political globes show countries. Physical globes show landscape like mountains and rivers. Some globes are star charts and some are maps of distant worlds.

Global as an adjective is used to mean the entire world rather than any special place on the Earth. It is also used in fields like computers to mean dealing with a whole larger system, rather than its individual little parts.

History

That the Earth is a sphere was established by Hellenistic astronomy in the 3rd century BC. The terrestrial globe appeared in that period. Now, there are no terrestrial globes from Classical Antiquity or the Middle Ages in existence. The first example of terrestrial globe known was the one the ancient Greek Crates of Mallus made in the middle of the 2nd century BC. The Greek geographer Strabo wrote about Crates's terrestrial globe during the early years of the Roman Empire.

A celestial globe from Classical Antiquity is in the Naples National Archaeological Museum, Italy. The globe is part of a sculpture – the globe is held up by a statue of Atlas, a Titan from Greek mythology. The ancient Romans made the globe and statue in the 2nd century AD. They may have copied the sculpture from Hellenistic art (art of the ancient Greek type) from the Hellenistic period (the 1st, 2nd, and 3rd centuries BC). The sculpture's name is the Farnese Atlas.

Early globes of the Earth that show all of the Old World were constructed in the Islamic Golden Age. One such example was made in the 9th century AD by Muslim geographers and cartographers working under the Abbasid caliph, al-Ma'mun. Another example was the globe introduced to Beijing by the Persian astronomer, Jamal ad-Din, in 1267.

Martin Behaim designed a globe in 1492, in Nuremberg, Bavaria (Germany). Georg Glockendon was the artist who created the actual map drawings following Behaim's specifications. This globe is the oldest terrestrial globe still in existence. A copy of the globe showing America was made by Martin Waldseemueller in 1507. Another early globe, the Hunt-Lenox Globe, ca. 1507, is thought to be the source of the phrase "Here be dragons". Another "remarkably modern-looking" globe of the Earth was made in Constantinople (Istanbul) by Taqi al-Din at his Constantinople Observatory during the 1570s.

Manufacture

Mass-produced globes are typically covered by a printed paper map. The most common type has long, thin gores (strips) of paper. These strips narrow to a point at the North Pole and the South Pole. Then a small disk is used to paper over the irregularities at the poles. The more gores there are, the less stretching and crumpling is needed to make the paper map fit the sphere.

From a geometric point of view, all points on a sphere are the same. One could select any point on the planet, and create a paper map that covers the globe with strips that come together at that point and the opposite point.

A globe that represents the Earth is usually mounted at a 23.5° angle on bearings.  This mounting represents the axial tilt of the spinning planet, in relation to the Sun.  This makes it easy to visualize how days and seasons change.

References

Cartography

Globe


Nautilus is the common name for cephalopods of the family Nautilidae. They are the only living members of the subclass Nautiloidea. In appearance, they have not changed much in millions of years. The term chambered nautilus is also used for any species of the Nautilidae.

Many biologists see them as 'living fossils', because they are the only living descendents of the group which gave rise to the ammonites in the Palaeozoic era.

Today, there are six living species in two genera. The species Nautilus pompilius, from Western Australia is the largest and best known. It get up to 27 cm in diameter. However, most other nautiluses never exceed 20 cm. The Bellybutton nautilus, Nautilus macromphalus, is the smallest species, usually measuring only 16 cm.

The radula is wide and has exactly nine teeth. There are two pairs of gills.

The shell

Nautiluses are the only cephalopods who have an external shell. This is an ancestral or basal, feature. The animal can withdraw completely into its shell. It can the close the opening with a leathery hood formed from two specially folded tentacles. The shell is coiled and calcareous, with mother-of-pearl lining. It is pressure resistant, but will implode at a depth of about 800 m. The nautilus shell is made of two layers: the outer layer is a dull white, while the inner layer is a striking white with iridescence. The innermost portion of the shell is pearlescent, blue-gray. A osmena pearl is not a pearl, but a jewellery product got from this part of the shell.

The shell is internally divided into chambers. All the walls between chambers ('septa') are pierced in the middle by a duct, the siphuncle. Through this the animal can change the air pressure in the chambers. As the nautilus matures its body moves forward, sealing each chamber in turn with a new septum (wall). The last fully open chamber, the largest one, is used as the living chamber. The number of chambers increases from around four at the moment of hatching to thirty or more in adults.

The shell colour keeps the animal hidden in the water. When seen from the top, the shell is darker in color and marked with irregular stripes, which makes it blend into the darkness of the water below. The underside is almost completely white, making the animal indistinguishable from brighter waters near the ocean surface. This mode of camouflage is called countershading.

The nautilus shell is one of the finest natural examples of a logarithmic spiral.

Natural history

Buoyancy and movement
In order to swim, the nautilus draws water into and out of the living chamber and makes use of jet propulsion. When water is inside the chamber, the siphuncle extracts salt from it and diffuses it into the blood. When water is pumped out, the animal adjusts its buoyancy with the gas contained in the chamber. Buoyancy can be controlled by the  pumping of gas and fluid into or from the chambers using the siphuncle. The control of buoyancy like this limits the nautilus; they cannot operate under extreme pressure.

In the wild, nautilus usually lives in depths of about 300 m, rising to around 100 m at night for feeding, mating and egg laying. The shell of the nautilus cannot withstand depths greater than approximately 800 m.

Diet and sensory system
Nautiluses are predators and feed mainly on shrimp, small fish and crustaceans, which are captured by the tentacles. However, due to the very little energy they devote to swimming, they need only eat once a month. Unlike other cephalopods, they do not have good vision; their eye structure is highly developed but lacks a solid lens. They have a simple "pinhole" lens through which water can pass. Instead of vision, the animal is thought to use olfaction as the primary sensory means during foraging, locating or identifying potential mates.

Evolution
Fossil records indicate that nautiloids have not changed much (at least in their body form) for the last 500 million years. Nautiloids were much more common and varied 200 million years ago. Many of the early ones were straight-shelled, as in the extinct genus Lituites. They developed in the Cambrian and became a significant sea predator in the Ordovician period. Some species reached over 2.5 meters in size. The other cephalopod subclass, Coleoidea, diverged from the Nautilidae long ago and the nautilus has remained relatively unchanged since. Extinct relatives of the nautilus include ammonites, which were very important for many millions of years.  Other groups were the baculites and goniatites.

Reproduction and lifespan
Nautiluses are sexually dimorphic and reproduce by laying eggs. Attached to rocks in shallow waters, the eggs take twelve months to develop before hatching out at around 30 mm long. Females spawn once per year and regenerate their gonads, making nautiluses the only cephalopods to repeat reproduction. The lifespan of nautiluses is about 20 years, which is exceptionally long for a cephalopod.

Distribution
Nautiluses are only found in the tropical Indian and Pacific Oceans, from 30° N to 30° S latitude and 90° to 185° W longitude. They live on the deep slopes of coral reefs.

Related pages
Ammonite

References

Ward, P.D. 1988. In search of Nautilus. Simon and Schuster.

Other websites

Nautilus pompilius at ARKive
 Nautilidae discussion forum at TONMO.com
 Waikïkï Aquarium: Marine Life Profile: Chambered Nautilus
A molecular and karyological approach to the taxonomy of Nautilus

Cephalopods
Living fossils

Nautilus


The marketing mix is the combination of elements necessary to the planning and execution of the total marketing operation. The total marketing operation mainly consists of three
processes : Analyzing the situation of companies, STP, and Marketing mix. Marketing
managers first analyzes the situation of the company and then conduct STP : Segmentation,
Targeting, Positioning. In STP, they segment the market, choose the target market in which
the company can have competitive advantages and determine what is to be their products'
position in the market, and then they design the marketing mix. In the process, marketing
managers should design an internally consistent and mutually supportive marketing mix.

It was specifically divided into the 4Ps : Product, Placement, Price and
Promotion by E.Jerome McCarthy in 1960, and from then it has been dealt with various
variables. These variables include not only goods and services but usage,
value, image and so on.

Relative importance among 4Ps
In devising the marketing strategy : the total marketing operation,  it is usually said that getting a competitive advantage should be considered importantly than any other factors. Thus, product and price is firstly determined because in the case they could be seen more related to the competitive advantage. In that case, decision-making about place and promotion is considered relatively later after determining the strategic position of the products by comparing rival products and then giving relative quality of products, price of products to the market. However, it is usual way of using marketing mix, and the relative importance of each mix can be different in many cases in terms of the situation of the market, the characteristic of the product, the characteristic of the company and so on. Thus, place or promotion can be the highest priority in any cases. In conclusion, marketing managers must pursue the most appropriate marketing mix with a bid to achieving competitive advantage using minimum effort.

References
 Kotler, Philip (2003) "Marketing Management", Prentice Hall, .
 Chae Seo Il (2007) "Marketing", B&M Books,
 Silbiger, Steven (2005) "The Ten-day MBA", HarperCollins,

Marketing

Marketing mix


Alternative Livelihood Programs are the name given to government attempts, especially in South America to replace the illicit cultivation of banned substances, such as opium or coca, with alternative, legal crops or other activities as a source of income.

Alternative Livelihood has sometimes been referred as Alternative Development.

References

External links
 A case study of alternative livelihood in Laos by UNODC (PDF)
 Alternative Development Cooperation in East Asia by UNODC
 Alternative Livelihoods Program in Northeastern Afghanistan - a project of CIDA implemented by Aga Khan Foundation Canada
 Integrated Alternative Livelihoods Program in Kandahar Province - a project of CIDA
 Research in Alternative Livelihoods Fund (RALF) - administered by the U.K.'s Department for International Development (DFID)
 Afghanistan Alternative Livelihoods Program - implemented by PADCO, a member of the International Peace Operations Association

Drug control law
International development

Alternative Livelihood


Asunción (puura naam: Nuestra Señora Santa María de la Asunción), Paraguay ke sab se barraa aur capital city hae. City ke populationhas more than 1.2 million inhabitants, and the metropolitan area has more than 1.8 million inhabitants.

The Spanish word "asunción" means assumption in English. It refers to the Assumption of Mary; the full name means Our Lady, Holy Mary of the Assumption.

A
Capital cities
Paraguay

Asunción


The People's Choice Awards is an awards show recognizing the people and the work of popular culture. The show has been held every year since 1975 and is voted on by regular people. The People's Choice Awards air on CBS and are produced by Procter & Gamble and Mark Burnett. Global promotion is handled by Kroszover Entertainment.

The award show's creator, Bob Stivers, made the first show in 1975. The first awards recognized The Sting as 1974's Favorite Picture, Barbra Streisand as the year's Favorite Film Actress, and John Wayne as its Favorite Film Actor. Ratings for the show peaked in 1977, when the 3rd People's Choice Awards attracted 35.3 million viewers who saw Farrah Fawcett-Majors win for Favorite Female TV Star, Star Wars win as the Favorite Picture, and Streisand and Wayne win again in the Film Actress and Actor categories.

Procter & Gamble, the show's first and only sponsor, bought the show from Stivers in 1982.

In 2008, the People's Choice Awards introduced a new category: Favorite Sci-Fi Show. The nominees were Stargate Atlantis, Battlestar Galactica, and Doctor Who. The winner was Stargate Atlantis.

Categories
The award categories have changed over the years. For example, the 16th People's Choice Awards had categories including Favorite All-Around Movie (Batman), and both a Favorite Movie Actor (Tom Cruise) and a World Favorite Movie Actor (Dustin Hoffman). At the 23rd People's Choice Awards, Rob Reiner was named the People's Choice Awards Honoree. More recently, the 32nd People's Choice Awards (shown in January 2006) included categories such as Favorite On-Screen Match-Up (Vince Vaughn and Owen Wilson in Wedding Crashers), Favorite Leading Lady (Reese Witherspoon), Favorite Tour (U2), and an award named after a Procter & Gamble brand: Nice 'n Easy Fans Favorite Hair (Faith Hill).

Poll-based awards
In the 20th century, the awards were based on results from Gallup polls. Each year, Gallup took a survey of different categories for favorite actor, actress, movie, artist, television show or group. There was no limit on who or what could be chosen. The public could choose whomever or whatever it liked. The results of the annual survey were announced in the form of the People's Choice Awards.

Since polls have margins of error, many years' awards have had ties in at least one category, when Gallup declared that the voting was so close that a single winner could not be chosen. For example, in 2003, both Spider-Man and The Lord of The Rings: The Fellowship of the Ring were recognized as Favorite Motion Picture.

Switch to online voting
The winners of the 31st People's Choice Awards (shown January 9, 2005) were decided by online voting rather than Gallup polls. The nominees submitted for Internet voting were picked using an unpublished process involving editors at Entertainment Weekly, the show's production team, and a group of pop culture fans.

The nominees for the 32nd People's Choice Awards were picked by web research company Knowledge Networks, which took what it described as a nationally representative sample of men and women ages 18 to 54, with and without Internet access, to come up with the nominees after being presented with a list of candidates determined by national ratings averages, box office grosses and album sales, and they had the option to write in their favorites. Knowledge Networks picks these people by calling them and gives a Web TV and Internet access to people without Internet access so they can see what other people think.

The nominees for the 2010 People's Choice Awards were determined by media research company Visible Measures, which specializes in measuring Internet Video audience behavior.  They used a program called True Reach to find out people's reactions to online videos.

Kids' Choice Awards
In 1988, Nickelodeon created the Nickelodeon Kids' Choice Awards, a children's version of the People's Choice Awards. Though they have similar names, the two shows are owned by different companies. Also similar but unrelated is the Teen Choice Awards.

References

Other websites
 People's Choice Awards website
 People's Choice blog
 Information and results  at the Internet Movie Database
 35th Anniversary article from "The Hollywood Reporter"

Entertainment awards
Movie awards

People's Choice Awards


A femtosecond is one billionth of one millionth, or one quadrillionth of a second. It can be written as 10-15 second. Even shorter than this is an attosecond.

 200 femtoseconds  -  the fastest chemical reactions
 300 femtoseconds  -  how long it takes for the atoms of an iodine molecule to vibrate

Orders of magnitude
Units of time

Femtosecond


A floating point unit (FPU) is a part of a computer system specially designed to carry out operations on floating point numbers. Typical operations are addition, subtraction, multiplication, division, and square root.

Some systems can also perform more advanced functions as exponential or trigonometric functions calculations, however in most modern processors these functions are done with software library routines, not by hardware.

In most modern general purpose CPUs, one or more FPUs are integrated inside the CPU; however many embedded microcontrollers, especially older designs, do not have hardware support for floating point operations.

In the past, some systems as the Intel 8086 have implemented floating point via a coprocessor that is controlled by the CPU rather than as an integrated unit.

Most modern computers have integrated FPU hardware; but not all. In the absence of an FPU, many FPU functions can be emulated, which saves the added hardware cost of an FPU but is significantly slower.

Related pages
 Arithmetic logic unit (ALU)
 Execution unit

Computer science

Floating point unit


Ash Hill, or Hitching Post Hill, is a two-story brick dwelling erected ca. 1840, and located on Rosemary Lane, in Hyattsville, Prince George's County, Maryland. The house was built by Robert Clark, an Englishman who was seeking space and quiet in contrast to the crowded city of Washington, D.C. In 1875, General Edward Fitzgerald Beale bought the property. Beale was well connected and known to have entertained President U.S. Grant (a close personal friend who kept his two Arabian horses, Leopard and Linden, stabled at Ash Hill), President Grover Cleveland and Buffalo Bill Cody. The house, with its foot-thick brick walls and hilltop site, is an imposing one, made even more so by the massive pillared porch which surrounds it on three sides. The porch was added by Admiral Chauncey Thomas who purchased the property in 1895.

It was listed on the National Register of Historic Places in 1977.

References

External links

, including photo in 2003, at Maryland Historical Trust website

Houses in Prince George's County, Maryland
Hyattsville, Maryland
Houses completed in 1840
Houses on the National Register of Historic Places in Maryland
National Register of Historic Places in Prince George's County, Maryland
Historic American Buildings Survey in Maryland

Ash Hill (Maryland)


The Kent Family Chronicles (also known as The American Bicentennial Series) is a series of eight novels by John Jakes written for Lyle Engel of Book Creations, Inc. to commemorate the 200th anniversary of the Declaration of Independence of the United States. The books became best sellers, with no novel in the series selling fewer than 3.5 million copies. With The Rebels, The Seekers, and The Furies, Jakes became the first author to have three books on the New York Times bestseller list in a single year, 1975.

The books feature different members of the Kent family, connecting them with historical events at the time of the American Revolution. The first novel begins just before the American Revolution, with Frenchman Phillipe Charboneau, who travels to England and later to the New World, changing his name to Philip Kent along the way and meeting several key figures of the Revolution, including the Marquis de Lafayette, Benjamin Franklin, Samuel Adams, Paul Revere, Joseph Warren, and others. The saga ends some generations later in 1890, with the death of Gideon Kent in The Americans. The series was originally intended to continue until 1976, covering 200 years.

The first two novels in the series were made into telefilms in 1978 and 1979, both starring Andrew Stevens as Philip Kent, with the third adapted as a 1979 telefilm starring Randolph Mantooth as the son, Abraham Kent. "Operation Prime Time" premiered in syndication with the first of these.

Novels
 The Bastard (1974)
 The Rebels (1975)
 The Seekers (1975)
 The Furies (1976)
 The Titans (1976)
 The Warriors (1977)
 The Lawless (1978)
 The Americans (1979)

Footnotes

External links
 John Jakes Official Homepage
 The Bastard on IMDb
 The Rebels on IMDb
 The Seekers on IMDb

Family saga novels
Novels by John Jakes
American historical novels
Novels set during the American Civil War
Fictional families
1970s novels
United States Bicentennial

The Kent Family Chronicles