Westworld (film)
Westworld is a 1973 American science fiction Western thriller film written and directed by Michael Crichton. Its plot concerns amusement park begin killing visitors, it stars Yul Brynner as an android in a futuristic Western-themed amusement park, Richard Benjamin and James Brolin as guests of the park. The film was from an original screenplay by novelist Crichton, served as his feature directorial debut, it was the first feature film to use digital image processing to pixellate photography to simulate an android point of view. The film was nominated for Hugo and Saturn awards. Westworld was followed by a sequel, a short-lived television series, Beyond Westworld. A new television series based on the film debuted in 2016 on HBO. In the then-future year of 1983, a high-tech realistic adult amusement park called Delos features three themed "worlds": Western World, Medieval World, Roman World; the resort's three "worlds" are populated with lifelike androids that are indistinguishable from human beings, each programmed in character for their assigned historical environment.
For $1,000 per day, guests may indulge in any adventure with the android population of the park, including sexual encounters and a simulated fight to the death. Delos's tagline in its advertising promises "Boy, have we got a vacation for you!" Peter Martin, a first-time Delos visitor, his friend John Blane, on a repeat visit, go to Westworld. One of the attractions is an android programmed to instigate gunfights; the firearms issued to the park guests have temperature sensors that prevent them from shooting anything with a high body temperature, such as humans, but allow them to "kill" the cold-blooded androids. The Gunslinger's programming allows guests to draw their guns and kill it, with the android always returning the next day for another duel; the technicians running Delos notice problems beginning to spread like an infection among the androids: the androids in Romanworld and Medievalworld begin experiencing an increasing number of breakdowns and systemic failures, which are said to have spread to Westworld.
When one of the supervising computer scientists scoffs at the "analogy of an infectious disease", he is told by the chief supervisor "We aren't dealing with ordinary machines here. These are complicated pieces of equipment as complicated as living organisms. In some cases, they've been designed by other computers. We don't know how they work." The malfunctions become more serious when a robotic rattlesnake bites Blane in Westworld, against its programming, an android refuses a guest's advances in Medieval World. The failures escalate; the resort's supervisors try to regain control by shutting down power to the entire park. However, the shutdown traps them in central control when the doors automatically lock, unable to turn the power back on and escape. Meanwhile, the androids in all three worlds run amok. Martin and Blane, recovering from a drunken bar-room brawl, wake up in Westworld's brothel, unaware of the park's massive breakdown; when the Gunslinger challenges the men to a showdown, Blane treats the confrontation as an amusement until the android outdraws and shoots, killing him.
Martin runs for the android implacably follows. Martin flees to the other areas of the park, but finds only dead guests, damaged androids, a panicked technician attempting to escape Delos, shortly thereafter shot by the Gunslinger. Martin climbs down through a manhole in Roman World into the underground control complex and discovers that the resort's computer technicians suffocated in the control room when the ventilation system shut down; the Gunslinger stalks him through the underground corridors,so he runs away until he enters an android-repair laboratory. When the Gunslinger enters the room, Martin pretends to be an android, throws acid into the Gunslinger's face, flees, returning to the surface inside the Medieval World castle. With its optical inputs damaged by the acid, the Gunslinger is unable to track Martin visually and tries to find Martin using its infrared scanners. Martin stands beneath the flaming torches of the Great Hall to mask his presence from the android, before setting it on fire with one of the torches.
The burned shell of the Gunslinger attacks him on the dungeon steps before succumbing to its damage. Martin sits on the dungeon steps in a state of near-exhaustion and shock, as the irony of Delos' slogan resonates: "Boy, have we got a vacation for you!" Crichton said he did not wish to make his feature directorial debut with science fiction but "That's the only way I could get the studio to let me direct. People think I'm good at it I guess."Crichton's agent introduced him to producer Paul N. Lazarus III; the script was written in August 1972. Lazarus says; the script was offered to all the major studios. They all turned down the project except for Metro-Goldwyn-Mayer under head of production Dan Melnick and president James T. Aubrey. Crichton: "MGM had a bad reputation among filmmakers. There were too many stories of unreasonable pressure, arbitrary script changes, inadequate post production, cavalier recutting of the final film. Nobody who had a choice made a p
Edge detection
Edge detection includes a variety of mathematical methods that aim at identifying points in a digital image at which the image brightness changes or, more formally, has discontinuities. The points at which image brightness changes are organized into a set of curved line segments termed edges; the same problem of finding discontinuities in one-dimensional signals is known as step detection and the problem of finding signal discontinuities over time is known as change detection. Edge detection is a fundamental tool in image processing, machine vision and computer vision in the areas of feature detection and feature extraction; the purpose of detecting sharp changes in image brightness is to capture important events and changes in properties of the world. It can be shown that under rather general assumptions for an image formation model, discontinuities in image brightness are to correspond to: discontinuities in depth, discontinuities in surface orientation, changes in material properties and variations in scene illumination.
In the ideal case, the result of applying an edge detector to an image may lead to a set of connected curves that indicate the boundaries of objects, the boundaries of surface markings as well as curves that correspond to discontinuities in surface orientation. Thus, applying an edge detection algorithm to an image may reduce the amount of data to be processed and may therefore filter out information that may be regarded as less relevant, while preserving the important structural properties of an image. If the edge detection step is successful, the subsequent task of interpreting the information contents in the original image may therefore be simplified. However, it is not always possible to obtain such ideal edges from real life images of moderate complexity. Edges extracted from non-trivial images are hampered by fragmentation, meaning that the edge curves are not connected, missing edge segments as well as false edges not corresponding to interesting phenomena in the image – thus complicating the subsequent task of interpreting the image data.
Edge detection is one of the fundamental steps in image processing, image analysis, image pattern recognition, computer vision techniques. The edges extracted from a two-dimensional image of a three-dimensional scene can be classified as either viewpoint dependent or viewpoint independent. A viewpoint independent edge reflects inherent properties of the three-dimensional objects, such as surface markings and surface shape. A viewpoint dependent edge may change as the viewpoint changes, reflects the geometry of the scene, such as objects occluding one another. A typical edge might for instance be the border between a block of yellow. In contrast a line can be a small number of pixels of a different color on an otherwise unchanging background. For a line, there may therefore be one edge on each side of the line. Although certain literature has considered the detection of ideal step edges, the edges obtained from natural images are not at all ideal step edges. Instead they are affected by one or several of the following effects: focal blur caused by a finite depth-of-field and finite point spread function.
Penumbral blur caused by shadows created by light sources of non-zero radius. Shading at a smooth objectA number of researchers have used a Gaussian smoothed step edge as the simplest extension of the ideal step edge model for modeling the effects of edge blur in practical applications. Thus, a one-dimensional image f which has one edge placed at x = 0 may be modeled as: f = I r − I l 2 + I l. At the left side of the edge, the intensity is I l = lim x → − ∞ f, right of the edge it is I r = lim x → ∞ f; the scale parameter σ is called the blur scale of the edge. Ideally this scale parameter should be adjusted based on the quality of image to avoid destroying true edges of the image. To illustrate why edge detection is not a trivial task, consider the problem of detecting edges in the following one-dimensional signal. Here, we may intuitively say that there should be an edge between the 5th pixels. If the intensity difference were smaller between the 4th and the 5th pixels and if the intensity differences between the adjacent neighboring pixels were higher, it would not be as easy to say that there should be an edge in the corresponding region.
Moreover, one could argue. Hence, to state a specific threshold on how large the intensity change between two neighbouring pixels must be for us to say that there should be an edge between these pixels is not always simple. Indeed, this is one of the reasons why edge detection may be a non-trivial problem unless the objec
Digital signal processing
Digital signal processing is the use of digital processing, such as by computers or more specialized digital signal processors, to perform a wide variety of signal processing operations. The signals processed in this manner are a sequence of numbers that represent samples of a continuous variable in a domain such as time, space, or frequency. Digital signal processing and analog signal processing are subfields of signal processing. DSP applications include audio and speech processing, sonar and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, among others. DSP can involve linear or nonlinear operations. Nonlinear signal processing is related to nonlinear system identification and can be implemented in the time and spatio-temporal domains; the application of digital computation to signal processing allows for many advantages over analog processing in many applications, such as error detection and correction in transmission as well as data compression.
DSP is applicable to static data. To digitally analyze and manipulate an analog signal, it must be digitized with an analog-to-digital converter. Sampling is carried out in two stages and quantization. Discretization means that the signal is divided into equal intervals of time, each interval is represented by a single measurement of amplitude. Quantization means. Rounding real numbers to integers is an example; the Nyquist–Shannon sampling theorem states that a signal can be reconstructed from its samples if the sampling frequency is greater than twice the highest frequency component in the signal. In practice, the sampling frequency is significantly higher than twice the Nyquist frequency. Theoretical DSP analyses and derivations are performed on discrete-time signal models with no amplitude inaccuracies, "created" by the abstract process of sampling. Numerical methods require a quantized signal, such as those produced by an ADC; the processed result might be a set of statistics. But it is another quantized signal, converted back to analog form by a digital-to-analog converter.
In DSP, engineers study digital signals in one of the following domains: time domain, spatial domain, frequency domain, wavelet domains. They choose the domain in which to process a signal by making an informed assumption as to which domain best represents the essential characteristics of the signal and the processing to be applied to it. A sequence of samples from a measuring device produces a temporal or spatial domain representation, whereas a discrete Fourier transform produces the frequency domain representation; the most common processing approach in the time or space domain is enhancement of the input signal through a method called filtering. Digital filtering consists of some linear transformation of a number of surrounding samples around the current sample of the input or output signal. There are various ways to characterize filters. Linear filters satisfy the superposition principle, i.e. if an input is a weighted linear combination of different signals, the output is a weighted linear combination of the corresponding output signals.
A causal filter uses only previous samples of the output signals. A non-causal filter can be changed into a causal filter by adding a delay to it. A time-invariant filter has constant properties over time. A stable filter produces an output that converges to a constant value with time, or remains bounded within a finite interval. An unstable filter can produce an output that grows without bounds, with bounded or zero input. A finite impulse response filter uses only the input signals, while an infinite impulse response filter uses both the input signal and previous samples of the output signal. FIR filters are always stable. A filter can be represented by a block diagram, which can be used to derive a sample processing algorithm to implement the filter with hardware instructions. A filter may be described as a difference equation, a collection of zeros and poles or an impulse response or step response; the output of a linear digital filter to any given input may be calculated by convolving the input signal with the impulse response.
Signals are converted from time or space domain to the frequency domain through use of the Fourier transform. The Fourier transform converts the time or space information to a magnitude and phase component of each frequency. With some applications, how the phase varies with frequency can be a significant consideration. Where phase is unimportant the Fourier transform is converted to the power spectrum, the magnitude of each frequency component squared; the most common purpose for analysis of signals in the frequency domain is analysis of signal properties. The engineer can study the spectrum to determine which frequencies are present in the input signal and which are missing. Frequency domain analysis is called spectrum- or spectral analysis. Filtering in non-realtime work can be achieved in the frequency domain, applying the filter and converting back to the time domain; this can be an efficient implementation and can g
Wavelet
A wavelet is a wave-like oscillation with an amplitude that begins at zero and decreases back to zero. It can be visualized as a "brief oscillation" like one recorded by a seismograph or heart monitor. Wavelets are intentionally crafted to have specific properties that make them useful for signal processing. Using a "reverse, shift and integrate" technique called convolution, wavelets can be combined with known portions of a damaged signal to extract information from the unknown portions. For example, a wavelet could be created to have a frequency of Middle C and a short duration of a 32nd note. If this wavelet were to be convolved with a signal created from the recording of a song the resulting signal would be useful for determining when the Middle C note was being played in the song. Mathematically, the wavelet will correlate with the signal if the unknown signal contains information of similar frequency; this concept of correlation is at the core of many practical applications of wavelet theory.
As a mathematical tool, wavelets can be used to extract information from many different kinds of data, including – but not limited to – audio signals and images. Sets of wavelets are needed to analyze data fully. A set of "complementary" wavelets will decompose data without gaps or overlap so that the decomposition process is mathematically reversible. Thus, sets of complementary wavelets are useful in wavelet based compression/decompression algorithms where it is desirable to recover the original information with minimal loss. In formal terms, this representation is a wavelet series representation of a square-integrable function with respect to either a complete, orthonormal set of basis functions, or an overcomplete set or frame of a vector space, for the Hilbert space of square integrable functions; this is accomplished through coherent states. The word wavelet has been used for decades in digital signal exploration geophysics; the equivalent French word ondelette meaning "small wave" was used by Morlet and Grossmann in the early 1980s.
Wavelet theory is applicable to several subjects. All wavelet transforms may be considered forms of time-frequency representation for continuous-time signals and so are related to harmonic analysis. All useful discrete wavelet transforms use discrete-time filterbanks; these filter banks are called scaling coefficients in wavelets nomenclature. These filterbanks may infinite impulse response filters; the wavelets forming a continuous wavelet transform are subject to the uncertainty principle of Fourier analysis respective sampling theory: Given a signal with some event in it, one cannot assign an exact time and frequency response scale to that event. The product of the uncertainties of time and frequency response scale has a lower bound. Thus, in the scaleogram of a continuous wavelet transform of this signal, such an event marks an entire region in the time-scale plane, instead of just one point. Discrete wavelet bases may be considered in the context of other forms of the uncertainty principle.
Wavelet transforms are broadly divided into three classes: continuous and multiresolution-based. In continuous wavelet transforms, a given signal of finite energy is projected on a continuous family of frequency bands. For instance the signal may be represented on every frequency band of the form for all positive frequencies f > 0. The original signal can be reconstructed by a suitable integration over all the resulting frequency components; the frequency bands or subspaces are scaled versions of a subspace at scale 1. This subspace in turn is in most situations generated by the shifts of one generating function ψ in L2, the mother wavelet. For the example of the scale one frequency band this function is ψ = 2 sinc − sinc = sin − sin π t with the sinc function. That, Meyer's, two other examples of mother wavelets are: The subspace of scale a or frequency band is generated by the functions ψ a, b = 1 a ψ, where a is positive and defines the scale and b is any real number and defines the shift.
The pair defines a point in the right halfplane R+ × R. The projection of a function x onto the subspace of scale a has the form x a = ∫ R W T ψ ⋅ ψ a, b d b with wavelet coefficients W T ψ (
Massachusetts Institute of Technology
The Massachusetts Institute of Technology is a private research university in Cambridge, Massachusetts. Founded in 1861 in response to the increasing industrialization of the United States, MIT adopted a European polytechnic university model and stressed laboratory instruction in applied science and engineering; the Institute is a land-grant, sea-grant, space-grant university, with a campus that extends more than a mile alongside the Charles River. Its influence in the physical sciences and architecture, more in biology, linguistics and social science and art, has made it one of the most prestigious universities in the world. MIT is ranked among the world's top universities; as of March 2019, 93 Nobel laureates, 26 Turing Award winners, 8 Fields Medalists have been affiliated with MIT as alumni, faculty members, or researchers. In addition, 58 National Medal of Science recipients, 29 National Medals of Technology and Innovation recipients, 50 MacArthur Fellows, 73 Marshall Scholars, 45 Rhodes Scholars, 41 astronauts, 16 Chief Scientists of the US Air Force have been affiliated with MIT.
The school has a strong entrepreneurial culture, the aggregated annual revenues of companies founded by MIT alumni would rank as the tenth-largest economy in the world. MIT is a member of the Association of American Universities. In 1859, a proposal was submitted to the Massachusetts General Court to use newly filled lands in Back Bay, Boston for a "Conservatory of Art and Science", but the proposal failed. A charter for the incorporation of the Massachusetts Institute of Technology, proposed by William Barton Rogers, was signed by the governor of Massachusetts on April 10, 1861. Rogers, a professor from the University of Virginia, wanted to establish an institution to address rapid scientific and technological advances, he did not wish to found a professional school, but a combination with elements of both professional and liberal education, proposing that: The true and only practicable object of a polytechnic school is, as I conceive, the teaching, not of the minute details and manipulations of the arts, which can be done only in the workshop, but the inculcation of those scientific principles which form the basis and explanation of them, along with this, a full and methodical review of all their leading processes and operations in connection with physical laws.
The Rogers Plan reflected the German research university model, emphasizing an independent faculty engaged in research, as well as instruction oriented around seminars and laboratories. Two days after MIT was chartered, the first battle of the Civil War broke out. After a long delay through the war years, MIT's first classes were held in the Mercantile Building in Boston in 1865; the new institute was founded as part of the Morrill Land-Grant Colleges Act to fund institutions "to promote the liberal and practical education of the industrial classes" and was a land-grant school. In 1863 under the same act, the Commonwealth of Massachusetts founded the Massachusetts Agricultural College, which developed as the University of Massachusetts Amherst. In 1866, the proceeds from land sales went toward new buildings in the Back Bay. MIT was informally called "Boston Tech"; the institute adopted the European polytechnic university model and emphasized laboratory instruction from an early date. Despite chronic financial problems, the institute saw growth in the last two decades of the 19th century under President Francis Amasa Walker.
Programs in electrical, chemical and sanitary engineering were introduced, new buildings were built, the size of the student body increased to more than one thousand. The curriculum drifted with less focus on theoretical science; the fledgling school still suffered from chronic financial shortages which diverted the attention of the MIT leadership. During these "Boston Tech" years, MIT faculty and alumni rebuffed Harvard University president Charles W. Eliot's repeated attempts to merge MIT with Harvard College's Lawrence Scientific School. There would be at least six attempts to absorb MIT into Harvard. In its cramped Back Bay location, MIT could not afford to expand its overcrowded facilities, driving a desperate search for a new campus and funding; the MIT Corporation approved a formal agreement to merge with Harvard, over the vehement objections of MIT faculty and alumni. However, a 1917 decision by the Massachusetts Supreme Judicial Court put an end to the merger scheme. In 1916, the MIT administration and the MIT charter crossed the Charles River on the ceremonial barge Bucentaur built for the occasion, to signify MIT's move to a spacious new campus consisting of filled land on a mile-long tract along the Cambridge side of the Charles River.
The neoclassical "New Technology" campus was designed by William W. Bosworth and had been funded by anonymous donations from a mysterious "Mr. Smith", starting in 1912. In January 1920, the donor was revealed to be the industrialist George Eastman of Rochester, New York, who had invented methods of film production and processing, founded Eastman Kodak. Between 1912 and 1920, Eastman donated $20 million in cash and Kodak stock to MIT. In the 1930s, President Karl Taylor Compton and Vice-President Vannevar Bush emphasized the importance of pure sciences like physics and chemistry and reduced the vocational practice required in shops and drafting studios; the Compton reforms "renewed confidence in the ability of the Institute to develop leadership in science as well as in engineering". Unlike Ivy League schools, MIT catered more to middle-class families, depended more on tuition than on endow
Self-organizing map
A self-organizing map or self-organizing feature map is a type of artificial neural network, trained using unsupervised learning to produce a low-dimensional, discretized representation of the input space of the training samples, called a map, is therefore a method to do dimensionality reduction. Self-organizing maps differ from other artificial neural networks as they apply competitive learning as opposed to error-correction learning, in the sense that they use a neighborhood function to preserve the topological properties of the input space; this makes SOMs useful for visualization by creating low-dimensional views of high-dimensional data, akin to multidimensional scaling. The artificial neural network introduced by the Finnish professor Teuvo Kohonen in the 1980s is sometimes called a Kohonen map or network; the Kohonen net is a computationally convenient abstraction building on biological models of neural systems from the 1970s and morphogenesis models dating back to Alan Turing in the 1950s.
While it is typical to consider this type of network structure as related to feedforward networks where the nodes are visualized as being attached, this type of architecture is fundamentally different in arrangement and motivation. Useful extensions include using toroidal grids where opposite edges are connected and using large numbers of nodes, it has been shown that while self-organizing maps with a small number of nodes behave in a way, similar to K-means, larger self-organizing maps rearrange data in a way, fundamentally topological in character. It is common to use the U-Matrix; the U-Matrix value of a particular node is the average distance between the node's weight vector and that of its closest neighbors. In a square grid, for instance, we might consider the closest 4 or 8 nodes, or six nodes in a hexagonal grid. Large SOMs display emergent properties. In maps consisting of thousands of nodes, it is possible to perform cluster operations on the map itself. Like most artificial neural networks, SOMs operate in two modes: mapping.
"Training" builds the map using input examples, while "mapping" automatically classifies a new input vector. The visible part of a self-organizing map is the map space, which consists of components called nodes or neurons; the map space is defined beforehand as a finite two-dimensional region where nodes are arranged in a regular hexagonal or rectangular grid. Each node is associated with a "weight" vector, a position in the input space. While nodes in the map space stay fixed, training consists in moving weight vectors toward the input data without spoiling the topology induced from the map space. Thus, the self-organizing map describes a mapping from a higher-dimensional input space to a lower-dimensional map space. Once trained, the map can classify a vector from the input space by finding the node with the closest weight vector to the input space vector; the goal of learning in the self-organizing map is to cause different parts of the network to respond to certain input patterns. This is motivated by how visual, auditory or other sensory information is handled in separate parts of the cerebral cortex in the human brain.
The weights of the neurons are initialized either to small random values or sampled evenly from the subspace spanned by the two largest principal component eigenvectors. With the latter alternative, learning is much faster because the initial weights give a good approximation of SOM weights; the network must be fed a large number of example vectors that represent, as close as possible, the kinds of vectors expected during mapping. The examples are administered several times as iterations; the training utilizes competitive learning. When a training example is fed to the network, its Euclidean distance to all weight vectors is computed; the neuron whose weight vector is most similar to the input is called the best matching unit. The weights of the BMU and neurons close to it in the SOM grid are adjusted towards the input vector; the magnitude of the change decreases with time and with the grid-distance from the BMU. The update formula for a neuron v with weight vector Wv is W v = W v + θ ⋅ α ⋅,where s is the step index, t an index into the training sample, u is the index of the BMU for the input vector D, α is a monotonically decreasing learning coefficient.
Depending on the implementations, t can scan the training data set systematically, be randomly drawn from the data set, or implement some other sampling method. The neighborhood function Θ depends on the grid-distance between the BMU and neuron v. In the simplest form, it is 1 for all neurons close enough to BMU and 0 for others, but a Gaussian function is a common choice, too. Regardless of the functional form, the neighborhood funct
Artificial neural network
Artificial neural networks or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. The neural network itself is not an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs; such systems "learn" to perform tasks by considering examples without being programmed with any task-specific rules. 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 results to identify cats in other images, they do this without any prior knowledge about cats, for example, that they have fur, tails and cat-like faces. Instead, they automatically generate identifying characteristics from the learning material that they process. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain.
Each connection, like the synapses in a biological brain, can transmit a signal from one artificial neuron to another. An artificial neuron that receives a signal can process it and signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, the output of each artificial neuron is computed by some non-linear function of the sum of its inputs; the connections between artificial neurons are called'edges'. Artificial neurons and edges have a weight that adjusts as learning proceeds; the weight decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer, to the last layer after traversing the layers multiple times; the original goal of the ANN approach was to solve problems in the same way that a human brain would.
However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial 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. Warren McCulloch and Walter Pitts created a computational model for neural networks based on mathematics and algorithms called threshold logic; this model paved the way for neural network research to split into two approaches. One approach focused on biological processes in the brain while the other focused on the application of neural networks to artificial intelligence; this work led to work on their link to finite automata. In the late 1940s, D. O. Hebb created a learning hypothesis based on the mechanism of neural plasticity that became known as Hebbian learning. Hebbian learning is unsupervised learning; this evolved into models for long term potentiation. Researchers started applying these ideas to computational models in 1948 with Turing's B-type machines.
Farley and Clark first used computational machines called "calculators", to simulate a Hebbian network. Other neural network computational machines were created by Rochester, Holland and Duda. Rosenblatt created an algorithm for pattern recognition. With mathematical notation, Rosenblatt described circuitry not in the basic perceptron, such as the exclusive-or circuit that could not be processed by neural networks at the time. In 1959, a biological model proposed by Nobel laureates Hubel and Wiesel was based on their discovery of two types of cells in the primary visual cortex: simple cells and complex cells; the first functional networks with many layers were published by Ivakhnenko and Lapa in 1965, becoming the Group Method of Data Handling. Neural network research stagnated after machine learning research by Minsky and Papert, who discovered two key issues with the computational machines that processed neural networks; the first was. The second was that computers didn't have enough processing power to handle the work required by large neural networks.
Neural network research slowed. Much of artificial intelligence had focused on high-level models that are processed by using algorithms, characterized for example by expert systems with knowledge embodied in if-then rules, until in the late 1980s research expanded to low-level machine learning, characterized by knowledge embodied in the parameters of a cognitive model. A key trigger for renewed interest in neural networks and learning was Werbos's backpropagation algorithm that solved the exclusive-or problem by making the training of multi-layer networks feasible and efficient. Backpropagation distributed the error term back up through the layers, by modifying the weights at each node. In the mid-1980s, parallel distributed. Rumelhart and McClelland described the use of connectionism to simulate neural processes. Support vector machines and other, much simpler methods such as linear classifiers overtook neural networks in machine learning popularity. However, using neural networks transformed some domains, such as the prediction of protein structures.
In 1992, max-pooling was introduced to help with least shift invariance and tolerance to deformation to aid in 3D object recognition. In 2010, Backpropagation training through max-pooli
where θ = π/6 =30°
