Understanding Computers And Cognition Pdf

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Researchers are learning more about how networks of biological neurons may learn by studying algorithms in artificial deep networks.

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Researchers are learning more about how networks of biological neurons may learn by studying algorithms in artificial deep networks.

The conference had rejected their request for an official workshop; deep neural nets were still a few years away from taking over AI. Today, deep nets rule AI in part because of an algorithm called backpropagation, or backprop. The algorithm enables deep nets to learn from data, endowing them with the ability to classify images, recognize speech, translate languages, make sense of road conditions for self-driving cars, and accomplish a host of other tasks.

But real brains are highly unlikely to be relying on the same algorithm. Bengio and many others inspired by Hinton have been thinking about more biologically plausible learning mechanisms that might at least match the success of backpropagation. Three of them — feedback alignment, equilibrium propagation and predictive coding — have shown particular promise. Some researchers are also incorporating the properties of certain types of cortical neurons and processes such as attention into their models.

All these efforts are bringing us closer to understanding the algorithms that may be at work in the brain. This principle, with some modifications, was successful at explaining certain limited types of learning and visual classification tasks. But it worked far less well for large networks of neurons that had to learn from mistakes; there was no directly targeted way for neurons deep within the network to learn about discovered errors, update themselves and make fewer mistakes.

Nevertheless, it was the best learning rule that neuroscientists had, and even before it dominated neuroscience, it inspired the development of the first artificial neural networks in the late s. Each artificial neuron in these networks receives multiple inputs and produces an output, like its biological counterpart.

By the s, it was clear that such neurons could be organized into a network with an input layer and an output layer, and the artificial neural network could be trained to solve a certain class of simple problems. During training, a neural network settled on the best weights for its neurons to eliminate or minimize errors. No one knew how to effectively train artificial neural networks with hidden layers — until , when Hinton, the late David Rumelhart and Ronald Williams now of Northeastern University published the backpropagation algorithm.

The algorithm works in two phases. When a network makes an inference with a given set of synaptic weights, it ends up at some location on the loss landscape. To learn, it needs to move down the slope, or gradient, toward some valley, where the loss is minimized to the extent possible. Backpropagation is a method for updating the synaptic weights to descend that gradient. This calculation proceeds sequentially backward from the output layer to the input layer, hence the name backpropagation.

The invention of backpropagation immediately elicited an outcry from some neuroscientists, who said it could never work in real brains. The most notable naysayer was Francis Crick, the Nobel Prize-winning co-discoverer of the structure of DNA who later became a neuroscientist. Backprop is considered biologically implausible for several major reasons.

The first is that while computers can easily implement the algorithm in two phases, doing so for biological neural networks is not trivial. But in a biological network, neurons see only the outputs of other neurons, not the synaptic weights or internal processes that shape that output.

Any biologically plausible learning rule also needs to abide by the limitation that neurons can access information only from neighboring neurons; backprop may require information from more remote neurons. Nonetheless, Hinton and a few others immediately took up the challenge of working on biologically plausible variations of backpropagation.

Over the past decade or so, as the successes of artificial neural networks have led them to dominate artificial intelligence research, the efforts to find a biological equivalent for backprop have intensified. Take, for example, one of the strangest solutions to the weight transport problem, courtesy of Timothy Lillicrap of Google DeepMind in London and his colleagues in Their algorithm, instead of relying on a matrix of weights recorded from the forward pass, used a matrix initialized with random values for the backward pass.

Once assigned, these values never change, so no weights need to be transported for each backward pass. Because the forward weights used for inference are updated with each backward pass, the network still descends the gradient of the loss function, but by a different path.

The forward weights slowly align themselves with the randomly selected backward weights to eventually yield the correct answers, giving the algorithm its name: feedback alignment. Researchers have also explored ways of matching the performance of backprop while maintaining the classic Hebbian learning requirement that neurons respond only to their local neighbors.

Backprop can be thought of as one set of neurons doing the inference and another set of neurons doing the computations for updating the synaptic weights. If such a network is given some input, it sets the network reverberating, as each neuron responds to the push and pull of its immediate neighbors. Eventually, the network reaches a state in which the neurons are in equilibrium with the input and each other, and it produces an output, which can be erroneous.

The algorithm then nudges the output neurons toward the desired result. This sets another signal propagating backward through the network, setting off similar dynamics. The network finds a new equilibrium. The constraint that neurons can learn only by reacting to their local environment also finds expression in new theories of how the brain perceives.

Beren Millidge , a doctoral student at the University of Edinburgh and a visiting fellow at the University of Sussex, and his colleagues have been reconciling this new view of perception — called predictive coding — with the requirements of backpropagation. Predictive coding posits that the brain is constantly making predictions about the causes of sensory inputs. The process involves hierarchical layers of neural processing.

To produce a certain output, each layer has to predict the neural activity of the layer below. If the highest layer expects to see a face, it predicts the activity of the layer below that can justify this perception. The layer below makes similar predictions about what to expect from the one beneath it, and so on. The lowest layer makes predictions about actual sensory input — say, the photons falling on the retina. In this way, predictions flow from the higher layers down to the lower layers.

But errors can occur at each level of the hierarchy: differences between the prediction that a layer makes about the input it expects and the actual input. The bottommost layer adjusts its synaptic weights to minimize its error, based on the sensory information it receives.

This adjustment results in an error between the newly updated lowest layer and the one above, so the higher layer has to readjust its synaptic weights to minimize its prediction error. These error signals ripple upward. The network goes back and forth, until each layer has minimized its prediction error. Millidge has shown that, with the proper setup, predictive coding networks can converge on much the same learning gradients as backprop. However, for every backward pass that a traditional backprop algorithm makes in a deep neural network, a predictive coding network has to iterate multiple times.

Whether or not this is biologically plausible depends on exactly how long this might take in a real brain. Crucially, the network has to converge on a solution before the inputs from the world outside change. Still, if some inaccuracy is acceptable, predictive coding can arrive at generally useful answers quickly, he said. Some scientists have taken on the nitty-gritty task of building backprop-like models based on the known properties of individual neurons.

Standard neurons have dendrites that collect information from the axons of other neurons. But not all neurons have exactly this structure. In particular, pyramidal neurons — the most abundant type of neuron in the cortex — are distinctly different. Pyramidal neurons have a treelike structure with two distinct sets of dendrites.

The trunk reaches up and branches into what are called apical dendrites. The root reaches down and branches into basal dendrites. Models developed independently by Kording in , and more recently by Blake Richards of McGill University and the Quebec Artificial Intelligence Institute and his colleagues, have shown that pyramidal neurons could form the basic units of a deep learning network by doing both forward and backward computations simultaneously. The key is in the separation of the signals entering the neuron for forward-going inference and for backward-flowing errors, which could be handled in the model by the basal and apical dendrites, respectively.

Information for both signals can be encoded in the spikes of electrical activity that the neuron sends down its axon as an output. In the late s, he and his colleagues showed that when monkeys fix their gaze on an object, neurons that represent that object in the cortex become more active. The dopamine levels act like a global reinforcement signal. In theory, the attentional feedback signal could prime only those neurons responsible for an action to respond to the global reinforcement signal by updating their synaptic weights, said Roelfsema.

He and his colleagues have used this idea to build a deep neural network and study its mathematical properties. The team presented this work at the Neural Information Processing Systems online conference in December. Nevertheless, concrete empirical evidence that living brains use these plausible mechanisms remains elusive. Meanwhile, Yamins and his colleagues at Stanford have suggestions for how to determine which, if any, of the proposed learning rules is the correct one.

By analyzing 1, artificial neural networks implementing different models of learning, they found that the type of learning rule governing a network can be identified from the activity of a subset of neurons over time. Given such advances, computational neuroscientists are quietly optimistic.

Backpropagation is useful. I presume that evolution kind of gets us there. Get highlights of the most important news delivered to your email inbox. Abusive, profane, self-promotional, misleading, incoherent or off-topic comments will be rejected. Moderators are staffed during regular business hours New York time and can only accept comments written in English. Read Later. Impossible for the Brain The invention of backpropagation immediately elicited an outcry from some neuroscientists, who said it could never work in real brains.

Staying More Lifelike Take, for example, one of the strangest solutions to the weight transport problem, courtesy of Timothy Lillicrap of Google DeepMind in London and his colleagues in The Quanta Newsletter Get highlights of the most important news delivered to your email inbox.

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Understanding Computers and Cognition - Winograd & Flores

By Dr. Saul McLeod , published At the very heart of cognitive psychology is the idea of information processing. Cognitive psychology sees the individual as a processor of information, in much the same way that a computer takes in information and follows a program to produce an output. The development of the computer in the s and s had an important influence on psychology and was, in part, responsible for the cognitive approach becoming the dominant approach in modern psychology taking over from Behaviorism. The computer gave cognitive psychologists a metaphor, or analogy, to which they could compare human mental processing.


Understanding Computers and Cognition: A New Foundation for Design by Terry Winograd PDF: Terry Winograd. Published Date 10/12/20


Computers in Human Behavior

Addison-Wesley Publishing Company, Inc. Can pigs have wings? Organizations as networks of commitments Decision support systems Tools for conversation 12 Using computers: A direction for design

Understanding computers and cognition - a new foundation for design

Some have voiced fears that artificial intelligence could replace humans altogether. A more valuable approach may be to view machine and human intelligence as complementary, with each bringing its own strengths to the table. View the Cognitive Technologies collection. Subscribe to receive related content.

Summary: This volume is a theoretical and practical approach to the design of computer technology. This text is aimed at a one-term course taken by allied health and agricultural science students. Le's First Aid for the USMLE Step 1, this essential study guide offers board-style questions and answers, easy-to-navigate, high yield explanations for correct and incorrect answers, and more than accompanying images. Quantitative Human Physiology: An Introduction presents a course in quantitative physiology developed for undergraduate students of Biomedical Engineering at Virginia Commonwealth University.

Cognitive computing CC refers to technology platforms that, broadly speaking, are based on the scientific disciplines of artificial intelligence and signal processing. These platforms encompass machine learning , reasoning , natural language processing , speech recognition and vision object recognition , human—computer interaction , dialog and narrative generation, among other technologies. At present, there is no widely agreed upon definition for cognitive computing in either academia or industry. CC applications link data analysis and adaptive page displays AUI to adjust content for a particular type of audience. As such, CC hardware and applications strive to be more affective and more influential by design. Cognitive computing-branded technology platforms typically specialize in the processing and analysis of large, unstructured datasets. Word processing documents, emails, videos, images, audio files, presentations, webpages, social media and many other data formats often need to be manually tagged with metadata before they can be fed to a computer for analysis and insight generation.


Feb 24, - Understanding Computers and Cognition PDF By:Terry Winograd,​Fernando Flores Published on by Intellect Books Understanding.


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Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Winograd and F. Winograd , F. Flores Published Psychology, Computer Science. Associating the rationalist tradition with the goal of building a human mind, the authors propose that a hermeneutic approach must adopt the goal of constructing prostheses which magnify the human mind. This paper argues that what AI needs is not so much a hermeneutic approach as a better appreciation of biology and psychology.

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Artificial Neural Nets Finally Yield Clues to How Brains Learn

Чем бы они ни занимались - посещали Смитсоновский институт, совершали велосипедную прогулку или готовили спагетти у нее на кухне, - Дэвид всегда вникал во все детали. Сьюзан отвечала на те вопросы, на которые могла ответить, и постепенно у Дэвида сложилось общее представление об Агентстве национальной безопасности - за исключением, разумеется, секретных сторон деятельности этого учреждения.

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Cognitive computing

Каждый затраханный файл может спасти мир. - И что же из этого следует.

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Information Processing

1 Comments

  1. Cuiquarcaycha 11.04.2021 at 18:39

    Include Synonyms Include Dead terms.