Artificial neural networks
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This document provides an overview of artificial neural networks (ANNs). It begins with basic definitions of ANNs and their goal of modeling the human brain. It then discusses the structure of the human brain and early ANN models like the perceptron. The document outlines different ANN architectures including feed forward and feedback networks. It also covers learning methods in ANNs like supervised, unsupervised, and reinforcement learning. Applications of ANNs are then listed before the document concludes with a discussion of potential destructions if ANNs are not properly designed and controlled.
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Ariificial brain
ARTIFICIAL brain ......AI
Artificial brain (or artificial mind) is a term commonly used in the media[1] to describe research that aims to develop software and hardware with cognitive abilities similar to those of the animal or human brain. Research investigating "artificial brains" and brain emulation plays three important roles in science:
An ongoing attempt by neuroscientists to understand how the human brain works, known as cognitive neuroscience.
A thought experiment in the philosophy of artificial intelligence, demonstrating that it is possible, at least in theory, to create a machine that has all the capabilities of a human being.
A long term project to create machines exhibiting behavior comparable to those of animals with complex central nervous system such as mammals and most particularly humans. The ultimate goal of creating a machine exhibiting human-like behavior or intelligence is sometimes called strong AI.
An example of the first objective is the project reported by Aston University in Birmingham, England[2] where researchers are using biological cells to create "neurospheres" (small clusters of neurons) in order to develop new treatments for diseases including Alzheimer's, motor neurone and Parkinson's disease.
The second objective is a reply to arguments such as John Searle's Chinese room argument, Hubert Dreyfus' critique of AI or Roger Penrose's argument in The Emperor's New Mind. These critics argued that there are aspects of human consciousness or expertise that can not be simulated by machines. One reply to their arguments is that the biological processes inside the brain can be simulated to any degree of accuracy. This reply was made as early as 1950, by Alan Turing in his classic paper "Computing Machinery and Intelligence".[3]
The third objective is generally called artificial general intelligence by researchers.[4] However, Ray Kurzweil prefers the term "strong AI". In his book The Singularity is Near, he focuses on whole brain emulation using conventional computing machines as an approach to implementing artificial brains, and claims (on grounds of computer power continuing an exponential growth trend) that this could be done by 2025. Henry Markram, director of the Blue Brain project (which is attempting brain emulation), made a similar claim (2020) at the Oxford TED conference in 2009.
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Ariificial brain
ARTIFICIAL brain ......AI
Artificial brain (or artificial mind) is a term commonly used in the media[1] to describe research that aims to develop software and hardware with cognitive abilities similar to those of the animal or human brain. Research investigating "artificial brains" and brain emulation plays three important roles in science:
An ongoing attempt by neuroscientists to understand how the human brain works, known as cognitive neuroscience.
A thought experiment in the philosophy of artificial intelligence, demonstrating that it is possible, at least in theory, to create a machine that has all the capabilities of a human being.
A long term project to create machines exhibiting behavior comparable to those of animals with complex central nervous system such as mammals and most particularly humans. The ultimate goal of creating a machine exhibiting human-like behavior or intelligence is sometimes called strong AI.
An example of the first objective is the project reported by Aston University in Birmingham, England[2] where researchers are using biological cells to create "neurospheres" (small clusters of neurons) in order to develop new treatments for diseases including Alzheimer's, motor neurone and Parkinson's disease.
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In this paper, we show that cache partitioning does
not necessarily ensure predictable cache performance in modern
COTS multicore platforms that use non-blocking caches to exploit
memory-level-parallelism (MLP).
Through carefully designed experiments using three real COTS
multicore platforms (four distinct CPU architectures) and a cycleaccurate
full system simulator, we show that special hardware
registers in non-blocking caches, known as Miss Status Holding
Registers (MSHRs), which track the status of outstanding cachemisses,
can be a significant source of contention; we observe up
to 21X WCET increase in a real COTS multicore platform due
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We propose a hardware and system software (OS) collaborative
approach to efficiently eliminate MSHR contention for
multicore real-time systems. Our approach includes a low-cost
hardware extension that enables dynamic control of per-core
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Artificial neural networks (ANNs) are computing systems inspired by biological neural networks. ANNs learn from examples to perform tasks such as pattern recognition or data classification. While much simpler than actual human brains, ANNs can solve complex problems through their interconnected structure and ability to learn from examples rather than being explicitly programmed. ANNs are well-suited for problems that are too complex for conventional algorithms or where the exact nature of the problem is unknown. Their ability to learn from examples, handle complex nonlinear problems, and be fault tolerant make ANNs a promising approach for future computing applications.
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This document discusses artificial brains and recent developments in creating artificial intelligence that mimics the human brain. It describes projects like the Blue Brain Project, which aims to simulate the human brain down to the molecular level using a supercomputer. Another project discussed is the CAM Brain Machine, which uses artificial neurons on a parallel platform. The document also compares key differences between the human brain and artificial brains currently in development, such as the number of neurons (1014 in the human brain vs. around 40,000 in current AI systems) and speed of processing. Overall the goal of creating an artificial brain is to develop a machine with general human-level intelligence that can solve real-world problems.
Neural Netwrok
A basic concept and example of neural network is presented here . I also have included a brief history about the concept of neural network.
Artificial intellegence by Bhanuprakash
Bhanupraksh
B.Tech.(CSE)
eagalabhanuprakash.eagala@gmail.com
It gives Basic Knowledge on Artificial Intellegence
SoftComputing5
This document provides an overview of artificial neural networks (ANNs). It discusses the history of ANNs beginning in the 1940s and important developments like the perceptron in 1957 and backpropagation algorithms in the 1970s and 1980s. The document defines ANNs as consisting of interconnected processing units (neurons) that communicate by sending signals to each other via weighted connections, and learns from experience through training. It also compares ANNs to the human brain in using a highly parallel and distributed approach to problem solving.
Aditya ann
The document provides an overview of artificial neural networks. It discusses how artificial neural networks are inspired by biological neural networks in the human brain and are made up of interconnected artificial neurons that can learn relationships from data. The document outlines the history of artificial neural networks, their capabilities and applications, issues with implementation, and advantages and disadvantages.
Neural Networks Basics
Short overview of Neural Networks Basics from our Software Developer Anatolii Shkurpylo
+ What is Neural Networks?
+ How NN works?
+ Simple NN in Python
Artificial Neural Network Abstract
1) Artificial neural networks (ANNs) are processing systems inspired by biological neural networks, consisting of interconnected nodes that process information via algorithms or hardware components. ANNs can accurately model functions like visual processing in the retina.
2) ANNs are useful for problems like facial recognition that are difficult to solve with algorithms due to their ability to learn from examples in a way similar to the human brain.
3) ANNs have many applications, including pattern recognition, modeling complex relationships in large datasets, and real-time systems due to their parallel architecture.
blue brain
The Blue Brain Project aims to create a virtual brain by simulating the brain on a supercomputer. It was started in 2005 as a collaboration between IBM and the Brain Mind Institute in Switzerland. The project uses the IBM Blue Gene supercomputer and neuron simulation software to model brain circuits at the molecular level with the goal of understanding brain function and treating neurological diseases. The Blue Brain simulation has shown flashes of activity resembling those in natural brains, demonstrating its potential to provide insights into human consciousness.
History of neural networks
Neural networks were first proposed in 1943 by McCulloch and Pitts to model how neurons in the brain work. Research declined in the 1960s-70s due to limitations of technology and theoretical results suggesting neural networks were not powerful. Interest renewed in the 1980s with the development of backpropagation, which allowed training of multi-layer networks. Breakthroughs in deep learning since 2006 using unsupervised pre-training, GPUs, and large datasets like ImageNet have led to state-of-the-art performance in areas like image recognition and natural language processing.
Artificial Intelligence
The document discusses a presentation on artificial intelligence given by Biswajit Mondal, including a definition of AI as making computers able to mimic human brain functions, the various fields that contribute to AI like philosophy and computer engineering, and examples of applications like game playing and robotics.
[IJET V2I2P20] Authors: Dr. Sanjeev S Sannakki, Ms.Anjanabhargavi A Kulkarni
[IJET V2I2P20] Authors: Dr. Sanjeev S Sannakki, Ms.Anjanabhargavi A KulkarniIJET - International Journal of Engineering and Techniques
Artificial neural networks are fundamental means for providing an attempt at modelling the information
processing capabilities of artificial nervous system which plays an important role in the field of cognitive
science. This paper focuses the features of artificial neural networks studied by reviewing the existing research
works, these features were then assessed and evaluated and comparative analysis. The study and literature
survey metrics such as functional capabilities of neurons, learning capabilities, style of computation, processing
elements, processing speed, connections, strength, information storage, information transmission,
communication media selection, signal transduction and fault tolerance were used as basis for comparison. A
major finding in this paper showed that artificial neural networks served as the platform for neuron computing
technology in the field of cognitive science.Amith blue brain
This document discusses the Blue Brain project, which aims to create a virtual human brain through detailed biological simulations. It describes how the Blue Brain is being constructed by scanning brain tissue to measure neuronal properties, translating these into mathematical models, and running large-scale simulations. The Blue Brain would function similarly to a natural brain, processing inputs, interpreting them through neuronal states, and producing outputs. It could allow human intelligence and memory to be uploaded and stored digitally. The Blue Brain may one day help treat conditions like memory loss and allow life-like artificial intelligence.
Blue Brain Seminar Report
The document discusses the Blue Brain project, which aims to simulate the human brain on a
supercomputer. It provides details on how the project uses neuron-level modeling and supercomputers
like IBM's Blue Gene to simulate small networks of neurons and ultimately work towards simulating the
entire human brain. The document also discusses how uploading and simulating an actual human brain
may be possible using nanobots to scan brain structure and activity at a microscopic level.
Blue Brain
The document summarizes the Blue Brain Project, which aims to simulate the mammalian brain through detailed modeling and reverse engineering. Key points include:
- The project uses supercomputers and neuronal modeling software to simulate brain circuits and functions.
- It involves data acquisition of real neurons, building virtual neurons and networks, and simulating their electrical activity.
- Long term goals include fully simulating the human brain to understand cognition and treat neurological diseases.
Week 1.pdf
This document provides an introduction to artificial intelligence (AI) including:
- Definitions of AI as the study of intelligent agents that perceive their environment and take actions to maximize success.
- The four main approaches to AI: systems that think like humans, think rationally, act like humans, and act rationally.
- An overview of the history of AI from its origins in the 1940s-50s to current applications including autonomous systems, game playing, robotics, and language processing.
1.ppt
The Blue Brain project seeks to create the first virtual brain through reverse engineering the human brain at the cellular level using supercomputer simulation. It involves acquiring brain data through microscopy, simulating neural networks on IBM's Blue Gene supercomputer, and visualizing results. The end goal is to fully simulate the human brain within decades to better understand brain function and potentially upload human consciousness into computers.
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Introduction to objective, scope and outcome the subject
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Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
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- 2. Agenda Basic definitions Debates Human Brain Anatomy Network Structure Learning methods Applications Conclusion
- 3. Basic Definitions An artificial neural network (ANN), usually called "neural network" (NN), is a mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks.... This is a type of computer program which aims to model the human brain to solve complex problems. Also known as an ANN. Computer technology that attempts to build computers that operate like a human brain. The machines possess simultaneous memory storage and work with ambiguous information. Sometimes called, simply, a neural network.
- 4. Debates Or
- 5. Perceptron The perceptron is one of the earliest neural networks. Invented at the Cornell Aeronautical Laboratory in 1957 by Frank Rosenblatt, the Perceptron was an attempt to understand human memory, learning, and cognitive processes. In 1960, Rosenblatt demonstrated the Mark I Perceptron. The Mark I was the first machine that could “learn” to identify optical patterns.
- 7. Human Brain Anatomy We need to know the anatomy of a human brain to understand how it is implemented in an artificial manner
- 8. Artificial Neuron McCulloch-Pitts in 1943 The next major development in neural network technology arrived in 1949 with a book, "The Organization of Behavior" written by Donald Hebb John von Neumann thought of imitating simplistic neuron functions by using telegraph relays or vacuum tubes. This led to the invention of the von Neumann machine.
- 9. Network Structure Feed forward Feed back or Recurrent
- 11. Feed back or Recurrent
- 12. Learning 3 types of Learning as Supervised Learning Unsupervised Learning Reinforcement Learning
- 20. Destruction Destructions made when the robots are designed with some strange logics. Let us watch a video trailer which illustrate how the destructions takes place.
- 21. Conclusion Artificial Neural Network is an efficient way to find an useful pattern from the available information. Even though ANN provide high degree of accuracy its training time becomes a major destructive fact. If the machines are subject to the control of human beings then there is no problem around us. Otherwise it will lead to disasters.