The document discusses artificial neural networks (ANNs). It provides an overview of ANNs, including their biological inspiration from neurons in the brain, their composition of interconnected processing elements called neurons, and how they are configured for applications like pattern recognition. The document also covers different types of ANNs, their computational power, capacity for learning, convergence abilities, and use for generalization. Examples are given of ANN applications in various business domains like marketing, sales forecasting, finance, insurance, and telecommunications. Risks of ANNs discussed include needing a large and diverse training set, overfitting data, and high hardware resource requirements. A hybrid symbolic-neural network approach is also mentioned.
Mr. Koushal Kumar Has done his M.Tech degree in Computer Science and Engineering from Lovely Professional University, Jalandhar, India. He obtained his B.S.C and M.S.C in computer science from D.A.V College Amritsar Punjab. His area of research interests lies in Artificial Neural Networks, Soft computing, Computer Networks, Grid Computing, and data base management systems
Mr. Koushal Kumar Has done his M.Tech degree in Computer Science and Engineering from Lovely Professional University, Jalandhar, India. He obtained his B.S.C and M.S.C in computer science from D.A.V College Amritsar Punjab. His area of research interests lies in Artificial Neural Networks, Soft computing, Computer Networks, Grid Computing, and data base management systems
Artificial Intelligence (AI) | Prepositional logic (PL)and first order predic...Ashish Duggal
The following are the topics in this presentation Prepositional Logic (PL) and First-order Predicate Logic (FOPL) is used for knowledge representation in artificial intelligence (AI).
There are also sub-topics in this presentation like logical connective, atomic sentence, complex sentence, and quantifiers.
This PPT is very helpful for Computer science and Computer Engineer
(B.C.A., M.C.A., B.TECH. , M.TECH.)
The project was started with a sole aim in mind that the design should be able to recognize the voice of a person by analyzing the speech signal. The simulation is done in MATLAB. The design of the project is based on using the Linear prediction filter coefficient (LPC) and Principal component analysis (PCA) on data (princomp) for the speech signal analysis. The Sample Collection process is accomplished by using the microphone to record the speech of male/female. After executing the program the speech is analyzed by the analysis part of our MATLAB program code and our design should be able to identify and give the judgment that the recorded speech signal is same as that of our desired output.
Brain Computer Interface Next Generation of Human Computer InteractionSaurabh Giratkar
In the area of HCI research the main focus is on defining new ways of human interaction with computer system. With the passes of time a number of inventions have been made in this field. In initial days we used only keyboards to access our computer system (e.g. in Unix Terminal). In Second phase, after invention of mouse and other pointing devices, we started using graphical user interface using pointing devices like mouse which make the use of computer more easy and comfortable. Nowadays we are using pressure-driven mechanism, i.e. touch screen, which is common at ATMs, Mobile phones and PDAs etc. Although it is not as common in daily works but the release of tablet PCs and its popularity shows that the day is not much far when we wouldn’t be having keyboards and mouse at all.
All of these inventions have been made for balancing the requirements of society and user. E.g. Games, Multimedia Applications etc are not possible using only-Keyboard so we need mouse driven system for such applications, similarly we cannot have large keyboard on mobile so we need a touch screen system for mobiles. In addition to these traditional HCI models, there are some more advance HCI technology too for adding more flexibility and hence making the product more useful. E.g. swap card system at office doors for attendance and ATM-swap card for shopping. Speech processing systems are also there where we can access our computer system using our speech. Fig 1 shows most popular traditional HCI system.
Neural network for real time traffic signal controlSnehal Takawale
Real-time traffic signal control is an integral part of the urban traffic control system, and providing effective real-time traffic signal control for a large complex traffic network is an extremely challenging distributed control problem. This paper adopts the multi-agent system approach to develop distributed unsupervised traffic responsive signal control models, where each agent in the system is a local traffic signal controller for one intersection in the traffic network. The first multi-agent system is developed using hybrid computational intelligent techniques. Each agent employs a multi stage online learning process to update and adapt its knowledge base and decision-making mechanism. The second multi-agent system is developed by integrating the simultaneous perturbation stochastic approximation theorem in fuzzy neural networks (NN).
Artificial Intelligence (AI) | Prepositional logic (PL)and first order predic...Ashish Duggal
The following are the topics in this presentation Prepositional Logic (PL) and First-order Predicate Logic (FOPL) is used for knowledge representation in artificial intelligence (AI).
There are also sub-topics in this presentation like logical connective, atomic sentence, complex sentence, and quantifiers.
This PPT is very helpful for Computer science and Computer Engineer
(B.C.A., M.C.A., B.TECH. , M.TECH.)
The project was started with a sole aim in mind that the design should be able to recognize the voice of a person by analyzing the speech signal. The simulation is done in MATLAB. The design of the project is based on using the Linear prediction filter coefficient (LPC) and Principal component analysis (PCA) on data (princomp) for the speech signal analysis. The Sample Collection process is accomplished by using the microphone to record the speech of male/female. After executing the program the speech is analyzed by the analysis part of our MATLAB program code and our design should be able to identify and give the judgment that the recorded speech signal is same as that of our desired output.
Brain Computer Interface Next Generation of Human Computer InteractionSaurabh Giratkar
In the area of HCI research the main focus is on defining new ways of human interaction with computer system. With the passes of time a number of inventions have been made in this field. In initial days we used only keyboards to access our computer system (e.g. in Unix Terminal). In Second phase, after invention of mouse and other pointing devices, we started using graphical user interface using pointing devices like mouse which make the use of computer more easy and comfortable. Nowadays we are using pressure-driven mechanism, i.e. touch screen, which is common at ATMs, Mobile phones and PDAs etc. Although it is not as common in daily works but the release of tablet PCs and its popularity shows that the day is not much far when we wouldn’t be having keyboards and mouse at all.
All of these inventions have been made for balancing the requirements of society and user. E.g. Games, Multimedia Applications etc are not possible using only-Keyboard so we need mouse driven system for such applications, similarly we cannot have large keyboard on mobile so we need a touch screen system for mobiles. In addition to these traditional HCI models, there are some more advance HCI technology too for adding more flexibility and hence making the product more useful. E.g. swap card system at office doors for attendance and ATM-swap card for shopping. Speech processing systems are also there where we can access our computer system using our speech. Fig 1 shows most popular traditional HCI system.
Neural network for real time traffic signal controlSnehal Takawale
Real-time traffic signal control is an integral part of the urban traffic control system, and providing effective real-time traffic signal control for a large complex traffic network is an extremely challenging distributed control problem. This paper adopts the multi-agent system approach to develop distributed unsupervised traffic responsive signal control models, where each agent in the system is a local traffic signal controller for one intersection in the traffic network. The first multi-agent system is developed using hybrid computational intelligent techniques. Each agent employs a multi stage online learning process to update and adapt its knowledge base and decision-making mechanism. The second multi-agent system is developed by integrating the simultaneous perturbation stochastic approximation theorem in fuzzy neural networks (NN).
Techniques for Smart Traffic Control: An In-depth ReviewEditor IJCATR
Inadequate space and funds for the construction of new roads and the steady increase in number of vehicles has prompted
scholars to investigate other solutions to traffic congestion. One area gaining interest is the use of smart traffic control systems (STCS)
to make traffic routing decisions. These systems use real time data and try to mimic human reasoning thus prove promising in vehicle
traffic control and management. This paper is a review on the motivations behind the emergence of STCS and the different types of
these systems in use today for road traffic management. They include – fuzzy expert systems (FES), artificial neural networks (ANN)
and wireless sensor networks (WSN). We give an in depth study on the design, benefits and limitations of each technique. The paper
cites and analyses a number of successfully tested and implemented STCS. From these reviews we are able to derive comparisons of
the STCS discussed in this paper. For instance, for a learning or adaptive system, ANN is the best approach; for a system that just
routes traffic based on real time data and does not need to derive any data patterns afterwards, then FES is the best approach; for a
cheaper alternative to the FES, then WSN is the least costly approach. All prove effective in traffic control and management with
respect to the context in which each of them is used.
ANN Modeling of Monthly and Weekly Behaviour of the Runoff of Kali River Catc...IOSR Journals
Model is a system, by whose operation; the characteristics of other similar systems can be ascertained. Experimental observation made on a model bear a definite relationship with prototype. So, the model analysis or modeling is actually an experimental method of finding solution of complex flow problems like surface water modeling, sub-surface water modeling etc. Many flow situations are not amenable to theoretical analysis. Modeling is a valuable means of obtaining better understanding of particular situation. Inspired by the functioning of the brain and biological nervous system, Artificial Neural Networks (ANNs) has been applied to various hydrological problems in last two decades. In this study, two ANN models using feed forward – back propagation network are developed to correlate a relationship between rainfall and runoff on monthly and weekly basis for Kali river catchment up to Supa dam in Uttara Kannada District of Karnataka State, India. The developed two models are compared and evaluated using standard statistical parameters to know strength and weaknesses. This performance can be further refined by incorporating more input parameters of catchment properties like soil moisture index; land use and land cover details etc.
It is also known as Indirect search method or Steepest descent method,this method firstly found to Augustin Louis Cauchy in 1857.The method is used to solve optimization problems.
100-Concepts-of-AI by Anupama Kate .pptxAnupama Kate
🧠 Dive Deep into the World of Neural Networks! Explore our latest SlideShare to unravel the complexities of the technology that’s transforming AI. Learn about the structure, operation, and vast applications of neural networks across various industries. Perfect for tech enthusiasts and professionals eager to understand the building blocks of modern artificial intelligence. #AI #NeuralNetworks #MachineLearning #TechnologyTrends
Power of Convolutional Neural Networks in Modern AI | The Lifesciences MagazineThe Lifesciences Magazine
Convolutional neural networks (CNNs) stand out as a ground-breaking technique with significant ramifications across multiple areas in the rapidly changing field of artificial intelligence (AI).
Stock Prediction Using Artificial Neural Networksijbuiiir1
Accurate prediction of stock price movements is highly challenging and significant topic for investors. Investors need to understand that stock price data is the most essential information which is highly volatile, non-linear, and non-parametric and are affected by many uncertainties and interrelated economic and political factors across the globe. Artificial Neural Networks (ANN) have been found to be an efficient tool in modeling stock prices and quite a large number of studies have been done on it. In this paper ANN modeling of stock prices of selected stocks under BSE is attempted to predict closing prices. The network developed consists of an input layer, one hidden layer and an output layer and the inputs being opening price, high, low, closing price and volume. Mean Absolute Percentage Error, Mean Absolute Deviation and Root Mean Square Error are used as indicators of performance of the networks. This paper is organized as follows. In the first section, the adaptability of ANN in stock prediction is discussed. In section two, we justify the using of ANNs in forecasting stock prices. Section three gives the literature review on the applications of ANNs in predicting the stock prices. Section four gives an overview of artificial neural networks. Section five presents the methodology adopted. Section six gives the simulation and performance analysis. Last section concludes with future direction of the study
Comparative Analysis of Computational Intelligence Paradigms in WSN: Reviewiosrjce
Computational Intelligence is the study of the design of intelligent agents. An agent is something that
react according to an environment—it does something. Agents includes worms, dogs, thermostats, airplanes,
humans, and society. The purpose of computational intelligence is to understand the principles that make
intelligent behavior possible, in real or artificial systems. Techniques of Computational Intelligence are
designed to model the aspects of biological intelligence. These paradigms include that exhibit an ability to
learn or adapt to new situations,to generalize, abstract, learn and associate. This paper gives review of
comparison between computational intelligence paradigms in Wireless Sensor Network and Finally,a short
conclusion is provided.
Deep learning and neural network convertedJanu Jahnavi
https://www.learntek.org/blog/industries-blockchain-disrupt/
https://www.learntek.org/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
categories
The Next Step For Aritificial Intelligence in Financial ServicesAccenture Insurance
As financial services firms strive to transform their businesses for a digital world, realize efficiencies, improve the customer experience and revitalize their growth, they increasingly see artificial intelligence-based (AI) technologies as key. For firms, the next wave of AI innovation are artificial neural networks.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
2. An Artificial Neural Network (ANN) is an
information processing paradigm that is
inspired by biological nervous systems.
It is composed of a large number of highly
interconnected processing elements called
neurons.
An ANN is configured for a specific
application, such as pattern recognition or
data classification
3. The neuron receives signals from other neurons,
collects the input signals, and transforms the
collected input signal.
Human information processing takes place
through the interaction of many billions of
neurons connected to each other, each
sending excitatory or inhibitory signals to
other neurons (excite in positive/suppress in
negative)
4. Artificial neural networks (ANNs) are biologically
inspired computer programs designed to simulate the way
in which the human brain processes information. ANNs
gather their knowledge by detecting the patterns and
relationships in data and learn (or are trained) through
experience.
5. Types of artificial neural networks
Artificial neural network types vary from those with
only one or two layers of single direction logic, to
complicated multi–input many directional feedback
loops and layers. On the whole, these systems use
algorithms in their programming to determine control
and organization of their functions.
6. Computational power
The multi-layer perception (MLP) is a universal
function approximate, as proven by the universa
approximation theorem. However, the proof is not
constructive regarding the number of neurons required
or the settings of the weights. Work by Hava
Siegelmann and Eduardo D. Sontag has provided a
proof that a specific recurrent architecture with rational
valued weights (as opposed to full precision real
number-valued weights) has the full power of
a Universal Turing Machine using a finite number of
neurons and standard linear connections. Further, it has
been shown that the use of irrational values for weights
results in a machine with super-Turing power.
7. Capacity
Artificial neural network models have a property called
'capacity', which roughly corresponds to their ability to
model any given function. It is related to the amount of
information that can be stored in the network and to the
notion of complexity.
Convergence
Nothing can be said in general about convergence since it
depends on a number of factors. Firstly, there may exist
many local minima. This depends on the cost function and
the model. Secondly, the optimization method used might
not be guaranteed to converge when far away from a local
minimum. Thirdly, for a very large amount of data or
parameters, some methods become impractical. In general,
it has been found that theoretical guarantees regarding
convergence are an unreliable guide to practical application.
8. Generalization and statistics
In applications where the goal is to create a system that generalizes
well in unseen examples, the problem of over-training has emerged.
This arises in convoluted or over-specified systems when the capacity
of the network significantly exceeds the needed free parameters. There
are two schools of thought for avoiding this problem: The first is to
use cross-validation and similar techniques to check for the presence of
overtraining and optimally select hyperparameters such as to minimize
the generalization error. The second is to use some form
of regularization This is a concept that emerges naturally in a
probabilistic (Bayesian) framework, where the regularization can be
performed by selecting a larger prior probability over simpler models;
but also in statistical learning theory, where the goal is to minimize
over two quantities: the 'empirical risk' and the 'structural risk', which
roughly corresponds to the error over the training set and the predicted
error in unseen data due to overfitting.
9.
10. Over the last decade, neural networks have found
application across a wide range of areas from business,
commerce and industry. Following an overview is provided
of the kinds of business problems to which neural networks
are suited, with a brief discussion of some of the reported
studies Relevant to each area.
11. The goal of modern marketing exercises is to
identify customers who are likely to respond
positively to a product, and to target any
advertising or solicitation towards these
customers. Target marketing involves market
segmentation, whereby the market is divided into
distinct groups of customers with very different
consumer behavior. Market segmentation can be
achieved using neural networks by segmenting
customers according to basic characteristics
including demographics, socio-economic status,
geographic location, purchase patterns, and
attitude towards a product.
12. Businesses often need to forecast sales to make
decisions about inventory, sta$ng levels, and
pricing. Neural networks have had great success
at sales forecasting, due to their ability to
simultaneously consider multiple variables such
as market demand for a product, consumers'
disposable income, the size of the population,
the price of the product, and the price of
complementary products. Forecasting of sales in
supermarkets and wholesale suppliers has been
studied and the results have been shown to
perform well when compared to traditional
statistical techniques like regression, and human
experts.
13. One of the main areas of banking and "nance
that has been affected by neural networks is
trading and financial forecasting. Neural
networks have been applied successfully to
problems like derivative securities pricing and
hedging , futures price forecasting, exchange
rate forecasting and stock performance and
selection prediction.
14. There are many areas of the insurance industry
which can benefit from neural networks. Policy
holders can be segmented into groups based
upon their behaviors, which can help to
determine effective premium pricing. Prediction
of claim frequency and claim cost can also help
to set premiums, as well as find an acceptable
mix or portfolio of policy holders characteristics.
The insurance industry, like the banking and
finance sectors, is constantly aware of the need
to detect fraud, and neural networks can be
trained to learn to detect fraudulent claims or
unusual circumstances.
15. Like other competitive retail industries, the
telecommunications industry is concerned with the concepts
of churn (when a customer joins a competitor) and win back
(when an ex-customer returns). Neural Technologies Inc., is
a UK-based company which has marketed a product called
DA Churn Manager. Specifically tailored to the
telecommunications industry, this product uses a series of
neural networks to: analyze customer and call data; predict
if, when and why a customer is likely to churn; predict the
elects of forthcoming promotional strategies; and
interrogate the data to find the most profitable customers.
Telecommunications companies are also concerned with
product sales, since the more reliant a customer becomes on
certain products
17. Four parts of a typical
nerve cell : -
DENDRITES: Accepts the
inputs
SOMA : Process the
inputs
AXON : Turns the
processed inputs into
outputs.
SYNAPSES : The
electrochemical
contact between the
neurons.
18. Inputs to the network are
represented by the
mathematical symbol, xn
Each of these inputs are
multiplied by a connection
weight
These products are simply
summed, fed through the
transfer function, f( ) to
generate a result and then
output.
f
w1
w2
xn
x2
x1
wn
f(w1 x1 + ……+ w
19. output layer
connections
Input layer
Hidden layers
Neural
network
Including
connections
(called
weights)
between
neuron
Com
pare
Actual
output
Desired
output
Input
output
Figure showing adjust of
neural network
: artificial neural network model
CONTD
20. The neural network in
which every node is
connected to every other
nodes, and these
connections may be
either excitatory
(positive weights),
inhibitory (negative
weights), or irrelevant
(almost zero weights).
These are networks in
which nodes are
partitioned into subsets
called layers, with no
connections from layer j
to k if j > k.
Input node
Input node
output node
output node
Hidden node
Layer 1 Layer2
Layer0
(Input layer) (Output layer)
21. Neurons in an animal’s brain are “hard
wired”. It is equally obvious that animals,
especially higher order animals, learn as
they grow.
How does this learning occur?
What are possible mathematical models of
learning?
In artificial neural networks, learning refers
to the method of modifying the weights of
connections between the nodes of a
specified network.
The learning ability of a neural network is
determined by its architecture and by the
algorithmic method chosen for training.
23. A common risk of neural networks, particularly in
robotics, is that they require a large diversity of
training for real-world operation. This is not
surprising, since any learning machine needs
sufficient representative examples in order to capture
the underlying structure that allows it to generalize to
new cases. Dean Pomerleau, in his research
presented in the paper "Knowledge-based Training of
Artificial Neural Networks for Autonomous Robot
Driving," uses a neural network to train a robotic
vehicle to drive on multiple types of roads (single
lane, multi-lane, dirt, etc.). A large amount of his
research is devoted to extrapolating multiple training
scenarios from a single training experience, and
24. Preserving past training diversity so that the
system does not become over trained (if, for
example, it is presented with a series of right turns –
it should not learn to always turn right). These
issues are common in neural networks that must
decide from amongst a wide variety of responses,
but can be dealt with in several ways, for example
by randomly shuffling the training examples, by
using a numerical optimization algorithm that does
not take too large steps when changing the network
connections following an example, or by grouping
examples in so-called mini-batches.
25. To implement large and effective software neural networks,
considerable processing and storage resources need to be
committed. While the brain has hardware tailored to the task of
processing signals through a graph of neurons, simulating even a
most simplified form on Von Neumann technology may compel a
neural network designer to fill many millions of database rows
for its connections – which can consume vast amounts of
computer memory and hard disk space. Furthermore, the
designer of neural network systems will often need to simulate
the transmission of signals through many of these connections
and their associated neurons – which must often be matched with
incredible amounts of CPU processing power and time. While
neural networks often yield effective programs, they too often do
so at the cost of efficiency (they tend to consume considerable
amounts of time and money).
26. Some other risks come from advocates of
hybrid models (combining neural networks
and symbolic approaches), who believe that
the intermix of these two approaches can
better capture the mechanisms of the human
mind.