This document discusses artificial intelligence (AI) technologies and their applications. It begins by defining AI as the recreation of human intelligence processes by machines. It then describes different types of AI, including weak AI which is designed for specific tasks, and strong AI which exhibits generalized human-level cognition. The document outlines several AI technologies like machine learning, machine vision, and natural language processing. It provides examples of how these technologies are used in applications such as self-driving cars, medical imaging, and digital assistants.
Artificial Intelligence Role in Modern Science Aims, Merits, Risks and Its Ap...ijtsrd
Artificial Intelligence AI is a growing field at the intersection of computer science, mathematics, and engineering, focused on creating machines capable of intelligent behavior. Over the years, AI has evolved from rule based systems to data driven approaches, prominently leveraging machine learning and deep learning. This evolution has led to AI systems capable of complex tasks such as pattern recognition, natural language processing, and decision making. The applications of AI are vast and diverse, permeating industries like healthcare, finance, automotive, retail, and education. AI driven technologies enable efficient automation, precise data analysis, personalized experiences, and improved decision making. However, with these advancements come ethical and culture concerns, including biases, data privacy, job displacement, and the responsible development and deployment of AI. Striking a balance between AIs potential and its associated risks necessitates a holistic approach, incorporating transparency, fairness, robust regulations, and ongoing research. This abstract encapsulates AIs transformative potential, emphasizing the importance of responsible AI development to ensure a positive impact on society while mitigating risks. Manish Verma "Artificial Intelligence Role in Modern Science: Aims, Merits, Risks and Its Applications" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-5 , October 2023, URL: https://www.ijtsrd.com/papers/ijtsrd59910.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/59910/artificial-intelligence-role-in-modern-science-aims-merits-risks-and-its-applications/manish-verma
The power and potential of artificial intelligence cannot be overstated. It has transformed how we interact with technology, from introducing us to robots that can perform tasks with precision to bringing us to the brink of an era of self-driving vehicles and rockets. And this is just the beginning. With a staggering 270% growth in business adoption in the past four years, it has been clear that AI is not just a tool for solving mathematical problems but a transformative force that will shape the future of our society and economy.
Artificial Intelligence (AI) has become an increasingly common presence in our lives, from robots that can perform tasks with precision to autonomous cars that are changing how we travel. It has become an essential part of everything, from large-scale manufacturing units to the small screens of our smartwatches. Today, companies of all sizes and industries are turning to AI to improve customer satisfaction and boost sales. AI is the next big thing, making its way into the inner workings of Fortune 500 companies to help them automate their business processes. Investing in AI can be beneficial for businesses looking to stay competitive in a fast-paced business world.
Building an AI App: A Comprehensive Guide for BeginnersChristopherTHyatt
"Discover the steps to create your own AI app: Choose a framework, define your app's purpose, collect and prepare data, train the model, integrate a user-friendly interface, and deploy successfully."
Artificial Intelligence Vs Machine Learning Vs Deep Learningvenkatvajradhar1
This technology is no longer a matter of science fiction. Instead, we see artificial intelligence in every part of our lives. Smart assistants are on our phones and speakers, helping us find information and complete everyday tasks. At work, chatbots are affiliated with the Customer Support Team, with estimates that they will be responsible for 85% of customer service by next year.
A Case Study of Artificial Intelligence is being used to Reshape BusinessAI Publications
AI is one of the emerging technologies with such a long record which is constantly changing and growing in the corporate world. We will explain the modern AI basics and various aspects, applications of AI, and its future in business throughout this paper. Many businesses benefit from AI technology by lowering operational expenses, improving efficiency, and expanding the customer base. AI is made up of a variety of tools that allow computers to process massive amounts of data using smart technologies such as machine learning and natural language processing. Many customers now value AI-powered everyday technologies such as credit card fraud detection, e-mail spam filters, and predictive traffic alerts. The field of artificial intelligence is shifting toward developing intelligent systems that can effectively collaborate with people, including innovative ways to develop interactive and scalable ways for people to teach robots. The Vehicle Integrated Artificial Intelligence System is the focus of this paper.
leewayhertz.com-How to build an AI app.pdfrobertsamuel23
The power and potential of artificial intelligence cannot be overstated. It has transformed
how we interact with technology, from introducing us to robots that can perform tasks
with precision to bringing us to the brink of an era of self-driving vehicles and rockets
This step-by-step guide will show you how to build and use an AI app. Whether you are a researcher, business owner or just curious about AI technology, these instructions will help you navigate the steps of creating an AI system that can transform your industry.
Artificial intelligence (AI) is a field of computer science that focuses on solving cognitive programs associated with human intelligence, such as pattern recognition, problem-solving and learning. AI refers to the use of advanced technology, such as robotics, in futuristic scenarios.
Artificial Intelligence Role in Modern Science Aims, Merits, Risks and Its Ap...ijtsrd
Artificial Intelligence AI is a growing field at the intersection of computer science, mathematics, and engineering, focused on creating machines capable of intelligent behavior. Over the years, AI has evolved from rule based systems to data driven approaches, prominently leveraging machine learning and deep learning. This evolution has led to AI systems capable of complex tasks such as pattern recognition, natural language processing, and decision making. The applications of AI are vast and diverse, permeating industries like healthcare, finance, automotive, retail, and education. AI driven technologies enable efficient automation, precise data analysis, personalized experiences, and improved decision making. However, with these advancements come ethical and culture concerns, including biases, data privacy, job displacement, and the responsible development and deployment of AI. Striking a balance between AIs potential and its associated risks necessitates a holistic approach, incorporating transparency, fairness, robust regulations, and ongoing research. This abstract encapsulates AIs transformative potential, emphasizing the importance of responsible AI development to ensure a positive impact on society while mitigating risks. Manish Verma "Artificial Intelligence Role in Modern Science: Aims, Merits, Risks and Its Applications" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-5 , October 2023, URL: https://www.ijtsrd.com/papers/ijtsrd59910.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/59910/artificial-intelligence-role-in-modern-science-aims-merits-risks-and-its-applications/manish-verma
The power and potential of artificial intelligence cannot be overstated. It has transformed how we interact with technology, from introducing us to robots that can perform tasks with precision to bringing us to the brink of an era of self-driving vehicles and rockets. And this is just the beginning. With a staggering 270% growth in business adoption in the past four years, it has been clear that AI is not just a tool for solving mathematical problems but a transformative force that will shape the future of our society and economy.
Artificial Intelligence (AI) has become an increasingly common presence in our lives, from robots that can perform tasks with precision to autonomous cars that are changing how we travel. It has become an essential part of everything, from large-scale manufacturing units to the small screens of our smartwatches. Today, companies of all sizes and industries are turning to AI to improve customer satisfaction and boost sales. AI is the next big thing, making its way into the inner workings of Fortune 500 companies to help them automate their business processes. Investing in AI can be beneficial for businesses looking to stay competitive in a fast-paced business world.
Building an AI App: A Comprehensive Guide for BeginnersChristopherTHyatt
"Discover the steps to create your own AI app: Choose a framework, define your app's purpose, collect and prepare data, train the model, integrate a user-friendly interface, and deploy successfully."
Artificial Intelligence Vs Machine Learning Vs Deep Learningvenkatvajradhar1
This technology is no longer a matter of science fiction. Instead, we see artificial intelligence in every part of our lives. Smart assistants are on our phones and speakers, helping us find information and complete everyday tasks. At work, chatbots are affiliated with the Customer Support Team, with estimates that they will be responsible for 85% of customer service by next year.
A Case Study of Artificial Intelligence is being used to Reshape BusinessAI Publications
AI is one of the emerging technologies with such a long record which is constantly changing and growing in the corporate world. We will explain the modern AI basics and various aspects, applications of AI, and its future in business throughout this paper. Many businesses benefit from AI technology by lowering operational expenses, improving efficiency, and expanding the customer base. AI is made up of a variety of tools that allow computers to process massive amounts of data using smart technologies such as machine learning and natural language processing. Many customers now value AI-powered everyday technologies such as credit card fraud detection, e-mail spam filters, and predictive traffic alerts. The field of artificial intelligence is shifting toward developing intelligent systems that can effectively collaborate with people, including innovative ways to develop interactive and scalable ways for people to teach robots. The Vehicle Integrated Artificial Intelligence System is the focus of this paper.
leewayhertz.com-How to build an AI app.pdfrobertsamuel23
The power and potential of artificial intelligence cannot be overstated. It has transformed
how we interact with technology, from introducing us to robots that can perform tasks
with precision to bringing us to the brink of an era of self-driving vehicles and rockets
This step-by-step guide will show you how to build and use an AI app. Whether you are a researcher, business owner or just curious about AI technology, these instructions will help you navigate the steps of creating an AI system that can transform your industry.
Artificial intelligence (AI) is a field of computer science that focuses on solving cognitive programs associated with human intelligence, such as pattern recognition, problem-solving and learning. AI refers to the use of advanced technology, such as robotics, in futuristic scenarios.
Artificial intelligence (AI) broadly refers to any human-like behavior displayed by a machine or system. AI has progressed from enabling computers to play games like checkers against humans to now being part of our daily lives through solutions in areas like healthcare, manufacturing, financial services, and entertainment. HPE is pioneering AI by harnessing data and gaining insights at the edge to help customers realize the value of their data faster and leverage opportunities for innovation, growth, and success. A brief history of AI discusses its early development in the 1950s and milestones like defeating chess masters and developing speech recognition.
UNCOVERING FAKE NEWS BY MEANS OF SOCIAL NETWORK ANALYSISpijans
This document discusses techniques for identifying fake news using social network analysis. It first reviews literature on existing fake news identification methods that use feature extraction from news content and social context. Deep learning models are then proposed to classify news as real or fake using datasets of news and social network information. The implementation achieves 99% accuracy on binary classification of news. Social network analysis factors like bot accounts, echo chambers, and information spread are discussed as enabling the spread of fake news online.
UNCOVERING FAKE NEWS BY MEANS OF SOCIAL NETWORK ANALYSISpijans
The short access to facts on social media networks in addition to its exponential upward push also made it
tough to distinguish among faux information or actual facts. The quick dissemination thru manner of sharing has more high quality its falsification exponentially. It is also essential for the credibility of social media networks to avoid the spread of fake facts. So its miles rising research task to robotically check for
misstatement of information thru its source, content material, or author and save you the unauthenticated
assets from spreading rumours. This paper demonstrates an synthetic intelligence primarily based completely approach for the identification of the fake statements made by way of the use of social network
entities. Versions of Deep neural networks are being applied to evalues datasets and have a look at for
fake information presence. The implementation setup produced most volume 99% category accuracy, even
as dataset is tested for binary (real or fake) labelling with multiple epochs.
Artificial intelligence is the field of study that describes the capability of machines to learn and act intelligently like humans. The document discusses the history of AI beginning in 1956 and provides descriptions of common AI algorithms and models including support vector machines and artificial neural networks. Examples of applications of AI discussed include voice recognition, virtual assistants like Siri, machine learning platforms, and robotic process automation. The document also outlines the typical phases of AI systems as getting data, preparing the data, training a model, and testing the model.
This document is a project report on the topic of artificial intelligence and whether it is a boon or bane. It includes an introduction on AI, a brief history of AI, the importance and features of AI, as well as the advantages and disadvantages. The report discusses findings from the study, suggestions, the objective and methodology. It concludes that AI could potentially threaten humanity if its social impacts are ignored and not properly addressed through policy frameworks.
Every thing about Artificial Intelligence Vaibhav Mishra
Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.
Artificial Intelligence, Areas of Artificial Intelligence, Examples of Artificial Intelligence, Applications of Artificial Intelligence, Data Mining, Robot etc.
This document provides an overview of artificial intelligence (AI) including definitions of different types of AI, a brief history of AI, potential application fields and use cases, and the future outlook for AI. It defines AI as ranging from everyday applications to self-driving cars. It discusses narrow AI, general AI, and superintelligence. The document also summarizes key milestones in the development of AI from 1955 to the present and potential opportunities and challenges of AI including automation, ethics, and politics. It provides examples of Austrian AI startups and their technologies. The outlook suggests that human-level AI may be achieved by 2040 and superintelligence by 2060 with impacts on robotics, climate change, human enhancement, and autonomous
The upsurge of deep learning for computer vision applicationsIJECEIAES
Artificial intelligence (AI) is additionally serving to a brand new breed of corporations disrupt industries from restorative examination to horticulture. Computers can’t nevertheless replace humans, however, they will work superbly taking care of the everyday tangle of our lives. The era is reconstructing big business and has been on the rise in recent years which has grounded with the success of deep learning (DL). Cyber-security, Auto and health industry are three industries innovating with AI and DL technologies and also Banking, retail, finance, robotics, manufacturing. The healthcare industry is one of the earliest adopters of AI and DL. DL accomplishing exceptional dimensions levels of accurateness to the point where DL algorithms can outperform humans at classifying videos & images. The major drivers that caused the breakthrough of deep neural networks are the provision of giant amounts of coaching information, powerful machine infrastructure, and advances in academia. DL is heavily employed in each academe to review intelligence and within the trade-in building intelligent systems to help humans in varied tasks. Thereby DL systems begin to crush not solely classical ways, but additionally, human benchmarks in numerous tasks like image classification, action detection, natural language processing, signal process, and linguistic communication process.
This document provides an overview of key concepts in data science including machine learning, deep learning, artificial intelligence, and how they relate. It defines machine learning as using algorithms to learn from data without being explicitly programmed. Deep learning is a subset of machine learning using artificial neural networks. Artificial intelligence is the broader field of machines performing intelligent tasks. The document also discusses supervised, unsupervised, and reinforcement machine learning algorithms and how data science uses skills from statistics, machine learning, and visualization to analyze and manipulate large datasets.
In today's tech-driven world, the integration of artificial intelligence (AI) into applications has become increasingly prevalent. From personalized recommendations to intelligent chatbots, AI enhances user experiences and optimizes processes. However, building an AI app can seem daunting to those unfamiliar with the process. Fear not! This guide aims to demystify the journey, offering step-by-step insights into how to build an AI app from scratch.
Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision. AI works by ingesting large amounts of labeled training data to analyze patterns and correlations and use these to make predictions. New AI techniques can generate realistic text, images, music and other media. The four main types of AI are reactive machines, those with limited memory, theory of mind, and self-awareness. AI is incorporated into automation, machine learning, machine vision, natural language processing, robotics, self-driving cars, and text, image and audio generation.
AI EXPLAINED Non-Technical Guide for PolicymakersBranka Panic
This guide is meant to help policymakers and citizens understand the basics of Artificial Intelligence (AI) and how it affects our society. It offers explanations and additional resources to help policymakers prepare for the current
and future AI developments.
What is Artificial Intelligence and Machine Learning (1).pptxprasadishana669
Artificial Intelligence refers to the development of computer systems that can perform tasks that typically require human intelligence. These tasks include problem-solving, learning, perception, speech recognition, and language translation, among others. Machine Learning is a subset of AI that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit programming.
The construction industry is faced with a variety of intricate problems, such as time and cost overruns, worries about health & safety, productivity issues, and labour availability. The industry’s expansion is consequently severely constrained.
Artificial intelligence (AI) is the simulation of human intelligence by machines, especially computer systems. AI works by ingesting large amounts of labeled training data to analyze patterns and correlations in order to make predictions. The main types of AI are reactive machines (task-specific without memory), limited memory systems (can use past experiences), theory of mind systems (understand human emotions and intentions), and self-aware systems (have consciousness). AI is important because it can provide insights by analyzing large amounts of data faster than humans and in some cases perform tasks better. However, AI also has disadvantages such as being expensive, requiring expertise, and only knowing what it has been exposed to through data.
What is artificial intelligence Definition, top 10 types and examples.pdfAlok Tripathi
What is artificial intelligence?
Although many definitions of artificial intelligence (AI) have emerged over the past few decades, John McCarthy provided the following definition in this 2004 paper (link is located outside ibm.com): MASU. Especially intelligent computer programs. It deals with the same task of using computers to understand human intelligence, but AI does not need to be limited to biologically observable methods.
Definition of artificial intelligence
Artificial intelligence is the imitation of human intelligence processes by machines, especially computer systems. Typical applications of AI include expert systems, natural language processing, speech recognition, and machine vision.
How does artificial intelligence (AI) work?
As the hype around AI grows, vendors are making efforts to promote how AI is used in their products and services. Often, what they call AI is just a component of technologies like machine learning. AI requires specialized hardware and software infrastructure to write and train machine learning algorithms. Although no single programming language is synonymous with AI, Python, R, Java, C++, and Julia have features that are popular among AI developers.
Generally, AI systems work by ingesting large amounts of labeled training data, analyzing correlations and patterns in the data, and using these patterns to predict future situations. This way, given examples of text, chatbots can learn to generate authentic-like conversations with people. Image recognition tools can also learn to recognize and describe objects in images by considering millions of examples. New and rapidly advancing generic AI technology allows you to create realistic text, images, music, and other media.
Artificial intelligence programming focuses on cognitive skills such as:
• Learn: This aspect of AI programming focuses on taking data and creating rules to turn it into actionable information. Rules, called algorithms, provide step-by-step instructions for computing devices to accomplish a particular task.
• Logic. This aspect of AI programming focuses on selecting the appropriate algorithm to achieve the desired result.
• Self-correction: This aspect of AI programming is designed to continuously improve the algorithms and provide the most accurate results possible.
• Creativity. This aspect of AI uses neural networks, rule-based systems, statistical methods, and other AI techniques to generate new images, new text, new music, and new ideas.
Differences between AI, machine learning and deep learning
AI, machine learning, and deep learning are common terms in enterprise IT, especially when companies use them interchangeably in marketing materials. But there are differences too. The term AI was coined in the 1950s and refers to the emulation of human intelligence by machines. A constantly changing set of capabilities is incorporated as new technologies are developed. Technologies falling under the umbrella of AI include machine learning and deep lea
Pin By Rhonda Genusa On Writing Process Teaching Writing, WritingJeff Nelson
The document discusses the contrasting philosophies of W.E.B. Du Bois and Booker T. Washington regarding the best approach for African Americans to overcome racial discrimination after the Civil War. Du Bois advocated for increased access to education and political rights, while Washington believed African Americans should focus first on industrial education and economic empowerment through occupations like farming and domestic work. Both men aimed to uplift the black community, but had differing views on the path forward.
In The Great Gatsby, F. Scott Fitzgerald uses the color green to symbolize wealth, aspirations for the American Dream, and the pursuit of happiness. Green is prominently featured in descriptions of Jay Gatsby's lavish parties and mansion, representing his immense fortune and desire to attain status. The novel suggests that true happiness cannot be bought, as represented by the fading of green at the end of the story along with Gatsby's dreams.
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Artificial intelligence (AI) broadly refers to any human-like behavior displayed by a machine or system. AI has progressed from enabling computers to play games like checkers against humans to now being part of our daily lives through solutions in areas like healthcare, manufacturing, financial services, and entertainment. HPE is pioneering AI by harnessing data and gaining insights at the edge to help customers realize the value of their data faster and leverage opportunities for innovation, growth, and success. A brief history of AI discusses its early development in the 1950s and milestones like defeating chess masters and developing speech recognition.
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UNCOVERING FAKE NEWS BY MEANS OF SOCIAL NETWORK ANALYSISpijans
The short access to facts on social media networks in addition to its exponential upward push also made it
tough to distinguish among faux information or actual facts. The quick dissemination thru manner of sharing has more high quality its falsification exponentially. It is also essential for the credibility of social media networks to avoid the spread of fake facts. So its miles rising research task to robotically check for
misstatement of information thru its source, content material, or author and save you the unauthenticated
assets from spreading rumours. This paper demonstrates an synthetic intelligence primarily based completely approach for the identification of the fake statements made by way of the use of social network
entities. Versions of Deep neural networks are being applied to evalues datasets and have a look at for
fake information presence. The implementation setup produced most volume 99% category accuracy, even
as dataset is tested for binary (real or fake) labelling with multiple epochs.
Artificial intelligence is the field of study that describes the capability of machines to learn and act intelligently like humans. The document discusses the history of AI beginning in 1956 and provides descriptions of common AI algorithms and models including support vector machines and artificial neural networks. Examples of applications of AI discussed include voice recognition, virtual assistants like Siri, machine learning platforms, and robotic process automation. The document also outlines the typical phases of AI systems as getting data, preparing the data, training a model, and testing the model.
This document is a project report on the topic of artificial intelligence and whether it is a boon or bane. It includes an introduction on AI, a brief history of AI, the importance and features of AI, as well as the advantages and disadvantages. The report discusses findings from the study, suggestions, the objective and methodology. It concludes that AI could potentially threaten humanity if its social impacts are ignored and not properly addressed through policy frameworks.
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This document provides an overview of artificial intelligence (AI) including definitions of different types of AI, a brief history of AI, potential application fields and use cases, and the future outlook for AI. It defines AI as ranging from everyday applications to self-driving cars. It discusses narrow AI, general AI, and superintelligence. The document also summarizes key milestones in the development of AI from 1955 to the present and potential opportunities and challenges of AI including automation, ethics, and politics. It provides examples of Austrian AI startups and their technologies. The outlook suggests that human-level AI may be achieved by 2040 and superintelligence by 2060 with impacts on robotics, climate change, human enhancement, and autonomous
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Artificial intelligence (AI) is additionally serving to a brand new breed of corporations disrupt industries from restorative examination to horticulture. Computers can’t nevertheless replace humans, however, they will work superbly taking care of the everyday tangle of our lives. The era is reconstructing big business and has been on the rise in recent years which has grounded with the success of deep learning (DL). Cyber-security, Auto and health industry are three industries innovating with AI and DL technologies and also Banking, retail, finance, robotics, manufacturing. The healthcare industry is one of the earliest adopters of AI and DL. DL accomplishing exceptional dimensions levels of accurateness to the point where DL algorithms can outperform humans at classifying videos & images. The major drivers that caused the breakthrough of deep neural networks are the provision of giant amounts of coaching information, powerful machine infrastructure, and advances in academia. DL is heavily employed in each academe to review intelligence and within the trade-in building intelligent systems to help humans in varied tasks. Thereby DL systems begin to crush not solely classical ways, but additionally, human benchmarks in numerous tasks like image classification, action detection, natural language processing, signal process, and linguistic communication process.
This document provides an overview of key concepts in data science including machine learning, deep learning, artificial intelligence, and how they relate. It defines machine learning as using algorithms to learn from data without being explicitly programmed. Deep learning is a subset of machine learning using artificial neural networks. Artificial intelligence is the broader field of machines performing intelligent tasks. The document also discusses supervised, unsupervised, and reinforcement machine learning algorithms and how data science uses skills from statistics, machine learning, and visualization to analyze and manipulate large datasets.
In today's tech-driven world, the integration of artificial intelligence (AI) into applications has become increasingly prevalent. From personalized recommendations to intelligent chatbots, AI enhances user experiences and optimizes processes. However, building an AI app can seem daunting to those unfamiliar with the process. Fear not! This guide aims to demystify the journey, offering step-by-step insights into how to build an AI app from scratch.
Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision. AI works by ingesting large amounts of labeled training data to analyze patterns and correlations and use these to make predictions. New AI techniques can generate realistic text, images, music and other media. The four main types of AI are reactive machines, those with limited memory, theory of mind, and self-awareness. AI is incorporated into automation, machine learning, machine vision, natural language processing, robotics, self-driving cars, and text, image and audio generation.
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This guide is meant to help policymakers and citizens understand the basics of Artificial Intelligence (AI) and how it affects our society. It offers explanations and additional resources to help policymakers prepare for the current
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Artificial Intelligence refers to the development of computer systems that can perform tasks that typically require human intelligence. These tasks include problem-solving, learning, perception, speech recognition, and language translation, among others. Machine Learning is a subset of AI that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit programming.
The construction industry is faced with a variety of intricate problems, such as time and cost overruns, worries about health & safety, productivity issues, and labour availability. The industry’s expansion is consequently severely constrained.
Artificial intelligence (AI) is the simulation of human intelligence by machines, especially computer systems. AI works by ingesting large amounts of labeled training data to analyze patterns and correlations in order to make predictions. The main types of AI are reactive machines (task-specific without memory), limited memory systems (can use past experiences), theory of mind systems (understand human emotions and intentions), and self-aware systems (have consciousness). AI is important because it can provide insights by analyzing large amounts of data faster than humans and in some cases perform tasks better. However, AI also has disadvantages such as being expensive, requiring expertise, and only knowing what it has been exposed to through data.
What is artificial intelligence Definition, top 10 types and examples.pdfAlok Tripathi
What is artificial intelligence?
Although many definitions of artificial intelligence (AI) have emerged over the past few decades, John McCarthy provided the following definition in this 2004 paper (link is located outside ibm.com): MASU. Especially intelligent computer programs. It deals with the same task of using computers to understand human intelligence, but AI does not need to be limited to biologically observable methods.
Definition of artificial intelligence
Artificial intelligence is the imitation of human intelligence processes by machines, especially computer systems. Typical applications of AI include expert systems, natural language processing, speech recognition, and machine vision.
How does artificial intelligence (AI) work?
As the hype around AI grows, vendors are making efforts to promote how AI is used in their products and services. Often, what they call AI is just a component of technologies like machine learning. AI requires specialized hardware and software infrastructure to write and train machine learning algorithms. Although no single programming language is synonymous with AI, Python, R, Java, C++, and Julia have features that are popular among AI developers.
Generally, AI systems work by ingesting large amounts of labeled training data, analyzing correlations and patterns in the data, and using these patterns to predict future situations. This way, given examples of text, chatbots can learn to generate authentic-like conversations with people. Image recognition tools can also learn to recognize and describe objects in images by considering millions of examples. New and rapidly advancing generic AI technology allows you to create realistic text, images, music, and other media.
Artificial intelligence programming focuses on cognitive skills such as:
• Learn: This aspect of AI programming focuses on taking data and creating rules to turn it into actionable information. Rules, called algorithms, provide step-by-step instructions for computing devices to accomplish a particular task.
• Logic. This aspect of AI programming focuses on selecting the appropriate algorithm to achieve the desired result.
• Self-correction: This aspect of AI programming is designed to continuously improve the algorithms and provide the most accurate results possible.
• Creativity. This aspect of AI uses neural networks, rule-based systems, statistical methods, and other AI techniques to generate new images, new text, new music, and new ideas.
Differences between AI, machine learning and deep learning
AI, machine learning, and deep learning are common terms in enterprise IT, especially when companies use them interchangeably in marketing materials. But there are differences too. The term AI was coined in the 1950s and refers to the emulation of human intelligence by machines. A constantly changing set of capabilities is incorporated as new technologies are developed. Technologies falling under the umbrella of AI include machine learning and deep lea
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This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
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used to train an AI program, there is inherent
potential for human bias and it must be closely
monitored. To make the world and its physical
objects truly autonomous, we need a machine
learning (ML) [2] that emulates human learning, as
well as a framework module for data analysis (DA)
[3]. ML would develop techniques to promote
learning in different network components / devices
to make them automated and self-sufficient, while
DA would evaluate / analyze all data generated over
time to determine past trends and be more efficient /
efficient in the future.
II. ARTIFICIAL INTELLIGENCE
AI is the science of machine intelligence, so that they
are able to carry out tasks that the human mind
historically needed. AI-based systems are rapidly
evolving with respect to implementation, adaptation,
processing speed and capabilities. Machines are
becoming more and more able to take on fewer
repetitive tasks. AI is simply about' choosing' a
correct decision at the right time. To put it plainly,
there is a lack of AI imagination in the decision
humans should take. It may be argued that human
ingenuity will always change the role of productive
work, but AI-based systems have reduced the
repetition of human efforts quite elegantly, and could
yield results in relatively little time [8].
Most of AI's ongoing work can be called' Narrow AI.'
This means that technology is only supporting those
functions. We're looking for something far more than
that though. Therefore, many areas have conjugated
to drive the advancement of AI [1]. Furthermore, AI
relies heavily on data science techniques. For the
creation of software, the ideas come primarily from
computer science that is mainly concerned with
algorithmic efficiency and scalability of data. The
ideas come from a lot more varied sources to examine.
Methodologies are borrowed from both the natural
sciences (such as physics, mathematics, graph theory)
and social sciences (such as economics, sociology,
political science). Also very common in data science
are different techniques that are naturally
interdisciplinary, such as ML [3], data mining, DBMS
[9].
ML is one of the principal tools for achieving AI. The
human brain can solve certain types of problems in
learning. For example, there are plenty of optical
neurons in the visual system which make object
recognition easy for humans. Learning is not only
restricted to humans, it is diversified to animals,
plants etc. Our very survival depends on the ability to
learn and adjust to the environment. Machines can be
equivalently made to learn and adjust themselves to
mimic the natural learning process, to be called' ML'
for better performance. Training (including ML) is
mostly performed in three ways: supervised,
reinforced and unregulated. Researchers have often
talked about the way in which we eventually create
human-like AI as a certainty. With increasing pace
we are definitely heading towards that target. A
significant part of the success we've seen in recent
years is all due to the fundamental changes in how
we interpret AI working, which were primarily
brought about by ML. Therefore, granting ML the
credit of instilling smartness into computers wouldn't
be wrong.
A. Goals of AI
➢ To generate Expert Systems – The systems
which reveal intelligent behaviour, learn,
demonstrate, explain, and advice its users.
➢ To realize Human Intelligence in Machines −
Creating systems that recognize, think, learn,
and behave like humans.
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III. TYPES OF ARTIFICIAL INTELLIGENCE
Arend Hintze, an assistant professor at Michigan
State University of Integrative Biology and Computer
Science and Engineering, categorizes AI into four
groups, ranging from the type of AI systems that
currently exist to sensory systems that do not yet
exist. His categories are as follows: [5].
A. Reactive machines: Deep Blue, the IBM chess
program which beat Garry Kasparov in the 1990s, is
one example. Deep Blue can recognize and anticipate
pieces on the chess board but it has no memory and
can't use past experiences to inform future ones. This
analyzes potential move its own and opponent and
chooses the most strategic move. Deep Blue and
Google's AlphaGO have been developed for limited
purposes and cannot be extended easily to a different
situation.
B. Limited memory: Such AI systems can use past
experiences to inform future decisions. Some of the
decision-making mechanisms of self-driving cars are
planned in this way. Observations guide activities
taking place in a not-so-distant future, such as a car
changing lanes. These observations are not stored
enduringly.
C. Theory of mind: This psychology word refers to
the understanding that others have their own beliefs,
requirements and intent ions that force the decisions
they make. This type of AI does not yet exist [10]
D. Self-awareness: In this category, AI systems have
an intelligence of self, have consciousness. Machines
with self-awareness recognize their present state and
can utilize the information to conclude what others
are feeling. This category of AI does not yet exist.
IV. TECHNOLOGIES OF AI
AI is included into a variety of diverse types of
technology. Technologies of AI are shown in Figure 1
[13].
A. Machine learning: The science of obtaining a
machine without programming to function Deep
learning is a type of machine learning that can be
called, in very simple terms, as predictive analytics
automation. There are three types of machine
learning algorithms:
➢ Supervised learning: Data sets are classified in
order to detect patterns and use them to mark
new data sets.
➢ Unsupervised learning: Data sets are not labeled
and are sorted by similarities or differences.
➢ Reinforcement learning: Data sets are not labeled,
but feedback is given to the AI system after
performing an action or several actions.
Figure 1. Technologies of AI
B. Machine vision: The knowledge of allowing
computers to see. This technology uses a camera,
analog to-digital conversion, and digital signal
processing to capture and analyze visual information.
It is often contrasted with human eyesight because,
for example, machine vision is not constrained by
nature and can be programmed to see through walls.
It is used in a variety of applications, from signature
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recognition to the study of medical images. Computer
vision, which focuses on computer-based image
processing, is frequently associated with machine
view.
C. Natural Language Processing (NLP): A computer
program processes human and not computer language.
One of the oldest and best-known examples of NLP is
spam detection, which looks at an email's subject line
and text and determines whether it is garbage.
Current approaches to NLP are based on machine
learning. NLP tasks include text translation, an
analysis of feelings and recognition of speech.
D. Robotics: An engineering field focused on the
design and fabrication of robots. Robots are often
used to perform tasks which are difficult for humans
to consistently perform or perform. These are used to
move large objects in space in assembly lines for
vehicle manufacturing, or by NASA. Researchers also
make use of machine learning to build robots that can
interact in social environments.
E. Self-driving cars: These use a grouping of
computer vision, image recognition and deep
learning to construct automated skill at piloting a
vehicle while staying in a given lane and avoiding
unpredicted obstructions, such as pedestrians.
1)
V. AI APPLICATIONS
An application of AI is shown in Figure 2.
Figure 2 : Applications of AI
A. AI in Healthcare: The best bets are to improve
patient satisfaction and cut costs. Enterprises apply
machine learning to make diagnosis easier and
quicker than humans. IBM Watson is one of the best
known health-care systems. This knows the natural
language and is able to answer questions asked about
it. The program uses patient data and other available
data sources to form a theory, which it then presents
with a scoring scheme for trust.
Certain AI implementations include chatbots, a
computer program that is used online to answer
questions and support clients, to help plan follow-up
appointments or to help patients through the
accounting process and virtual health assistants that
provide basic medical feedback [15].
B. AI in Business: Robotic automation of processes is
applied to highly repetitive tasks normally carried out
by humans. Machine learning algorithms are built
into analytics and CRM applications to discover
knowledge on how to represent consumers more
efficiently. Chatbots were built into the websites to
provide customers with instant service. Automation
of job positions has also turn out to be a discussion
point among academics and IT analysts.
C. AI in Education: AI will automate testing,
allowing more time for the educators. AI will
appraise and adjust students to their needs, helping
them to work at their own pace. AI tutors can
provide the students with additional support to
ensure that they remain on track. AI could change
where and how students learn, and maybe even
replace some teachers.
D. AI in Law: In law, the process of discovery, sifting
through records, is often daunting for humans.
Automating this method is using the energy more
efficiently. Startups are also developing virtual
assistants to ask and answer questions that can sift
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programmed-to-answer questions by analyzing the
taxonomy and ontology related with a database.
E. AI in Manufacturing: This is an area that was at
the forefront of embedding robots into the workflow.
Industrial robots used to perform single tasks and
were isolated from human workers, but as technology
progressed this changed.
VI. AI ENABLED IOT
IoT is a broad term that includes too many Internet-
interconnected devices, actuators, data storage and
data processing capabilities. Therefore, any IoT-
enabled device can sense its environment, transmit,
store and process the collected data and act
accordingly. The final step to act accordingly depends
entirely on the processing stage. An IoT service's true
smartness is strong-minded by the level of processing
and/or acting it can do. A non-smart IoT system will
have imperfect capacity and can't evolve with the
data. A smarter IoT framework, however, will have
AI and could serve the actual goal of automation and
adaptation. In this context, few examples of existing
IoT services are discussed here with the working of
AI behind them [1].
A. Voice Assistants: These are cloud-based voice
services that do something as table-top personal
assistants to users. They carry out various tasks in
their immediate vicinity via third party applications
and other smart devices. These are able of answering
questions, calling cabs, production of restaurant
reservations, playing music, flipping on / off smart
lights, and a lot of more user-based voice commands.
Few of the well-known voice assistants are:
➢ Alexa is Amazon's voice assistant, used in devices
such as Amazon Echo, Amazon Tap etc. There is a
specific set of competencies known as the Alexa
Skills Kit (ASK) that can be changed and revised
to personalize or develop those competences.
➢ Apple Inc.'s Apple Homepod uses Siri which
serves a similar purpose.
Such voice assistants are able to perform multiple
tasks primarily because of the implementation of
various AI subfields. Automatic far-field voice
recognition, wake-up word identification, speech-to-
text translation, natural language processing and
interpretation, contextual reasoning, dialog
management, question answering, conversational AI
etc. are constantly conducted to allow the voice
assistants perform tasks in real time.[14].
B. Robots: Recent advances in this field of robotics
have led to the formation of robots that have
increased human likeness and are capable of
interacting with humans while understanding,
reciprocating and expressing some human emotions.
Robots are IoTs in themselves as they contain
multiple sensors and actuators along with AI which
helps them learn and adapt over time.
➢ SoftBank Robotics Pepper is a human-shaped
robot considered a humanoid companion capable
of interacting with people. It can understand the
emotion of a human being through his / her
facial expression, movement of the body, tone of
voice, words used etc. It is capable of identifying
four human emotions, namely happiness,
sorrow, risk and reciprocation. It is able to move
around and interact with people and other
devices in its vicinity. Pepper is commercially
used in a variety of stores to act together with
customers.
➢ Hnson's Sophia Robotics is a social humanoid
robot that is incredibly human-like and can
express emotions through over 50 facial
expressions. Sophia is the first robot in the world
to get full nationality from a country. She also
gave interviews and sang in a concert.
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➢ Moley Robotics Robotic Kitchen is an superior
fully functional robot that is incorporated into a
kitchen. It has robotic arms, microwave, hob,
and a human interaction touch screen device,
and is intelligent to prepare expert food from its
recipe collection.
In these robots, the application of natural language
processing, computer vision, shape recognition,
object recognition, detection and tracking, block-
chain technology to interpret inputs and responses,
facial recognition, voice recognition, speech-to-text
technology, obstacle recognition, haptics etc. has
been broadly used to allow them to function
effectively.
C. Smart Devices: There are Smart objects (SO) /
devices present in an IoT apart from the voice
assistants and robots, which make the job simpler for
humans. AI-enabled SOs use object recognition
applications, facial identification, voice recognition,
identification of speech and expression, deep neural
networks, learning transfer, computer vision etc.
By June Smart Oven aims to cook food every time
completely It has an HD camera and food
thermometer that helps monitor the food being
cooked inside the oven automatically and, if
necessary, can switch cooking modes. This oven can
be run by Alexa and by analyzing the user's likings
will suggest and customize automated cook-program.
Deako's Smart Lights can be remotely controlled
through smartphones, and Alexa or Google Assistant.
They are linked via the Internet and may take
delivery of from time to time software upgrades.
Affectiva's automotive AI is an in-cabin sensing AI
that can be used in autonomous taxis and highly
automated vehicles. It detects the emotional and
cognitive state of the occupants in the vehicle from
their faces and voices through in-cabin cameras and
microphones.
D. Industrial IoT: Besides being used inside smart
homes, IoT has an enormous area of application in
the a variety of industries. These solutions carry out a
company as a whole's statistical and financial analysis
and supply predictions using some AI and ML
algorithms [11].
Primer is an Alluvium product which offers
industrial solutions. Primer produces an overview of
the stability score in real time, based on the collected
data, the device sensors and properties. This aims at
identifying potential problems well in advance and
lets operators know the irregularities and make
necessary adjustments to the entire facility from
something as simple as a sensor.
Plutoshift is yet another IoT-based industrial solution.
It allows industrial firms to incessantly trail their
asset performance, compute financial impact and
supply support for informed decision-making.
VII. CHALLENGES FOR AI
Nearly all of the current AI research areas would
discover applications in future real-time AI systems.
However, we can identify several AI areas that may
have a significant impact on the development of real-
time AI systems as they are related to these systems
fundamental operational constraints [4].
• Reduced Search Variance.
• Approximate and incremental problem-
solving.
• Custom Troubleshooting.
• Calculation.
• Reactions to the representation, planning and
learning.
• Modeling based on the utilities.
• Representation and rationale temporary.
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• System Representation and Projection.
• Planning and implementation at a time.
• Reasoning and Engagement multi-agent.
VIII. ARTIFICIAL INTELLIGENCE TOOLS
AND FRAMEWORKS
Developing neural networks is a long process that
requires a lot of thinking behind the design and a
whole bunch of complexities that actually make up
the system. These nuances can easily finish up being
confusing and not all can be easily tracked. Therefore,
require for such tackle arises where humans handle
the major architectural decisions leaving to such tools
other tasks of optimisation. Imagine architecture
with only 4 possible Boolean hyper parameters, it
would take 4 to test every possible combination Races.
The retraining 24 times of the same system is
definitely not the best use of time and energy. In fact,
most of the newer algorithms include a whole host of
hyper parameters. It is here that new tools come into
the picture. These tools not only assist to build up but
also to optimize these networks [10]. From the dawn
of humanity, we as a species have forever tried to
make things that will help us in everyday tasks. From
stone tools to modern machinery, to tools for creating
programs to help us in our daily lives. Some of the
main tools and frameworks are:
A. Scikit Learn: Scikit-learn is one of the most
common ML libraries. It underpins many calculations
for administered and unsupervised learning.
Precedents include straight and measured relapses,
trees of preference, bunching, etc. It builds on two
important Python, NumPy and SciPy libraries. This
includes a lot of standard AI and data mining
assignment calculations including bunching, relapse
and ordering. Indeed, even undertakings such as
changing information, determining features and
techniques for ensembles can be executed in a few
lines.
B. Tensorflow: On the off chance that you're in the
field of Artificial Intelligence, you've most likely
figured out, attempted or conducted some kind of
deep learning calculation.
C. Theano: Theano is beautifully folded over Keras,
an odd state library of neural systems that runs
almost parallel to the library Theano. It was designed
to make the updating of profound learning models as
quick and easy as possible for innovative work. It
continues to run on Python 2.7 or 3.5 and can be
implemented on GPUs and CPUs reliably.
Constructed with scalability in mind (fairly easy-to-
use multi-GPU and multi machine training support).
Many cool features, such as writing custom layers
easily in high-level languages.
D. Computational Network Toolkit(CNTK): CNTK
allows users to easily understand and unite common
types of models such as feed-forward Deep Neural
Network(DNN),Convolutional Neural Network
(CNN), and Recurrent Neural networks
(RNNs/LSTMs).This implements stochastic gradient
learning across multiple graphics processing unit
(GPUs) and servers with automatic differentiation
and parallelization. CNTK is available under an open-
source license, for anyone to try.
E. Google ML Kit: Google ML Kit, Google's beta
learning machine Software Development Kit (SDK)
for mobile developers, is calculated to enable
developers to construct custom features on Android
and IOS phones. The kit allows developers to
integrate machine learning technologies with app-
based APIs running on the device or in the cloud.
These contain features such as recognition of face and
text, barcode scanning, image labelling, and more.
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IX. CONCLUSION
In the future, people will wear smart devices, eat
smart pills that measure the medicine's impact on the
body, live in smart homes, and so on. Everything will
be smart and connected to the Internet. To build
something of a major value, all branches of science
must cooperate. Machines, for example, can now take
on less-routine tasks, and this transition is taking
place during an era in which many workers are
already struggling. Our lives will continue to be more
technologically driven and we will rely on AI-
enabled systems for everything.
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9. Volume 6, Issue 4, May-June-2020 | http://ijsrcseit.com
Haripriya S et al Int J Sci Res CSE & IT, July-August-2020; 6 (4) : 336-344
344
AUTHOR PROFILE
HARIPRIYA S is currently pursuing
M.Tech in Computer Science and
Engineering at Musaliar College of
Engineering and Technology,
Pathanamthitta, Kerala, INDIA. She
has received B Tech degree in Computer Science and
Engineering from College of Engineering Adoor,
Pathanamthitta, Kerala, INDIA. Her research area of
interest includes the field of Artificial Intelligence,
machine learning and Internet of Things.
Dr. L. C. Manikandan is working as
Professor at Musaliar College of
Engineering and Technology,
Pathanamthitta, Kerala, INDIA. He
has received Ph.D. degree in
Computer and Information
Technology from Manonmaniam Sundaranar
University, M.Tech Degree in Computer and
Information Technology from Manonmaniam
Sundaranar University, M.Sc., Degree in Computer
Science from Bharathidasan University and B.Sc.
Degree in Computer Science from Manonmaniam
Sundaranar University. His main research interest
includes Video Surveillance, Image Compression &
Video Coding in image processing.
Cite this article as :
Haripriya S, Dr. L. C. Manikandan, "A Study on
Artificial Intelligence Technologies and its
Applications", International Journal of Scientific
Research in Computer Science, Engineering and
Information Technology (IJSRCSEIT), ISSN : 2456-
3307, Volume 6 Issue 4, pp. 336-344, July-August
2020. Available at
doi : https://doi.org/10.32628/CSEIT206455
Journal URL : http://ijsrcseit.com/CSEIT206455