We found 35 results that contain "artificial intelligence"
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Artificial Intelligence
The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
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Artificial Intelligence
Since the invention of computers or machines, their capability to perform various tasks went on growing exponentially. Humans have developed the power of computer systems in terms of their diverse working domains, their increasing speed, and reducing size with respect to time.
A branch of Computer Science named Artificial Intelligence pursues creating the computers or machines as intelligent as human beings.
A branch of Computer Science named Artificial Intelligence pursues creating the computers or machines as intelligent as human beings.
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Artificial intelligence
Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with an intelligent being.The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.
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BENEFITS & RISKS OF ARTIFICIAL INTELLIGENCE
1- “Everything we love about civilization
2. That is a product of intelligence,
2. so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before – as long as we manage to keep the technology beneficial.“
2. That is a product of intelligence,
2. so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before – as long as we manage to keep the technology beneficial.“
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EXAMPLES OF ARTIFICIAL INTELLIGENCE IN USE TODAY
Artificial Intelligence (AI) is the branch of computer sciences that emphasizes the development of intelligence machines, thinking and working like humans. For example, speech recognition, problem-solving, learning, and planning.
Today, Artificial Intelligence is a very popular subject that is widely discussed in the technology and business circles. Many experts and industry analysts argue that AI or machine learning is the future – but if we look around, we are convinced that it’s not the future – it is the present.
With the advancement in technology, we are already connected to AI in one way or the other – whether it is Siri, Watson, or Alexa. Yes, the technology is in its initial phase and more and more companies are investing resources in machine learning, indicating a robust growth in AI products and apps in the near future.
The following statistics will give you an idea of growth!
– In 2014, more than $300 million was invested in AI startups, showing an increase of 300%, compared to the previous year (Bloomberg)
– By 2018, 6 billion connected devices will proactively ask for support. (Gartner)
– By the end of 2018, “customer digital assistants” will recognize customers by face and voice across channels and partners (Gartner)
Today, Artificial Intelligence is a very popular subject that is widely discussed in the technology and business circles. Many experts and industry analysts argue that AI or machine learning is the future – but if we look around, we are convinced that it’s not the future – it is the present.
With the advancement in technology, we are already connected to AI in one way or the other – whether it is Siri, Watson, or Alexa. Yes, the technology is in its initial phase and more and more companies are investing resources in machine learning, indicating a robust growth in AI products and apps in the near future.
The following statistics will give you an idea of growth!
– In 2014, more than $300 million was invested in AI startups, showing an increase of 300%, compared to the previous year (Bloomberg)
– By 2018, 6 billion connected devices will proactively ask for support. (Gartner)
– By the end of 2018, “customer digital assistants” will recognize customers by face and voice across channels and partners (Gartner)
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Neural networks, or artificial neural networks
Neural networks, or artificial neural networks (ANNs), are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network. The “deep” in deep learning is just referring to the depth of layers in a neural network. A neural network that consists of more than three layers—which would be inclusive of the inputs and the output—can be considered a deep learning algorithm or a deep neural network. A neural network that only has two or three layers is just a basic neural network.
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This supercomputer will perform 1,000,000,000,000,000,000 operations per second
A government laboratory in Illinois will receive the fastest supercomputer in the United States in 2021, and it will be the first to hit what’s called exascale-level processing. The mammoth machine, called Aurora, will live at Argonne National Laboratory, and will be able to accomplish tasks like simulating complex systems, running artificial intelligence, and conducting materials-science research.
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How this AI is starting a music playlist revolution
Machine-learning algorithms made possible by a combination of deep learning and artificial intelligence has dominated 2017. The dramatic rises in consumer expectation levels are also forcing businesses to simplify and personalize everything in a bid to remain relevant to their tech savvy customers.
We now have access to unlimited music to accompany us on our travels thanks to all-you-can-eat packages offered by the likes of Apple Music and Spotify. However, discovery algorithms on these digital services could never replace the art of manually creating your playlist, or could they?
We now have access to unlimited music to accompany us on our travels thanks to all-you-can-eat packages offered by the likes of Apple Music and Spotify. However, discovery algorithms on these digital services could never replace the art of manually creating your playlist, or could they?
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Categorization of Artificial Intelligence
Categories of AI
Artificial intelligence:
can be divided into two different categories: weak and strong. Weak artificial intelligence embodies a system designed to carry out one particular job. Weak AI systems include video games such as the chess example from above and personal assistants such as Amazon's Alexa and Apple's Siri. You ask the assistant a question, it answers it for you.
Strong artificial intelligence systems are systems that carry on the tasks considered to be human-like. These tend to be more complex and complicated systems. They are programmed to handle situations in which they may be required to problem solve without having a person intervene. These kinds of systems can be found in applications like self-driving cars or in hospital operating rooms.
Artificial intelligence:
can be divided into two different categories: weak and strong. Weak artificial intelligence embodies a system designed to carry out one particular job. Weak AI systems include video games such as the chess example from above and personal assistants such as Amazon's Alexa and Apple's Siri. You ask the assistant a question, it answers it for you.
Strong artificial intelligence systems are systems that carry on the tasks considered to be human-like. These tend to be more complex and complicated systems. They are programmed to handle situations in which they may be required to problem solve without having a person intervene. These kinds of systems can be found in applications like self-driving cars or in hospital operating rooms.
Authored by: Rupali
Assessing Learning
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Business Intelligence and Data Science
Business Intelligence and Data Science are two of the most recurring terms in the digital era. While both of them involve the use of data, they are totally different from one another. Data Science is the bigger pool containing greater information, BI can be thought of as a part of the bigger picture.
What is Business Intelligence?
Business Intelligence is a process of collecting, integrating, analyzing, and presenting the data. With Business Intelligence, executives and managers can have a better understanding of decision-making. This process is carried out through software services and tools.
Using Business Intelligence, organizations are able to several strategic and operational business decisions. Furthermore, BI tools are used for the analysis and creation of reports. They are also used for producing graphs, dashboards, summaries, and charts to help the business executives to make better decisions.
What is Business Intelligence?
Business Intelligence is a process of collecting, integrating, analyzing, and presenting the data. With Business Intelligence, executives and managers can have a better understanding of decision-making. This process is carried out through software services and tools.
Using Business Intelligence, organizations are able to several strategic and operational business decisions. Furthermore, BI tools are used for the analysis and creation of reports. They are also used for producing graphs, dashboards, summaries, and charts to help the business executives to make better decisions.
Authored by: Rupali
Assessing Learning
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Developing self-awareness and emotional intelligence: Understanding one's own emotions and those of
\Life skills education focuses on equipping individuals with the abilities needed to navigate everyday challenges and lead fulfilling lives. It encompasses a range of psychosocial and interpersonal skills that enable informed decision-making, effective communication, and healthy relationships. This type of education goes beyond traditional academic subjects, emphasizing practical skills applicable to personal, social, and professional contexts.
Key aspects of life skills education include:
Developing self-awareness and emotional intelligence:
Understanding one's own emotions and those of others, managing stress and anxiety, and building healthy relationships are crucial components.
Enhancing critical thinking and problem-solving:
Learning to analyze information, identify problems, and develop effective solutions is essential for navigating complex situations.
Improving communication and interpersonal skills:
Effective communication, both verbal and nonverbal, is vital for building strong relationships and resolving conflicts.
Promoting decision-making and goal-setting:
Learning to make informed decisions, set realistic goals, and develop plans to achieve them are important life skills.
Fostering adaptability and resilience:
Developing the ability to adapt to change, cope with setbacks, and bounce back from challenges is essential for navigating life's ups and downs.
Encouraging responsible citizenship:
Understanding personal responsibility, contributing to the community, and promoting ethical behavior are important aspects of life skills education.
Key aspects of life skills education include:
Developing self-awareness and emotional intelligence:
Understanding one's own emotions and those of others, managing stress and anxiety, and building healthy relationships are crucial components.
Enhancing critical thinking and problem-solving:
Learning to analyze information, identify problems, and develop effective solutions is essential for navigating complex situations.
Improving communication and interpersonal skills:
Effective communication, both verbal and nonverbal, is vital for building strong relationships and resolving conflicts.
Promoting decision-making and goal-setting:
Learning to make informed decisions, set realistic goals, and develop plans to achieve them are important life skills.
Fostering adaptability and resilience:
Developing the ability to adapt to change, cope with setbacks, and bounce back from challenges is essential for navigating life's ups and downs.
Encouraging responsible citizenship:
Understanding personal responsibility, contributing to the community, and promoting ethical behavior are important aspects of life skills education.
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Navigating Context
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THE TOP MYTHS ABOUT ADVANCED AI
common myths
for Advanced
AI:A captivating conversation is taking place about the future of artificial intelligence and what it will/should mean for humanity. There are fascinating controversies where the world’s leading experts disagree, such as AI’s future impact on the job market; if/when human-level AI will be developed; whether this will lead to an intelligence explosion; and whether this is something we should welcome or fear. But there are also many examples of boring pseudo-controversies caused by people misunderstanding and talking past each other.
TIMELINE MYTHS
The first myth regards the timeline: how long will it take until machines greatly supersede human-level intelligence? A common misconception is that we know the answer with great certainty.
One popular myth is that we know we’ll get superhuman AI this century. In fact, history is full of technological over-hyping. Where are those fusion power plants and flying cars we were promised we’d have by now? AI has also been repeatedly over-hyped in the past, even by some of the founders of the field. For example, John McCarthy (who coined the term “artificial intelligence”), Marvin Minsky, Nathaniel Rochester, and Claude Shannon wrote this overly optimistic forecast about what could be accomplished during two months with stone-age computers: “We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College […] An attempt will be made to find how to make machines use language, form abstractions, and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.”
CONTROVERSY MYTHS
Another common misconception is that the only people harboring concerns about AI and advocating AI safety research are Luddites who don’t know much about AI. When Stuart Russell, author of the standard AI textbook, mentioned this during his Puerto Rico talk, the audience laughed loudly. A related misconception is that supporting AI safety research is hugely controversial. In fact, to support a modest investment in AI safety research, people don’t need to be convinced that risks are high, merely non-negligible — just as a modest investment in home insurance is justified by a non-negligible probability of the home burning down.
for Advanced
AI:A captivating conversation is taking place about the future of artificial intelligence and what it will/should mean for humanity. There are fascinating controversies where the world’s leading experts disagree, such as AI’s future impact on the job market; if/when human-level AI will be developed; whether this will lead to an intelligence explosion; and whether this is something we should welcome or fear. But there are also many examples of boring pseudo-controversies caused by people misunderstanding and talking past each other.
TIMELINE MYTHS
The first myth regards the timeline: how long will it take until machines greatly supersede human-level intelligence? A common misconception is that we know the answer with great certainty.
One popular myth is that we know we’ll get superhuman AI this century. In fact, history is full of technological over-hyping. Where are those fusion power plants and flying cars we were promised we’d have by now? AI has also been repeatedly over-hyped in the past, even by some of the founders of the field. For example, John McCarthy (who coined the term “artificial intelligence”), Marvin Minsky, Nathaniel Rochester, and Claude Shannon wrote this overly optimistic forecast about what could be accomplished during two months with stone-age computers: “We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College […] An attempt will be made to find how to make machines use language, form abstractions, and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.”
CONTROVERSY MYTHS
Another common misconception is that the only people harboring concerns about AI and advocating AI safety research are Luddites who don’t know much about AI. When Stuart Russell, author of the standard AI textbook, mentioned this during his Puerto Rico talk, the audience laughed loudly. A related misconception is that supporting AI safety research is hugely controversial. In fact, to support a modest investment in AI safety research, people don’t need to be convinced that risks are high, merely non-negligible — just as a modest investment in home insurance is justified by a non-negligible probability of the home burning down.
Authored by: Rupali
Assessing Learning
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GA Art
Summary
Deep learning is the new state-of-the-art for artificial intelligence. Deep learning architecture is composed of an input layer, hidden layers, and an output layer. The word deep means there are more than two fully connected layers.
There is a vast amount of neural network, where each architecture is designed to perform a given task. For instance, CNN works very well with pictures, RNN provides impressive results with time series and text analysis.
Deep learning is the new state-of-the-art for artificial intelligence. Deep learning architecture is composed of an input layer, hidden layers, and an output layer. The word deep means there are more than two fully connected layers.
There is a vast amount of neural network, where each architecture is designed to perform a given task. For instance, CNN works very well with pictures, RNN provides impressive results with time series and text analysis.
Authored by: Jagruti Joshi
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What is natural language processing?
Natural language processing (NLP) refers to the branch of computer science—and more specifically, the branch of artificial intelligence or AI—concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.
NLP combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment. https://byjus.com/biology/flower/
NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time. There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processehttps://byjus.com/biology/flower/
NLP combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment. https://byjus.com/biology/flower/
NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time. There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processehttps://byjus.com/biology/flower/
Authored by: Pranjali
Disciplinary Content
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Industrial Revolution 4.0
What better way to start this new century than to go over the pros and cons of the 4th Industrial Revolution. The 4th industrial revolution is a term coined by Professor Klaus Schwab. He is the founder and Executive chairman of the World Economic Forum, so he has some good credentials. He described the 4th industrial revolution as a “current and developing environment in which disruptive technologies and trends such as the Internet of Things, robotics, virtual reality and Artificial Intelligence are changing the way people live and work”. So this is the era of AI and machine learning, genome editing, 3D printing, Internet of Things, augmented reality, autonomous vehicles, and much more. And we’re not talking about the future here. These things are currently affecting our personal and work life and they are ever evolving.
Authored by: Divya Sawant
Assessing Learning
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Data availability
Just over 3 billion people are online with an estimated 17 billion connected devices or sensors. [9] That generates a large amount of data which, combined with decreasing costs of data storage, is easily available for use. Machine learning can use this as training data for learning algorithms, developing new rules to perform increasingly complex tasks.
Just over 3 billion people are online with an estimated 17 billion connected devices or sensors. That generates a large amount of data which, combined with decreasing costs of data storage, is easily available for use. Machine learning can use this as training data for learning algorithms, developing new rules to perform increasingly complex tasks.
Just over 3 billion people are online with an estimated 17 billion connected devices or sensors. [9] That generates a large amount of data which, combined with decreasing costs of data storage, is easily available for use. Machine learning can use this as training data for learning algorithms, developing new rules to perform increasingly complex tasks.
Just over 3 billion people are online with an estimated 17 billion connected devices or sensors. That generates a large amount of data which, combined with decreasing costs of data storage, is easily available for use. Machine learning can use this as training data for learning algorithms, developing new rules to perform increasingly complex tasks.
Just over 3 billion people are online with an estimated 17 billion connected devices or sensors. That generates a large amount of data which, combined with decreasing costs of data storage, is easily available for use. Machine learning can use this as training data for learning algorithms, developing new rules to perform increasingly complex tasks.
Just over 3 billion people are online with an estimated 17 billion connected devices or sensors. [9] That generates a large amount of data which, combined with decreasing costs of data storage, is easily available for use. Machine learning can use this as training data for learning algorithms, developing new rules to perform increasingly complex tasks.
Just over 3 billion people are online with an estimated 17 billion connected devices or sensors. That generates a large amount of data which, combined with decreasing costs of data storage, is easily available for use. Machine learning can use this as training data for learning algorithms, developing new rules to perform increasingly complex tasks.
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Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.
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Assessing Learning
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A career in Artificial Intelligence requires a strong background in programming, systems analysis, and/or fluency in several computer languages. A bachelor's degree in mathematics, data science, statistics, and computer science can qualify you for entry-level positions in the Artificial Intelligence field.
Posted by: Rupali Jagtap
Assessing Learning
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Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.
Artificial intelligence is based on the principle that human intelligence can be defined in a way that a machine can easily mimic it and execute tasks, from the most simple to those that are even more complex. The goals of artificial intelligence include learning, reasoning, and perception.
link: https://www.youtube.com/watch?v=oV74Najm6Nc
Artificial intelligence is based on the principle that human intelligence can be defined in a way that a machine can easily mimic it and execute tasks, from the most simple to those that are even more complex. The goals of artificial intelligence include learning, reasoning, and perception.
link: https://www.youtube.com/watch?v=oV74Najm6Nc
Posted by: Rupali Jagtap
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Artificial intelligence: Artificial intelligence includes computers that play chess and self-driving cars. Each of these machines must weigh the consequences of any action they take, as each action will impact the end result. In chess, the end result is winning the game. For self-driving cars: the computer system must account for all external data and compute it to act in a way that prevents a collision.
Artificial intelligence also has applications in the financial industry, where it is used to detect and flag activity in banking and finance such as unusual debit card usage and large account deposits—all of which help a bank's fraud department.
Artificial intelligence also has applications in the financial industry, where it is used to detect and flag activity in banking and finance such as unusual debit card usage and large account deposits—all of which help a bank's fraud department.
Posted by: Rupali Jagtap
Assessing Learning
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Artificial Intelligence, also known as AI, and Data Science, has become the two most important sought after technologies in today's time. Many a time, people think of it as the same thing, but they are not the same thing in reality. Artificial Intelligence is used in the field of Data Science for its operations
Posted by: Rupali Jagtap
Assessing Learning
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Artificial intelligence (AI) aims to or is required to synthesize goal-orientated processes such as problem-solving, decision-making, environmental adaptation, learning, and communication found in humans and animals.
https://www.w3.org/TR/UNDERSTANDING-WCAG20/visual-audio-contrast-scale.html
artificial intelligence research has been necessarily cross-disciplinary, drawing on areas of expertise such as applied mathematics, symbolic logic, semiotics, electrical engineering, neurophysiology, and social intelligence.
https://www.w3.org/TR/UNDERSTANDING-WCAG20/visual-audio-contrast-scale.html
artificial intelligence research has been necessarily cross-disciplinary, drawing on areas of expertise such as applied mathematics, symbolic logic, semiotics, electrical engineering, neurophysiology, and social intelligence.
Posted by: Rupali Jagtap
Assessing Learning
