We found 26 results that contain "machine"
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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)
ASSESSING LEARNING
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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.
ASSESSING LEARNING
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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?
ASSESSING LEARNING
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Language Recognizer
In the global Internet environment processing of information in multiple languages has a great importance. Intellexer Language Recognizer identifies the language and character encoding of incoming documents. It supports more than 30 languages, covering major European and Asian languages. Intellexer Language Recognizer can be successfully used: - as a pre-filtering step to improve the quality of input text data (because of most natural processing algorithms deal with monolingual texts and inclusion of other languages can decrease the performance of document management systems); - in mining bilingual texts for machine translation from online resources; - for retrieval, grouping and understanding relevant information (user’s texts, emails and etc.) in multilingual environment.Language Recognizer
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Overlapping
Artificial intelligence is further defined as “narrow AI” or “general AI”. Narrow AI, which we interact with today, is designed to perform specific tasks within a domain (e.g. language translation). General AI is hypothetical and not domain specific, but can learn and perform tasks anywhere. This is outside the scope of this paper. This paper focuses on advances in narrow AI, particularly on the development of new algorithms and models in a field of computer science referred to as machine learning.
DISCIPLINARY CONTENT
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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.
NAVIGATING CONTEXT
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What is physiotherapy?
Physiotherapy is an important part of treatment for most people with arthritis. It’s run by physiotherapists, who are part of a team of healthcare professionals who help you to resume or maintain an active and independent life both at home and work. They’re experts in assessing movement and can show you how to protect your joints. Your physiotherapist will:
offer advice and reassurance
help you to feel confident about managing your condition
address any concerns or uncertainties
set appropriate goals to keep you as active as possible.
Specialist physiotherapists are trained in diagnosing and treating joint and muscle problems, and your GP may refer you to a specialist physiotherapist rather than to a rheumatologist or orthopaedic surgeon.
Your physiotherapist will start by asking you questions and examining the joint(s) you’re finding painful. This assessment will let them tailor the treatment to your needs. Treatment may include:
a programme of specific exercises
general advice on increasing your activity level and avoiding exercise-related injuries
pain-relief treatments such as heat or ice packs, TENS (transcutaneous electrical nerve stimulation) machines, massage, manipulation, acupuncture or taping
providing walking aids or splints to help you stay mobile and independent.
offer advice and reassurance
help you to feel confident about managing your condition
address any concerns or uncertainties
set appropriate goals to keep you as active as possible.
Specialist physiotherapists are trained in diagnosing and treating joint and muscle problems, and your GP may refer you to a specialist physiotherapist rather than to a rheumatologist or orthopaedic surgeon.
Your physiotherapist will start by asking you questions and examining the joint(s) you’re finding painful. This assessment will let them tailor the treatment to your needs. Treatment may include:
a programme of specific exercises
general advice on increasing your activity level and avoiding exercise-related injuries
pain-relief treatments such as heat or ice packs, TENS (transcutaneous electrical nerve stimulation) machines, massage, manipulation, acupuncture or taping
providing walking aids or splints to help you stay mobile and independent.
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Are there any advances in this direction that you think hold promise?
The basic idea of intelligence:An explosion is that once machines reach a certain level of intelligence, they’ll be able to work on AI just like we do and improve their own capabilities — redesign their own hardware and so on — and their intelligence will zoom off the charts. There’s an area emerging called “cyber-physical systems” about systems that couple computers to the real world. With a cyber-physical system, you’ve got a bunch of bits representing an air traffic control program, and then you’ve got some real airplanes, and what you care about is that no airplanes collide. You’re trying to prove a theorem about the combination of the bits and the physical world. What you would do is write a very conservative mathematical description of the physical world — airplanes can accelerate within such-and-such envelope — and your theorems would still be true in the real world as long as the real world is somewhere inside the envelope of behaviors.
Yet you’ve pointed out that it might not be mathematically possible to formally verify AI systems.
There’s a general problem of “undecidability” in a lot of questions you can ask about computer programs. Alan Turing showed that no computer program can decide whether any other possible program will eventually terminate and output an answer or get stuck in an infinite loop. So if you start out with one program, but it could rewrite itself to be any other program, then you have a problem, because you can’t prove that all possible other programs would satisfy some property.
Yet you’ve pointed out that it might not be mathematically possible to formally verify AI systems.
There’s a general problem of “undecidability” in a lot of questions you can ask about computer programs. Alan Turing showed that no computer program can decide whether any other possible program will eventually terminate and output an answer or get stuck in an infinite loop. So if you start out with one program, but it could rewrite itself to be any other program, then you have a problem, because you can’t prove that all possible other programs would satisfy some property.
Authored by: Rupali
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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Human computer interaction (HCI)
Introduction
Humans interact with computers in any way the interface between humans and computers is crucial to facilitate this interaction. Desktop applications, internet browsers, handheld computers, ERP, and computer kiosks make use of the prevalent graphical user interfaces (GUI) of today.
Voice user interfaces (VUI) are used for speech recognition and synthesizing systems, and the emerging multi-modal and Graphical user interfaces (GUI) allow humans to engage with embodied character agents in a way that cannot be achieved with other interface paradigms. The growth in the human-computer interaction field has been in the quality of interaction, and indifferent branching in its history. Instead of designing regular interfaces, the different research branches have had a different focus on the concepts of multimodality rather than unimodality, intelligent adaptive interfaces rather than command/action based ones, and finally active rather than passive interfaces.
An important facet of HCI is user satisfaction (or simply End-User Computing Satisfaction). "Because human-computer interaction studies a human and a machine in communication, it draws from supporting knowledge on both the machine and the human side. On the machine side, techniques in computer graphics, operating systems, programming languages, and development environments are relevant.
Humans interact with computers in any way the interface between humans and computers is crucial to facilitate this interaction. Desktop applications, internet browsers, handheld computers, ERP, and computer kiosks make use of the prevalent graphical user interfaces (GUI) of today.
Voice user interfaces (VUI) are used for speech recognition and synthesizing systems, and the emerging multi-modal and Graphical user interfaces (GUI) allow humans to engage with embodied character agents in a way that cannot be achieved with other interface paradigms. The growth in the human-computer interaction field has been in the quality of interaction, and indifferent branching in its history. Instead of designing regular interfaces, the different research branches have had a different focus on the concepts of multimodality rather than unimodality, intelligent adaptive interfaces rather than command/action based ones, and finally active rather than passive interfaces.
An important facet of HCI is user satisfaction (or simply End-User Computing Satisfaction). "Because human-computer interaction studies a human and a machine in communication, it draws from supporting knowledge on both the machine and the human side. On the machine side, techniques in computer graphics, operating systems, programming languages, and development environments are relevant.
Authored by: Rupali
Assessing Learning
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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
Posted on: #iteachmsu
3 Kinds of Exercise That Boost Heart Health
Aerobic Exercise
What it does: Aerobic exercise improves circulation, which results in lowered blood pressure and heart rate, Stewart says. In addition, it increases your overall aerobic fitness, as measured by a treadmill test, for example, and it helps your cardiac output (how well your heart pumps). Aerobic exercise also reduces the risk of type 2 diabetes and, if you already live with diabetes, helps you control your blood glucose.
How much: Ideally, at least 30 minutes a day, at least five days a week.
Examples: Brisk walking, running, swimming, cycling, playing tennis, and jumping rope. Heart-pumping aerobic exercise is the kind that doctors have in mind when they recommend at least 150 minutes per week of moderate activity.
Resistance Training (Strength Work)
What it does: Resistance training has a more specific effect on body composition, Stewart says. For people who are carrying a lot of body fat (including a big belly, which is a risk factor for heart disease), it can help reduce fat and create leaner muscle mass. Research shows that a combination of aerobic exercise and resistance work may help raise HDL (good) cholesterol and lower LDL (bad) cholesterol.
How much: At least two nonconsecutive days per week of resistance training is a good rule of thumb, according to the American College of Sports Medicine.
Examples: Working out with free weights (such as hand weights, dumbbells, or barbells), on weight machines, with resistance bands or through body-resistance exercises, such as push-ups, squats, and chin-ups.
Stretching, Flexibility, and Balance
What they do: Flexibility workouts, such as stretching, don’t directly contribute to heart health. What they do is benefit musculoskeletal health, which enables you to stay flexible and free from joint pain, cramping, and other muscular issues. That flexibility is a critical part of being able to maintain aerobic exercise and resistance training, says Stewart.
“If you have a good musculoskeletal foundation, that enables you to do the exercises that help your heart,” he says. As a bonus, flexibility and balance exercises help maintain stability and prevent falls, which can cause injuries that limit other kinds of exercise.
How much: Every day and before and after another exercise.
Examples: Your doctor can recommend basic stretches you can do at home, or you can find DVDs or YouTube videos to follow (though check with your doctor if you’re concerned about the intensity of the exercise). Tai chi and yoga also improve these skills, and classes are available in many communities.Testing
What it does: Aerobic exercise improves circulation, which results in lowered blood pressure and heart rate, Stewart says. In addition, it increases your overall aerobic fitness, as measured by a treadmill test, for example, and it helps your cardiac output (how well your heart pumps). Aerobic exercise also reduces the risk of type 2 diabetes and, if you already live with diabetes, helps you control your blood glucose.
How much: Ideally, at least 30 minutes a day, at least five days a week.
Examples: Brisk walking, running, swimming, cycling, playing tennis, and jumping rope. Heart-pumping aerobic exercise is the kind that doctors have in mind when they recommend at least 150 minutes per week of moderate activity.
Resistance Training (Strength Work)
What it does: Resistance training has a more specific effect on body composition, Stewart says. For people who are carrying a lot of body fat (including a big belly, which is a risk factor for heart disease), it can help reduce fat and create leaner muscle mass. Research shows that a combination of aerobic exercise and resistance work may help raise HDL (good) cholesterol and lower LDL (bad) cholesterol.
How much: At least two nonconsecutive days per week of resistance training is a good rule of thumb, according to the American College of Sports Medicine.
Examples: Working out with free weights (such as hand weights, dumbbells, or barbells), on weight machines, with resistance bands or through body-resistance exercises, such as push-ups, squats, and chin-ups.
Stretching, Flexibility, and Balance
What they do: Flexibility workouts, such as stretching, don’t directly contribute to heart health. What they do is benefit musculoskeletal health, which enables you to stay flexible and free from joint pain, cramping, and other muscular issues. That flexibility is a critical part of being able to maintain aerobic exercise and resistance training, says Stewart.
“If you have a good musculoskeletal foundation, that enables you to do the exercises that help your heart,” he says. As a bonus, flexibility and balance exercises help maintain stability and prevent falls, which can cause injuries that limit other kinds of exercise.
How much: Every day and before and after another exercise.
Examples: Your doctor can recommend basic stretches you can do at home, or you can find DVDs or YouTube videos to follow (though check with your doctor if you’re concerned about the intensity of the exercise). Tai chi and yoga also improve these skills, and classes are available in many communities.Testing
Authored by: Viju
Navigating Context
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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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Machine-generated data is information automatically generated by a computer process, application, or other mechanism without the active intervention of a human. While the term dates back over fifty years,[1] there is some current indecision as to the scope of the term. Monash Research's Curt Monash defines it as "data that was produced entirely by machines OR data that is more about observing humans than recording their choices."[2] Meanwhile, Daniel Abadi, CS Professor at Yale, proposes a narrower definition, "Machine-generated data is data that is generated as a result of a decision of an independent computational agent or a measurement of an event that is not caused by a human action."[3] Regardless of definition differences, both exclude data manually entered by a person.[4] Machine-generated data crosses all industry sectors. Often and increasingly, humans are unaware their actions are generating the data.[
Posted by: Chathuri Super admin..
Assessing Learning
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Reinforcement Learning
Reinforcement learning is a subfield of machine learning in which systems are trained by receiving virtual "rewards" or "punishments," essentially learning by trial and error. Google's DeepMind has used reinforcement learning to beat a human champion in the Go games. Reinforcement learning is also used in video games to improve the gaming experience by providing smarter bot.
One of the most famous algorithms are:
Q-learning
Deep Q network
State-Action-Reward-State-Action (SARSA)
Deep Deterministic Policy Gradient (DDPG)
Reinforcement learning is a subfield of machine learning in which systems are trained by receiving virtual "rewards" or "punishments," essentially learning by trial and error. Google's DeepMind has used reinforcement learning to beat a human champion in the Go games. Reinforcement learning is also used in video games to improve the gaming experience by providing smarter bot.
One of the most famous algorithms are:
Q-learning
Deep Q network
State-Action-Reward-State-Action (SARSA)
Deep Deterministic Policy Gradient (DDPG)
Posted by: Chathuri Super admin..
Navigating Context
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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
Posted on: #iteachmsu
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.
Posted by: Rupali Jagtap
Assessing Learning
Posted on: #iteachmsu
Second post
: 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 (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.
Posted by: Roni Smith
Navigating Context
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Management Information Systems is of paramount importance to reach effective decisions in an organization. The literature presented in this study explained the significant role of MIS in the decision-making process enhancement in an organization. MIS is deemed to be an integrated user-machine system that provides information to support operations, management, and decision-making functions at various levels of an organization. Organizations are aware that MIS is a special-purpose system useful for management objectives. The study has highlighted that MIS should be accessible in supplying appropriate and high-quality information from its generation to its users. To MIS, to be vital and effective, a carefully conceived, designed, and executed database should exist to communicate the adaptive decisions.
Posted by: Rupali Jagtap
Assessing Learning
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Data Science is a process of extracting, manipulating, visualizing, maintaining data as well as generating predictions.
A Data Scientist is supposed to have knowledge of various data operations as well as machine learning algorithms. Using Data Science, industries are able to extract insights and forecast their performance.
A Data Scientist is supposed to have knowledge of various data operations as well as machine learning algorithms. Using Data Science, industries are able to extract insights and forecast their performance.
Posted by: Chathuri Super admin..
Assessing Learning
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The robot misconception is related to the myth that machines can’t control humans.
Intelligence enables control: humans control tigers not because we are stronger, but because we are smarter. This means that if we cede our position as smartest on our planet, it’s possible that we might also cede control.
Intelligence enables control: humans control tigers not because we are stronger, but because we are smarter. This means that if we cede our position as smartest on our planet, it’s possible that we might also cede control.
Posted by: Chathuri Super admin..
Assessing Learning
Host: MSU Libraries
Sew What? Getting Started with a Sewing Machine
Discover the joy of sewing! This beginner-friendly session will guide you through the essentials of how a sewing machine works, using a mechanical sewing machine. Learn valuable skills while creating a project to take home. Perfect for anyone eager to learn the basics, this workshop is designed to boost your confidence and spark creativity!
Navigating Context