Deep learning is a way to teach concepts in a deeper and more meaningful way. This method is increasingly popular, especially in STEM areas. This can also apply to K-12 education. This article will cover some of its characteristics. This will enable educators to see how deep learning can be beneficial for students and their future jobs.
Characteristics that characterize deep learning in education
Deep learning, a type teaching method, promotes higher-level thinking. It requires students to critically analyze and link new ideas with principles and concepts they already understand. It also involves problem-solving in unfamiliar contexts. It seeks to instill a deep understanding that students can apply for their entire lives. Deep learners are self-directed, collaborative, and have high skills in meta-cognition.
Deep learning, in its simplest form uses multiple processing levels. It is able to create highly advanced, data-driven models that can improve over time. It is also capable to learn from large quantities of data. Deep learning, for example, can detect fraudulent transactions in a video clip. It can also analyze data collected from sensors and webcams. This technology is also useful for government programs, such as reducing fraudulent transactions, speeding up legal processes, and implementing more efficient policies.
Deep learning is a subset of machine learning. It employs multiple layers of neural networks in order to recognize complex patterns and learn from them. Deep learning systems are able to identify objects and even understand human speech. They analyze large amounts of data, then apply the results to new situations.
Characteristics of deep Learning in STEM Fields
Deep learning can be used to analyze large amounts of data. It is frequently used in the fields molecular and cell biology. Microscopical observation of cultured cells in these fields is vital. Different cells exhibit distinct morphological features and distinctive gene expression patterns. Humans are unable to distinguish differentiated cells visually, so researchers have been using deep learning to improve cell biology research.
Deep learning is also useful in the field of drug discovery. It can assist in the classification of drugs based their molecular attributes. For example, a deep algorithm called Atomwise can help identify drugs based on specific criteria. Researchers can also study the 3-D structures of molecules, such as proteins and small molecules.
Deep learning is also beneficial in biomedical information analysis. In this case, it can decrease the labor-intensive process involved in feature extraction. This could help alleviate the enormous challenges that biomedical big data presents. Deep learning is also used to recognize speech or natural language.
Deep learning characteristics in K-12
Deep learning encourages students to develop high-level critical thinking skills. It challenges students with complex problems and teaches them how to analyze data and construct well-thought out points of view. It encourages students to be curious and develop critical thinking skills. It can be used in any level of learning and across all subject fields.
In K-12 education, depth learning can have a huge impact on student achievement. Deep learning can offer a powerful set problem-solving skills that will enable children to answer complicated questions about the world. It can also help educators engage students with STEM subjects. Deep learning networks have been reported to increase self-efficacy, collaboration skills, as well as motivation. These schools also scored higher on state-standardized exams.
While deep learning is not new to the field of education, it is still largely in its infancy. Teachers feel uncomfortable helping their colleagues learn. They fear losing their own content. In addition, there is a widespread lack of willingness among teachers to mentor other teachers in learning.
FAQ
Who created AI?
Alan Turing
Turing was first born in 1912. His father was a priest and his mother was an RN. After being rejected by Cambridge University, he was a brilliant student of mathematics. However, he became depressed. He took up chess and won several tournaments. He worked as a codebreaker in Britain's Bletchley Park, where he cracked German codes.
He died in 1954.
John McCarthy
McCarthy was born 1928. McCarthy studied math at Princeton University before joining MIT. There, he created the LISP programming languages. By 1957 he had created the foundations of modern AI.
He died in 2011.
Is AI good or bad?
AI is both positive and negative. The positive side is that AI makes it possible to complete tasks faster than ever. No longer do we need to spend hours programming programs to perform tasks such word processing and spreadsheets. Instead, we ask our computers for these functions.
The negative aspect of AI is that it could replace human beings. Many believe that robots will eventually become smarter than their creators. This means that they may start taking over jobs.
How does AI work?
Understanding the basics of computing is essential to understand how AI works.
Computers save information in memory. Computers interpret coded programs to process information. The code tells the computer what it should do next.
An algorithm is a set or instructions that tells the computer how to accomplish a task. These algorithms are usually written in code.
An algorithm could be described as a recipe. A recipe can include ingredients and steps. Each step may be a different instruction. A step might be "add water to a pot" or "heat the pan until boiling."
How do you think AI will affect your job?
AI will take out certain jobs. This includes drivers, taxi drivers as well as cashiers and workers in fast food restaurants.
AI will create new jobs. This includes business analysts, project managers as well product designers and marketing specialists.
AI will simplify current jobs. This includes accountants, lawyers as well doctors, nurses, teachers, and engineers.
AI will make existing jobs more efficient. This includes salespeople, customer support agents, and call center agents.
What uses is AI today?
Artificial intelligence (AI), a general term, refers to machine learning, natural languages processing, robots, neural networks and expert systems. It is also known as smart devices.
Alan Turing created the first computer program in 1950. He was curious about whether computers could think. He presented a test of artificial intelligence in his paper "Computing Machinery and Intelligence." The test asks whether a computer program is capable of having a conversation between a human and a computer.
John McCarthy, in 1956, introduced artificial intelligence. In his article "Artificial Intelligence", he coined the expression "artificial Intelligence".
Today we have many different types of AI-based technologies. Some are very simple and easy to use. Others are more complex. They range from voice recognition software to self-driving cars.
There are two main types of AI: rule-based AI and statistical AI. Rule-based uses logic in order to make decisions. A bank account balance could be calculated by rules such as: If the amount is $10 or greater, withdraw $5 and if it is less, deposit $1. Statistics are used for making decisions. To predict what might happen next, a weather forecast might examine historical data.
What is the most recent AI invention?
Deep Learning is the latest AI invention. Deep learning is an artificial intelligent technique that uses neural networking (a type if machine learning) to perform tasks like speech recognition, image recognition and translation as well as natural language processing. Google was the first to develop it.
The most recent example of deep learning was when Google used it to create a computer program capable of writing its own code. This was done using a neural network called "Google Brain," which was trained on a massive amount of data from YouTube videos.
This enabled it to learn how programs could be written for itself.
IBM announced in 2015 that they had developed a computer program capable creating music. Also, neural networks can be used to create music. These are known as NNFM, or "neural music networks".
Statistics
- By using BrainBox AI, commercial buildings can reduce total energy costs by 25% and improves occupant comfort by 60%. (analyticsinsight.net)
- In 2019, AI adoption among large companies increased by 47% compared to 2018, according to the latest Artificial IntelligenceIndex report. (marsner.com)
- The company's AI team trained an image recognition model to 85 percent accuracy using billions of public Instagram photos tagged with hashtags. (builtin.com)
- A 2021 Pew Research survey revealed that 37 percent of respondents who are more concerned than excited about AI had concerns including job loss, privacy, and AI's potential to “surpass human skills.” (builtin.com)
- More than 70 percent of users claim they book trips on their phones, review travel tips, and research local landmarks and restaurants. (builtin.com)
External Links
How To
How to make Siri talk while charging
Siri can do many different things, but Siri cannot speak back. This is because there is no microphone built into your iPhone. Bluetooth or another method is required to make Siri respond to you.
Here's a way to make Siri speak during charging.
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Select "Speak When locked" under "When using Assistive Touch."
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To activate Siri, double press the home key twice.
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Ask Siri to Speak.
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Say, "Hey Siri."
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Speak "OK"
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Say, "Tell me something interesting."
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Say "I'm bored," "Play some music," "Call my friend," "Remind me about, ""Take a picture," "Set a timer," "Check out," and so on.
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Speak "Done"
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If you'd like to thank her, please say "Thanks."
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Remove the battery cover (if you're using an iPhone X/XS).
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Reinsert the battery.
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Put the iPhone back together.
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Connect your iPhone to iTunes
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Sync your iPhone.
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Switch on the toggle switch for "Use Toggle".