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Deep learning is a concept in machine learning and artificial intelligence which is inspired by how the human brain works. It helps scientists and machine developers to create systems that consistently analyze logically structured data and drawing conclusions as humans. Deep learning depends on neural network design to accomplish this. Neural networks are created to mimic the structure of the human brain.
The Internet of Things (IoT)
IoT describes the millions and millions of computer devices all across the world that are linked via a network (internet). These devices can collect and share data thanks to the ubiquity of wireless networks. While physical devices are often dumb, a level of digital intelligence can be added by using sensors to create a connection. Doing this enables these devices to communicate in real-time without human help.
Computer vision is one of the most compelling subsets of artificial intelligence that we often interact with even without knowing. It is a concept that draws its inspiration from the complexity of human vision. Computer vision strives to replicate the human vision system to enable machines to process objects (images and videos) like we humans do. However, little advancement has been made in this field and it has only worked in a constrained capacity.
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Natural language processing is a branch of artificial intelligence that focuses on creating machines that can understand human language. The main areas of natural language computing are:
- Machine translation
- Google translate
- Question answering
- Language gaming
Natural language processing uses AI to process real-world data and analyze it in a way that a computer can understand. Artificial intelligence aims to make machines act like humans. Just like we have sensors such as eyes and ears, machines also have programs that can listen to audio and read texts. There are two major phases of natural language processing:
- Data preprocessing: This first phase involves making the data ready for machine analysis. The data is cleaned and converted to a workable form.
- Algorithm development: An algorithm is needed to process the prepared data. The most commonly used natural language algorithms are machine learning-based system algorithms and rule-based systems.
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Cybernetics and brain simulations are the future of artificial intelligence. Although these areas have only been explored in science fiction, today experts are trying to introduce some of these ideas into a scientific reality. Cybernetics is concerned with regulatory systems. It studies the structure, limitations, possibilities, and controls of these systems. In other words, we can define cybernetics as the use of technology to study the controls of any system. The main aim of this study is to have a thorough understanding of the processes and functions of a system.
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Statistical learning theory is a concept in machine learning that tries to provide a theoretical explanation of how algorithms work. This theory can help us understand:
- Approximating functions
- Upper error bound
- Minimum sample size
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The idea of agents is used in artificial intelligence to represent knowledge. To make good decisions, intelligent agents must know their world. Knowledge-based agents consist of:
- Knowledge base: domain-specific content
- Inference mechanism: domain-independent algorithms
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