Artificial Intelligence

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Artificial Intelligence

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Introduction HHHHHHHHH ionDEIntroduction G Artificial intelligence AI is the simulation of human intelligence processes by machines especially computer systems. ... When presented with an unfamiliar task a strong AI system is able to find a solution without human intervention. ● Speech recognition ● Learning ● Planning Introduction

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Types of artificial intelligence Type 1: Reactive machines Type 2: Limited memory. Type 3: Theory of mind. Type 4: Self-awareness

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Examples of AI technology 1 . Automation: 2 . Machine learning: 3 . Machine vision: 4 . Natural language processing NLP 5 . Robotics: 6 . Self-driving cars:

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Automation: What makes a system or process function automatically. For example robotic process automation RPA can be programmed to perform high-volume repeatable tasks that humans normally performed. RPA is different from IT automation in that it can adapt to changing circumstances.

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Machine learning: The science of getting a computer to act without programming . Deeplearning is a subset of machine learning that in very simple terms can be thought of as the automation of predictive analytics

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Machine vision: The science of allowing computers to see. This technology captures and analyzes visual information using a camera analog-to-digital conversion and digital signal processing. It is often compared to human eyesight but machine vision isnt bound by biology and can be programmed to see through walls for example. It is used in a range of applications from signature identification to medical image analysis. Computer vision which is focused on machine-based image processing is often conflated with machine vision.

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Natural language processing NLP: The processing of human -- and not computer -- language by a computer program. One of the older and best known examples of NLP is spam detection which looks at the subject line and the text of an email and decides if its junk. Current approaches to NLP are based on machine learning. NLP tasks include text translation sentiment analysis and speech recognition.

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Robotics: A field of engineering focused on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. They are used in assembly lines for car production or by NASA to move large objects in space. Researchers are also using machine learning to build robots that can interact in social settings.

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Self-driving cars: These use a combination of computer vision image recognition and deep learning to build automated skill at piloting a vehicle while staying in a given lane and avoiding unexpected obstructions such as pedestrians.

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AI applications AI in business. AI in education. AI in finance AI in law. AI in manufacturing

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Regulation of AI technology Despite these potential risks there are few regulations governing the use AI tools and where laws do exist the typically pertain to AI only indirectly. For example federal Fair Lending regulations require financial institutions to explain credit decisions to potential customers which limit the extent to which lenders can use deep learning algorithms which by their nature are typically opaque. Europes GDPR puts strict limits on how enterprises can use consumer data which impedes the training and functionality of many consumer-facing AI applications. For more visit at-https://technologymoon.com/

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