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Automotive One example of where digital twins are used in the automotive industry is to gather and analyze operational data from a vehicle to assess its status in real time.
Healthcare The medical sector has benefitted from digital twins in
areas such as organ donation, surgery training, and de-risking of procedures. Systems have also modeled the flow of people through hospitals and tracked where infections may exist.
Disaster Management Global climate change has had an impact across the world in recent years, yet digital twins can help to combat this through the informed creation
of smarter infrastructures, emergency response plans, and climate change monitoring.
Smart Cities The digital twin can also be used to help cities
become more economically, environmentally, and socially sustainable. Virtual models can guide planning decisions & offer solutions to the many complex challenges faced by modern cities.
AI and ML AI: Artificial intelligence (AI, also
known as machine intelligence, is a branch of computer science that focuses on building and managing technology that can learn to autonomously make decisions and carryout actions on behalf of a human being.
ML: Machine learning (ML) is a subtopic of artificial intelligence (AI) that focuses on building algorithmic models that can identify patterns and relationships in data. In this context,
the word machine is a synonym fora computer program,
and the word learning describes how ML algorithms will automatically become more accurate as they receive additional data.
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Difference between AI and ML Artificial Intelligence Machine Learning The goal of AI is to simulate natural intelligence to solve complex problems. The goal of ML is to learn from data on certain tasks to maximize the performance of the machine on this task. It leads to developing a system to mimic humans to respond and behave in the circumstances. It involves creating self-learning algorithms. AI leads to intelligence or wisdom. ML leads to knowledge.
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