How do ML and Deep Learning can be related with AI?
What is AI?
Artificial Intelligence is the simulation of human intelligence process by machines, especially computer syetems.The best example for defining AI is Apple’s Siri, Google Now, Amazon’s Alexa, and Microsoft’s Cortana are one of the main examples of AI in everyday life. These digital assistants help users perform various tasks, from checking their schedules and searching for something on the web, to sending commands to another app.
AI has become an essential part of daily life an it helps in day to day tasks in different ways. AI helps in increasing work speed and reduces time and it also helps in many fields like Health and Hospitality, Travel and Transpoartation, Business advancements, Research and Development, Space studies, Agriculture adavancements,Military, Entertainment industary,Robotics, Education advancements etc,. like this AI is used in different fields.
AI can be seen in everyday life like un the form Facial Detection and recognition, maps and navigation, Chatbots, Social Media, E-Payments, retail.
Advantages of Aritificial Intelligence:
Reduction in Human Error
New Innovative Inventions
All time Availability
No biased decisions
AI Personalized assistants
How do ML and Deep Learning can be related with AI
ML and DL are both are integrated types in AI. We can even say ML is AI and it can be used to adapt the human interference to perform better and DL is a subset of ML that can be used in artificial neural networks to mimic the learning process of human brain.
AI and ML can be automate repetitive tasks such as data entry, checking and filtering emails and analysis.One of the biggest advantages of using deep learning approach is the algorithms can identify and can evaluate the data and combine them for faster learning without implementing each and everything manually.
Deploying innovative AI models in different production environments becomes a common problem as AI applications become more ubiquitous in our daily lives. Deployment of both training and inference workloads bring great challenges as we start to support a combinatorial choice of models and environment. Additionally, real world applications bring with a multitude of goals, such as minimizing dependencies, broader model coverage, leveraging the emerging hardware primitives for performance, reducing memory footprint, and scaling to larger environments.
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