AI studies how the human brain thinks, learns, makes decisions, and functions. AI research aims for reasoning, knowledge representation, planning, learning, natural language processing, realization, and the ability to move and manipulate objects.
In short, AI provides state-of-the-art technology for processing complex data that humans cannot process. AI automates redundant jobs, allowing employees to focus on high-value tasks. If you want to learn more about it, you can enroll in Great Learning’s artificial intelligence online course.
Types of Artificial Intelligence
Artificial intelligence can be categorized into different types. Three main types of main categories based on the capabilities of AI:
- Weak AI/ Narrow AI: Narrow AI is a type of AI that can perform specialized tasks with intelligence. The most widely and currently available AI is narrow AI, and narrow AI is only trained for a particular task and cannot function beyond its discipline or limits. Therefore, it is also called a weak AI.
- General AI: AI, in general, is a type of intelligence that can perform all intellectual tasks as efficiently as humans. Currently, no system corresponds to general AI and performs all tasks completely like human beings. Currently, researchers worldwide are focusing on developing machines that use common AI.
- Super AI: Super AI is the intelligence level of a system that allows machines to outperform human intelligence and perform any task with better cognitive traits than humans. This is the result of common AI.
Machine Learning: Introduction
It is proven that Machine Learning is one of the most disruptive technological advances in the last decade. In an increasingly competitive world, ML enables enterprises to accelerate digital transformation and enter the era of automation. Machine Learning comes under Artificial Intelligence.
Machine learning (ML) is a category of algorithms that allows you to predict results more accurately, even if your software application is not explicitly programmed. The basic premise of machine learning is to develop an algorithm that can take input data, predict the output using statistical analysis, and update the output when new data becomes available.
Types of Machine Learning
Currently, there are three main methods used:
- Supervised learning: A small training dataset is given to supervised learning to use for the ML algorithm. This training dataset is a small part of a large dataset that helps provide algorithms with basic ideas for problems, solutions, and data points to handle.
- Unsupervised learning: Unsupervised learning creates a hidden structure because there are no labels to work with. The creation of these hidden structures makes unsupervised learning algorithms versatile. Instead of defined and fixed problems, unsupervised learning algorithms can adapt to the data by dynamically modifying hidden structures.
- Reinforcement learning: Reinforcement learning is directly inspired by how people learn from data in their lives. It has algorithms that use trial and error methods to self-improve and learn from new situations. Beneficial results are encouraged or “enhanced,” and unfavorable results are recommended or “punished.”
Conclusion
Hope this article was helpful for you in learning the basics of AI and Ml. Sign up for Great Learning’s Artificial Intelligence and machine learning courses that can help you learn more about the concepts involved.
