Broadly speaking, artificial intelligence (AI) refers to any human-like behavior exhibited by a machine or system. The most basic form of AI is the programming of computers so that they can "simulate" human behavior based on vast amounts of data collected from similar behaviors in the past.
Deep Dive into Artificial Intelligence
Whether it is deep learning, strategic thinking, or other types of AI, the basis for its application is a scenario that requires extremely fast response. With the support of AI technology, machines can operate efficiently and analyze massive amounts of data at once to solve various problems through supervised, unsupervised or reinforcement learning.
Early AI
While early AI enabled computers to play games like checkers with humans, today’s AI is an integral part of everyday life. AI solutions are now used not only for quality control, video analytics, speech-to-text conversion (natural language processing), and autonomous driving, but also for healthcare, manufacturing, financial services, and entertainment.
Types of artificial intelligence
There are two main categories of AI: Function-based AI and Capability-based AI. Function Based
Responsive Machines - This type of AI has no memory and cannot learn from past behavior, such as IBM's "Deep Blue."
Theory of Mind - This type of AI is still in development and aims to gain insight into the human mind. Self-aware AI - Such AIs that can understand and evoke human emotions and possess their own are still hypothetical.
Competency Based Specialized Artificial Intelligence (ANI) – Systems that focus on performing narrowly programmed tasks. This type of AI is a combination of responsive machines and limited memory, and most AI applications today fall into this category.
Artificial General Intelligence (AGI) - This type of AI has the human-like ability to train, learn, understand, and execute.
Super Artificial Intelligence (ASI) - This type of AI has superior data processing, memory, and decision-making abilities to perform tasks better than humans. There are currently no application examples. The relationship between Artificial Intelligence, Machine Learning and Deep Learning
Artificial Intelligence
Artificial intelligence is a branch of computer science that aims to simulate human intelligence with machines. AI systems are based on algorithms, using techniques such as machine learning and deep learning to display "intelligent" behavior.
Machine Learning
A machine is “learning” when software on a computer is able to successfully predict and respond to an emerging scenario based on past results. Machine learning refers to a learning process for computers, in which the computer can form pattern recognition or continuously learn and make predictions based on data, and finally make corresponding adjustments without special programming. Machine learning is a form of artificial intelligence that effectively automates the analytical modeling process, enabling computers to independently adapt to new scenarios. Four steps of machine learning modeling: Select and prepare the training dataset needed to solve the problem. These data can be marked or not. Select the algorithm to run on the training data. For labeled data, you can run regression algorithms, decision trees, or instance-based algorithms. For unlabeled data, clustering algorithms, association algorithms, or neural networks can be run. 3) Train the algorithm and build the model. 4) Use and improve the model. There are three approaches to machine learning: "supervised" learning uses labeled data and requires less training. "Unsupervised" learning can classify unlabeled data by identifying patterns and relationships. "Semi-supervised" learning is trained on a small set of labeled datasets, and then tackles classification tasks on large unlabeled datasets.
Deep Learning
Deep learning is a branch of machine learning that significantly outperforms some traditional machine learning methods. Inspired by scientific research that has led to new understandings of human brain behavior, deep learning uses a combination of multiple layers of artificial neural networks, data-intensive training, and computationally-intensive training. This approach is so effective that it even surpasses human capabilities in many fields such as image recognition, speech recognition, and natural language processing. Deep learning models can handle massive amounts of data, often in unsupervised or semi-supervised form.
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