There are five types of Artificial Intelligence:
1. Reactive machines: These are the simplest AI systems. They do not have any memory and they only react to the current situation. For example, a chess program that can only make moves based on the current state of the board is a reactive machine.
2. Limited memory: These AI systems have some memory and they can use it to learn from past experiences. For example, a self-driving car that remembers the position of other vehicles in order to make decisions is an AI system with limited memory.
3. Theory of mind: This type of AI system is able to understand human emotions and intentions. For example, a chatbot that can carry on a conversation with a human is an example of theory of mind AI.
4. Self-aware: This is the most advanced type of AI system which is aware of its own existence and can intro spectively reflect on its own thoughts and feelings. Currently, there are no fully self-aware AI systems in existence but there are some prototype systems that exhibit some degree of self-awareness..
Machine learning (ML) Machine learning is a sub-component of Artificial Intelligence
Machine learning is a sub-component of Artificial Intelligence that deals with the construction and study of algorithms that can learn from and make predictions on data. ML algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so.
ML algorithms are used in a variety of applications, such as email filtering, detection of network intruders, and computer vision.
Deep learning. This branch of AI tries to closely mimic the human mind
Deep learning is a branch of AI that tries to closely mimic the human mind. It is based on the idea that the best way to learn something is to do it yourself. This means that deep learning systems learn by example, just like humans do.
Deep learning systems are able to learn from data in ways that previous generations of AI could not. They can identify patterns and correlations that humans might miss, and they can do it at scale. This makes them well-suited for tasks like image recognition, where they can outperform humans by a large margin.
Deep learning is still in its early days, and there are many open questions about how it works and how it can be used effectively. However, the potential applications of deep learning are vast, and it is already having a major impact on industries such as healthcare, finance, and manufacturing.
Natural language processing (NLP)
NLP research deals with problems such as automatic translation, text understanding, and text generation. In order to solve these problems, NLP methods are used to represent text data in a form that can be processed by machine learning algorithms. NLP techniques are also used to develop systems that can automatically generate responses to user queries.
Applications of NLP include voice recognition, chatbots, and machine translation. In voice recognition, NLP is used to convert speech into text. Thistext can then be processed by other applications such as a search engine or a decision-making system. Chatbots use NLP to generate responses to user queries in natural language. Machine translation uses NLP methods to translate texts from one language into another.
Computer vision
Artificial intelligence has been used in a variety of fields including medical diagnosis, stock trading, robot control, law and many more. In each of these fields, AI is used to make predictions or recommendations based on data.
One area where AI is becoming increasingly popular is computer vision. Computer vision is the process of using computers to interpret images. This can be done for a variety of purposes such as object recognition, facial recognition, and motion detection.
There are many different algorithms that can be used for computer vision tasks. Some of the most popular include convolutional neural networks (CNNs), support vector machines (SVMs), and decision trees. CNNs are particularly well suited for image classification tasks while SVMs are better at detecting patterns in data. Decision trees can be used for both classification and prediction tasks.
Computer vision is a rapidly growing field with many potential applications. It has already been used for things like automated driving and security systems. As the technology continues to improve, it will likely find its way into even more areas of our lives.
“Machine learning is a type of artificial intelligence that allows computer systems to improve automatically through experience.” -Anand