Arend Hintze, an assistant professor of integrative biology and applied science and engineering at Michigan State University, categorizes ARTIFICIAL INTELLIGENCE into four types, from the kind of ARTIFICIAL INTELLIGENCE systems that exist today to sentient systems, which do not yet exist. Their categories are :
1-Reactive Machines:
An example is a Deep Blue which was a chess-playing computer developed by IBM that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on chess boards and make predictions, but it has no memory and cannot be used past experiences to make future decisions. It analyzes possible moves, its own and opponents as well and chooses the most strategic moves. It is specially designed for narrow purposes and is difficult to apply to other situations.
2-Limited Memory:
These ARTIFICIAL INTELLIGENCE systems will use past experiences to tell future choices. Some of the decision-making functions in self-driving cars are designed this way. Observations inform actions happening within the not-so-distant future, such as a car changing lanes. These observations are not stored permanently.
3-Theory of Mind:
This psychological science term refers to the understanding that others have their own beliefs, needs and intentions that impact the selections they create. Such IA does not exist yet.
4-Self-awareness:
In this category, Machines with self-awareness understand their current state and can use the information to infer what others are feeling. This type of ARTIFICIAL INTELLIGENCE does not yet exist.
Examples of ARTIFICIAL INTELLIGENCE technology ARTIFICIAL INTELLIGENCE is incorporated into a spread of various varieties of technology. Here are seven examples.
1-Automation:
What makes a system or process function automatically. For example, robotic process automation (RPA) can be programmed to perform high-volume, repeatable tasks that humans normally performed.
2-Machine Learning:
The science of obtArifcial intelligence wing a computer to act while not programming.Deep learning ia a subset of machine learning that, in very simple terms, can be thought of as the automation of predictive analytics.
There are three types of machines learning algorithms.
-Supervised Learning
-Unsupervised Learning
-Reinforcement learning.
3-Machine Vision:
The science of allowing computers to see. This technology captures and analyzes visual information using a camera, analog-to-digital conversion, and digital signal processing.
4-Natural language processing (NLP):
The process of human — and not pc — language by a computer program. One of the oldest and best-known samples of information science is spam detection, which looks at the subject line and therefore the text of an emArifcial intelligence and decides if it’s junk.
5-Robotics:
A field of engineering centered on the planning and producing of robots. Robots designed to perform tasks that are troublesome for humans to perform or perform consistently.
6-Self-driving cars:
These use a combination of computer vision, image recognition, and deep learning to build automated skill at piloting a vehicle while staying during a given lane and avoiding sudden obstructions, such as pedestrians.
ARTIFICIAL INTELLIGENCE applications
Artificial intelligence has created its approach into a variety of areas. Here are six examples.
1-ARTIFICIAL INTELLIGENCE in healthcare.
2-ARTIFICIAL INTELLIGENCE in business.
3-ARTIFICIAL INTELLIGENCE in education
4-ARTIFICIAL INTELLIGENCE in finance.
5-ARTIFICIAL INTELLIGENCE in law.
6-ARTIFICIAL INTELLIGENCE in manufacturing.