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JAKARTA – Literasi Digital ID – Artificial Intelligence (AI) has become something of concern because it affects human work. But what exactly is AI? In short, it refers to the simulation of human intelligence in machines programmed to think like humans and imitate their actions.
The term can also be applied to any machine that exhibits traits associated with the human mind. Where the process includes learning (acquiring information and rules for using information), reasoning (using rules to reach definite approximate conclusions) and self-correction, reported by search enterprise AI and Investopedia. The ideal characteristic of AI is its ability to rationalize and take the actions that have the best chance of achieving certain goals.
Deep into AI
The first thing people usually think of when they hear the term AI are robots. Because popular movies and novels tell of human-like machines that wreak havoc on Earth. Artificial intelligence is based on the principle that human intelligence can be defined in such a way that machines can easily imitate it and carry out tasks, from the simplest to the more complex. The goals of artificial intelligence include learning, reasoning, and perception.
As technology advances, the previous benchmarks defining artificial intelligence are becoming obsolete. For example, machines that compute basic functions or recognize text through optimal character recognition are no longer considered artificial intelligence, as these functions are now considered inherent computer functions. AI is constantly evolving to benefit many different industries. Machines are transferred using a cross-disciplinary approach based in mathematics, computer science, linguistics, psychology, and more.
Category AI
AI has 2 categories, namely weak or strong. Weak AI also known as narrow AI is an AI system that is designed and trained for a specific task. Virtual personal assistants, like Apple’s Siri, are a weak form of AI. While strong AI (strong AI), also known as general artificial intelligence is an AI system with general human cognitive abilities. When presented with a specific task, powerful AI systems can find solutions without human intervention.
Types of AI
Arend Hintze, assistant professor of integrative biology and computer science and engineering at Michigan State University, categorizes AI into 4 types, from the types of AI systems that exist today to living systems, which do not yet exist. The categories are as follows:
Type 1: Reactive engine. Take Deep Blue, for example, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but he has no memory and cannot use past experience to tell his next move. It analyzes the possible moves of the opponent and itself and chooses the most strategic moves. Deep Blue and GoogleGOGO are designed for narrow purposes and cannot easily be applied to other situations.
Type 2: Limited memory. These AI systems can use past experiences to inform future decisions. Several decision-making functions in self-driving cars are designed in this way. Observations inform actions that take place in the not-too-distant future, such as a car changing path. These observations are not stored permanently.
Type 3: Theory of mind. This psychological term refers to the notion that other people have their own beliefs, desires and intentions that influence the decisions they make. This type of AI does not exist until now.
Type 4: Self-awareness. In this category, AI systems have a sense of self, have awareness. Machines with self-awareness understand their current state and can use the information to infer how the other person is feeling. This type of AI does not exist until now.
Examples of AI Applications
Automation: A system or process that functions automatically. For example, robotic process automation (RPA) can be programmed to perform high-volume, repetitive tasks that humans normally perform. RPA differs from IT automation in that it can adapt to changing circumstances.
Machine learning: The science of making computers act without programming.
Machine vision: The science that allows computers to see. This technology captures and analyzes visual information using the camera’s analog-to-digital conversion and digital signal processing. This is often compared to human vision, but machine vision is not bound by biology and can be programmed to see through walls. It is used in a variety of applications from signature identification to medical image analysis. Computer vision, which is focused on machine-based image processing, is often associated with machine vision.
Natural language processing (NLP): The processing of human language by computer programs. One of the older and best known examples of NLP is spam detection, which looks at the subject line and text of an email and decides whether it is junk. The current approach to NLP is based on machine learning. NLP tasks include text translation, sentiment analysis and speech recognition.
Robotics: An engineering field that focuses on the design and manufacture of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. They are used in assembly lines for automobile production or by NASA to move large objects in space. Researchers are also using machine learning to build robots that can interact in social settings. Self-driving cars: These use a combination of computer vision, image recognition and deep learning to build automated skills in driving vehicles while staying in specific lanes and avoiding unexpected obstacles, such as pedestrians. (Sal@)
