Artificial intelligence is currently one of the significant trends. More and more companies are currently examining the use of this technology in suitable applications. Artificial intelligence is currently one of the significant trends. More and more companies are currently examining the use of this technology in suitable applications. In addition to robotic process automation, big data, and blockchain. Nevertheless, many executives remain skeptical about whether AI can deliver what it promises for their digital strategy.
Artificial intelligence is a branch of IT that aims to create intelligent machines. The focus is on the simulation of human intelligence processes using machines, especially computer systems. These processes include acquiring information and rules for using the information, using rules to draw approximate or definitive conclusions, and self-correcting. In general, the term artificial intelligence refers to the imitation of human decision-making behavior using simple algorithms.
Differentiation Between Weak And Strong Artificial Intelligence
In theory, we talk about AI when a computer solves complex problems, which require human intelligence to solve. A distinction is made between weak and strong AI. The weak AI is a system that has been developed and trained for a specific task.
Virtual assistants like Siri are a form of weak AI. Strong AI, also known as Artificial General Intelligence, has generalized human cognitive abilities. It is supposed to mechanize human behavior. She can find a solution to unfamiliar tasks without the need for human intervention. It should help to support people in the thought process.
When Is A Machine Intelligent?
As an accepted measuring tool, the Turing test can be used to determine whether a machine can think like a human. According to this, a computer is certified as Artificial Intelligence if, under certain conditions, it can imitate human answers so that a person cannot correctly determine whether the answers to the questions asked were given by a computer or by another person.
How Does Artificial Intelligence Work In Digital Strategy?
The fields of application of artificial intelligence are very diverse. AI is used to ward off cyber attacks, as an assistant in medical diagnostics, and to realize the idea of autonomous driving. AI is already being used successfully, especially in medicine. Intelligent machines are already performing numerous surgical steps today, more precisely than human surgeons.
AI-based systems convert the computed topographies into three-dimensional images, allowing doctors to get a specific picture of every part of the body. More and more expert systems that are used in specialized areas of application are making use of AI.
Digital Strategy: Chatbots In Customer Service
Chatbots function as text-based dialogue systems, especially in customer service using AI-based speech recognition technologies. Digital assistants like Google Assistant are getting better and better because they automatically learn with every new request. Large, complex, or weakly structured mass data can hardly be used productively without AI.
Intelligent algorithms help to recognize patterns in substantial amounts of data and to divide them into clear categories. AI allows automation in customer service and commercial processes. Thanks to their cognitive abilities, the systems learn with every customer contact and every business transaction and react more precisely to requirements. Computers with artificial intelligence have the potential to make future forecasts based on their wealth of experience. Intelligent algorithms can use previous buying behavior to predict when a customer will want to purchase a particular product.
AI is tied into a variety of different types of technology:
- Automation: For example, Robotic Process Automation (RPA) can automatically perform high-volume, repetitive tasks generally done by humans. RPA differs from IT automation in that it can adapt to changing circumstances.
- Machine learning: It is considered the core technology of artificial intelligence. In simple terms, this is the automation of predictive analytics. The more sample or training data the learning process receives, the more it can improve its model. Learning algorithms extract statistical regularities from the data provided and develop models that can react to new, previously unseen data by classifying them and generating predictions or suggestions.
- There are three types of machine learning algorithms :
- Supervised learning: Data sets are labeled so that patterns can be recognized and used to label new data sets.
- Unsupervised learning: records are not labeled and are sorted based on similarities or differences.
- Reinforcement learning: records are not labeled, but the AI system receives feedback after performing one or more actions.
- Machine Vision: This technology captures and analyzes visual information with the help of a camera, analog-to-digital conversion, and digital signal processing. Machine vision can be programmed to see through walls, for example. The fields of application range from signature identification and the classification of product parts to medical image analysis.
- Natural Language Processing (NLP): NLP is about processing human language by a computer program. One of the best-known examples of use is spam detection, in which the subject line and the text of an email are checked, and a decision is made as to whether it is junk. NLP is mainly used for text translation, sentiment analysis, and speech recognition.
- Robotics: It deals with the design and manufacture of robots. They are not only used in production or by NASA to move large objects in space. Using machine learning, robots can also interact in social environments.
- Self-driving cars: The combination of computer vision and image recognition enables vehicles to drive, stay in lane, avoid obstacles, and park in an automated manner without the influence of a human driver.
Corporate digital strategy: AI permeates everyday life
AI penetrates our everyday life at an unprecedented speed in the form of digital assistants, cooperative robots, autonomous vehicles, and drones. Big data and American Internet companies are driving the development of artificial intelligence, supported by increasingly powerful hardware and software platforms. They are the tools that machine learning needs to process large amounts of data, recognize complex relationships and learn from them without explicit programming.
It will not be long before the first intelligent, predictive systems monitor themselves, provide forecasts and independently suggest or implement measures. Research is still in the early stages of the digital strategy so that technological optimization is currently associated with enormous added value for users and companies.
