Welcome to Codingcompiler. The goal of Artificial Intelligence (AI) is not only to simulate but also to complement human thinking. Meanwhile, the AI is used in the economy many times.
Augmented Intelligence extends and supports human intelligence with the help of artificial intelligence. Computer-provided results and data analysis enable faster and more accurate human decisions. The artificial intelligence evaluates large amounts of data and provides people with decision-making.
What is Artificial Intelligence?
The term “artificial intelligence” was first mentioned at a scientific conference in the US city of Dartmouth in 1954. The scientist Marvin Minsky, who is considered one of the founding fathers of the AI, defined the term in 1966 as follows:
Artificial intelligence is when machines do things for which they assume intelligence in humans. In the late 1960s, the General Problem Solver was introduced. This was an AI system that was able to solve simple problems.
Also at the end of the 1960s, the ELIZA program developed at MIT attracted attention. The chat program was able to simulate a therapy conversation.
Better processor performance and storage capabilities helped to improve the AI’s capabilities in the following years. In 2011, IBM introduced the computer program Watson . Watson was able to win in the quiz show Jeopardy against two human opponents.
Users of computers or mobile devices are now coming into contact with artificial intelligence through programs such as Siri or Cortana. Siri and Cortana are intelligent wizards used in iOS and Windows 10 operating systems.
[Related Article: Artificial Intelligence Trends]
Methods for the Generation of Artificial Intelligence
The basic assumption of AI is that human intelligence is the result of various calculations. The AI itself can be generated in different ways. There are now AI systems whose main task is to identify patterns and, as a result, perform appropriate actions.
There are also the so-called knowledge-based AI systems. These try to solve problems based on the knowledge stored in a database. Other systems, in turn, use probabilistic methods to adequately respond to given patterns.
[Related Article: Amazon Redshift]
Modern AI characteristics
Some of the most recent forms of artificial intelligence include approaches such as cognitive computing , neural networks and natural language processing . Cognitive computing is a concept that aims to adapt existing information systems to today’s needs. In this way, the interaction between computer system and human is to be improved.
A neural network consists of artificial neurons and is oriented to the human brain in terms of its structure and functioning. This should enable a neural network to create very realistic calculations. Neural networks are now used in numerous fields of science and industry.
[Related Article: Python For Machine Learning]
For example, Google uses a neural network for its AI system, DeepMind, and combines it with machine learning methods . The goal of DeepMind and the machine learning approach is not just to equip computers with intelligence, but also to better understand how the brain works.
For this one also applies the so-called deep learning . Deep learning is a part of machine learning. Along with neural networks, Deep Learning offers the best way to recognize images and language at the moment.
Natural language processing means the processing of natural language. Focusing on Natural Language Processing as well as Cognitive Computing deals with the interaction between computer and user.
[Related Article: What is Amazon Machine Learning]
Purpose and limitations
For AI systems, there are now many possible applications. Businesses often use the ability to streamline their communication with customers through the use of chatbots . Warehouse management or purchases can now also be taken over by AI-based systems.
Artificial intelligence in the form of robotics is used in the production of machines or devices. In addition, KI can also be used in the automotive sector. There one uses artificial intelligence, for example, for the development and implementation of self-driving cars. While the AI is now useful in many areas, its use is always associated with problems and risks.
The dangers of using AI were highlighted by a Google research group in March 2016. To this end, the researchers formulated a questionnaire that was intended to substantiate potential security risks of self-learning and intelligent systems. Among other things, the researchers asked the question of how a robot can explore its environment in such a way that it does not endanger humans.
[Related Article: What Is Amazon Kinesis Data Streams]
What is Augmented Intelligence?
Augmented intelligence extends and supports human intelligence with the help of artificial intelligence. Computer-provided results and data analysis enable faster and more accurate human decisions. The artificial intelligence evaluates large amounts of data and provides people with decision-making.
The English term augmented intelligence means “extended intelligence”. It is a term from the environment of Artificial Intelligence (AI). Augmented Intelligence combines the artificial intelligence of computers with human intelligence, thereby improving human decision-making capabilities.
In the Big Data environment, Augmented Intelligence extends the power of human intelligence by analyzing large amounts of data that people can barely grasp and by providing found patterns or dependencies as a basis for decision-making.
The goal of augmented intelligence is not to replace humans, but to benefit from the combination of data science, machine learning, and human intelligence in decision-making.
Computers can evaluate data in seconds and support people. In some definitions, Augmented Intelligence requires a computer brain interface. This technology is not available today and is being replaced by natural interfaces such as listening, seeing or reading in current applications. Typical applications of advanced intelligence are health or education.
Differentiation between Artificial and Augmented Intelligence
Often the terms artificial intelligence and advanced intelligence are used in similar contexts or synonymously. However, they differ fundamentally and can be clearly distinguished from each other.
Artificial intelligence focuses on computers and machine learning. Ultimately, the machines make decisions based on previously acquired knowledge. Through the interaction of computer science, data science, computing power and algorithms, machines are capable of independently solving problems and performing tasks previously reserved for humans.
Artificial intelligence replaces human intelligence and computers are given complete autonomy in some areas. An interaction with humans is no longer necessary.
Augmented intelligence completely leaves the decision-making process to people. The expanded intelligence is only intended to support human thinking and decision-making. Man and machine interact. The sovereignty over the actual action and any decisions remains with humans.
[Related Article: Guide To Understand The Machine Learning, Deep Learning, AI]
Augmented intelligence in the big data environment
The big data environment offers ideal prerequisites for the use of extended intelligence. Machines can analyze the huge amounts of data that are virtually unrecognizable by humans and provide results as a basis for human action. Augmented intelligence is based on the following basic approach:
- collect data
- Machine data analysis
- Provision of analysis results
- Evaluation of the results by humans
- Make decisions and act
In the first step, all data relevant for later decision-making has to be collected, saved and made available to the other processes. Using machine learning, computers analyze the data and independently find patterns, relationships, trends or other dependencies.
The results found prepare the computers for subsequent assessment by humans. The preparation of the data can be done in graphic, text-based or other form. Only through the human interpretation of the machine-based results, the actual basis for decision-making arise. In the last step, the human being makes the decision and initiates the resulting actions.
[Related Article: Machine Learning Interview Questions]
Application examples for augmented intelligence
Augmented Intelligence offers applications wherever large quantities of structured or unstructured data are present, where humans are unable to capture and interpret them without computer systems. Computers, algorithms, machine learning and artificial intelligence support and expand human intelligence in these cases.
An important area of application of advanced intelligence is healthcare. Large amounts of collected patient and health data can be evaluated by artificial intelligence. For example, symptoms and illnesses of millions of different patients can be compared in a very short time and dependencies or causes of illness can be found.
Image data such as x-rays or MRI images can also be analyzed with artificial intelligence. The results of the data analysis support physicians in making therapy decisions.
[Related Article :Programming Languages For Data Science]
Other application examples can be found in the teaching area. Both students and teachers can benefit from augmented intelligence. Learning outcomes can be enhanced by providing material found through machine learning and relevant to the learning or teaching process.
In companies, advanced intelligence supports corporate governance in their decisions. External or internal company data is analyzed by Artificial Intelligence and the results are provided to the management in the form of qualified reports or graphics. Based on the analysis, sound decisions are made and the risk of wrong decisions is reduced.
Related Technical Articles: