AI News, Expert System in Artificial Intelligence: What is, Applications, Example artificial intelligence

Explore The Types Of Artificial Intelligence

If I were to name a technology that completely revolutionized the 21st century, it would be Artificial Intelligence.

He defined AI as: ‘The science and engineering of making intelligent machines.’ Artificial Intelligence can also be defined as the development of computer systems that are capable of performing tasks that require human intelligence, such as decision making, object detection, solving complex problems and so on.

Also known as Weak AI, ANI is the stage of Artificial Intelligence involving machines that can perform only a narrowly defined set of specific tasks.

Also known as Strong AI, AGI is the stage in the evolution of Artificial Intelligence wherein machines will possess the ability to think and make decisions just like us humans.

Strong AI is considered a threat to human existence by many scientists, including Stephen Hawking who stated that: “The development of full artificial intelligence could spell the end of the human race….

Humans, who are limited by slow biological evolution, couldn’t compete and would be superseded.” Artificial Super Intelligence is the stage of Artificial Intelligence when the capability of computers will surpass human beings.

ASI is currently a hypothetical situation as depicted in movies and science fiction books, where machines have taken over the world.

Based on the functionality of AI-based systems, AI can be categorized into the following types: This type of AI includes machines that operate solely based on the present data, taking into account only the current situation.

For example, self-driving cars use sensors to identify civilians crossing the road, steep roads, traffic signals and so on to make better driving decisions.

Artificial Intelligence can be used to solve real-world problems by implementing the following processes/ techniques: Machine Learning is the science of getting machines to interpret, process and analyze data in order to solve real-world problems.

Under Machine Learning there are three categories: Deep Learning is the process of implementing Neural Networks on high dimensional data to gain insights and form solutions.

Natural Language Processing (NLP) refers to the science of drawing insights from natural human language in order to communicate with machines and grow businesses.

Twitter uses NLP to filter out terroristic language in their tweets, Amazon uses NLP to understand customer reviews and improve user experience.

Fuzzy logic is a computing approach based on the principles of “degrees of truth” instead of the usual modern computer logic i.e.

Expert systems are mainly used in information management, medical facilities, loan analysis, virus detection and so on.

Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review

A review of the curriculum is an administrative and arduous process, which strongly speaks to the need for machine automation to ease the process.

One plausible reason for the lack of adoption of AI in curriculum review is the limited digitalization in medical education learning management systems, which is essential for creation of a curriculum map.

Currently, there are two main approaches to obtaining data—accessing records from prior digitalization of the curriculum and transferring hard copy data into a soft copy, which is a time-consuming process.

Useful feedback should essentially assist students in identifying conceptual misunderstandings, critique their performance, and be structured enough to help students achieve their learning objectives [53].

One of the limitations of automated and immediate feedback provision with AI is limitation in the quality of the feedback [18], as the feedback generated is based on the knowledge base and model of the AI system, which, as of now, has room for improvement.

A study by Shin et al [55] demonstrated that undergraduates who adopted problem-based learning are more up to date in medical information as compared to their counterparts who experienced a traditional curriculum.

Without the digitalization of examinations, it remains an arduous task to transfer hard copy examination results into soft copy to meet the data pool requirements necessary to develop an AI system.

In addition, the sensitive nature of summative assessments and examinations limits the use of AI: A malfunction or improper coding of the AI system may cause the results to be incorrect, which may have dire consequences on the students involved.

In this regard, AI may be better used in areas in which human performance would increase when assisted by AI and when humans are unable to perform by themselves, such as in adaptive assessment and programmatic assessment.

Programmatic assessment involves the use of an AI system to design an assessment program tailored to optimize learning outcomes and ensure curriculum quality at a systemic level [59].

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