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

Meeting the Challenge of Artificial Intelligence

The term “artificial intelligence” was first coined by John McCarthy, who defined it as “the science and engineering of making intelligent machines.” In 1955, McCarthy coauthored a research proposal to study AI with Marvin Minsky, Nathaniel Rochester, and Claude Shannon (A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, https://stanford.io/2KdBeeK), which launched it as a research field.

ML studies the algorithms and statistical models that computers use to effectively perform a specific task (e.g., predicting corporate financial stress or bankruptcy).

One of the models for ML is the artificial neural network (ANN), a collection of connected units or nodes called artificial neurons that emulates a human’s biological neural network and develops algorithms from a set of given samples.

David Yang and Miklos Vasarhelyi reported that there were 167 accounting-related ES studies in the ’80s and ’90s covering five areas: auditing, taxation, financial accounting, personal financial planning, and management accounting (“The Application of Expert Systems in Accounting,” Artificial Intelligence in Accounting and Auditing, vol.

It doesn’t have to be Artificial Intelligence vs. Machine Learning: the most effective approach merges the best of both worlds

In other words, it’s a black box.  Your only option is to feed more examples to the algorithm, but this doesn’t guarantee greater accuracy because you need to provide specific training documents to cover all the use cases.

In that case, other AI approaches, such as Natural Language Understanding based on a knowledge graph, offer concrete benefits for a range of cognitive tasks and can also be used in scenarios that involve a small, distributed set of sample documents with an average level of complexity.

Thanks to the deep and wide representation of knowledge, AI solutions that leverage a knowledge graph understand and process natural language and any kind of unstructured texts faster and more accurately than a ML approach.

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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