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Is Problem-Solving a Problem for AI Development?

The use of artificial intelligence (AI) is growing across a wide array of business segments. From medical sciences to space engineering, the influence of AI in business is widespread. According to a recent report, the global AI software market is showing signs of rapid growth and is expected to reach around US $126 billion by 2025 (Liu, 2021).

These trends point to the fact that AI technology is being deployed to build solutions for all sorts of uses, both from a business and customer perspective. One of the key functions of AI, therefore, is to provide a decision-making answer as an output to a user-driven input or activity. As such, it won’t be an exaggeration to say that the AI system operates on the notion of giving an answer to a question in relation to the problem that the user may be interested in. Artificial intelligence technologies such as machine learning/deep learning and natural language processing (NLP) essentially infer and predict from the data fed into them and provide an output that is just like an answer to a particular question or query for decision-making and further information processing purposes.

For instance, an AI system trained on MRI scans helps with cancer diagnosis and treatment protocols. The data fed into the system is processed by the system to generate MRI scans that help human decision-makers answer the probability of the presence or absence of cancer in the patient (NIH, 2018; Recht & Sodickson, 2020).

Similarly, an AI system using natural language processing (NLP) technology to convert voice to text is simply solving a problem of having text transcripts of voice data.

What it means is that AI’s functioning is predominantly problem solving oriented. Luger (2005; p.25) concurs and highlights that AI programmes are designed to solve useful problems. As such, the term “intelligence” in artificial intelligence causes confusion for people, as AI does not possess any genuine intelligence per se.

When it comes to human intelligence, it is important to recognize the various challenges that we face to describe and define what intelligence is.

First, despite a lot of progress and development of knowledge about ‘how the brain functions”, the understanding of what intelligence is remains elusive. Is it a collection of various abilities or a single faculty? What is perception, intuition and creativity and how such concepts are developed? What are cognitive capabilities and how they are developed? (Luger, 2005). Second, human intelligence is not limited to problem solving and decision-making alone. It is much more than that. Human intelligence involves interaction with the environment dynamically and responding to emerging scenarios and situations. The inherently dynamic nature of human intelligence involving cognition capabilities is not question-answer sequence focused. Third, how do creativity and intuitive intelligence drive the actions and behaviours of humans? How do creativity and intuitive intelligence manifest in real-time? These are some other areas that need more work to understand the human cognitive processes.

For AI machines to be called intelligent in any extant imagination, they need to possess at least some limited intelligence capabilities similar to those a human mind possesses. It raises the question of whether the current AI systems’ abilities and functioning are limited by their focus on problem-solving. Is problem-solving focus a problem for the evolution and development of AI? If so, what should be done for further development of AI?

Note: This article was previously published on: www.academiasolution.com

References

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Jiwat Ram
Jiwat Ramhttps://pmknowhow.wordpress.com/
Jiwat is currently working as a Professor in Project management at Excelia Business School France. He did his Ph.D. from the University of South Australia and MBA in International Business from AIT Thailand. Jiwat has over 20 years experience of working in industry across banking, construction, service, and education sectors in an international setting. For the last more than 10 years, Jiwat has worked in academia teaching at Executive Education, Master’s, and bachelor’s levels. His teaching includes courses on Artificial Intelligence, project management, management, and research methodology. Jiwat has published his research work in top-tier, high-impact factor journals including the International Journal of Production Economics, the International Journal of Project Management, Computers in Human Behaviour, the Journal of Global Information Management, and Enterprise Information Systems, among others. Combining academic and non-academic work, he has published over 100 articles in journals, conferences and industry outlets. His published work has been well received and four of his published papers have ranked in the Top 25 most downloaded papers from ScienceDirect. His two papers have been ranked in the Top 25 Most Cited articles as well. Jiwat’s research is focused on the impacts of technologies such as Social Media, Big Data, and Artificial Intelligence on businesses and society. Jiwat likes to understand how we can leverage upon the use of innovative technologies for business growth and productivity. Jiwat regularly contributes towards the development of new thought and ideas in business and technology management. As such, he has a growing portfolio of publications on some of the contemporary issues in the management of projects and organizations. Jiwat also publishes his work on social media platform Linkedin to connect and reach out to other industry professionals. His work has received a good following with a significant number of posts cited as reaching top 1% engagement on Linkedin. Jiwat’s content on LinkedIn can be accessed at: #ideannovation_jiwat Please feel free to connect with Jiwat on LinkedIn by clicking on the Icon above.

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