Is Problem Solving a Problem for AI Development? – Analytics Insight

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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. AI technologies such as machine learning/deep learning, natural language processing (NLP) essentially infer and predict from the data fed into it 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 in 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 in answering the probability of presence or absence of cancer in the patient (NIH, 2018; Recht and Sodickson, 2020).
Similarly, an AI system using natural language processing (NLP) technology to convert voice to text is simply solving the 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 programs 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. Firstly, despite a lot of progress and development of knowledge about ‘how the brain functions”, the understanding of what intelligence remains elusive. Is it a collection of various abilities or a single faculty? What are 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 behaviors 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 if 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 the further development of AI?
For further AI developments, there is a need to focus on understanding and building technologies that could mimic or mirror some form of human cognition and behaviors beyond just problem-solving.
As David (2020) puts it, there is a need to start such a journey by looking at how the human brain develops from early childhood. Increasing our understanding of synaptic development activity in the human brain from infancy to adulthood will lead to the development of better neural networks that can mimic human cognition developmental processes.
Understanding early childhood brain development will also help improve our understanding of how stronger and weaker synapses are formed in the human brain, as well as how such developments to aid in creative and intuitive activities. Such an understanding will help in building more sophisticated neural networks with increased synaptic plasticity. It will also help in building new forms of training processes and models for use by artificial neural networks (David, 2020).
However, all the developments in AI must not be at the cost of bringing adverse effects on humanity. While leveraging the potential of AI technology is important, any developments must be within the bounds of ethical and harmless use and should be aimed at aiding the human living experience.
Professor Jiwat Ram
Jiwat writes about some of the contemporary issues in technology and business management. He has a growing portfolio of publications on emergent issues in technology, including AI, social media, and big data. He has over 120+ publications, including in journals, conferences, and industry outlets.

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.
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