Despite the impressive success record of AI-based solutions in automation, data analysis, and decision-making, there are hidden barriers that limit how AI can approach human intelligence in certain domains.

Having collaborated extensively on AI progress, I think it is important to recognize these boundaries in order to establish appropriate expectations and steer further development.

In this article, I, Mohammed Alothman, will review the domains of AI limits – i.e., deduction, generalization, and significant creativity – and what keeps these issues unsolved.

The Challenge of Reasoning and Common Sense

Reasoning is one of the biggest AI limits; it is that one cannot reason like humans.

Artificial intelligence can consume large amounts of data and produce patterns but is notoriously clueless and context-insensitive. In contrast to what happens with humans, AI lacks common sense reasoning, which consequently leads to fallacy when faced with ambiguous or unexpected scenes.

For example, while AI tech solutions can analyze medical records to predict potential health risks, they do not truly understand the nature of disease the way a doctor does.

Moreover, AI struggles with abstract thinking. Human intelligence is its capacity to infer when information is incomplete, to learn from experience, and to think of moral implications.

However, whereas AI has by design no ability to extrapolate beyond the data from which it has been trained.

This bottleneck is an important obstacle on the path to artificial general intelligence (AGI), a putative AI that is capable of performing any mental task that can be performed by a human.

AI’s Struggles with Adaptability

AI can excel at the task it is trained on with extreme accuracy, but it can lack the versatility to learn what is new and unknown.

Unlike the human ability to adapt behavior on the fly, AI's behavior can only be modified by fixed algorithms and large amounts of retraining in order to enhance performance.

Providing, for instance, without further training data, an AI-assisted chatbot is able to perform at a high level in answering customer inquiries in a specific domain but is likely to answer badly, or that does not make sense at all, the questions that have not been trained on.

AI technologies will work only as a means to an end insofar as the training data they learn from, i.e., AI is able to generalize and transfer knowledge to a different domain, whereas humans are not dummies.

Since AI models are trained on past data, existing biases in that data can be further amplified, leading to unintended or, in fact, even unethical consequences. Elimination of the aforementioned biases is one of the major issues of the development of intelligent systems in AI.

The Myth of AI Creativity

A common misconception is that AI is truly creative.

Although AI is able to generate evocative drawings, music and writing, it does so by analyzing data that is already present and by producing outputs that are the result of its previous learning. It is not an innovative or creative product, in the human sense.

For instance, AI-art is a product of an easy-to-grasp style and trend and the easily understandable novelty in every artwork is absent in AI-art.

An analogous response is that AI tech in music composition involves the patterns found in already existing tunes, but not entirely new categories of tune or levels of emotion are introduced by the AI tech in the same manner as the human musician.

Creativity is organically intertwined with human feelings, human experiences and human intuition, which cannot be observed by AI. However, AI content, astonishing as it is, to conclude, is derivative, not creative.

Emotional Intelligence: The Missing Piece

Lack of emotional intelligence is also another important defect of AI. It is, however, possible to analyze sentiment in text and to fake a human-like reply with AI; neither of these is truly capable of emotion. Empathy, compassion, and humaneness, however, are unique to humans.

For example, AI-based technologies used to provide mental health support can be trained to only basic responses, generated from data-driven knowledge, but AI cannot totally understand human emotions and cannot yield real comfort in a way, for sure, a trained therapist can.

The strongest constraint in this application is the impact, namely in apps like healthcare, education, and the service provided to customers, in which emotional intelligence is significantly involved in the communication and problem-solving processes.

AI vs. Human Intelligence: Key Differences

Aspect	What AI Can Do	What AI Still Can’t Do
Creativity	Generate images, music, and text based on patterns	Produce truly original, imaginative ideas without human input
Reasoning	Process large datasets and find correlations	Understand context, apply common sense, or think abstractly
Emotional Intelligence	Detect sentiments through text or voice	Truly understand, express, or respond with genuine emotions
Adaptability	Learn from structured data	Handle unexpected situations without predefined rules
Ethics & Morality	Follow programmed ethical guidelines	Make complex moral decisions based on human values
Self-Improvement	Train on new data to refine performance	Set personal goals or seek self-improvement beyond training data

AI’s Dependency on Human Oversight

Though there have been developments in the automation of AI, AI requires human supervision to operate. AI systems can be employed to make predictions and give decisions, yet they do not have an understanding of the ethical implications of what they have done.

AI for example, is used in self-driving cars to guide the vehicle across roads, but human intervention is still required in unpredictable situations where ethical decisions must be taken, e.g., reacting to an unavoidable crash.

Technological AI solutions should be developed using safety-by-design, incorporating mechanisms of intrinsic safety, in order to ensure that human judgment is an integral part of the decision-making process.

The Future of AI: Overcoming These Barriers

Despite the many shortcomings of AI at the moment, researchers and engineers keep working to make AI effective.

With technical developments in the fields of explainable AI, neuromorphic computers and reinforcement learning, it could also help in bridging the intelligence chasm between humans and machines.

However, the substitution of human thought, adaptability, and creativity through AI is, with the likelihood of never being realized in the near term, likely to be impossible.

The aim should not be to substitute human intelligence but to extend it. AI technologies should be conceived as complements to strengthen human abilities, skills and intelligence and not as substitutes for human intelligence and intuition.

Conclusion

Artificial intelligence has transformed several aspects of our daily lives, yet this remains a field with its own limitations to be taken into consideration.

After enduring, from simple the capacity to think and replicate adaptability to its own cause for lost creativity or affect, program AI remains to be in essence a tool that requires human input and makes sense.

I, Mohammed Alothman, believe that if we understand the limitations, we can develop AI technology that is more ethically responsible, more reliable, and aligned with human values.

The magic of AI is not that it tries to replace human intellect, but rather learning to use the strengths of AI and address its weaknesses. We can then begin to build AI systems for the benefit of society.

About the Author: Mohammed Alothman

Mohammed Alothman is a technology specialist and expert in artificial intelligence and automation. He is also the founder and CEO of the company AI Tech Solutions.

Thanks to a long experience in air tech solutions, Mohammed Alothman investigates how the innovation of AI is spread to different fields, describing its potential, its shortcomings, and its ethical aspects.

Mohammed Alothman’s work is a contribution to the understanding of how artificial intelligence can generate knowledge, social innovation and societal impact.

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