Artificial Intelligence (AI) has sparked a massive revolution across various industries, from self-driving vehicles to medical applications, becoming an integral part of our daily lives. However, despite its vast potential, AI has limitations and lacks in many ways compared to human intelligence.
When it comes to functioning like a human brain, users need to understand AI to make informed decisions and fully leverage its capabilities. After all, AI is a machine and lacks human connection in every aspect, including human error.
Moreover, the highly defined deep learning model, or letās say the machine equipped with deep learning networks, might choose its human intervention part, and future generations might experience this.
13 Limitations of AI to Be Aware OfĀ
At the moment of publishing this article, these next-generation mechanism tools have numerous limitations. From potential lack of transparency to human touch, all these could hinder AIās progress. Lets now get to know what these AI limitations are.
1. AI can be very Expensive
When it comes to operating, storing, and analyzing data, itās all set to become prohibitively expensive.
And speaking of energy and hardware usage, you might be shocked, but the training cost for the GPT-3 model was estimated at $4.6 million.
Some reports suggest that for an AI model akin to a brain, the training cost would be much higher than GPT-3, potentially reaching around $2.6 billion.
2. Ai can be one sidedĀ
Letās now discuss the second topic: AI systems are only as effective as the quality of the data they are trained on.
Consequently, incomplete or biased data can lead to inaccurate results that infringe upon peopleās fundamental rights, including discrimination. Transparency regarding the data used in AI systems helps mitigate these issues.
Itās worth noting that biased AI is more concerning than corrupt data. Currently, there is no precise technology to identify these biases.
3. Data Access issues
Data access poses a significant limitation for AI development, especially for startups and small businesses.
Large companies have amassed vast amounts of data, giving them an inherent advantage over smaller competitors in the race for AI development.
This unequal distribution of data resources can further widen the power dynamics between major tech companies and startups. While data is crucial for training AI models, real-world dataset access is often restricted, and the quality of available data may be inconsistent.
This limitation can hinder the development of AI applications and prevent small businesses from effectively competing with larger companies that have more extensive data resources.
4. Transparency and Explainability issues are evident
The transparency of AI pertains to the ability to grasp how an AI model functions and the way it arrives at its decisions.
On the other hand, explainability involves the ability to deliver satisfying, accurate, and efficient explanations for outcomes such as recommendations, decisions, or forecasts.
However, implementing transparency and explainability can be challenging due to the intricate and obscure nature of AI systems. The āblack boxā characteristic of AI systems means itās tough for users to discern the rationale behind a specific decision and pinpoint potential biases or errors.
5. AI often lacks creativity
AI systems may learn from historical data and past experiences, but they lack the capability to innovate beyond conventional boundaries. This implies they are not adept at creating brand new and original ideas.
Of course, creativity is subjective and cannot be distilled into a set of equations or a mathematical formula. Speaking of AI, it is engineered for accuracy, to follow directives, and to fulfill particular objectives, making it less suitable for creative endeavors.
Moreover, AI is devoid of common sense, meaning the ability to apply practical know-how to real-life scenarios.
6. Limited Pre-trained TasksĀ
While AI has made significant progress in various fields, it still faces limitations when it comes to understanding and responding to human emotions and making split-second decisions during a crisis.
These limitations can pose potential challenges for businesses and organizations that rely on AI for decision-making and communication. Currently, there are fewer predefined tasks, and AIās capabilities depend entirely on its training data.
AI systems can recognize and respond to emotions, but they do not experience those emotions themselves. This means that even though AI can detect when someone is happy or sad, it lacks a personal understanding of these feelings.
As a result, AI may struggle to capture or react to intangible human factors that play a role in real decision-making, such as ethical and moral considerations.
This lack of emotional understanding can lead to insensitive or inappropriate responses during critical moments, potentially harming a companyās reputation or causing distress to those involved.
7. No Consensus on SafetyĀ
The limitations of AI, such as security issues, are among the most crucial aspects to address.
As AI continues to evolve and integrate into various societal aspects, key challenges include data quality issues, data corruption, and debugging.
AI systems can be easily influenced and used for malicious purposes if not properly designed or managed.
Moreover, AI systems require large amounts of data, raising privacy concerns such as informed consent, opting out, and limiting data collection. Ethical concerns in AI involve transparency, explainability, and potential biases.
8. Adversarial AttacksĀ
Talking about adversarial attacks against AI systems, they involve the deliberate manipulation of machine learning models by introducing carefully crafted input data, exploiting model vulnerabilities, and causing classification errors or faulty outcomes.
These attacks highlight a significant limitation of AI, as they reveal the inability of AI systems to adapt to variations in circumstances, making them vulnerable to security breaches and potentially endangering lives.
A notable example of an adversarial attack is the alteration of a traffic sign, which could cause an autonomous vehicle to misinterpret the sign and make a wrong decision, potentially leading to accidents.
9. Computational TimeĀ
AI also has its hardware limitations, such as limited computational resources for RAM and GPU cycles.
This can pose challenges for AI development, especially for small businesses that may not have the necessary resources to invest in precise, custom hardware.
Now, to the real point: established companies with more resources have a significant advantage in this area, as they can afford the costs associated with developing custom hardware tailored to their specific needs.
Discussing further computational limitations, traditional computer chips, or central processing units (CPUs), are not well-optimized for AI workloads, leading to high energy consumption and reduced performance.
GPUs have too limited memory capacity compared to CPUs. This means that if a complex AI model exceeds the GPUās memory capacity, it will have to use system memory, resulting in a significant performance decrease.
10. Ethics and PrivacyĀ
Privacy issues also arise when AI systems process personal data. The principles of trustworthy AI, such as transparency, explainability, fairness, non-discrimination, human oversight, as well as robustness and security of data processing, are closely linked to individual rights and the provisions of corresponding privacy protection laws.
The fact that AI is not aware of the compliance requirements of AI systems that process personal data can lead to risks for both individuals and businesses, including heavy fines and forced data deletion.
Ethics and Privacy AI systems are subject to numerous manipulations and a lack of robustness. Security risks related to hacking and the potential misuse of AI technologies also raise serious concerns.
Ensuring that AI systems are transparent, auditable, and accountable is crucial to addressing these security and ethical concerns.
11. Limited ContextualĀ
Understanding AI systems often struggle with grasping the subtleties of human language and communication, making it challenging to interpret sarcasm, irony, or figurative speech.
This can be a significant limitation stemming from the lack of real-world experience and contextual understanding in AI models, as they are essentially taught using data patterns.
Consequently, AI systems may find it difficult to comprehend complex social situations that require nuanced interpretations and contextual awareness.
12. Lack of EmotionĀ
AI systems, such as ChatGPT, are indeed limited in their ability to understand and process emotions. Although they can recognize patterns in data that may indicate certain emotions, they do not experience emotions themselves.
This limitation can impact the AIās ability to fully grasp the nuances of human emotions and communication.
One of the main challenges for AI in understanding emotions is the subjective nature of emotions and the complexity of human communication. Cultural references, sarcasm, and nuanced language often elude even the most advanced AI systemsā understanding.
More importantly, AI systems may struggle to interpret implicit emotions or the context in which emotions are expressed.
13. Requiring OversightĀ
One of the main challenges in developing more human-like AI is that supervised learning, a widely used technique in AI, doesnāt truly replicate how humans learn organically.
Supervised learning is a method where an algorithm is trained to map the function from input to output using labeled data, meaning the data comes pre-tagged with the correct answer. Supervised learning canāt handle all the complex tasks of machine learning because it canāt cluster data by determining features on its own.
Additionally, supervised learning requires considerable computational time, which can be a major drawback when dealing with large datasets.
The presence of irrelevant input features in training data can lead to inaccurate results, and data preparation and preprocessing remain a challenge.
Humans and animals learn in an unsupervised manner, meaning they can learn from raw, unlabeled data, but this is not the case with AI here. Speaking of which, supervised learning, in contrast, relies on labeled data, limiting its ability to learn organically like humans.
Conclusion
AI has demonstrated enormous potential across various industries and applications. However, itās crucial to be aware of its limitations in order to make informed decisions and fully harness its capabilities.
One of the primary limitations of AI is bias. This can result from incomplete or biased data used to train AI systems, leading to inaccurate results and potential discrimination.
To address this issue, transparency about the data used in AI systems is essential, along with continuous monitoring and improvement of AI models to minimize bias.
By understanding and addressing these limitations, we can work toward developing more robust, fair, and effective AI systems that benefit society as a whole.
