AI: A Multi-Million Dollar Limitation?
Artificial Intelligence (AI) has brought a huge revolution into a number of industries, from autonomous vehicles, self driving cars to even medical applications, and it has become an integral part of our everyday life. However, despite its immense potential, AI has limitations and unlike human intelligence, the AI lacks in a lot of ways.
When it comes to working as a human brain, the users must be aware of AI to make informed decisions and harness its full capabilities. After all the AI is a machine and lacks human connection in all ways, which also includes the human error by the way. Also, the super defined deep learning model or let's say the machine with deep learning networks might take a pick on its human intervention part, and may be future generations might be able to experience it.
The Other Side of AI: 14 Limitations You Need to Know
There are a lot of limitations that these new generation mechanism tools have. From potential lack of transparency to the human touch, these all might affect the the advancements of AI.
1. Huge Cost
When it comes to mining, storing as well as analyzing data, all of this is about to become too costly. And as we speak of energy and hardware use, you would be shocked but the training cost for GPT 3 model was estimated to be $4.6 million. As per a few reports, it is predicted that in an AI model that is similar to a brain, the training cost would get much higher than that of GPT 3, which might be around $2.6 billion.
One more thing that we would like to inform you all, is that the AI prompt engineers are rare at present and hence it would be too costly for the companies to hire them and work with them. They come with an additional costs.
Now coming to the second topic, AI systems are only as efficient as the quality of data they are trained on and hence incomplete or biased data can lead to inaccurate outcomes that infringe on people's fundamental rights, including discrimination. Transparency about the data used in AI systems helps to mitigate these issues.
One thing we would like to bring to your knowledge is that biased AI is more threatening than a tainted data. Also in many ways, a biased AI can slip through and at present there is no exact technology that could identify these issues.
3. Access to data
Access to data is a significant limitation for AI development, particularly for startups and smaller companies. Large corporations have amassed vast troves of data, giving them an inherent advantage over smaller competitors in the AI development race. This unequal distribution of data resources can further widen the power dynamic between big tech companies and startups.
Data is essential for training AI models, as it allows them to learn patterns, make predictions, and support decision-making processes with minimal human intervention. However, access to real-world datasets is often restricted, and the quality of available data can be inconsistent. This limitation can hinder the development of AI applications and prevent smaller companies from competing effectively with larger corporations that have more extensive data resources.
4. Transparency and explainability
The AI’s transparency refers to the ability to understand the workings of an AI model and how it reaches its decisions. On the other hand, its explainability is the ability to provide satisfactory, accurate, as well as the efficient explanations of the results, such as recommendations, decisions, or predictions.
However, implementing transparency and explainability can be challenging due to the complexity and opacity of AI systems. The “black box” nature of AI systems makes it difficult for users to understand why the system made a particular decision and identify potential biases or errors.
5. Lack of creativity
AI systems can learn from data and past experiences but are not able to think outside the box. When we say this, we mean that they are not able to generate new and fundamental ideas.
Well, of course, creativity is subjective and cannot be reduced to a set of equations or a mathematical formula. Now talking about the AI, it is designed to be precise, follow instructions, and achieve specific goals, which makes it less suitable for creative tasks. Additionally, AI lacks common sense, which is the ability to apply practical knowledge to real-life situations.
6. Limited pre fed tasks
AI has indeed made a number of significant advancements in a lot of fields, but it still faces limitations when it comes to understanding and responding to human emotions and making split-second decisions during the crisis.
These limitations can lead to potential issues for businesses and organizations that rely on AI for decision-making and communication. This is because there are fewer pre fed tasks at present and also, that the AI is totally based as well as dependent upon what it is fed.
AI systems can recognize and respond to emotions but do not experience them. This means that while AI can detect when someone is happy or sad, it does not feel those emotions itself and is unaware of what exactly those feeling or emotions mean.
As a result, AI may struggle to capture or respond to intangible human factors that go into real-life decision-making, such as ethical and moral considerations. This lack of emotional understanding can lead to insensitive or inappropriate responses during times of crisis, potentially harming a company's reputation or causing distress to affected individuals.
7. No consensus on safety
The limitations of AI, such as safety concerns, are one of the most crucial aspects that need to be addressed. Here, as AI continues to develop and integrate into various aspects of society some of the main challenges include data quality issues, data corruption, and debugging.
AI systems can be easily influenced and can be used for malicious intent if not properly designed or managed. Additionally, AI systems require vast amounts of data, which raises privacy concerns like informed consent, opting out, and limiting data collection. Ethical concerns in AI involve transparency, explainability, and potential biases.
8. Adversarial attacks
When we talk about the adversarial attacks on AI systems, they involve the deliberate manipulation of machine learning models by introducing carefully crafted input data, exploiting the model's vulnerabilities, and causing misclassifications or faulty outputs.
These attacks highlight a significant limitation of AI, as they expose the inability of AI systems to adapt to deviations in circumstances, making them vulnerable to security breaches and potentially putting lives at risk. One prime example we can talk of as an adversarial attack is the modification of a street sign. This could cause an autonomous vehicle to misinterpret the sign and make a wrong decision, potentially leading to accidents.
9. Computing time
AI has even got some of its very own hardware limitations, such as limited computation resources for RAM and GPU cycles. This is something that can pose challenges for AI development, particularly for smaller companies that may not have the resources to invest in custom and precise hardware. Now coming to the actual point, the 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.
Talking more about the computational limitations, the traditional computer chips, or central processing units (CPUs), are not well-optimized for AI workloads, leading to high energy consumption and declining performance. GPUs are too limited in memory capacity compared to CPUs. This means that if a complex AI model exceeds the GPU's memory capacity, it will need to use system memory, resulting in a significant performance decrease.
10. Ethics and privacy
Privacy concerns also arise when AI systems process personal data. Principles of trustworthy AI, such as transparency, explainability, fairness, non-discrimination, human oversight, and robustness and security of data processing, are closely related to individual rights and provisions of corresponding privacy laws. The AI not being aware of compliance requirements for AI systems that process personal data can lead to risks for both individuals and companies, including hefty fines and forced deletion of data.
AI systems are susceptible to a lot of manipulation as well as the lack of robustness. Security risks from hacking and potential misuse of AI technologies also pose significant concerns. Ensuring AI systems are transparent, auditable, and accountable is crucial for addressing these safety and ethical concerns.
11. Limited understanding of context
AI systems often struggle with understanding the nuances of human language and communication, making it difficult to interpret sarcasm, irony, or figurative language.
This in turn can be a huge limitation that arises from AI models lacking real-world experience and contextual understanding, as they are actually taught patterns in data. Consequently, AI systems may have difficulty comprehending complicated social situations that require nuanced interpretations and contextual awareness.
12. Lack of emotion
AI systems, such as ChatGPT, are indeed limited in their ability to understand and process emotions. While they can recognize patterns in data that may indicate certain emotions, they do not experience emotions themselves. This limitation can impact AI's ability to fully comprehend 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 escape the understanding of even the most advanced AI systems. Most importantly the AI systems may struggle to interpret unspoken emotions or the context through which emotions are expressed.
13. Require monitoring
One of the main challenges in developing a more human-like AI is that supervised learning, a widely used technique in the field of AI, does not actually replicate how humans learn organically. Supervised learning is a technique where an algorithm is designed to map the function from input to output using labeled data. This means that the data is already tagged with the correct answer.
Supervised learning cannot handle all complex tasks in machine learning. This is because it cannot cluster data by figuring out its features on its own. Also, supervised learning requires vast computation time, which can be a significant drawback when dealing with large datasets.
The presence of irrelevant input features in the training data can lead to inaccurate results, and data preparation and pre-processing are always a challenge. Humans and animals learn in an unsupervised manner, which means they can learn from raw, unlabeled data, but the same is not with AI here.
Speaking of which, supervised learning, on the other hand, relies on labeled data, which limits its ability to learn organically like humans.
14. Moral dilemmas
As AI has now become more integrated into our lives, it raises ethical concerns and also a few moral dilemmas. Machines making decisions that impact human lives can lead to questions about responsibility, accountability, and the potential for AI to make decisions counter to human values. These concerns need careful consideration as they pose limitations for AI development and implementation.
One major area of ethical concern is privacy and surveillance. This is where we would like to shed a bit of light. As AI systems collect and process vast amounts of data, there is a risk of violating individuals' privacy rights. Another significant concern is bias and discrimination, as AI systems can inadvertently perpetuate existing biases and stereotypes, leading to unfair and discriminatory outcomes. This can occur in various sectors, including healthcare, employment, creditworthiness, and criminal justice.
Accountability here is a cornerstone of AI governance. However, it is often defined too imprecisely due to the multifaceted nature of AI systems and the sociotechnical structure they operate within. As AI technologies become more sophisticated and autonomous, it is high time to ensure that there are mechanisms in place to hold relevant stakeholders accountable for the AI system's actions and outcomes.
How is AI responsible for Job displacement?
Today we all know that the AI has already begun to replace human jobs, particularly when it comes to repetitive tasks. In May 2023, AI contributed to nearly 4,000 job losses. However, AI can also create new job opportunities and enhance human productivity across various sectors.
Let's talk a bit about how AI can potentially generate new jobs. It is possible by enabling new sectors and business models, such as AI-powered digital assistants and smart home appliances, which opens up new career prospects for hardware engineers, data analysts, and software developers.
The key to addressing the limitations of AI in terms of job displacement is to strike a balance between AI implementation and human workforce development. Policymakers need to consider the implications of human-AI collaborations and AI that which enhance human performance, such as generative AI tools.
They should develop smart, targeted strategies to address future job displacement based on research into the differential impact of automation by sector, occupation, and demographic group. To mitigate the risk of job displacement, governments can offer special welfare programs to support and retrain the newly unemployed.
Now talking about the workforce development practitioners, the job seekers can leverage AI technologies to analyze and tackle barriers to job searching, recruitment, and career pathways for those with varying qualifications. Companies can adopt more expansive hiring approaches and invest in retraining their employees to adapt to the changes brought about by AI.
Final Verdict about the limitations of AI in 2023 and Beyond
AI has shown tremendous potential in various industries and applications. However, it is essential to be aware of its limitations to make informed decisions and harness its full capabilities. One of the key limitations of AI is that it is biased. This can arise from incomplete or biased data used to train AI systems, leading to inaccurate outcomes and potential discrimination.
Addressing this issue requires transparency about the data used in AI systems, as well as continuous monitoring and improvement of AI models to minimize bias. By understanding and addressing these limitations, we can work towards developing more robust, fair, and efficient AI systems that can benefit society as a whole.
Also, besides these AI tools being biased, there are a few more limitations such as the computational costs that we discussed above, also if the AI misinterprets any of its commands, it can lead to a life threating conditions, especially when it comes to the driverless vehicle. Yes, AI based technology is advanced but there are still a lot of chances of errors and complex issues.