Artificial Intelligence (AI) has become a common feature of modern-day living, bringing with it more efficient systems and services that elevate our standard of living. Across diverse sectors including healthcare, transportation, finance, and e-commerce, technological advancements have been embraced to augment industry functions. In healthcare, for instance, AI is utilised for patient records management, disease diagnosis, and even surgical procedures. Meanwhile, in the automotive industry, self-driving cars are a prime example of AI-powered technology, while e-commerce platforms leverage it to offer customers personalised product recommendations.What are the unresolved problems in artificial intelligence?
Despite its advantages, AI technology does have its drawbacks. Misapplication of this technology can result in incorrect outputs, which is commonly known as ‘garbage in, garbage out’. Additionally, there are logistical concerns that need to be addressed, such as the requirement for skilled personnel and the substantial cost associated with delivering desired outcomes for businesses.
Although AI technology has demonstrated its efficacy in various business settings, it still confronts several challenges that are yet to be resolved. This article outlines the top five challenges faced by AI, including their likely implications and the current efforts to address them.
The concept of inherent biases in Artificial Intelligence (AI) is widely recognized. This poses a risk of discrimination in various domains including housing, employment, and the legal system. Amazon, for example, uncovered that its use of AI in screening job candidates resulted in numerous female applicants being flagged as unsuitable. Such prejudice stems from deficiencies in the data used to train the AI algorithms.
The video below demonstrates how women are also susceptible to discrimination through facial recognition technology.
With Artificial Intelligence (AI) technology becoming increasingly integrated in our daily lives, there is a growing concern regarding the perpetuation of biases. It is crucial for users to be equipped with the knowledge to recognize biases and to comprehend the limitations of outcomes produced by machines. Overcoming the issue of prejudices is feasible, but it may necessitate additional resources and staff.
Inadequate Skilled Workforce
The IT sector is currently grappling with a shortage of qualified candidates, making the recruitment of suitable professionals a challenge for businesses. Despite expectations of an increase in available talent, there remains a dearth of skilled data scientists specialised in AI. In response, certain companies are investing in customized employee training and development programs to fill their own talent gaps. While this is a long-term solution that requires patience, it could potentially alleviate the current labour shortage.
It’s a Costly and Time-Consuming Process
The expenses related to AI can be substantial, particularly during the early phases. These expenditures can encompass hardware, software, and training for newly-recruited personnel, in addition to possible equipment procurement. The already significant costs may have further increased due to the higher demand for this technology, such as for bitcoin mining.
Regrettably, this is only the beginning of the AI implementation process. Operators of AI systems must devote time to training the system, which can take several weeks or even months depending on the task at hand. Furthermore, gathering, organising, and classifying data is a time-consuming operation. Although utilizing existing datasets can reduce the effort required, it might be necessary to construct new data sets if the current ones are unsuitable.
The application of AI in cybersecurity has the potential to enhance security measures and simultaneously pose novel threats. Critical threats that AI programs may encounter are outlined as follows.
Unauthorized Intrusion:Malicious actors may utilize audio recordings, images, and facial recognition technology to trick speech and facial recognition systems, thereby gaining illicit access to sensitive information.
Inaccurate Predictions:Cybercriminals can intentionally feed the AI with deceptive data to generate false predictions.
Manipulated Information:Amending data sets may lead to incorrect inferences being drawn. To prevent this, corporations should establish strict privileged access management (PAM) procedures.
Transferable Knowledge:In this scenario, the hacker deceives an AI that has been programmed to execute a particular task, thereby obtaining knowledge that can be exploited elsewhere.
Online User Manipulation:Operating an Artificial Intelligence (AI) system carries the potential risk of malicious actors attempting to manipulate it by feeding it with false data or training it to produce wrong outcomes.
Data privacy is a crucial aspect of security. Those responsible for managing AI systems are responsible for preserving the confidentiality of users’ data, particularly personal or financial information. Conversely, systems may be vulnerable to exploitation, and sensitive information may be exposed to theft if security measures are inadequate.
Although Artificial Intelligence (AI) is incredibly adept at logical reasoning, it may not align with morality, as AI has not been designed to comprehend morals. Therefore, engineers have been unable to teach AI to identify human behavior. Consequently, decisions made by AI using algorithms may conflict with human ethical values.
The employment of Artificial Intelligence (AI) presents a number of ethical quandaries. For example, workers may feel apprehensive about their positions being replaced by AI systems. Is it ethically justifiable for businesses to automate positions, despite the possibility of high rates of unemployment? Additionally, self-driving cars may endanger pedestrians; is it ethically responsible to permit the use of such vehicles by children? Moreover, surveillance techniques powered by AI may constitute a violation of privacy; should such activities be deemed morally acceptable?
Moreover, it is not always clear to those who work with AI systems how these systems arrive at their conclusions. As Artificial Intelligence (AI) is increasingly responsible for decisions that cannot be explained, this is partly due to AI’s opaque nature.
AI Issues Pose a Difficulty for Small Enterprises.
The obstacles expounded above present a substantial hurdle for larger corporations and may also prevent smaller businesses from embracing AI solutions, as they may not have access to the necessary resources, training, or means to address bias when recruiting.
Small enterprises that opt-out of employing Artificial Intelligence (AI) technology may find themselves at a disadvantage in terms of competitiveness. However, accessibility to AI resources may still present a challenge.