Top Challenges/Major Issues in Artificial Intelligence

In the modern era, practically all of our labour is done by computers and other devices that are entirely reliant on artificial intelligence (AI) and machine learning technologies, which are necessary for doing our daily activities. In fact, a number of digital behemoths, including Google, Facebook, Uber, and others, have incorporated AI and machine learning into their overall business strategies. These innovations mark a significant advancement in computer science and data processing that is rapidly changing a wide range of businesses. It also turns out to be a fantastic supply of amazing chances for expense reduction and revenue expansion.

As is well known, the business and industrial world is undergoing a significant digital transformation and is dealing with a vast amount of data that is both extremely valuable and becoming progressively more difficult to collect, process, and analyse. Therefore, businesses need new tools, approaches, and processes to manage the massive amounts of data being gathered in order to derive valuable insights and take appropriate action when those insights are found. Vise versa, the demand for AI and machine learning experts has also increased who have attended an AI and Machine learning course to perform their tasks in a better way. But it is also the truth that these experts have to face many unwanted situations during their careers.

So this article is going to discuss some challenges and major issues in the Artificial intelligence field that almost every professional must be aware of.

What is Artificial Intelligence?

Artificial Intelligence( AI) is a way of study in which computers/systems/machines/ robots can be made to copy or mimic the way humans think, learn, respond, and act. In other words, it is the simulation of human intelligence processes by machines and computer systems. Generally, AI applications involves expert systems, speech/voice recognition, natural language processing, machine vision, etc. AI generally refers to the ability of computers and robots to perform tasks that are associated with intelligent beings. So the advanced applications of AI can be seen in sectors such as manufacturing robots, self-driving cars, healthcare management, smart assistants, social media monitoring, marketing chatbots, automated financial investing, virtual travel booking agent, etc.

Basically, AI involves a basic of specialized software and hardware for writing and training machine learning algorithms. AI is a unique approach that is written in programming languages such as R, Python, and Java. These programs are based on three main approaches: learning Processes, reasoning Processes, and Self-correction Processes. On the other side, AI is not only about process and capability for super-powered thinking and data analysis, but it also refers to any specific function or format that can bring up images of high-functioning, human-like robots taking over the world, etc.

Top Challenges or Major Issues In Artificial Intelligence

As AI is a newer field in the world of technology that is making our lives easier. But this field also has its challenges and issues faced by organizations, professionals, as well as users. There are several issues with AI development, and implementation one can face while operating AI devices. Let us have a look at some of the common problems in this domain.

  • Computing Power Issue- As AI and ML algorithms are highly power-consuming processes that keep most developers away from implementing them in their organization. This setup requires a supercomputer’s computing power which is an extravagant way of computing that demands an ever-increasing number of cores and GPUs to work efficiently. So not everyone can afford that with issues like unprecedented amounts of data and rapidly increasing complex algorithms.
  • The Right Data Set Issue- it is also a major challenge in artificial intelligence to determine the right and appropriate data set, as the quality and availability of data are essential for AI capabilities. There could be issues regarding the trusted source of relevant data that should be clean, secured, well-governed, and accessible. It is also very complicated to implement AI algorithms with the flow of inaccurate and low-quality data.
  • Processing Unstructured Data- After determining the right data sets, it is also a big issue to process unstructured data that holds immense value for the business. But several organizations and developers are unable to get meaningful insights because they can’t be analyzed with conventional systems and cant be stored in an RDBMS (Relational Database Management System), so it is a big issue to process and analyze them.
  • Trust Issue- It is also a major cause of worry as AI algorithms are known for their unknown nature of deep learning models that can predict the accurate output. It is also doubtful how a specific set of inputs can provide the right solution for different kinds of problems and issues.
  • Security and Storage Issue- Almost all AI development services are based on the availability of large amounts of data to train the algorithms that can provide better business opportunities. But here, the issue is all about data storage and security, which is one of the major challenges of AI services.
  • Lack of AI Talents- It is predicted that almost 97.9 billion will be spent on AI technologies by the end of the year 2023 as this field is continuously growing and a huge amount of people and organizations are implementing this evolving technology. Ultimately it increased the demand for AI experts or developers. However, there need to be more AI professionals with the required qualifications and skills to create fully-functional systems.
  • Lack of Awareness- Lack of awareness of AI techniques and services is also one of the major challenges in this sector, as only researchers, college students, and technology enthusiasts are aware of the real potential of artificial intelligence.

Besides these challenges, there are several big and small challenges faced by organizations and AI experts while implementing AI services. For example, Data Privacy and Security, Human-level, Trust Deficiency, AI tools for marketing, Transparency, Integration to Augmented Intelligence, AI integration with the cloud, Supporting IT systems, Cybersecurity issues, Improving Speech and Text AI, Established AI Ethics, etc. So it is clear that AI technologies are in their initial stages where many types of research, improvements, and solutions are about to come in order to make our lives easier.

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