6 Common Challenges Encountered While Building A Career In AI

Career In AI

The term “artificial intelligence” (AI) refers to machines that can carry out tasks that would normally need human cognition.

Artificial intelligence relies on algorithms that are trained to make judgments based on predetermined rules or, in the case of machine learning, that sift through enormous amounts of data in search of repeatable patterns.

The use of artificial intelligence is now widespread throughout many sectors of the economy, from marketing and retail to transportation and manufacturing. The need for AI professionals has increased dramatically in the last four years, while the field itself is expanding rapidly.

This is because many businesses believe that AI may raise productivity by as much as 40% and can be used for anything from astronomical object tracking to disease prediction, from terrorism research to product design.

In this article, we’ll discuss some of the common challenges associated while building a career in AI.

1. Lack Of Trust

The uncertainty introduced by the lack of knowledge about the methods by which deep learning models forecast the output is a major concern for AI. The process through which a given set of inputs can generate a solution for a wide variety of issues is complex and not easily explained to a non-specialist.

In many parts of the world, people are either completely unaware of or uninterested in Artificial Intelligence and the ways in which it is already present in the products like smartphones or banking systems, they use on a daily basis.

2. AI Policy

As more and more AI applications enter the market, governments are now playing catch-up. Despite the global scope of AI, there is currently no coherent policy framework for its governance or its application of data.

Governments must effectively regulate the private sector to provide “guardrails” for growth. However, countries like the US where the bulk of development is taking place, along with a majority of the rest of the globe, have not implemented this at this time. Significant ethical and safety concerns are raised surrounding AI.

3. Expensive Skill

Most programmers stay away from these kinds of algorithms because of how much power they consume. The foundations of modern AI are machine learning and deep learning, which require an increasing number of CPU cores and graphics processing units.

They need the processing power of a supercomputer, which is expensive. Developers can work more efficiently on AI systems with the help of Cloud Computing and parallel processing systems, but this comes at a cost. The exponential growth in both the volume and complexity of algorithms means that not everyone can afford such a luxury.

4. AI Ethics

There are significant moral questions raised by AI. Unfortunately, until AI is really put into practice, we may not realize these consequences. There have been several instances of unethical behavior throughout the history of artificial intelligence, including invasions of privacy, displays of bias, and the inability to question the results of an AI’s choice.

Therefore, it is essential to recognize and address ethical concerns during the AI development process and thereafter.

5. Limited Information

Despite the fact that Artificial Intelligence may be used to replace antiquated methods in many different industries, it has yet to find widespread adoption. The lack of understanding in the field of AI is the actual obstacle. Only a small subset of the population understands AI’s potential; this includes only tech enthusiasts, students, and academics.

Many SMEs (Small and Medium-Sized Enterprises) might benefit from scheduling their work or learning new, creative ways to boost their output. However, they are unaware of the power of AI at present time.

6. The Issue Of Bias

The quality of an AI system is highly dependent on the quantity of data used for training. Therefore, the key to successful AI systems in the future is the ability to acquire high-quality data. Unfortunately, the quality of the data collected on a daily basis is low and therefore has little value by itself.

They are prejudiced and can only characterize the characteristics of a small group of people who share a certain set of characteristics or interests on the basis of their religion, gender, community, or other racial biases. Determining methods that effectively monitor these issues is the only way to bring about meaningful change.

AI will be hard to trust without a legislative framework guiding its funding, design, and use. Ethics, bias, privacy, and other considerations pose obstacles. It is therefore an urgent need to initiate an open discourse to raise understanding about how this technology is used today and to collectively designate where AI should or should not be employed.

By Olivia Bradley

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