Top 9 Challenges in Artificial Intelligence in 2021 | Rohan Girdhani

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How Artificial Intelligence Can Bring Problems

The toast of every technology-driven company is Artificial Intelligence (AI). AI integration gives a company enormous opportunities for transformation to leverage the value chain. AI technologies are a roller-coaster journey, no matter how professional it sounds.

According to a report from Deloitte, approximately 94% of companies face potential problems with artificial intelligence.

As consumers and developers of AI technology, we need to know the benefits and challenges of adopting AI. Knowing these narrow-minded aspects of any technology helps users/developers to mitigate and benefit from the risks associated with the technology.

It is essential to know how a developer should deal with / handle IA problems in the real world. AI technologies should not be seen as an enemy but as a friend.

Here is the list of problems associated with artificial intelligence

Technical Knowledge

The organization must know the latest AI progress and technologies and their shortcomings to integrate, deploy and implement AI applications for the organization. In most organizations, the lack of technical know-how prevents the adoption of this niche domain.

Currently, just six percent of enterprises have a smooth AI ride. The enterprise needs a specialist to identify the roadblocks during the deployment process. The Return team will also be able to track the adoption of AI/ML solutions with skilled human resources.

Price Factor

Small and medium-sized organizations struggle hard to use AI technologies because this is an expensive business. In fact, even major companies like Facebook, Apple, Microsoft, Google, Amazon (FAMGA) allocate an independent budget for AI technology adoption and implementation.

Acquisition of Data and Storage

The acquisition and storage of data are one major artificial intelligence problem. The systems of Business AI depend on the input of sensor data. A mountain of sensor data is gathered for AI validation. Failure to store and analyze irrelevant and noisy datasets may cause blockages.

AI works best if it contains a good number of good data. The algorithm is robust and works as the corresponding data grows. When enough data quality is not fed into it, the AI system fails badly.

With slight input variations in data quality with such profound results and forecasts, there is a real need to make Artificial Intelligence more stable and accurate. In addition, sufficient data, which restricts AI adoption, could not be available in some industries, such as industrial applications.

Expensive Workforce

As mentioned earlier, AI technologies require information scientists, data engineers, and other small and medium-sized enterprises (Subject Matter Experts). In today's market, these specialists are expensive and rare. Small and medium-sized companies do not have a tight budget to contribute to the workforce as required by the project.

Responsibility Issues

It is with great responsibility that AI applications are implemented. Every individual needs to bear the burden of hardware defects of any kind. In the past, determining whether an incident was the consequence of a user, developer, or manufacturer's activities was relatively easy.

Ethical Challenges

Ethics and morality are some of the biggest AI problems still to be addressed. The way the designers handle AI technology is perfect. They can imitate human conversations without issues and make it increasingly difficult to distinguish between a machine and a true customer service representative.

The algorithm of artificial intelligence forecasts based on training. The algorithm labels things according to the data on which it has been trained. Therefore, if the algorithm is introduced in data reflecting racism or sexism, the prediction results will mirror it instead of automatically correct.

It is therefore not possible to understand the correctness of data. Some current algorithms mislabel black people as "gorillas." Consequently, we must ensure that the algorithms are fair, mainly when private and corporate individuals use them.

Computation Speed

AI, machine learning, and solutions for deep understanding require a high degree of computing speeds that only high-end processors offer. The more significant infrastructure and pricing requirements associated with these processors have become an obstacle in their overall adoption of AI technology.

This scenario provides a powerful alternative to these computational requirements for cloud computing systems and some parallel processors.

As the volume of data available for processing increases exponentially, the requirements for calculating speed will increase. The development of next-generation computer infrastructure solutions is imperative.

Legal Challenges

The company may have legal challenges with an AI application with a wrong algorithm and data governance. Once again, this is one of the most significant issues facing a developer in the world of artificial intelligence.

A faulty algorithm made with an inadequate data set may leave a massive dent in the profit of an organization. An incorrect algorithm will always predict wrongly and unfavorably. Is it due to weak and poor data governance that problems such as data breaches are possible?

For an algorithm, the PII (personal information identifiable) is a feed inventory that can slip into the hands of hackers. The organization will therefore fall into legal traps.

Myths and Expectation

The actual potential of the AI system is quite different from that of the generation's expectations. Artificial intelligence, according to Media, is replacing human jobs with its cognitive abilities.

However, the IT industry faces a challenge by accurately conveying that AI is only a tool capable of operating with only the indulgence of the human brain. The outcome of replacing human roles such as routine automation and everyday work, optimizing all industrial activities, data-driven predictions, etc., can certainly be improved.

Vendors Assessing Difficulties

However, AI cannot substitute the caliber of the human brain and what it brings to the table on most occasions (particularly in highly skilled roles).

Tech procurement is very challenging in any emerging field since AI is especially vulnerable. Enterprises face many problems in knowing how to use AI as many non-AI firms do AI washing, and some companies overdo it. AI technology is indeed a luxury retreat because you can't monitor the organization's radical change.

However, a company needs experts that are difficult to find to implement. It requires high-grade calculation processing for successful adoption. Enterprises should focus instead of standing back and ignoring this ground-breaking technology to responsibly mitigate these artificial intelligence problems.

Happy Learning

Rohan Girdhani | IT Consultant & Technology Leader

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