Artificial intelligence has been a deep impact on human lives and the economy. By 2030 Artificial Intelligence can add about $15.7 trillion to the world economy.  It can boost the business by 40% which is a dramatic increase in the number of AI start-ups that has magnified 14 times since 2000. From space to earth AI has explored new and innovative ways which are really astonishing.  But there are some challenges associated with AI as well. Here we have discussed 10 such problems.

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Top 10 Common Challenges in AI

1. Computing Power

AI needs a lot of computing power to work properly. This is because of its power-hungry algorithms. Machine Learning and Deep Learning are the chores of Artificial Intelligence, and they demand an ever-increasing number of GPUs to work efficiently. AI is being used in various domains from asteroid tracking to healthcare deployment, tracing of cosmic bodies, and much more. This requires enormous computing power which is almost equal to a supercomputer’s computing power which is no doubt a costly factor.

However, with the intervention of cloud computing and parallel processing systems developers nowadays can work on AI systems more effectively. But this does not always happen as it is not possible for everyone to afford the inflow of unprecedented amounts of data along with rapidly increasing complex algorithms.

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2. A lack of technical expertise

A company must have a thorough understanding of AI technologies to integrate, install, and apply AI applications in the enterprise. Due to lack of technical know-how in most of the specialty sectors, the adoption of AI is hampered. Only 6% of businesses are now enjoying a smooth transition to AI technologies. An enterprise needs proper professional and skilled human resources to identify the barriers in the deployment process.

Apart from technology enthusiasts, college students, and researchers, there are only a limited number of people who are aware of the potential of AI.

For example, there are many small and medium enterprises that can have their work scheduled or learn innovative ways to increase their production, manage resources, sell and manage products online, learn and understand consumer behavior and react to the market effectively and efficiently. They are also not aware of service providers such as Google Cloud, Amazon Web Services, and others in the tech industry.

3. Data Privacy and Security

Most of the deep and machine learning models are based on the availability of data and resources to train them. It is not a problem to have data but as this data is generated from millions of users around the globe, there are high chances that this data can be used for bad purposes. It may be a problem of dark web. Some companies have already started working innovatively to bypass these barriers. It trains the data on smart devices, and hence it is not sent back to the servers, only the trained model is sent back to the organization. 

4. Data Scarcity

There is a surge of unethical use of data. As a result, many countries are imposing stringent IT rules to restrict the flow. Thus, these companies now face the problem of using local data for developing applications for the world, and that would result in bias.

The data is a very important aspect of AI, and labeled data is used to train machines to learn and make predictions. Some companies are trying to innovate new methodologies and are focused on creating AI models that can give accurate results despite the scarcity of data. With biased information, the entire system could become flawed.

5. Data gathering and storage

One of the most difficult challenges faced by artificial intelligence is data capturing and storage issues. Sensor data is used as input by business AI systems. A mountain of sensor data is gathered to validate AI. Irrelevant and noisy datasets might be a stumbling block because they are difficult to store and evaluate. AI performs best when a large amount of high-quality data to work with it. As the amount of relevant data grows, the algorithm becomes more powerful and performs well. When not enough high-quality data is supplied into the AI system, it fails miserably.

With slight differences in data quality having such a big impact on outcomes and predictions, there’s a clear need for Artificial Intelligence to be more stable and accurate. Furthermore, sufficient data may not be available in some domains, such as industrial applications, restricting AI adoption.

6. A scarce and costly labor

As previously said, the adoption and deployment of AI technologies necessitate the involvement of experts such as data scientists, data engineers, and other small businesses (Subject Matter Experts). In today’s market, these professionals are both pricey and scarce. Small and medium-sized businesses can’t afford to hire enough people to meet the project’s needs because of their limited budget.

7. Ethical dilemmas

Ethics and morality are some of the fundamental AI issues which are not yet resolved. The developers’ technological grooming of AI bots to the point where they can flawlessly simulate human conversations is making it increasingly difficult to distinguish between a computer and a real customer support representative. Artificial intelligence algorithms predict based on the training given to it. The algorithm will label things as per the assumption of data it is trained on. Hence, it will simply ignore the correctness of data, for example- if the algorithm is trained on data that reflects racism or sexism, the result of prediction will mirror back it instead of correcting it automatically. 

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8. Slowness of computing

AI, machine learning, and deep learning solutions necessitate high-speed computations, which needs high-end CPUs. Larger infrastructure needs and pricing associated with these processors have become a barrier to AI technology’s widespread adoption. In this case, a cloud computingenvironment with several processors working in parallel is a viable option for meeting these computational needs. As the amount of data accessible for processing increases exponentially, so will the computation speed requirements. The development of next-generation computational infrastructure solutions is critical.

9. Legal obstacles

Due to incorrect algorithm and data governance, a corporation may face legal issues. This is another of the most difficult Artificial Intelligence issues that a developer must deal with in the actual world. If it is a faulty algorithm that has been created with the wrong collection of data, it can wreak havoc on a company’s bottom line. A faulty algorithm will always produce inaccurate and negative results. It can fall into the hands of hackers.

10. Difficulty of assessing vendors

In any emerging field, tech procurement is quite challenging as AI is particularly vulnerable. Businesses face a lot of problems to know how exactly they can use AI effectively as many non-AI companies engage in AI washing, some organizations overstate. It’s true that AI technology is a

luxurious retreat because you cannot oversee the radical changes it brings into the organization. However, to implement it an organization needs experts who are hard to find. For successful adoption, it needs a high-degree computation processing. Enterprises should concentrate on how they can responsibly mitigate these artificial intelligence problems rather than staying back and ignoring this ground-breaking technology.