Artificial intelligence (AI) is omnipresent in a wide range of sectors, from consumer products to business intelligence, and will soon be one of the major forces boosting productivity, expansion, and innovation. There are still many obstacles to overcome, despite the fact that AI has advanced over the previous 60 years. To fully utilise AI's potential, some of these obstacles must be overcome.
1. Limited Data
A training set of data is necessary for an AI system to function. The majority of people may not be aware that labelling training data takes a significant amount of labour. Additionally, a sufficiently extensive and sizable data set must be available for use in the training process. In order to rate an AI system as 'excellent' or 'poor,' it is important to have access to high-quality data. For new apps or start-up businesses, these would frequently be the hurdles.
2. Computing Ability
Most developers avoid them because of how much power these power-hungry algorithms consume. The building blocks of this artificial intelligence are machine learning and deep learning, and in order to function effectively, they require a growing number of cores and GPUs. We have the expertise and ideas to apply deep learning frameworks in many different fields, including asteroid tracking, the delivery of healthcare, the tracing of celestial bodies, and many more.
They need the processing power of a supercomputer, which is expensive. Although developers are able to work on AI systems more successfully because of Cloud Computing and parallel processing systems, they are not without cost.
3. Lack of Trust
The fact that deep learning models' predictions of the output are unknown is one of the most significant aspects that worry AI. It is challenging for the average person to comprehend how a particular collection of inputs might provide a solution for many types of problems.Many people throughout the world are completely unaware of artificial intelligence, including how it works in daily objects like smartphones, smart TVs, banking systems, and even automobiles (at some level of automation).
4. Lack of Information
Despite the fact that there are many applications for artificial intelligence that are superior to current systems on the market, Understanding artificial intelligence is the true issue. There aren't many people who are aware of the possibilities of AI besides computer enthusiasts, college students, and researchers.Many SMEs (Small and Medium Enterprises), for instance, can arrange their work or find creative ways to enhance productivity, manage resources, sell and manage products online, learn and understand consumer behaviour, and respond to the market effectively and efficiently. Additionally, they lack knowledge of IT sector service providers like Google Cloud, Amazon Web Services, and others.
5. The bias issue
The quantity of data that an AI system is educated on truly determines how excellent or horrible it is. Consequently, the key to developing effective AI systems in the future is the capacity for gathering high-quality data. The daily information that the organisations gather, however, is subpar and has no intrinsic value.
They are prejudiced and only serve to describe the characteristics of a small group of individuals who share similar interests based on prejudices related to race, including those related to religion, ethnicity, gender, and community. Only by creating certain algorithms that can effectively track these issues will a real change be brought about.