What is The Most Common Problem in AI?

Artificial Intelligence is known to have shaken the world bringing 
massive changes and impressive advantages to almost all industries. Businesses have gained extensive transformation from leveraging the value chain. Healthcare systems have witnessed unprecedented improvements ranging from better diagnosis to treatment facilities. However, these disruptive innovations also are met with concerns over the limitations and numerous challenges of AI. Through this post, let us find out various problems of AI and learn in-depth about its drawbacks. 

Top Challenges of AI

Paucity of Technical Knowledge

The process of integrating, deploying, and implementing AI-powered machines and applications requires skills and knowledge, particularly of the current AI innovations and technologies. Organizations are crippled by the limitation of talents and skills that impedes the adoption and full utilization of AI at its fullest potential. Only a few enterprises are capable of capturing AI's fullest advantages. 


Expensive

This is one of the biggest challenges of AI as only big companies are in the capacity to afford advanced technologies and AI technologies. Most small and mid-sized companies are still struggling to adopt AI technologies owing to the high cost of these technologies. Even big tech firms like Google, Amazon, Facebook, Microsoft, and Apple go through separate funds and budget allocations to adopt and implement AI-powered applications, machines, and technologies. 


Data Collection and Storage

One of the significant problems of AI is the issue of data acquisition and storage. Businesses, to make AI applicable largely, require data. As AI machines work on data. Hence, a colossal number of data is collected, and in the process, numerous noisy and irrelevant datasets become problematic as they cause obstruction and make storing and analyzing difficult. The full potential of AI can only be seen in a good amount of quality data. The quality of the AI becomes stronger and better performance is gained when the data fed on the machines are of good quality. 


Issue of responsibility

With great AI work and implementation comes the bigger responsibility of bearing the burden of any malfunction in the hardware. Prior to AI implementation, manual human work makes it easier of detecting the causal action of any such incidents. It is easier to identify who among the users, manufacturers, or developers, is responsible for the malfunction. 

 

Ethical Challenges

Another one of the significant challenges of AI is in the areas concerning ethics and morality. AI technologies and applications are developed to mimic human capacities and to automate the working process. AI has been successful in generating human-like tasks it is often difficult to differentiate whether it is performed by a human or AI. This happens especially in customer support services where AI can converse and chat like a human. The algorithms of AI machines gives prediction based on the training they are given. Hence, the algorithms make assumptions about the data input and label things accordingly. They will produce results as per the data fed to them without any modification to suit the correctness of the results. 


Deficit in Computation Speed

For AI and its advanced sub-components like deep learning and machine learning to perform requires a high level of computation speeds which can only be provided by high-end processors. This involves procuring and installing larger infrastructures which are highly expensive making it a stumbling block in the essential adoption of the AI technology. While other methods provide parallel services and processing systems like cloud computing, it also comes with a price, and not everyone can afford it. The increasing flow of data at an unprecedented rate and the rapidly growing complex algorithms is a complex process that not everyone is in the capacity to afford. 


Legal challenges

Another one of the major problems of AI is its legal challenges. The instances of AI applications with faulty algorithms and data aggregation and governance are potential challenges legally for organizations. Inappropriate or defective datasets result in flawed algorithms which may result in a huge dent in the profits of the organization. An error in the algorithm will produce predictions that are incorrect and unfavorable. Hence, this poor governance may result in problems like data breaches. AI algorithms make use of a user's Personal Identifiable Information akin to feedstock, which runs the risk of falling into the hands of hackers leading to the organizations falling into legal challenge traps. 


Those are the significant ai problems that are working against the full potential of Artificial Intelligence technology.


Also, read:

What are the pros and cons of an AI-based Chatbot?


The Advantages and Disadvantages of Cyber Security: Exploring the Benefits and Drawbacks

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