In 2021, the most advanced companies have at least one tool that is enhanced by Artificial Intelligence. AI technologies give a lot of opportunities to businesses – from additional value to public recognition. Everyone wants to be on the top, with a modern tech stack. Nevertheless, AI technologies still have some leaks. In this article I try to explain the most important problems to solve in this sphere.
Despite the fact that Artificial Intelligence started to develop in 1956, computer scientists and engineers still have many issues to figure out and resolve. The pandemics gave a big boost to digitalization and before we make AI the routine technology (like it happened with optical character recognition), we should deal with several imperfections. What are the main AI challenges in 2021?
The cost of expertise
Adoption and deployment of Artificial Intelligence require Subject Matter Experts in this field. The cost of SMEs in machine learning, deep learning, and Big Data is high due to the lack of experts in this field.
Artificial Intelligence is comparably new technology and there are not so many specialists with proper expertise and work experience on the job market. Especially, it’s a big problem for small and medium size companies with their tight budgets.
Ethical problems
Ethical challenges are one of the most discussed topics in the world of Artificial Intelligence. We use digital assistants like Siri and expect the flawless, human-like response. However, not all experiments in the AI field end successfully in terms of ethics, even for big companies like Microsoft (Tay bot case). Here are the most common AI ethical problems described.
Data handling
One of the unsolved problems existing in AI is deeply connected with data management. AI systems engineered for business needs require sensor data. An ungodly amount of data coming from these sensors should be collected for AI validation, because AI trains better on big datasets.
The algorithms of AI learn better on big amounts of data with good quality. AI systems become strong if there’s a growth in the amount of relevant data. But if the quality is bad and the amount of data is limited – you’ll probably get a wrong result.
Too low computation speed
AI systems need top-tier processors because the high computational speed is required for ML and deep learning. Serious requirements for IT infrastructure can cost you a fortune and the software side of AI development is what holds the AI solutions market back from more disruptive growth. Multiple processors and cloud computing solutions can be a good alternative. In this case.
Legal issues
Incorrect algorithms and data governance can be a big problem for any IT company that decides to focus on AI development, not only by budget wasting operations, but also from the legal and regulatory side. The data used for learning the system to make predictions can be hacked if there are any breaches. It can cause wrong results as well as low computational speed, bad quality and low amount of data.
Summary
The next gen AI systems will be more intelligent and optimized for work not only with high-performance processors, but the technical progress is not the only thing we should keep our eyes on when we talk about the future of AI. As the number of AI-enabled devices, AI software and use cases grows, we need to make these technologies lawful (by scenarios of usage and flawless systems) and meeting the ethical standards.