A Note on Artificial Intelligence (AI) Challenges (Part 1 — The Basics)
Artificial Intelligence (AI) is progressing significantly, hitting its stride every moment. From automation to intelligent management & from personal assistants to autonomous vehicles — everything we see today is because of AI. Despite various AI challenges, it has a significant influence on our lives & the economy. According to Fortune Business Insights’s report, the global AI market will grow from 387.45 billion USD in 2022 to 1394.30 billion USD in 2029. According to them, the projected compound annual growth rate (CAGR) will be 20.1% during the forecast period. This article will highlight some critical AI challenges and ways to fix them.
Various AI challenges to consider
Despite its staggering growth, AI faces obstacles that enterprises should fix to keep AI utilization at the top of the industry trends. Here is a list of some challenges and how we should address them.
i. The data acquisition challenge:
It is the most pressing AI challenge that companies are facing. Various branches of AI, such as machine learning and deep learning, require the training of models. This model training phase requires a significant amount of first-party real-life data. Often it becomes difficult to gauge the amount of data a company needs to develop the model accurately or leverage the AI algorithm. This requirement often depends on conversion goals & rates, project goals, perfection requirements, analytics precision, etc.
To resolve this AI challenge, early startups and multi-billion dollar firms strive to accomplish a correct data acquisition strategy. Companies should understand what data they are collecting and from what sources before acquiring them for AI use. In situations where training datasets aren’t available (such as accidents), companies should develop or leverage algorithms to create dummy datasets for model training.
ii. Data privacy control:
Data is an essential commodity whose value keeps on increasing as it fits into modeling & training AI algorithms. As we discussed, the massive amount of real-world data goes into machine learning and deep learning training; there lies a possibility that the data gets stolen or used for illicit purposes. Any massive cyber attack or insider data leakage can cause problems to millions, if not billions, of users. In the worst-case scenario, anyone can sell them on the dark web for monetary benefit.
Enterprises handling real-world data (not dummy ones) must follow strict regulations like GDPR & other data-privacy-controlled policies followed worldwide. Awareness among customers of data privacy is also essential. Policy-makers and security firms should empower users to influence regulatory debate around data privacy. Businesses and enterprises can seek expert security and privacy guidance from security firms.
iii. AI has automated cyber frauds:
Cybercriminals and fraudsters also leverage AI and artificial bots to generate traffic, attack a system (DDoS), harvest fake followers and subscribers, and perform other bot farming schemes. If they get detected, the bot-farming fraudsters quickly devise new mechanisms to trick the system and continue the fraudulent process for monetary benefit. Digital advertisement fraud has also gained momentum with the advent of AI. According to a report, the total cost of ad fraud in 2022 is 81 billion USD. Their prediction says it will increase to 100 billion USD by 2023.
One approach to prevent such fraudulent actions is to implement bot-detection tools and fraud-detection algorithms that can use behavioral analysis and patterns to identify and eliminate such threats. These third-party tools filter traffic that is auto-generated or has anomalies.
iv. Trust deficiency:
Another AI challenge that most AI-based projects face is how deep learning models predict the output. It is worrying a lot of AI engineers and users. Firstly, the data used in training the model can have biasness. Secondly, it becomes more work for users to understand how a specific set of inputs can improvise a solution for distinct scenarios/problems. This level of automation and self-learning often confuses the user, especially the layman. Even numerous companies malpractice this AI training with biased datasets, leading to trust issues. Hence, users are boycotting the use of many AI tools.
To tackle such AI challenges, enterprises and researchers should promote more knowledge on how AI works the difference between supervised & unsupervised learning, etc. Researchers and AI engineers must also follow particular policies while leveraging datasets for training AI algorithms. These company policies should mention that there is no biasness in the datasets used during the training.
Conclusion
The AI challenges are endless and ever-changing. Although AI and its subsidiaries, like machine learning and deep learning, are in their infancy, researchers & engineers have to unlock the potential of AI. However, it could be possible only if they successfully tackle the existing AI challenges. This article highlighted four AI challenges and how enterprises and engineers can address them. To tackle AI security challenges, you can connect to my courses and learn how to defend against such modern threats. They have the expertise to break down various AI security challenges with preventive measures.
