The key challenges and limitations of current AI and ML technologies include data quality and bias, algorithmic complexity and interpretability, scalability, and cybersecurity concerns. Data quality and bias can lead to inaccurate or unfair outcomes, while complex algorithms can be difficult to interpret and trust. Scalability issues arise as systems grow, and cybersecurity threats pose risks to data and systems.
Researchers and developers are addressing these issues through various approaches. They are focusing on improving data collection and preprocessing to ensure quality and mitigate bias. Explainable AI (XAI) is being developed to make algorithms more transparent and understandable. Scalability is being tackled by optimizing algorithms and using distributed computing. To enhance cybersecurity, researchers are developing more robust intrusion detection systems and exploring quantum-resistant encryption methods. Additionally, interdisciplinary collaboration and ethical considerations are being emphasized to ensure responsible AI development.