According to estimates, 80% of the AI projects fail which is twice the failure of IT projects. The five leading root causes of the failure are: 1. The industry stakeholders misunderstand or miscommunicate what the problem needs to be solved using AI. 2. The industry lack the necessary data to train AI models effectively. 3. The more focus on the use of latest technology than solving the real problems for their intended users. 4. Lack of infrastructure to manage the data and deploy the AI models. 5. In some cases, AI projects fail because the technology is applied to problems that are too difficult for AI to solve. Source: The article (Avoiding the Anti-Patterns of AI, written by James Ryseff, Brandon F. De Bruhl, Sydne J. Newberry)
Adeel Akhtar
5mo
Annotated data, or ground truth, serves as the foundation of any machine learning or AI model, as it defines the real-world expectations against which your model's performance is measured. This labeled dataset, enriched with accurate and detailed annotations, allows your model to learn complex patterns, make precise predictions, and generalize effectively to new data. Without high-quality ground truth, even the most sophisticated models risk being inaccurate or biased, as they lack a reliable baseline for understanding the data's true structure. Therefore, investing in robust and carefully annotated datasets is crucial, as it directly impacts the model's accuracy, reliability, and overall performance in real-world applications.
Gabriel Bițună
5mo
6. Not using symbolic approaches together with the current "AI" models
PhD in Software Engineering | R&D Engineer | System Analyst with 10+ Years of Industry Experience | Dedicated to Practical Research that Fuels Industry Innovation.
5moWrite one research article on this :-) Its a good idea