Practical AI examples
| Autor: | Moye Nyuysoni Glein Perry |
| Art: | Project Work |
| Starttermin: | 14.11.2024 |
| Abgabetermin: | 31.03.2025 |
| Betreuer: | Prof. Dr.-Ing. Schneider |
Practical application examples
Advantages and disadvantages of AI compared to conventional data processing
Advantages of AI
Imagine you're using a smart assistant that learns and adapts with each interaction—that’s one of the key advantages of AI over traditional data processing:
1.Flexibility with Unstructured Data:
Unlike conventional systems that rely on pre-defined rules, AI can handle messy, unstructured data (like images, speech, or free-form text). This makes it incredibly versatile in fields like healthcare or social media analysis [Russell & Norvig, 2010 ].
2.Enhanced Pattern Recognition:
AI excels at spotting complex patterns in large datasets. For instance, it can predict trends or detect subtle anomalies in financial transactions, which might be missed by standard data processing methods [Goodfellow et al., 2016 ].
3.Real-Time Decision Making:
Many AI systems are designed to analyze data on the fly. Whether it’s a self-driving car adjusting its path or an e-commerce platform tailoring recommendations instantly, real-time processing is a strong suit of AI.
4.Continuous Learning and Adaptation:
With machine learning, AI systems improve over time. They learn from new data and adjust their strategies, much like how we refine our skills through experience.
5.Automation of Complex Tasks:
AI can take on tasks that require deep analysis or decision-making—tasks that would be too complex or tedious for traditional rule-based systems. This can free up human experts to focus on more strategic challenges.
Disadvantages of AI
While AI brings many benefits, it also comes with its own set of challenges:
1.Data Hunger:
AI models often need huge amounts of quality data to perform well. If the data is scarce or not representative, the model’s performance can suffer [Goodfellow et al., 2016 ].
2.Opacity in Decision-Making:
Many AI systems, especially those using deep learning, operate as “black boxes” where it’s not clear how they arrive at a decision. This lack of transparency can be problematic in fields where understanding the reasoning process is critical.
3.High Resource Requirements:
Training AI models typically requires significant computational power, specialized hardware, and energy. This can make AI implementations more expensive and resource-intensive compared to traditional methods.
4.Risk of Bias:
AI is only as good as the data it learns from. If the training data contains biases, the AI might perpetuate or even amplify these biases, leading to unfair or skewed outcomes.
5.Implementation Complexity:
Developing and maintaining AI systems requires specialized expertise. The complexity of these systems can lead to higher costs and longer development times compared to conventional data processing solutions.
6.Ethical and Regulatory Concerns:
Issues like data privacy, accountability, and ethical use of AI remain significant challenges. These concerns are prompting ongoing debates and new regulations worldwide [IBM Watson, 2020 ].
Conclusion
AI offers transformative advantages—its ability to learn from data, adapt in real time, and handle complex tasks sets it apart from traditional data processing. However, these benefits come with trade-offs, including high data requirements, opacity, and resource demands. Balancing these factors is crucial when deciding which approach best suits a particular application.
References
Russell, S., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach. Prentice Hall. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. IBM Watson (2020). AI in Business: Overcoming the Challenges of Implementing AI. IBM.