The Limitations of AI: Why Generalization is a Challenge
Artificial Intelligence (AI) has the potential to make significant strides in a number of fields, but it faces several limitations that could decelerate its widespread adoption. While AI has many applications, there are fundamental challenges associated with developing algorithms that are capable of generalizing to a wide range of situations. It is important to understand these limitations because they can affect the future of this technology as well as the field as a whole.
In this blog post, we will explore the challenges of AI generalization in the manufacturing industry and discuss the importance of adaptability through human involvement in overcoming these challenges.
One of the key challenges of AI generalization is that AI models are only as good as the data they are trained on. If an AI model is trained only on a limited data set, it will not possess the necessary knowledge and skills to perform effectively in other environments. For example, AI pioneer Andrew Ng has pointed out that an AI model trained on chest X-ray data from a modern hospital with state-of-the-art medical imaging equipment may perform well in diagnosing pneumonia on that specific data. However, this model may not perform as well when it encounters older images that have been captured using less sophisticated equipment by physicians working in a rural hospital without access to advanced diagnostic tools. As a result, it may not be able to accurately identify cases of pneumonia in these older images. In contrast, human radiologists could easily detect these cases because they are familiar with different imaging equipment and understand how to interpret the results of these scans. This shows that AI models can struggle with generalization because training them on a limited set of data can prevent them from gaining the knowledge and expertise that they need to operate successfully in the real world.
The challenges of AI generalization are particularly evident in the manufacturing industry, where AI models are often used for tasks such as predictive maintenance and quality control. These tasks are typically performed in production environments, which can vary significantly from the conditions that the model was trained on. As a result, these models may not perform as expected in these different environments. For instance, a model developed to identify defects on a specific batch of products may not detect the same defects when they are applied to a different batch. Another example would be if AI models were used to conduct predictive maintenance by identifying signs of anomalies on a production line and replacing the parts before they fail. However, if the model were trained to detect anomalies from just one machine, it may not detect these problems on other machines on the same production line. This shows how AI models can be problematic when dealing with variables that are unique to a particular environment or task. AI’s incapability to generalization makes it less feasible for broad applications across different industries. This is why human involvement is still needed to supplement these technologies, particularly in scenarios where the technology needs to be adapted for specific environments or industries.
The limitations of AI that are related to generalization may stem from various technical or conceptual factors. Below is a brief discussion of some of these factors and how they impact generalization of AI technologies:
- Quality and completeness of data: In order for an AI model to be able to generalize well to new situations, it must be trained on a diverse and representative dataset that accurately reflects the real-world conditions it will encounter. However, in many cases, the data available for training AI models in the manufacturing industry may be limited, biased, or incomplete, which can negatively impact the performance of the AI model and its ability to generalize to new situations. For instance, if the training data only includes data from machines that are well-maintained and operate under ideal conditions, the model may not be able to handle data from machines that are older or have been subjected to more wear and tear.
- Overfitting: Data Scientists’ biggest nightmare is when their models are “overfitted” to a particular dataset, causing them to perform poorly when exposed to new data. In other words, the model parameters become overly sensitive to changes in the initial training set and may struggle to handle data that has not been seen in past examples. Manufacturing processes are particularly prone to overfitting because the data collected is often sparse and noisy. This is one of the main reasons why Data Scientists often need to supplement their AI models with human expertise to make the final models more robust and accurate.
- Data labelling: Due to the diverse and complex nature of manufacturing processes, it may be difficult to accurately label all of the raw data in a manufacturing dataset (e.g., photos of products with quality defects) so that it can be used by the AI model in training and testing, in the case of using supervised learning. Supervised learning is a popular technique for training AI models, where the model is given a large dataset with labelled examples and learns to predict the correct label for new examples. However, in the manufacturing industry, obtaining large amounts of labelled data can be difficult and time-consuming, as it typically requires manual labour to label the data and ensure its quality and relevance. This can be a significant barrier to the use of supervised learning algorithms in the manufacturing industry, as it may not be practical or cost-effective to obtain the necessary amount of labelled data covering every machine or product group, production line and process.
Overall, the limitations of AI generalization are an important factor to consider when applying AI to the manufacturing industry. By understanding these limitations and taking steps to address them, it is possible to improve the performance and reliability of AI models in the manufacturing industry and to support their successful deployment in a wide range of manufacturing tasks and environments.
While writing this blog post, I used OpenAI’s ChatGPT, a highly capable AI language model launched on November 30th, 2022 to assist me in generating ideas and suggestions for the content.
This blog post is a case of “AI-ception”, where AI is used to write about AI, and to explore the limitations and challenges of AI within AI itself. This may seem ironic or even like a paradox. But here is the fact: While AI algorithms have their limitations and challenges, especially when it comes to generalization in various tasks and industries, they are also capable of impressive feats, such as writing a blog post about their own limitations. This demonstrates that the potential and limitations of AI vary based on different areas. By exploring the limitations of AI, we can gain a better understanding of its potential and limitations, and how it can be used effectively to improve our lives and industries.
* Interpretation of the illustration by the author.
