Recommendations & Top 3 Techniques
The attitude towards AI is becoming more positive, which is why more customers have started to prefer companies and brands that leverage AI (See Figure 1). From using chatbots to obtain faster customer service to more personalized healthcare, AI applications are observed everywhere, even in simple daily tasks such as using the navigation on your smartphone.
With this increasing level of trust in AI, the demand for it to be more accurate and unbiased also rises. However, AI and Ml models are known to degrade1 over time. It does not matter how sophisticated the algorithms are or how diverse the dataset is; if the model is not re-trained or improved over time, it can fail to deliver the required results.
Figure 1. Global opinion of AI
This article explores the top 5 approaches to improve your AI/Ml models and help developers and digital transformation leaders maintain or improve the level of quality achieved from their AI-powered solutions.
Recommendations on how to approach AI/ML model improvement
This section highlights 2 recommendations on what to do before implementing the 3 AI improvements techniques mentioned in the article:
Monitor performance
You can only improve something by knowing its areas to improve. This can be done by monitoring the features of the AI/ML model. However, if all the model features can not be monitored, only a selected number of key features can be observed to study variations in their output that can impact the model performance.
Hypothesis generation
Prior to selecting the right method, we recommend performing hypothesis generation. This is a pre-decisional process to structure the decision process and narrow down the options. This process involves gaining domain knowledge, studying the problem the AI/ML model is facing, and narrowing down readily available options that can tackle the identified issues.
Top 3 ways to improve an AI/ML model
1. Feed more data
Adding new and fresh data is one of the most common and effective methods of improving the accuracy of your machine-learning model. Now that AI solutions are becoming more complex and cater to a larger user base, better and more diverse data is required to develop them.
For instance, a recent paper2 by MIT presents a complex deep-learning model that helps object detection systems understand the interactions between two objects. The paper concluded that the model is susceptible3 to dataset bias and requires complex datasets to produce results.
Research4 has also shown a positive correlation between dataset size and AI model accuracy (See figure below).
Therefore, expanding the dataset that is used for retraining the model can be an effective way to improve AI/ML models. Make sure that the data changes according to the environment it is deployed. It is also important to follow proper data collection quality assurance practices.
Sponsored
Working with a data collection/harvesting service can effectively acquire large-scale and diverse datasets. Clickworker offers such datasets through its crowdsourcing platform. It works with over 4 million registered data collectors from different countries and offers scalable datasets to re-train and improve your AI models.
2. Enrich the data
Expanding the dataset is one of many ways to improve AI/ML models. Another important way of enhancing AI is enriching the data. This simply means that the new data that is collected to expand the dataset must be processed and of high quality. This can also mean improving the annotation of the existing dataset. Since new and improved labeling techniques are developed, they can be implemented on the existing or newly gathered dataset to improve model accuracy.
3. Improve the algorithm
Sometimes, the algorithm that was initially created for the model needs to be improved. This can be due to different reasons, including a change in the population that the model is deployed on.
For instance, studies show that patients with lower income levels have a greater health risk as compared to patients with higher income levels. Suppose a deployed AI/ML algorithm that evaluates the patient’s health risk and does not include the income level parameter is suddenly exposed to data of patients with lower income levels. In that case, it is unlikely to produce fair evaluations.
Therefore, upgrading the algorithm and adding new parameters to it can be an effective way to improve model performance.
For more in-depth knowledge on data collection for AI/ML models, feel free to download our whitepaper:
Get Data Collection Whitepaper
Further reading
If you need help finding a vendor or have any questions, feel free to contact us:
References
- Vela, D., Sharp, A., Zhang, R., Nguyen, T., Hoang, A., & Pianykh, O. S. (2022). Temporal quality degradation in AI models. Scientific Reports, 12(1), 1-12.
- Liu, N., Li, S., Du, Y., Tenenbaum, J., & Torralba, A. (2021). Learning to compose visual relations. Advances in Neural Information Processing Systems, 34, 23166-23178.
- Anyverse (February 28, 2022), “More complex deep learning models require more complex data.” Retrieved: November 22, 2022.
- Adey, Brenton ( Apr 27, 2021), “Investigating ML Model Accuracy as Training Size Increases” Retrieved: November 22, 2022.
The attitude towards AI is becoming more positive, which is why more customers have started to prefer companies and brands that leverage AI (See Figure 1). From using chatbots to obtain faster customer service to more personalized healthcare, AI applications are observed everywhere, even in simple daily tasks such as using the navigation on your smartphone.
With this increasing level of trust in AI, the demand for it to be more accurate and unbiased also rises. However, AI and Ml models are known to degrade1 over time. It does not matter how sophisticated the algorithms are or how diverse the dataset is; if the model is not re-trained or improved over time, it can fail to deliver the required results.
Figure 1. Global opinion of AI
This article explores the top 5 approaches to improve your AI/Ml models and help developers and digital transformation leaders maintain or improve the level of quality achieved from their AI-powered solutions.
Recommendations on how to approach AI/ML model improvement
This section highlights 2 recommendations on what to do before implementing the 3 AI improvements techniques mentioned in the article:
Monitor performance
You can only improve something by knowing its areas to improve. This can be done by monitoring the features of the AI/ML model. However, if all the model features can not be monitored, only a selected number of key features can be observed to study variations in their output that can impact the model performance.
Hypothesis generation
Prior to selecting the right method, we recommend performing hypothesis generation. This is a pre-decisional process to structure the decision process and narrow down the options. This process involves gaining domain knowledge, studying the problem the AI/ML model is facing, and narrowing down readily available options that can tackle the identified issues.
Top 3 ways to improve an AI/ML model
1. Feed more data
Adding new and fresh data is one of the most common and effective methods of improving the accuracy of your machine-learning model. Now that AI solutions are becoming more complex and cater to a larger user base, better and more diverse data is required to develop them.
For instance, a recent paper2 by MIT presents a complex deep-learning model that helps object detection systems understand the interactions between two objects. The paper concluded that the model is susceptible3 to dataset bias and requires complex datasets to produce results.
Research4 has also shown a positive correlation between dataset size and AI model accuracy (See figure below).
Therefore, expanding the dataset that is used for retraining the model can be an effective way to improve AI/ML models. Make sure that the data changes according to the environment it is deployed. It is also important to follow proper data collection quality assurance practices.
Sponsored
Working with a data collection/harvesting service can effectively acquire large-scale and diverse datasets. Clickworker offers such datasets through its crowdsourcing platform. It works with over 4 million registered data collectors from different countries and offers scalable datasets to re-train and improve your AI models.
2. Enrich the data
Expanding the dataset is one of many ways to improve AI/ML models. Another important way of enhancing AI is enriching the data. This simply means that the new data that is collected to expand the dataset must be processed and of high quality. This can also mean improving the annotation of the existing dataset. Since new and improved labeling techniques are developed, they can be implemented on the existing or newly gathered dataset to improve model accuracy.
3. Improve the algorithm
Sometimes, the algorithm that was initially created for the model needs to be improved. This can be due to different reasons, including a change in the population that the model is deployed on.
For instance, studies show that patients with lower income levels have a greater health risk as compared to patients with higher income levels. Suppose a deployed AI/ML algorithm that evaluates the patient’s health risk and does not include the income level parameter is suddenly exposed to data of patients with lower income levels. In that case, it is unlikely to produce fair evaluations.
Therefore, upgrading the algorithm and adding new parameters to it can be an effective way to improve model performance.
For more in-depth knowledge on data collection for AI/ML models, feel free to download our whitepaper:
Get Data Collection Whitepaper
Further reading
If you need help finding a vendor or have any questions, feel free to contact us:
References
- Vela, D., Sharp, A., Zhang, R., Nguyen, T., Hoang, A., & Pianykh, O. S. (2022). Temporal quality degradation in AI models. Scientific Reports, 12(1), 1-12.
- Liu, N., Li, S., Du, Y., Tenenbaum, J., & Torralba, A. (2021). Learning to compose visual relations. Advances in Neural Information Processing Systems, 34, 23166-23178.
- Anyverse (February 28, 2022), “More complex deep learning models require more complex data.” Retrieved: November 22, 2022.
- Adey, Brenton ( Apr 27, 2021), “Investigating ML Model Accuracy as Training Size Increases” Retrieved: November 22, 2022.
Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the hyperlink to the primary source is specified. All trademarks belong to their rightful owners, all materials to their authors. If you are the owner of the content and do not want us to publish your materials, please contact us by email – [email protected]. The content will be deleted within 24 hours.