Learn about AI and GDPR
Training of AI models
When training AI models it is important to achieve the best possible results. There are several different methods to use for training AI models.
Training of AI models
By training an AI model, it should then be able to perform a specific task. A general starting point is the more data it gets access to, the better results it can produce. However, more factors play a role in the result as well.
For example, the quality of the data and learning systems in the model. Remember to use relevant data for the purpose of the AI model.
Proper labelling of data
In order for an AI model to function effectively in supervised learning, proper labelling of the data plays an important role.
Examples of different training methods that can be used for AI models
Supervised learning
This is a learning method that involves giving the AI model labeled data (the correct answer). For example, give 1000 pictures of cars and trucks, so that the AI model can recognize the difference between them.
Unsupervised learning
This refers to an algorithm that has to find patterns itself, without it having been trained to predetermined answers. In other words, unmarked data is used in the training of the AI model.
Reinforcement learning
This is a form of machine learning that involves an algorithm learning to make optimal decisions. This is done by evaluating actions positively or negatively based on the desired end result or goal.
Semi-supervised learning
This is a combination of supervised and unsupervised learning. Normally, a smaller amount of marked data and a larger amount of unmarked data are used for learning and thus be able to predict results.
Results regarding the training of the AI model
The result of machine learning, including deep learning and amplification learning, becomes an AI model. Note that the AI model rarely stores the training data. Instead, it consists of a representation of the data that the model has used to train on. In some cases, however, it is possible to revoke the training data.
What is the difference between a static AI model and a dynamic AI model?
It can be helpful to know the difference between a static AI model and a dynamic AI model, in order to know which one is best suited when developing an AI model.
Static AI model
If an AI model uses a static model, it is trained to produce a result that will not change. In other words, it produces the same result throughout its life cycle. In this way, you can have better control over the model during use.
Dynamic AI model
Unlike a static model, a dynamic AI model can use data to improve and develop itself. One disadvantage is that you have less control over the changes, than when using static AI models.
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The AI Act in the EU
AI has had and will continue to have a major impact on businesses. There are many benefits to using AI models in everything from creating a good customer profile, recruitment, streamlining communication and much more. However, there are also risks associated with the use of AI. The EU has created the AI Act to create a safe and ethically sustainable environment for both citizens and innovation in AI. Among other things, the AI Act divides risks into four categories, one of which concerns AI models with unacceptable risk and which are prohibited. The GDPR and the AI Act apply in parallel.