Artificial Intelligence involves the pursuit of human-ness in technology. Like teaching a child, AI development involves two things. The first being providing the study material (training data) and second being the learning method (Machine Learning, Deep Learning, etc.).
For an ML model to perform well, it requires extensive training with a variety of training data. ML models consuming large amounts of training data allows them to understand diverse examples. And, a comprehensive training process increases the model’s odds of understanding and acting on the data at hand.
The common problem faced by most developers is a misapplication of what was mentioned above. Simple strategy based problems have quick fixes, but those can seem distant or non-existent during the thick of the development phase. Here are some of the common mistakes developers make while training AI models, along with tips to avoid them:
Poor training data development
Training data is the juice that keeps AI/ML models functioning. Bad quality training data leads to bad quality results. It’s as simple as that. Bad quality is a broad term here, so allow me to break it down:
Lack of training data
ML models need multiple examples of a situation to understand how to tackle it. When there is a lack of training data, your model will not be able to identify real-world examples effectively. Analogous to how we learn, an AI model can function as required only if there has been a large number of examples to learn from (in this case, a large amount of training data).
Having a large volume of training data is worth nothing if it’s quality is below par. Training data that’s riddled with errors will only confuse your ML model, which will render it unusable. Think about it, you can’t expect a student to learn if the reading material is filled with mistakes.
Common examples of unclean data include inaccurately annotated images and videos, irrelevant data points, faulty conversational datasets (generally poor grammar and tonal issues).
To add the element of human experience to your AI/ML model, developers need to train it to understand specific rare scenarios and edge cases. Many AI developers falter here. They build algorithmically sound models, but they don’t train it to perform well when encountered with uncommon scenarios. For example, if an autonomous vehicle isn’t trained to tackle rare situations (such as protestors on the street, kids randomly running, etc.), the end result could be fatal.
The straightforward but tedious solution to solving this is exploring all scenarios your model might encounter, and feed datasets that represent all possible circumstances.
AI/ML model development snags
Even if the training data is sound, the AI/ML model at hand needs to be powerful enough to not only consume that data but reproduce usable results. Here are some common mistakes:
Machine Learning where it isn’t necessary
Yes, in many scenarios, companies decide to implement machine learning even when it doesn’t serve the purpose or serves it inadequately. In many situations, procedural logic does the job, so determine the need for ML implementation accordingly.
Even if an ML model can perform the right processes with the data fed to it, there might be issues beyond training data and AI/ML algorithms that can restrict the model from functioning effectively. Consider this performance-related issue: if the model exhibits a lag while producing results, that might not help in certain use cases. Taking the example of an autonomous vehicle, if it takes even as long as a second to identify a pedestrian in the middle of a street, the vehicle might still end up causing an accident. Factors surrounding performance influence real-life consequences, so it’s important to identify such issues.
Mixing up correlation and causation
It’s easy to allow your ML model to function based on correlating certain data points consumed to determine a cause. Consider this conflation of correlation and causation: “The faster windmills rotate, the more wind is observed. Hence wind is caused by rotation.”
While that statement’s fault might seem obvious to us, it might be fair logic to an AI/ML model’s mind. In most cases, acting based on correlation may not have significant adverse consequences. But, it displays an inaccuracy in the model’s algorithm. Ideally, correlation and causation shouldn’t be misunderstood, even by an AI/ML model.
Training an AI model is no simple feat. It involves a comprehensive understanding of the human mind and a serious attempt to replicate it. We’re making great strides in the science of Artificial Intelligence, but we still have ways to go. We can traverse those ways faster if we identify and eliminate key mistakes that make our model’s performance suffer. And we can do that only if we understand the common mistakes that we need to avoid while training and developing our AI models.
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