A powerful AI model is trained from a rich data set, so curating purposeful data makes all the difference.
The first step is to make sure the content you're feeding it is relevant and impactful, do not waste time on adjacent or redundant material.
For example, if you were building a vehicle appraisal agent for a car dealership, you would not fill your image set with trucks or motorcycles.
After you have narrowed your data set to a sufficient niche, you then need to diversify within the data set. Make sure there is sufficient diversity of angles, lighting and sizes or proportions. This will make sure your AI model has experience viewing real world objects from the plethora of perspectives that it would encounter when actively assisting you.
Another helpful element for your data set is annotation and domain specificity, these often will go hand in hand. The more specialized your AI is the more exact and detailed your annotation and context will need to be. If it's in a medical context make sure your feeding it x rays with doctor notes so that future x rays can be properly analyzed by the AI.
While it seems counter intuitive, it is also very impactful to include fuzzy or noisy data, after all you can't learn what something is without some of what it isn't. Include things like misspellings, inaccuracies or incorrect answers to questions. With proper annotations and labeling this will help the AI recognize when things are not in line with its specialty and can aid you in pointing out anomalies that you will encounter in real world use cases.
Finally all this prep will only get you so far, the model will need to be reinforced by real world user feedback. Just like a real human employee on the job, even with their expertise there is always room for improvement, so make sure you are critiquing your AI's responses or actions on a regular basis for further refinement.
I hope this article kick starts your AI training regiment, but experimentation is a part of the fun and magic of technology, so make sure to adjust for your own specific case and let me know what sorts of data sets you found most helpful in training your AI!
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