good model can then be used to predict whatwould happen in the real world without performing real-world experi-ments
In this way, models could be said to be dynamic according to how often they're 'fed' or designed around training data. Does this mean every time a ML model or AI resource is 'updated,' it is referring to the model having been given more data to model and extract patterns from?
Experienced Artist
As a bioinformatics student, I often find that most Bioinformaticians have assumed the role of experienced artists. It seems the 'composing' in industry tends to fall to software engineers, and 'listening' to subject-matter expert scientists well-versed in theory and application. As a Bioinformatician, you are almost like a middleman between the two other professionals, translating algorithms and leveraging their outputs. It would appear then that a valuable skill to have as a bioinformatics specialist lies in your ability to describe and explain the inner workings of the ML models. This is certainly something I'll pay special attention to during my degree at Guelph.
But on top of that, there are many unintended ways in which feedback might arise, and these are more pernicious and harder to control.
This is an interesting concept. When navigating social media or online websites, we often encounter feedback systems that prompt users to rate their experience with the site or ask them to measure their level of satisfaction with the content. I usually find these prompts irritating as they interrupt my daily 'doom scroll'. I wonder if anyone else has any opinions on these feedback systems? Do you answer honestly or just click the first response that will make it go away?