I have an idea for training task-specific AI models using multi-stage training. The first stage would be training the AI on simple data. During subsequent steps, the model would be retrained with increasingly complicated data. However, I am deciding not to work on it for two main reasons.

 First of all, while the idea seems to make sense in my head, it may not be realistic to actually implement. Practical realities are likely to make multi-stage training not make sense. Researchers have tried to implement a multi-stage training process in the past and ran into difficulties. If resolving them is possible, the solution is probably going to be complex and require a more detailed understanding of AI model training.

Also, I think software should be designed from a user first perspective where a developer talks with people and uses the feedback to create a helpful product. Trying to focus on a specific solution would move me away from a user first perspective, even when the solution has uses. User needs for a solution will also fluctuate over time. It’s important to pivot away from working on a technology if it is no longer relevant to what a user is looking for. Also, if a new technology I develop turns out to be useful, other people will try to copy the technology or develop their own version.

When it comes to developing AI technology, I believe in being fully transparent about how it works. If it is based on some proprietary algorithm, people won’t be able to understand it and give feedback. It would also be far more disconnected from users. Also, models can be very complex, and trying to make one that is straightforward enough for people to understand without a background in AI is hard.