ByDarko Savic
Unrelated expertise exchange network
4 years ago
1
Create newCreate new
Idea
Darko Savic Jul 24, 2021
Repository of stuff that is likely to spark novel ideas in people
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Originality
Is it original or innovative?
Feasibility
Is it feasible?
Necessity
Is it targeting an unsolved problem?
Conciseness
Is it concisely described?
Bounty for the best solution
Provide a bounty for the best solution
Bounties attract serious brainpower to the challenge.
A repository of references to inspirational content that sparked novel ideas in people and is likely to do the same for others.
People would add links to videos, articles, books,... and quote the timestamp/text that sparked an idea in their mind. They should aim to describe their exact thought process at that eureka moment. They could specify which other pieces of knowledge influenced the final idea. Ideas are often remixes of various pieces of info and circumstances.
People would write a brief story on how they came up with their idea and links/quotes of the most influential factors.
The repository could be useful for drawing new conclusions about ideation, finding new patterns, or learning from each other's thought processes.
Integral part of an existing ideation platform
Something like this would make sense in a place where people talk about ideas. It could be integrated into an existing platform (like this one) as an optional field "How did you come up with this idea?".
I noticed that in our idea descriptions we often provide credit with "this idea was inspired by <link to other idea>". What I propose here is to make this more elaborate and add a dedicated section for it.
Inspirational content rating
The entire repository could be useful to ideators as idea starting material. They could browse through the links that inspired other people's ideas in hopes that it could do the same for them. Sort of like a fishing expetidion for sparking creativity.
People could rate each idea starter based on what it did for them. Underneath each reference there could be a button "Did this inspire your creativity?" yes/no
Based on feedback, the most idea-starting content would surface to the top of the list. Such a repository would then be an ideal tool for jump-starting creative thinking.
The repository would have to be moderated to keep self-promotion and spam at bay.
2
Creative contributions
Learn problem-solving and creative thinking
On quora, I often see questions like "How do you teach problem-solving skills?"
The repository of inspirational content and the accompanying stories of how people came up with ideas could be used for educational purposes. Readers could follow along and in time learn to mimic the thought processes until they adopt them fully.
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Stuff that surfaces to the top should be studied
Based on feedback from repository users, some content will surface to the top for their ability to spark creative thinking. These content pieces should be studied for similarities, extractable principles, identifiable elements, styles how they were made, and so on. This could result in people figuring out novel ways of creating content that sparking creative thinking in readers/viewers/listeners.
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salemandreus3 years ago
Learning from the best ideas certainly sounds like a great way to go!
Probably a more efficient/scalable way to group these similarities than the manual study approach would be using unsupervised machine learning, and more specifically using clustering (https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68). This is similar to how Netflix identifies which shows to recommend to users based on what viewers who watched what they watched also watch or enjoy.
The reasons for using this would be that firstly it will reveal less obvious and potentially changing trends which human observers might not be aware of.
Secondly, requiring no training data and thus no human to facilitate (or in this case limit) this allows it to scale efficiently with the expected big data, similarly to how Gmail spam filters can easily identify spam from previously unidentified and related factors without relying on a human to tweak the algorithm or someone on the back-end to identify exact keywords.
In this case, the input where individual users occasionally mark an email as spam or ignore them, or watch a Netflix series to the end or upvote similar movies are comparable to how activity and upvotes on posts and even new creative suggestions linking off those posts identify these new data and trends which would “surface to the top” as you said.
The best part is that we would not even necessarily need to study or monitor it even at a final “review” stage either - recommendations would be generated from the algorithm itself just as in the Gmail spam-filter and Netflix recommendations examples, so the algorithm would be identifying possibilities and correlations out there that even we humans would not have thought to put together (or which would have been way WAY too intensive for us to process that amount of data to correlate), sparking new creative inspirations!
Deep learning (https://www.ibm.com/cloud/learn/deep-learning) can be used in combination with clustering to gain deeper insights into these, similarly to how a human would derive these, again at scale for big data. Some useful links that go deeper into how to utilize deep learning with clustering:
https://deepnotes.io/deep-clustering
https://openreview.net/pdf?id=B1eT9VMgOX
https://divamgupta.com/unsupervised-learning/2019/03/08/an-overview-of-deep-learning-based-clustering-techniques.html
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