- Apr 2022
Since most of our feeds rely on either machine algorithms or human curation, there is very little control over what we actually want to see.
While algorithmic feeds and "artificial intelligences" might control large swaths of what we see in our passive acquisition modes, we can and certainly should spend more of our time in active search modes which don't employ these tools or methods.
How might we better blend our passive and active modes of search and discovery while still having and maintaining the value of serendipity in our workflows?
Consider the loss of library stacks in our research workflows? We've lost some of the serendipity of seeing the book titles on the shelf that are adjacent to the one we're looking for. What about the books just above and below it? How do we replicate that sort of serendipity into our digital world?
How do we help prevent the shiny object syndrome? How can stay on task rather than move onto the next pretty thing or topic presented to us by an algorithmic feed so that we can accomplish the task we set out to do? Certainly bookmarking a thing or a topic for later follow up can be useful so we don't go too far afield, but what other methods might we use? How can we optimize our random walks through life and a sea of information to tie disparate parts of everything together? Do we need to only rely on doing it as a broader species? Can smaller subgroups accomplish this if carefully planned or is exploring the problem space only possible at mass scale? And even then we may be under shooting the goal by an order of magnitude (or ten)?
- research workflows
- algorithmic feeds
- passive acquisition
- active acquisition
- library stacks
- controlled sloppiness
- artificial intelligence
- problem spaces
three steps required to solve the all-importantcorrespondence problem. Step one, according to Shenkar: specify one’s ownproblem and identify an analogous problem that has been solved successfully.Step two: rigorously analyze why the solution is successful. Jobs and hisengineers at Apple’s headquarters in Cupertino, California, immediately got towork deconstructing the marvels they’d seen at the Xerox facility. Soon theywere on to the third and most challenging step: identify how one’s owncircumstances differ, then figure out how to adapt the original solution to thenew setting.
Oded Shenkar's three step process for effective problem solving using imitation: - Step 1. Specify your problem and identify an analogous problem that has been successfully solved. - Step 2. Analyze why the solution was successful. - Step 3. Identify how your problem and circumstances differ from the example problem and figure out how to best and most appropriately adapt the original solution to the new context.
The last step may be the most difficult.
The IndieWeb broadly uses the idea of imitation to work on and solve a variety of different web design problems. By focusing on imitation they dramatically decrease the work and effort involved in building a website. The work involved in creating new innovative solutions even in their space has been much harder, but there, they imitate others in breaking the problems down into the smallest constituent parts and getting things working there.
Link this to the idea of "leading by example".
Link to "reinventing the wheel" -- the difficulty of innovation can be more clearly seen in the process of people reinventing the wheel for themselves when they might have simply imitated a more refined idea. Searching the state space of potential solutions can be an arduous task.
Link to "paving cow paths", which is a part of formalizing or crystalizing pre-tested solutions.
- Feb 2022
A user talks about why they've stopped using Roam Research.
I suspect that a lot of people have many of the same issues and to a great extent, it's a result of them not understanding the underlying use cases of the problems they're trying to solve.
This user is focusing on it solving the problem of where one is placing their data in hopes that it will fix all their problems, but without defining the reason why they're using the tool and what problems they hope for it to solve.
Note taking is a much broader idea space than many suppose.