Last time we discussed the difference between information and a question or a problem, and I suggested that the novelty-satisfied mode of information presentation isn’t as good as addressing actual questions or problems. In chapter 3 which I have not typed up thoughts about, A three step procedure is introduced
Topic: “I am studying …”
Question: ”… because I want to find out what/why/how …”
Significance: ”… to help my reader understand …”
As we elaborate on the different kinds of problems, we will vary this framework and launch exercises from it.
Some questions raise problems, others do not. A question raises a problem if not answering it keeps us from knowing something more important than its answer.
The basic feedback loop introduced in this chapter relates practical with conceptual problems and relates research questions with research answers.
Practical problem -> motivates -> research question -> defines -> conceptual/research problem -> leads to -> research answer -> helps to solve -> practical problem (loop)
What should we do vs. what do we know—practical vs conceptual problems
Opposite eachother in the loop are practical problems and conceptual problems. Practical problems are simply those which imply uncertainty over decisions or actions, while conceptual problems are those which only imply uncertainty over understanding. Concretely, your bike chain breaking is a practical problem because you don’t know where to get it fixed, implying that the research task of finding bike shops will reduce your uncertainty about how to fix the bike chain.
Conditions and consequences
The structure of a problem is that it has a condition (or situation) and the (undesirable) consequences of that condition. The consequences-costs model of problems holds both for practical problems and conceptual problems, but comes in slightly different flavors. In the practical problem case, the condition and costs are immediate and observed. However, a chain of “so what?” must be walked.
Readers judge the significance of your problem not by the cost you pay but by the cost they pay if you don’t solve it… To make your problem their problem, you must frame it from their point of view, so that they see its cost to them.
One person’s cost may be another person’s condition, so when stating the cost you ought to imagine a socratic “so what?” voice, forcing you to articulate more immediate costs until the socratic voice has to really reach in order to say that it’s not a real cost.
The conceptual problem case is where intangibles play in. The condition in that case is always the simple lack of knowledge or understanding of something. The cost in that case is simple ignorance.
Modus tollens
A helpful exercise is if you find yourself saying “we want to understand x so that we can y”, try flipping to “we can’t y if we don’t understand x”. This sort of shifts the burden on the reader to provide ways in which we can y without understanding x. You can do this iteratively: come up with _z_s which you can’t do without y, and so on.
Pure vs. applied research
Research is pure when the significance stage of the topic-question-significance frame refers only to knowing, not to doing. Research is applied when the significance step refers to doing. Notice that the question step, even in applied research, refers to knowing or understanding.
Connecting research to practical consequences
You might find that the significance stage is stretching a bit to relate the conceptual understanding gained from the question stage. Sometimes you can modify and add a fourth step to the topic-question-significance frame and make it into topic-conceptual question-conceptual significance-possible practical application. Splitting significance into two helps you draw reasonable, plausible applications. A claimed application is a stretch when it is not plausible. Note: the authors suggest that there is a class of conceptual papers in which you want to save practical implications entirely for the conclusion, that for a certain kind of paper practical applications do not belong in the introduction.
AI safety
One characterisitic of AI safety that makes it difficult both to do and interface with is the chains of “so what” are often very long. The path from deconfusion research to everyone dying or not dying feels like a stretch if not done carefully, and has a lot of steps when done carefully. As I mentioned in my last post, it’s easy to get sucked into the “novel information for it’s own sake” regime at least as a reader. More practical oriented approaches are perhaps those that seek new regimes for how to even train models, and the “so what?” is answered “so we have dramatically less OODR-failures” or something. The condition-costs framework seems really beneficial for articulating alignment agendas and directions.
Misc
“Researchers often begin a project without a clear idea of what the problem even is.”
Look for problems as you read. When you see contradictions, inconsistencies, incomplete explanations tentatively assume that readers would or should feel the same.
Ask not “Can I solve it?” but “will my readers think it ought to be solved?”
“Try to formulate a question you think is worth answering, so that down the road, you’ll know how to find a problem others think is worth solving.”
Questions and Problems—thoughts on chapter 4 of Craft of Doing Research
Last time we discussed the difference between information and a question or a problem, and I suggested that the novelty-satisfied mode of information presentation isn’t as good as addressing actual questions or problems. In chapter 3 which I have not typed up thoughts about, A three step procedure is introduced
Topic: “I am studying …”
Question: ”… because I want to find out what/why/how …”
Significance: ”… to help my reader understand …” As we elaborate on the different kinds of problems, we will vary this framework and launch exercises from it.
The basic feedback loop introduced in this chapter relates practical with conceptual problems and relates research questions with research answers.
What should we do vs. what do we know—practical vs conceptual problems
Opposite eachother in the loop are practical problems and conceptual problems. Practical problems are simply those which imply uncertainty over decisions or actions, while conceptual problems are those which only imply uncertainty over understanding. Concretely, your bike chain breaking is a practical problem because you don’t know where to get it fixed, implying that the research task of finding bike shops will reduce your uncertainty about how to fix the bike chain.
Conditions and consequences
The structure of a problem is that it has a condition (or situation) and the (undesirable) consequences of that condition. The consequences-costs model of problems holds both for practical problems and conceptual problems, but comes in slightly different flavors. In the practical problem case, the condition and costs are immediate and observed. However, a chain of “so what?” must be walked.
One person’s cost may be another person’s condition, so when stating the cost you ought to imagine a socratic “so what?” voice, forcing you to articulate more immediate costs until the socratic voice has to really reach in order to say that it’s not a real cost.
The conceptual problem case is where intangibles play in. The condition in that case is always the simple lack of knowledge or understanding of something. The cost in that case is simple ignorance.
Modus tollens
A helpful exercise is if you find yourself saying “we want to understand x so that we can y”, try flipping to “we can’t y if we don’t understand x”. This sort of shifts the burden on the reader to provide ways in which we can y without understanding x. You can do this iteratively: come up with _z_s which you can’t do without y, and so on.
Pure vs. applied research
Research is pure when the significance stage of the topic-question-significance frame refers only to knowing, not to doing. Research is applied when the significance step refers to doing. Notice that the question step, even in applied research, refers to knowing or understanding.
Connecting research to practical consequences
You might find that the significance stage is stretching a bit to relate the conceptual understanding gained from the question stage. Sometimes you can modify and add a fourth step to the topic-question-significance frame and make it into topic-conceptual question-conceptual significance-possible practical application. Splitting significance into two helps you draw reasonable, plausible applications. A claimed application is a stretch when it is not plausible. Note: the authors suggest that there is a class of conceptual papers in which you want to save practical implications entirely for the conclusion, that for a certain kind of paper practical applications do not belong in the introduction.
AI safety
One characterisitic of AI safety that makes it difficult both to do and interface with is the chains of “so what” are often very long. The path from deconfusion research to everyone dying or not dying feels like a stretch if not done carefully, and has a lot of steps when done carefully. As I mentioned in my last post, it’s easy to get sucked into the “novel information for it’s own sake” regime at least as a reader. More practical oriented approaches are perhaps those that seek new regimes for how to even train models, and the “so what?” is answered “so we have dramatically less OODR-failures” or something. The condition-costs framework seems really beneficial for articulating alignment agendas and directions.
Misc
“Researchers often begin a project without a clear idea of what the problem even is.”
Look for problems as you read. When you see contradictions, inconsistencies, incomplete explanations tentatively assume that readers would or should feel the same.
Ask not “Can I solve it?” but “will my readers think it ought to be solved?”
“Try to formulate a question you think is worth answering, so that down the road, you’ll know how to find a problem others think is worth solving.”