You can discover a new knowledge node through three different methods:
Logic
Empiricism
Communication
True in some sense, but misleading, insofar as “empiricism” usually brings to mind experiments. In practice, people get far, far more bits from observation of the day-to-day world around them than from experiments. Even in experiments, I think most of the value is usually from observing lots of stuff, more than from carefully controlling things.
The scientific method is the current algorithm individual researchers run to discover new knowledge in unknown knowledge space. Researchers are traditionally trained in the scientific method during PhD’s, which is basically an apprenticeship slathered in tradition, and sealed with a protected title. For any real world problem, the scientific method relies on iteration across empirical experiments, designed and analyzed using logic.
Careful here. There’s one thing usually called “the scientific method”, which is basically iteratively coming up with models, testing them experimentally, then updating the models based on the results. If you actually go look at how science is practiced, i.e. the things successful researchers actually pick up during PhD’s, there’s multiple load-bearing pieces besides just that. Some examples include:
fast-check techniques like fermi estimates, dimensional analysis, type tracking, limiting cases
creation or use of new measurement devices or visualization methods
skills for translating intuitions into mathematics, and vice-versa
heuristics for what features to pay attention to or ignore
...
Achieving the Distribution and Query-able properties will presumably hinge on improving coordination tools between researchers (e.g., journals, search engines, research databases, etc). Encoding knowledge space in a searchable format would be more in the order of a paradigm shift for how research is done.
Note that a much simpler first-pass on all these is just “spend a lot more time reading others’ work, and writing up and distilling our own”. Key background idea here is that the internet has already dramatically reduced the cost of finding or sharing information, and the internet is relatively new, so most people probably have not yet increased their information consumption and production as much as would be optimal given the much-reduced cost.
Even in experiments, I think most of the value is usually from observing lots of stuff, more than from carefully controlling things.
I think I mostly agree with you but have the “observing lots of stuff” categorized as “exploratory studies” which are badly controlled affairs where you just try to collect more observations to inform your actual eventual experiment. If you want to pin down a fact about reality, you’d still need to devise a well-controlled experiment that actually shows the effect you hypothesize to exist from your observations so far.
If you actually go look at how science is practiced, i.e. the things successful researchers actually pick up during PhD’s, there’s multiple load-bearing pieces besides just that.
Fair!
Note that a much simpler first-pass on all these is just “spend a lot more time reading others’ work, and writing up and distilling our own”.
I agree, but if people were both good at finding necessary info as an individual and we had better tools for coordinating (e.g.,finding each other and relevant material faster) then that would speed up research even further. And I’d argue that any gains in speed of research is as valuable as the same proportional delay in developing AGI.
True in some sense, but misleading, insofar as “empiricism” usually brings to mind experiments. In practice, people get far, far more bits from observation of the day-to-day world around them than from experiments. Even in experiments, I think most of the value is usually from observing lots of stuff, more than from carefully controlling things.
Careful here. There’s one thing usually called “the scientific method”, which is basically iteratively coming up with models, testing them experimentally, then updating the models based on the results. If you actually go look at how science is practiced, i.e. the things successful researchers actually pick up during PhD’s, there’s multiple load-bearing pieces besides just that. Some examples include:
fast-check techniques like fermi estimates, dimensional analysis, type tracking, limiting cases
creation or use of new measurement devices or visualization methods
skills for translating intuitions into mathematics, and vice-versa
heuristics for what features to pay attention to or ignore
...
Note that a much simpler first-pass on all these is just “spend a lot more time reading others’ work, and writing up and distilling our own”. Key background idea here is that the internet has already dramatically reduced the cost of finding or sharing information, and the internet is relatively new, so most people probably have not yet increased their information consumption and production as much as would be optimal given the much-reduced cost.
I think I mostly agree with you but have the “observing lots of stuff” categorized as “exploratory studies” which are badly controlled affairs where you just try to collect more observations to inform your actual eventual experiment. If you want to pin down a fact about reality, you’d still need to devise a well-controlled experiment that actually shows the effect you hypothesize to exist from your observations so far.
Fair!
I agree, but if people were both good at finding necessary info as an individual and we had better tools for coordinating (e.g.,finding each other and relevant material faster) then that would speed up research even further. And I’d argue that any gains in speed of research is as valuable as the same proportional delay in developing AGI.