I came across a 2001 paper in Science (PDF) recently that lines up with some thinking I'd been doing myself. The web is full of futuristic literature that envisions man's intellectual legacy being carried forward by computers at a greatly increased pace; this is one of the ideas covered under the umbrella term "technological singularity".
In machine learning there are lots of approaches and algorithms that are relevant to the scientific method. The ML folks have long been working on the problem of taking a bunch of data and searching it for organizing structure. This is an important part of how you would formulate a hypothesis when looking at the bunch of data. You would then design experiments to test the hypothesis. If you wanted to automate everything completely, you'd run the experiment in a robotic lab. Conceivably, science could be done by computers and robots without any human participation, and that's what the futurists envision.
The Science paper goes into pretty deep detail about the range and applicability of machine learning methods, as things stood in 2001. I find ML an interesting topic, but I can't claim any real knowledge about it. I'll assume that somebody somewhere can write code to do the things claimed by the paper's authors. It would be fascinating to try that myself some day.
To bring this idea closer to reality, what we need is a widely accepted machine-readable representation for hypotheses, experiments, and experimental results. Since inevitably humans would also participate in this process, we need representations for researchers (human, and possibly machine) and ratings (researcher X thinks hypothesis Y is important, or unimportant, or likely to be true, or likely to be false). So I have been puttering a little bit with some ideas for an XML specification for this sort of ontology.
Specifying experiments isn't that tricky: explain what equipment and conditions and procedure are required, and explain where to look for what outcome, and say which hypotheses are supported or invalidated depending on the outcome. Experimental results are likewise pretty simple. Results should refer to the experiments under test, identifying them in semantic web style with a unique permanently-assigned URI.
The tricky part is an ontology for scientific hypotheses. But you then need a machine-readable language flexible enough to express complex scientific ideas, and that's potentially challenging. Besides, some of these ideas are naturally expressible in ways humans can easily get, but in ways difficult for machines, for instance almost anything involving images.
Nevertheless an XML specification for describing hypotheses, experiments and results in a machine-readable way would be very interesting. I'm inclined to do some tinkering with all that, in my ridiculously abundant free time. Maybe I'll manage it.
In machine learning there are lots of approaches and algorithms that are relevant to the scientific method. The ML folks have long been working on the problem of taking a bunch of data and searching it for organizing structure. This is an important part of how you would formulate a hypothesis when looking at the bunch of data. You would then design experiments to test the hypothesis. If you wanted to automate everything completely, you'd run the experiment in a robotic lab. Conceivably, science could be done by computers and robots without any human participation, and that's what the futurists envision.
The Science paper goes into pretty deep detail about the range and applicability of machine learning methods, as things stood in 2001. I find ML an interesting topic, but I can't claim any real knowledge about it. I'll assume that somebody somewhere can write code to do the things claimed by the paper's authors. It would be fascinating to try that myself some day.
To bring this idea closer to reality, what we need is a widely accepted machine-readable representation for hypotheses, experiments, and experimental results. Since inevitably humans would also participate in this process, we need representations for researchers (human, and possibly machine) and ratings (researcher X thinks hypothesis Y is important, or unimportant, or likely to be true, or likely to be false). So I have been puttering a little bit with some ideas for an XML specification for this sort of ontology.
Specifying experiments isn't that tricky: explain what equipment and conditions and procedure are required, and explain where to look for what outcome, and say which hypotheses are supported or invalidated depending on the outcome. Experimental results are likewise pretty simple. Results should refer to the experiments under test, identifying them in semantic web style with a unique permanently-assigned URI.
The tricky part is an ontology for scientific hypotheses. But you then need a machine-readable language flexible enough to express complex scientific ideas, and that's potentially challenging. Besides, some of these ideas are naturally expressible in ways humans can easily get, but in ways difficult for machines, for instance almost anything involving images.
Nevertheless an XML specification for describing hypotheses, experiments and results in a machine-readable way would be very interesting. I'm inclined to do some tinkering with all that, in my ridiculously abundant free time. Maybe I'll manage it.
