Making Science Machine Readable 135
holy_calamity writes "New Scientist is reporting on a new open source tool for writing up scientific experiments for computers, not humans. Called EXPO, it avoids the many problems computers have with natural language, and can be applied to any experiment, from physics to biology. It could at last let computers do real science - looking at published results and theories for new links and directions."
EXPO has a serious naming problem (Score:5, Insightful)
And forgive me for thinking the university would be more helpful, but no, there's been a series of expos at the University of Aberystwyth, from art through VoIP.
I'd love to have found more info on the language, but my casual browsing got stopped right there.
If they'd named it something like EXPI or EXPLO at least it'd be uniquely locatable. Google might whine about the potential misspelling of Expo, but it would dutifully locate the search term as requested.
XML? (Score:1, Insightful)
Wait, what does it do? (Score:5, Insightful)
WTF? If you have to manually pre-parse every article that enters the system, it severely limits the rate you can enter information into the database, no?
Re:ok .... (Score:2, Insightful)
Hmmm (Score:4, Insightful)
Basically what I'd be worried about is the tendency of the tool to become the task. This is something of a problem in my field (biostats) because SAS is so ubiquitous -- often the question becomes "what can SAS tell us about this data set" rather than "what do we want to know from this data set, and what tool should we use to find out?" Fortunately other, more flexible analysis tools (particularly R, which encourages real programming rather than running a set of canned tests) are becoming more common in the field, and so this is starting to change, but it's still a problem.
It's also a problem that every techie is familiar with -- "We want to do this in $LANGUAGE on $PLATFORM," even when that particular language and platform may be an absolutely terrible choice for the task at hand.
That being said, it's certainly a potentially useful tool, and I'll be interested to see where it goes. It's just that when I read lines like "Journals could also insist that researchers submit papers in EXPO as well as written normally," I get twitchy.
Re:Wait, what does it do? (Score:2, Insightful)
Re:ok .... (Score:3, Insightful)
Re:EXPO has a serious naming problem (Score:3, Insightful)
Re:I don't mean to sound like a conspiricy theoris (Score:3, Insightful)
Re:I don't mean to sound like a conspiricy theoris (Score:3, Insightful)
the edge is always fuzzy (Score:1, Insightful)
Re:"At last" do real science? (Score:2, Insightful)
That's impressive. But it is engineering, not science. When computers start proposing new experiments to which will help us understand things unknown, then they will be doing science!
Key Aim (Score:3, Insightful)
1) These complicated hypotheses could still be tested relatively easy by human scientists because most computer suggestion systems for new hypothesis possibilities would likely suggest a few tests that would help to support/disprove these new hypotheses.
2) Even more simplification comes from the fact that experiments may not need to be repeated nearly as much as they do now in order to make a hypothesis -- there is an incredible amount of data already gathered, and typical AI/pattern matching algorithms keep some of the data back for testing later. If the system finds a possible hypothesis on some level, experiments as to that concepts validity have essentially already been done in a virtual sense.
3) If the somewhat positivist version of current thought in physics http://www.toequest.com/ [toequest.com], mathematics, chaos theory, complexity theory, cellular automata http://www.wolframscience.com/nksonline/toc.html [wolframscience.com], etc. is even vaguely valid, it is quite possible that, despite the complexity and dimensionality of the input data, the 'best' hypotheses developed even by purely automated means might still be simple and elegant and/or even yield insight into possible explanatory processes rather than just statistical indicators. This would be a valuable and beautiful victory for humanism and the importance of science as a truly elegant description of the world around us.