I'm being profiled on WIN-novators later this spring. As an preview, here's my take on the question in the title of this post:
I suppose there are basically two possible aspects on innovation. The first is taking something preexisting and familiar and making it just a little better. The other alternative is a radical break with tradition and doing things differently altogether. In a sense this echoes the distinction between normal science and scientific revolutions introduced into the philosophy of science by Thomas Kuhn. But in reality (rather than philosophy) these aspects are complementary rather than dichotomous.
To take a concrete example: the shift from, say, a 32 nanometre to a 22 nanometre process in semiconductor manufacturing is not immediately visible for the computer user and from this perspective it may seem like yet another meaningless number in the computer specifications orat best a small incremental change. From the manufacturing perspective, on the other hand, shedding those extra nanometres has required enormous technological advances. One additional constraint onthe design is also the amount of heat being generated into a smaller and smaller space that still has to be dissipated through a cross-section of comparable size, leading to the invention of 'darksilicon' (powering down parts of a chip that are unused at any given moment). And on the other hand, the cumulative effect of such changes in terms of processing power, storage capacity and so on (Moore's lawand all that) enable new approaches to all kinds of problems thatwould have been quite impossible a decade or two ago. After all, eventoday's smartphones are more powerful than supercomputers in most ofthe 1980s.
The ability to deal with immense amounts of data in real time isdefinitely one of the two biggest driving forces for artificialintelligence in the foreseeable future. Recently I looked into the history of machine translation, and one of the earliest systems actually took twice as long just to do what amounts to looking up each individual word in a dictionary and stringing them together into an approximation of a translation as what it would take for a human translator to produce a correct translation. A system like Google Translate, on the other hand, has ginormous collections of multilingual documents with aligned language elements and uses them together with some heavy statistical processing to do the same job and produce at least something understandable if not correct in justfractions of a second. Oh, and the other driving force? Bio-inspired AI, or seeing how Nature has solved a given problem and trying to reproduce that in an artificial design.
As for my own work, I try to keep these different perspectives in mind, and while I occasionally like to throw words like 'robot judge' around, it is more as an abstract target (and of course also asprovocation) rather than as something I am actually concretely interested in implementing. But it is certainly helpful in trying to keep in mind the whole range of issues potentially involved in working with legal AI, and not just the issues du jour the research community finds interesting at the moment. In my opinion, one part of the problem is also that mainstream legal theory does not study law and legal reasoning in particular as a form of cognitive activity and has managed to all but ignore all the scientific progress made in both linguistics and psychology over the past fifty years, and in much of my work I take theories from those disciplines and try to apply them to questions of legal theory in a very general sense (mostly because almost nobody else seems to be doing it). Still, the best way forward for me seems to be trying to model some very small corner of the legal system using some particular technique (and I think I'm stuck with fuzzy logic at least until I've finished my dissertation) to see whether it works and then trying to see whether there are some broader conclusions to be drawn based on it. And in the best case it might even do something useful (read: marketable) at the same time. In this respect, AI & law seems to be about twenty years behind language technology.
We don't particularly need robot judges, but for example judicial decision-support systems could help actual judges in making correct and consistent decisions more efficiently and reliably, thus perhaps enabling them to spend more time on cases where uniquely human capabilities are really required. And if at the same time technology also revolutionizes the way legal services are provided (as predicted by Richard Susskind in particular), maybe the parties do not even have to go to court in the first place.