6 ms·

Human language, on the other hand, has an infinite number of "nuances", and as such, a "translation computer will never be able to give a sufficiently satisfyin

• johnchristopher 14y ago
  That cynical person would confuse the internet with its usage :) The internet will still be there when AOL and netscape are gone.
  • ma2rten 14y ago
    I don't think a couple of network hubs and cables is what most people would refer to as the internet, e.g. "I went on the internet to find it", "I posted it on the internet".
  • PostOnce 14y ago
    I would imagine he/she was referring to the power of Google and Facebook to analyze & exploit the data of billions of people, something that is perhaps uniquely available to them, that the average man will never obtain or have access to this data
• drostie 14y ago
  Bear with me, this won't seem immediately relevant, but I'll get to the point. I have at various times created solvers and helpers for the sorts of logic puzzles you sometimes see in newspapers alongside the crosswords -- you can find a bunch of them online and with many of them, the limiting problem is simply time investment for applying automatic, pithy little rules. Just as an example, I wrote a helper script in Greasemonkey at one point for large versions of http://www.puzzle-loop.com/ http://www.puzzle-loop.com/ , the rules of which are "You have to draw lines between the dots to form a single loop without crossings or branches. The numbers indicate how many lines surround it." It's very satisfying to click once and then see an algorithm performing all of the drudge-work, leaving you to focus on the really interesting bits which you haven't yet been able to articulate as rules. One interesting problem in the above link is knowing when you're about to create a region which only has an odd number of ends inside of it. I found a very pretty way to do this in most cases. The central problem of the translation computer is that often, knowing which translation of word W is important depends on understanding the context around W, sometimes out a couple of sentences. The central problem of understanding the context of W, however, would demand a much more holistic approach than the above translation program seems to provide. The problem in both is precisely a declarative/imperative divide. We are very good at working out, given the adequacy conditions for the solution (the "rules of the game"), what sorts of automatic reflex actions we can do (the "moves" of the game, I suppose). We know the rules, we figure out the moves. Ideally, we would like to just tell a computer the rules of chess without specifying that there is something called 'value' of a piece, and that the computer should find values for e.g. the loss of a knight in comparison to the loss of a bishop. It should be able to work out the principles of effective play, if we want true machine learning. It is my contention, to tie this all together, that without something which appears like machine learning, we won't have anything which appears like machine understanding, and that a machine translation would appear like a machine understanding, due to context-sensitivity in translation: you will hand it "Knives were good for helping humans to hunt" and it will accidentally translate "good" as the equivalent for "ethical" rather than the more-likely-intended "efficient", because it correlates "helping" and "humans" with ethics.
  • Retric 14y ago
    There are programs that given enough time will come up with piece values for arbitrary board games like chess. Non random board games are actually one of the easier things to translate into computer code, the problem is games like go are inherently difficult. I suspect with perfect play the first go player completely dominates the board just like with smaller boards that have been solved, but the ultra aggressive gameplay required to pull this off is impossible to pull off with any sort of reasonable hardware. So, it's really a question of discovering heuristics that allow computationally reasonable pay to still create an acceptable level of imperfect play, and piece values are just one of those heuristics. Perfect play in chess can ignore positional concepts and just look for paths that either force a checkmate or a tie. The problem with language translation is you need to understand the context of an idea before you can accurately translate it which is why humans have such issues translating technical content they don't understand. That said, humans can decode things that are close to linguistic gibberish so even a poor translation is often good enough. Google translate is already rather useful though a great set of heuristics despite an endless array of edge cases it fumbles though. It's a useful path that get's ever more expensive the more demanding the problem.
• andreasvc 14y ago
  The current machine translation systems work with parallel texts, and they will for the foreseeable future. However, to properly translate you need to understand the meaning of a text, not just look up words or phrases in a table as is currently done (and using statistics does not address this). In other words, you need to have a connection between language and world, which is something only an intelligent agent has, hence the problem is AI complete. In terms of the nuances that were cited, it's not so much that they are infinite which is the problem, as other potentially infinite combinatorial systems such as syntax can be analysed effectively by computers. Rather it is the fact that choosing the right nuance depends on a completely arbitrary amount of context, such as the whole history of Russia when you're translating a Russian novel. So the problem is not impossible, but considering that even humans need years and years of study to do a good job at translation, I'd say machine translation that replaces humans is quite infeasible.