Translation Technology and Copyright

Translation technology (TT) has made significant technical advances over the past decade. Machine translation has become increasingly commonplace in everyday use through services such as Google Translate. At the same time, computer-aided translation systems are now an invaluable tool for the professional translator. Unlike the machine translation systems of yore, current systems are no longer based on explicit, formal models of language, but rather on machine learning and statistical methods using vast collections of multilingual documents with each language element aligned across language pairs. Because of this, entirely new questions of intellectual property become a crucial yet still poorly understood part of the enterprise. This article presents an introduction to translation technology and the legal questions potentially involved and then proceeds to the legal analysis of quantitative and qualitative requirements for copyright protection, the issue of potential (human and computer) authorship to translation technology output, and others.

Our article can be found here:

http://www.nir.nu/Journal/nir-2012-3

Sorry for the paywall. Our article has not undergone any language examination. According to the editors each text “shall be the voice of its author”.

What is Artificial Intelligence?

Prompted by the discussion at a presentation I gave yesterday on intelligent legal technology (titlend Älykäs oikeusteknologia, ie. in Finnish, for once) I guess I feel the need to export a part of the neural network that subsequently emerged in the form of a blog post. I was asked to define artificial intelligence, and since I refused to provide a definition, I was asked again. And again. So even if providing such a definition is quite irrelevant as I don’t research legal AI in general (in which case the delineation between AI and non-AI might be of interest) but rather some specific questions (modelling vagueness and uncertainty in law) which without a question are AI & law questions, here, for explanatory rather than definitive use, with no warranties for fitness for any particular purpose, yadda yadda yadda, are my two cents:

Artificial Intelligence is the cross-disciplinary enterprise of trying to do things with a computer which when done by people are said to require intelligence and which computers cannot (yet) do. (The careful reader may notice a certain degree of isomorphism with a popular definition within the extended cognition framework...)

(And comparison shoppers, here is Wikipedia’s current version: “Artificial intelligence (AI) is the intelligence of machines and robots and the branch of computer science that aims to create it.”)

So: Consistently with the bottom-up approach to AI I like to advocate in general, I don’t think allusions to the Turing test or the Singularity or whatever are all that interesting, as far as actual progress is concerned, the cognitive arts advance through innovations which are very small increments from the perspective of AI as a whole but can be quite dramatic for the topical discipline in question.

I do think that the trying (or aim[ing] to create) is an important part of what makes AI AI. Doing arithmetics also requires intelligence but has never been a part of AI since computers could do (and indeed were built to do) it properly from the beginning. And so on the way from notrespondingstilltrying to commercial viability, AI projects start being called computational whatever or whatever technology (hence legal technology). Of course the boundaries are vague and the whole boxological excercise of little use in anything other than turf-wars in academia.

And the fact that the definition refers to human intelligence just serves to illustrate the futility and question-beggitude of definitions for one simple reason: The psychological understanding of human intelligence just adds even more layers of complexity. For example, IQ tests cannot possibly measure human intelligence per se and in general. What they measure instead is a specific indicator known as the g factor (or general intelligence), which has been shown to correlate (reasonably) well with the more specific intelligent abilities.

Even if working on definitions can occasionally serve an useful purpose, personally I think in most cases the more expedient alternative is to follow Justice Potter Stewart in Jacobellis v. Ohio: “I know it when I see it.” For historical reasons, jurisprudence in Finland still has a particular affinity for concepts and definitions not really seen elsewhere to the same degree. I’m planning to address this issue in extenso at some point with the title Begriffsjurisprudenz 2.0. (Spoiler alert: may also offend ontologists.)

Augmenting Man

"The combination of machines and ICT has brought exponential development into the engineering world. As a result we see the emergence of autonomous machines. The complexity of tasks as well as the complexity of environments where these machines can work is steadily increasing. We can build cars that can navigate autonomously through city traffic finding destinations without any human intervention. Therefore, it is fair to say that machines have, on a functional level, already reached cognitive abilities comparable to horses or dogs. But this is not the end of development. Soon there will be no type of manual labor in which machines will not outperform humans. This is technically already true today. Currently, machines are merely held back by economic and societal constraints. The weakest of these constraints is the cost of hardware. Moore’s Law guarantees that computing power that today can only be found in supercomputers will be available in pocket sized devices in little a more than a decade. Some other constraints are more difficult to overcome. The more powerful and more complex a machine is, the more damage it can potentially create. This is the reason there are no self-steering cars on the roads yet. We have suggested a path of best practices and ethics to improve machines and reduce intentionally malicious behavior. Nevertheless, even those best practices leave us with a residual risk. This residual risk is not necessarily small. It may indeed be so large that certain types of machines will not be able to enter the market because of liability concerns. This limitation will only be overcome by the creation of an ultimate machine. For human parents responsibility and liability for a child ends with it becoming an adult. Similarly a machine can become an ultimate machine by emancipating itself from its manufacturer/owner and indeed becoming a distinct legal or even social entity. Interestingly, this can be done by creating a legal construct around this ultimate machine that in itself has economical value.

Nevertheless, the big question remains: how will our societies hold up to this rapid change? For example, currently our entire tax and social system, indeed most of our culture, is centered on the concept of work as the means of creating one’s livelihood. For example, the European Union has set a goal of increasing the part of the population (between 15 and 64 years of age) in gainful employment to 70 per cent up to 2010. Yet when machines are able to perform manual labor cheaper and more efficiently than humans, what jobs will remain? Former US Secretary of Labor, Robert Reich, assumes that manual labor will eventually be replaced completely by machines. Nevertheless he argues that there will still be a high demand for a human work force. These new workers will have to be highly educated and trained “symbolic analysts” – lawyers, doctors, journalists, consultants, and the like – which create value beyond mere manufacturing. However currently only a fraction of the labor force is capable of performing these jobs. Even though goverments have stated their intention to increase investment in education it is questionable whether this goal can be achieved for everyone. And even if it were possible, the advancement in information technology is not restricted to manual labor. Machines have augmented the physical performance of man to the point were he becomes superfluos. The same augmentation is also taking place with our cognitive abilities. The famous quote of the computer being a “bicycle for the mind” becomes evident when we consider the vast amount of data a single person can analyze with the help of a personal computer. Therefore, the observation that machines in the long run are not destroying jobs but creating new ones is merely that; an observation and not a law. There might well be a threshold of automation that changes the rules of the game entirely. If that should happen this would be one aspect in which we have to change our culture radically. In any case, how well we are prepared for these new machines will determin the social acceptance and ultimately the cost of the transition. Since development is still gradual, there will be several years left to create new practises. There is likely not a simple nor a single answer. The convergence of disciplines and the accelerating speed of technological progress will require a holistic approach and result in ad-hoc solutions. Fortunately, we can start learning about the problem and its solutions already today. After all, the future is already here, just not equally distributed."

William Brace, Anniina Huttunen, Vesa Kantola, Jakke Kulovesi, Lorenz Lechner, Kari Silvennoinen and Jukka Manner,
"Augmenting Man,"
in Bit Bang, Rays to the Future, Yrjö Neuvo and Sami Ylönen, Helsinki: Helsinki University Print, 2009, p. 236-263