“ Its as though the creative process is no longer contained within an individual skull, if indeed it ever was. Everything today is to some extent the reflection of something else “. William Gibson . (‘ Pattern Recognition ‘ , 2003)

We are now fairly used to AI . There is nothing very unexpected then about intelligent systems that write sports reports or business news . Or fully synthesised AI voiceovers in advertisements , which may also be created entirely by CGI . Creating new Beatles-alike pop songs is likewise six years in the past for the Sony CSL Lab Flow Machines . Even replacing the missing pieces of Rembrandt’s ‘Night Watch’ or creating a wholly new ‘Rembrandt’ seems not entirely unusual .Indeed, with GitHub’s Co-Pilot , we even have a picture of the machine sitting at our elbow writing the code that we were thinking about writing , a vision of some future scenario of autonomous machine creativity .  Given enough data , enough machine intelligence and enough machine learning capacity , and we believe that anything is possible . So why , in terms of our laws and regulations , do we fail to register the capacity of intelligent machines to generate original and creative work which cannot belong entirely to the owners of the machines or the writers of the programs , or the owners of the data ? 

A very informative seminar organised by IBiL, University College , London , tackled the issues surrounding copyright and AI last week . Excellent speakers from the UK Intellectual Property Office and from the music rights licensing body laid out the current steps to review the law and the conservative view of protecting the livelihoods of living creators . All of this is clear and understandable . We know that the law will always be five years behind the front line of innovation , and we certainly want the rights of the live creators of original works to be protected . But there is also a real worry here that we will delay or inhibit development work vital to the growth of a strong AI-based sector of the economy , and within it the creativity and originality that should be associated with this. Tobias McKenney of Google spoke graphically of the iterative processes of modelling , adjusting and remodelling and wondered if current provision protected the model , or the process , or the output ?  He also pointed out the need to regulate against bias , and the use of selective data , through audit and certification, and for the protection of AI creativity to be global . Martin Senftleben, from Amsterdam University , proved a fascinatingly different professor of IP in that he argued that the objectives of our society were more important than the legal objectives , and saw copyright as something aimed at restricting acts rather than encouraging them . So perhaps AI creativity did need a boost , and perhaps a new neighbouring right , a ‘single equitable payment ‘ , was needed to secure the data availability that would in turn stimulate development . 

The quotation at the head of this piece reminds us that all art , and science , is derivative in some sense . Not only do we stand on  “ the shoulders of giants “ but we look through their eyes and make use of their brains as well . Perhaps it would be better to shelve the debate on whether a machine can be original and creative , and concentrate on ensuring that the data is available and licensable to make AI the effective boon to our society that it certainly can be . This means arguing for something like a re-use right which makes data holders explain why their data cannot be used , neighbouring rights around standard terms and payments to licensing societies on data re-use , and standard core terms for data and text mining licences . Let’s get this up the agenda , and leave the arguments about ownership , creativity and originality until later, until we are prepared to debate whether a machine can have a legal personality – or , indeed , a simulacrum ot consciousness .  


Seminar :





AI voice synthesis www.WellSaidLabs.com and www.VocaliD.ai

https://www.youtube.com/watch?v=LSHZ_b05W7o  Daddy’s Car Flow Machines Sony CSL

NLG data narrative platforms.  www.narrativescience.com ; www.automatedinsights.com ; www.yseop.com;  www.primer.ai ; www.arria.com



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