Clarifying Assumptions About Artificial Intelligence Before Revolutionising Patent Law

Abstract

This paper examines several widespread assumptions about artificial intelligence, particularly machine learning, that are often taken as factual premises in discussions on the future of patent law in the wake of ‘artificial ingenuity’. The objective is to draw a more realistic and nuanced picture of the human-computer interaction in solving technical problems than where ‘intelligent’ systems autonomously yield inventions. A detailed technical perspective is presented for each assumption, followed by a discussion of pertinent uncertainties for patent law. Overall, it is argued that implications of machine learning for the patent system in its core tenets appear far less revolutionary than is often posited.

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“…Namely, there is a stand that a human is always behind all processes related to AI, including building and adjusting a model, choosing relevant data that, simultaneously, involves the direction of a model (stipulating the problem to be solved), predicting the output of inserted randomness, and others. In this regard, AI is solely an automated tool for humans because all AI activities derive from a direction pre-set by humans (Kim et al, 2022).…”

“…Another aspect that has been outlined is that humans are always behind the invention, since a human preprogrammed also randomness and because AI per se does not have any built-in instructions; therefore, it could not deviate from those programmed by humans (Kim et al, 2022). It should be noted that AI might also display an output by mistake that has not been pre-programmed by a human.…”

Application of supervised learning models in the Chinese futures market

“…Reinforcement learning is something that does not require a domain expert, but requires constant progress towards a predetermined goal. We all know that AphaGo, based on reinforcement learning theory, played a million matches against itself in DeepMind and eventually became the world champion [8,9].…”