http://ipkitten.blogspot.com/2020/09/the-mirage-of-ai-invention-nothing-more.html
As previously reported on IPKat, two European patent applications naming an AI algorithm as an inventor are currently making their way through the EPO appeal process. The applications (EP 18275163 and EP 18275174) were unsurprisingly rejected by the EPO because the applicant refused to designate a human inventor (IPKat). The appeal includes extensive arguments on the legality and ethics of human-only inventorship. Surprisingly, in their preliminary arguments, the applicant claims that the EPO has conceded that the AI algorithm was the actual divisor of the inventions. However, the patent applications themselves do not disclose the processes by which the AI invented. To this Kat, the very fact of “AI invention” is still very much open to question.
The algorithm allegedly behind the inventions in the patent application was devised by Dr Thaler. Dr Thaler is a curious character, who also claims that his algorithms (or “creativity machines”) are capable of dreaming, near-death experiences and sentience. However, we will leave aside Dr Thaler and his self-termed “AI child” for now. The broader argument has been made here on IPKat that more well-known AI algorithms, such as DeepMind’s AlphaGo, demonstrate that AI is now capable of invention. But is this the case?
Is AlphaGo inventive?
| Optimising her game |
AlphaGo is a machine learning algorithm devised by DeepMind to assist in playing the Chinese game of Go. Go is a far more complex problem to solve than chess. Dr Matt Fisher argued in an IPKat post earlier this year that AlphaGo was capable of reading and understanding the prior art of the game Go, finding novel and inventive solutions to defeat a world Go champion and communicating that solution to the DeepMind team member who placed the Go stones. However, when you look into the matter more closely, even the undeniably impressive achievement of AlphaGo cannot be equated with invention. When you take the lid off the metaphorical AI black-box, it turns out that the AlphaGo algorithm actually uses no more than advanced trial-and-error optimisation (in combination with huge computing power), instead of inventiveness to play the game.
Invention or trial and error?
The processes by which AlphaGo succeeded at Go can be equated with the trial and error optimization processes for designing jet engines that have been used in industry for years. In the field of jet engine design, an engineer will set up a (non-AI) computer simulation to suggest and test a variety of possible jet engines. The computer simulator will make many small changes to the jet engine design, and simulate the performance of these new engines. Each of the possible engines will be given a performance score by the simulator. The simulator is able to select the most efficient engine from these results. Notably, no-one has argued that computer simulators of this type should be named as inventors on patents.
So how does AlphaGo work? The AlphaGo algorithm is first fed data from Grand-master games of Go. AlphaGo does not need to search every possible move from a given state in the game, because it can use the Grand-master data set to narrow down the options. At each point in the game, the algorithm tests each of the possible “Grand-master” moves by simulating how the game will go from that move onward. The algorithm does this by playing a theoretical game of Go against itself using the Grand-master data set. AlphaGo can thereby score the likelihood of a win or loss for each possible move available to it at a particular point in the game. The algorithm reads out the highest scoring move to the person running the algorithm.
The only difference between AlphaGo and previous attempts to solve games such as Go and Chess is that AlphaGo is faster, uses more computing power and is more difficult to explain. AlphaGo is thus more mysterious to the casual observer. AlphaGo is so quick and effective that it appears to operate like a black-box of inventive activity. However, this impression is only a mirage of invention that is produced by very fast and effective processes of search and optimisation. This perhaps makes AlphaGo itself an invention, but does not elevate AlphaGo into the position of an inventor.
Embodiment versus invention
Importantly, AlphaGo is only capable of reading out single possible moves in a game of Go. AlphaGo does not devise Go strategies underlain by a unified inventive concept for winning Go. In the comparison above between jet engine design and AlphaGo, a single move in a single game of Go is analogous to a single jet engine embodiment from the computer simulator. Importantly, the individual jet engines identified by a computer simulator would not normally be understood as inventions. It is possible, even probable, that one of the jet engines identified by the simulator may embody an invention. However, the selected jet engine will comprise many extraneous features immaterial to the inventive concept. It requires a human jet engineer to recognise the broader inventive concept.
In a similar way, the AlphaGo algorithm identifies the single moves in a game of Go which score highest in the searches it has run. A human observer may be able to extrapolate from a series of these results a broader inventive strategy for winning games of Go. However, this would necessitate inventive activity on behalf of the human observer to recognise and communicate any broader principles of Go strategy evident in the games played by AlphaGo. Once again, AlphaGo comes short of being an inventor itself.
The argument about AI inventorship looks set to run and run. In the latest news, Dr Thaler is now suing the USPTO for not permitting an AI inventor to be designated on Dr Thaler’s US patent applications. However, whilst the thought experiment of AI inventorship is of potential academic interest, the discussion currently lacks practical relevance. Even the most advanced AI algorithms available today are more a testament to improvements in computing power than evidence of silicon-based inventive activity.
Throughout human history, the temptation to personify that which we do not fully understand has been ever present. To paraphrase A. C. Clarke, any sufficiently advanced algorithm will be indistinguishable from magic to those who do not understand it. We might therefore wish to approach the claims of AI magicians and their magic algorithms with perhaps a little more scepticism than has yet been demonstrated in the AI inventor debate.
Content reproduced from The IPKat as permitted under the Creative Commons Licence (UK).