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AlphaGo is an artificial intelligence which has beaten top human GO-players. Even the creators and instructors do not know how AlphaGo came to it's conclusion to put a stone just at this position. Top-Players thought, AlphaGo did a mistake and in further progress it appeared to be a genius move. In fact AlphaGo played like no player in thousands of years - and won that match.
How could AlphaGo create that novel move? Is it really a new creation or is it just a product of a machine?
This is actual a pretty old and often debated question. It is called "Lady Lovelace's Objection" and first appeared in Alan Turing's seminal paper "Computing Machinery and Intelligence".
Below is my response to Lovelace's Objection, as well as Alan Turing's response which I wrote for a philosophy course in 2015. Perhaps it will be of interest to you?
Turing created the imitation game as a way to talk about cognition in machines without being bogged down by the philosophical history of the word “think” and “machine”. In his discussion of the imitation game, he addresses Ada Lovelace's objection and refutes one aspect of it, however he does not cover all aspects. Namely, his rebuttal to Lovelace's objection seems to assume the universe is deterministic and by rephrasing Lovelace's argument, he may be missing some of it's more subtle implications.
Turing's imitation game has the objective of causing a human judge to be unable to correctly guess which amongst two players is a computer. The only tool at the judge's disposal is textual communication that can be directed to each of the players individually, similar to an Internet chat room or an e-mail exchange. If the machine can confuse the judge, given a certain time or question limit, the machine is said to have won the imitation game and to have passed the Turing test.
Lovelace's argument against a machine passing the Turing test and thus being able to “think”, is that a machine (specifically referring to the Analytical Machine) “has no pretences to originate anything. It can do whatever we know how to order it to perform” (Turing 1950, 450). Turing faces this argument by first rephrasing it into questioning whether a machine “can never do anything 'new'” (Turing 1950, 450). He refutes this reformulated objection by questioning whether human beings have ever done anything new. In other words, he claims that there is no verifiable proof that any creation from a human being was not the product of their education; whether formally studied or learned from the environment.
Turing's claim is a reasonable one, but he risks getting caught up in the debate of free will versus determinism, since he seems to be claiming that a human being is purely a function of this environment. He assumes that all that is necessary to imitate a human being is to determine the function that maps the external environment to human reasoning. The belief of the existence of this function is known as determinism within philosophy and is a matter of much debate. Alternatively, to avoid this philosophical quagmire, instead of appealing to determinism, we could instead look at what is required for a computer to create something “new”. Creating something “new” could be interpreted as synthesizing new information. The proof that a computer can accomplish this task can be proven by examining a rule-based system.
A rule-based system is based on the idea that most of human knowledge can be represented by rules and human reasoning can be approximated by the manipulation of these rules in a logical manner. For example, to represent a navigation plan, you can know a series of logical steps or rules describing routes, such as “Lester street connects my house to highway 69” and “highway 69 leads to Sudbury”. It is possible to program a computer using to search through these steps and come to a conclusion, in this case the route from Sudbury to Lester street (Thagard 2005, 47). You can then synthesize new rules by combining them using a process known as “chunking” or “composition”, and saving them so that the search doesn't need to be executed every time this objective (getting from Sudbury to Lester) needs to be accomplished (Thagard 2005, 49). Additionally, rules can be used to generate explanations or hypotheses by abductive inference (Thagard 2005, 50). Both the plan, the explanation and the summary did not exist until the computer sought them out and discovered them. This can be seen as the equivalent of a machine producing something “new” and acquiring “new” knowledge. Using mathematical terminology, it can be said that the system is non-monotonic.
There are many flaws to this simplistic rule-based system. It is computationally inefficient and does grasp all of the psychological complexity of a human being. Consequently, it could be argued that this model could never evolve to successfully imitate a human being. However these are superficial concerns. It would be a mistake to broaden this specific rule-based system's inability to evolve to all computational models. Instead, what should be retained from the example of the rule-based system is that it is possible to contain aspects of human reasoning and ingenuity within a computational model. Additionally, the flaws of computational inefficiency and psychological incompleteness can be and have been addressed by newer, more complete models. For example, ACT-R has been shown to be able to imitate certain attributes of a human being, such as natural language processing, in the form of language acquisition (Anderson 2002, 1).
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How could AlphaGo create that novel move? Is it really a new creation or is it just a product of a machine?
The answer is that it is a new creation which is a product of the intelligence programmed into the computer software. The move was not conceived by any human, therefore it was new and it was created by the software.
Another example can be found if you look at the computer folding project. Computers are creating new models of protein structures through the use of many computers around the world, and these folded structures tell researchers what role they play in all sorts of diseases throughout the body, including the brain.
They are working things out a lot faster than any human can and you cannot say anything other than the fact that these computers are creating new things. Things unknown to humans prior to their creation.
It's not a product of a mechanical device anymore! Yes, it's the result of calculations of a cluster of machines, but the complexity of the group is in no way similar to what we used to know as machines. It's more complex by orders of magnitudes. In a way, you could say the brain is also mechanical since it's a group of neurons and a neuron is close to mechanical. But when the number of neurons is so large, the group is no longer similar to an individual. This is also what happens to the deep learning cluster of machines. It's already super machinery. It's like a human body is no longer similar to a cell. So there is creativity in AlphaGo.
On the other hand, you could argue that mathematically there is a best strategy for Go. Knowing this strategy could lead to victory over any opponent, if not draw, including AlphaGo and world champions. So there is not creativity in any game. All is brutal calculations.
