“Cogito, ergo sum (I am thinking, therefore I exist)”, as Rene Descartes famously proposed in 1637, has spawned almost four hundred years of philosophical debate on the nature of thinking and existing.
In this argument, he proposed that the only thing that he was absolutely certain about is that he exists, as he is here to think about existence. This is quite an abstract thought process so when we see the quote below, it naturally suggests computers are capable of similar philosophical musings.
Computers are learning to think, read, and write, says Bloomberg Beta investor Shivon Zilis.
There have been many debates on the nature of thinking, especially over the last 50 years or so of the computer age. Most famous perhaps is the Chinese Room thought experiment proposed by the philosopher John Searle. His argument proposes that he is locked in a room. He does not speak or read any Chinese and only has a set of instructions which outline what Chinese symbols he is to respond with if he receives a set of Chinese symbols.
From outside the room, if we pass in a correct Chinese sentence or question to John, we will receive a correct response, even though John doesn’t speak Chinese and the instruction book certainly doesn’t either. We are led to deduce (erroneously) that there is a Chinese speaker in the room. (You can find out about his argument and some of the counter-arguments here on Wikipedia.)
Some of the achievements in Machine Learning are indeed impressive. But all such algorithms are the same as the Chinese Room. There is an actor (in this case the computer) carrying out a set of pre-defined instructions. Those instructions are complex and often achieve surprising results. But at no point can we safely deduce that the computer is actually thinking. We may say that today, computers do not think in the way that humans do and that the above quote is a bit of an exaggeration.
As the volume of articles written about Machine Learning and Artificial Intelligence grows, we have to be careful not to unduly overstate the capabilities of these algorithms. We need to avoid the mistakes of the past where much was promised for AI and little delivered, to preserve the interest and funding for research.
Impressive? Yes. Interesting? Definitely. Thinking? No.