Fake it till you make it approach in AI

The name ‘Turing Test’ is known by everyone in artificial intelligence field. It is considered one of the benchmarks of deciding a machine’s intelligence. Every year ‘Loebner Prize‘ which offers a prize of $100,000 and  solid 18 carat Gold medal to the program which can pass the Turing Test. But this article is not there to talk about the Turing test but to consider the impact it has on our approaches to solving the problems of intelligence.

So what is Turing Test. (See : Original Version of Turing Test)

In simple; there is a machine(program) and there are a board of judges in separate rooms, they can communicate through typed media (types messages will be passed). If the machine can convince the board of judges that it is a human being more than 30% of the time then it will pass the Test.

For the first time, in 2014 a program was able to achieve this task. From BBC news

A computer program called Eugene Goostman, which simulates a 13-year-old Ukrainian boy, is said to have passed the Turing test at an event organised by the University of Reading.

On 7 June Eugene convinced 33% of the judges at the Royal Society in London that it was human

In 2016 a MIT group made a program which has the ability to beat the Turing test for sound.

An MIT algorithm has managed to produce sounds able to fool human listeners and beat Turing’s sound test for artificial intelligence. (source)

Artificial intelligence has broken through a sound barrier. Researchers from Massachusetts Institute of Technology have developed an AI system that “watches” a silent video clip and generates a sound so convincing that most human viewers cannot tell whether it is computer-generated. (source)

I think these are incredible achievements. Bit by bit we are gaining knowledge on building smarter machines.

When we take a step back, one could argue that the working towards passing the Turing Test bench mark might have caused us to loose focus on some other aspects.

First the Turing Test is focused on Human intelligence. But as much as we like to think otherwise, we are not the only intelligent species on the planet. For example the intelligence in insect colonies are mind blowing. For cry not loud, we see how intelligent our pets are everyday. So by focusing on Turing test we are kind of restricting the search for intelligence.

The second fact is that by focusing on Turing test we are essentially more focused on machines which are capable of deceiving the judges. We are trying to create machines which can give the ‘illusion’ of intelligence, instead of finding ways to build machines which are truly intelligent.

This doesn’t mean that focusing on Turing test hasn’t yielded results. Of cause it has, so many developments. For example; the above mentioned program which broke the Turing test for sound has many applications.

“A robot could look at a sidewalk and instinctively know that the cement is hard and the grass is soft, and therefore know what would happen if it stepped on either of them,” he said. “Being able to predict sound is an important first step toward being able to predict the consequences of physical interactions with the world.” (source)

So like I said it on the title, ‘faking it till you make it‘ works; we just have to not loose focus of our primary goal; true intelligent machines.


One thought on “Fake it till you make it approach in AI

  1. Pingback: The Imitation Game – Original Version of Turing Test | The Puddle Jumper

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