IBM has come up with an automatic debating system called Project Debater that researches a topic, presents an argument, listens to a human rebuttal and formulates its own rebuttal. But does it pass the Turing test? Or does the Turing test matter anymore?
The Turing test was first introduced in 1950, often cited as year-one for AI research. It asks, “Can machines think?”. Today we’re more interested in machines that can intelligently make restaurant recommendations, drive our car along the tedious highway to and from work, or identify the surprising looking flower we just stumbled upon. These all fit the definition of AI as a machine that can perform a task normally requiring the intelligence of a human. Though as you’ll see below, Turing’s test wasn’t even for intelligence or even for thinking, but rather to determine a test subject’s sex.
Continue reading “Death Of The Turing Test In An Age Of Successful AIs”
Keeping animals from tropical regions of the world in a cold climate is an expensive business, they need a warm environment in their pens and sleeping areas. Marwell Zoo was spending a small fortune keeping its herd of nyalas (an antelope, not as the title suggests a deer, native to Southern Africa) warm with electric heating, so they went looking for a technology that could reduce their costs by only heating while an animal was in its pen.
One might expect that a passive IR sensor would solve the problem, but a sleeping nyala too soon becomes part of the background heat for these devices, and as a result, the heaters would not operate for long enough to keep the animals warm. The solution came from an unlikely source, a coffee queue monitoring project at the IBM Watson headquarters in Munich, that used an array of infra-red sensors to monitor the changing heat patterns and thus gauge the likelihood of a lengthy wait for a beverage.
In the zoo application, an array of thermal sensors hooked up to ESP8266 boards talk back to a Raspberry Pi that aggregates the readings and sends them to the IBM Watson cloud where they are analyzed by a neural net. The decision is then made whether or not a nyala is in the field of view, and the animal is toasted accordingly.
This project has some similarities with a Hackaday Prize entry, automated wildlife recognition, in its use of Watson.
Nyala image: Charlesjsharp [CC BY-SA 4.0 ].