DEEPMIND, Google's artificial intelligence divisions famous for jaw-dropping but arcane achievements like designing an algorithm that can beat the world's best player of Go -

Has shown off a prototype of a machine with an an equally impressive but this time real-world  application - scanning eyes for complex and sometimes for fatal diseases.

Two reasons. One : It is a practical breakthrough.
While justly proud of its triumph in Go, DeepMind co-founder Mustafa Suleyman's previous big example of an applied win was the company's prowess cooling Google's server farms - which, while important, is less sexy than saving lives.

Two : Deepmind promises that its device dishes out diagnoses every bit as good as world-leading eye doctors, but those doctors say that it can also explain the reasoning behind its diagnoses. Which brings us to the explainability.

As AI advances there is increasing concern that its working become so complex that while it may spit out the ''right'' answer, no one can understand or explain why.

Essentially, we can see the  data we feed into the AI machine, and we can see the results after it has been processed, but the actually crunching and calculation process in the middle is opaque.

This is known as the Black Box problem.

Black Boxes are a big hurdle to the real-world applications of AI because in many fields, like Justice or healthcare, explainability is critical.

For example a sentencing AI algorithm used to help judges make parole decisions may crunch all kinds background data to evaluate the risk of prisoner reoffending.

But a Yes or a No answer is not enough. It needs to be able to explain how it has weighed that data for its judgment to be trusted, especially since data sets are vulnerable to all kinds of bias.

AI black boxes are not related to the emergency recording devices in aircraft, but the recent Boeing  Max crashes are a reminder that explainability in complex systems is not just an AI problem.

The honor and serving of the latest operational research on AI continues to Part-2. The World Students Society thanks author and researcher Harry de Quetteville.


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