The hysteria about the future of artificial intelligence (AI) is everywhere.
This is the philosophy that, given enough data, machine learning algorithms cansolve all of humanitys problems.
But theres a big problem with this idea.
The pendulum has swung from the dystopian notion thatAI will destroy humanityto the utopian belief that ouralgorithmic savioris here.
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AI solutionism is on the rise and it is here to stay.
One of the most promising varieties of AI technologies areneural networks.
Many AI-based products use neural networks to infer patterns and rules from large volumes of data.
Most of the data remains stored in offline archives.
The few digitized sources of data that exist tend to be buried in bureaucracy.
For these reasons, the sensationalism over AI has attracted many critics.
One of the many difficulties in deploying machine learning systems is that AI is extremely susceptible toadversarial attacks.
Manyresearchershave warned against the rolling out of AI without appropriatesecurity standards and defense mechanisms.
Still, AI security remains an often overlooked topic.
This means we need to have a discussion aboutAI ethicsand the distrust that many people havetowards machine learning.
TheIBM Watson for Oncologyprogram was a piece of AI that was meant to help doctors treat cancer.
Even though it was developed to deliver the best recommendations, human experts found itdifficult to trust the machine.
As a result, the AI programwas abandonedin most hospitals where it was trialled.
Similar problems arose in the legal domain whenalgorithms were used in courtsin the US to sentence criminals.
An algorithm calculated risk assessment scores and advised judges on thesentencing.
The system was found to amplify structural racial discrimination and was later abandoned.
These examples demonstrate that there is no AI solution for everything.
Using AI simply for the sake of AI may not always be productive or useful.
Not every problem is best addressed by applying machine intelligence to it.
This article is republished fromThe ConversationbyVyacheslav Polonski, Researcher,University of Oxfordunder a Creative Commons license.
