Artificial Intelligence and the Privacy Challenge Posted on : Dec 26 - 2018

Proponents of artificial intelligence (AI) hail the advances in the ability of machines to make independent decisions based on an analysis on the environment as the next step in machine intelligence – and claim that it will revolutionize complex problem solving across a wide spectrum of human endeavor. The simplest definition of AI is that of an ‘intelligent’ machine that exhibits all the attributes of a flexible, rational agent that perceives its environment and makes decisions – and in many instances takes actions that maximize the chances of success when engaged in a particular task. If one looks at a popular definition, Artificial Intelligence machines mimic human cognitive function. They can learn and solve problems.

One of the oldest and most well accepted tests on whether a machine exhibits true AI is the Turing Test. Machine AI can pass the 65-year-old Turing Test if the computer is mistaken for a human more than 30% of the time during a series of five-minute keyboard conversations. In 2014, 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 organized by the University of Reading.

But many argue that this is of historical interest only – and is in effect an AI party trick. The definition of AI in the 21st century remains whether a machine can mimic that all important cognitive function – learning to solve problems using logic and not whether it can imitate a human being.

Artificial intelligence – Machine learning using massive data

So, how does this work in the real world? Without delving too deeply into the programming behind it, no matter how fascinating that might be – at its core it falls into different categories. However, when it comes to possible threats regarding privacy there is one subset of AI that should give those concerned with data privacy pause. It is so called ‘machine learning’. An explanation is almost superfluous. This field is focused on giving machines the ability to ‘learn’ without human intervention. This is achieved through advanced algorithms that spot and decode patterns – and then generate insights based on the data that they observe. This then influences the future decision making and predictive responses of the machine. It neatly sidesteps the need for a machine to be programmed to respond to every eventuality within the environment it operates – which in a chaotic environment (think of an AI car on a city street or the sheer amount of data available to big business at the moment) would be a practical impossibility. At least for human beings operating in real time. View More