In the past few months we have been deluged with headlines about new AI tools and how much they are going to change society.
Some reporters have done amazing work holding the companies developing AI accountable, but many struggle to report on this new technology in a fair and accurate way.
We—an investigative reporter, a data journalist, and a computer scientist—have firsthand experience investigating AI. We’ve seen the tremendous potential these tools can have—but also their tremendous risks.
As their adoption grows, we believe that, soon enough, many reporters will encounter AI tools on their beat, so we wanted to put together a short guide to what we have learned.
So we’ll begin with a simple explanation of what they are.
In the past, computers were fundamentally rule-based systems: if a particular condition A is satisfied, then perform operation B. But machine learning (a subset of AI) is different. Instead of following a set of rules, we can use computers to recognize patterns in data.
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For example, given enough labeled photographs (hundreds of thousands or even millions) of cats and dogs, we can teach certain computer systems to distinguish between images of the two species.
This process, known as supervised learning, can be performed in many ways. One of the most common techniques used recently is called neural networks. But while the details vary, supervised learning tools are essentially all just computers learning patterns from...
Police have registered an FIR against former Delhi minister Rajendra Pal Gautam for allegedly spreading misleading information through a video and attempting to disturb communal harmony after claim...