AI risksWhat an artificial intelligence researcher fears about AI
As an artificial intelligence researcher, I often come across the idea that many people are afraid of what AI might bring. It’s perhaps unsurprising, given both history and the entertainment industry, that we might be afraid of a cybernetic takeover that forces us to live locked away, “Matrix”-like, as some sort of human battery. Might I become “the destroyer of worlds,” as Robert Oppenheimer lamented after spearheading the construction of the first nuclear bomb? Perhaps the critics are right. Maybe I shouldn’t avoid asking: As an AI expert, what do I fear about artificial intelligence?
As an artificial intelligence researcher, I often come across the idea that many people are afraid of what AI might bring. It’s perhaps unsurprising, given both history and the entertainment industry, that we might be afraid of a cybernetic takeover that forces us to live locked away, “Matrix”-like, as some sort of human battery.
And yet it is hard for me to look up from the evolutionary computer models I use to develop AI, to think about how the innocent virtual creatures on my screen might become the monsters of the future. Might I become “the destroyer of worlds,” as Oppenheimer lamented after spearheading the construction of the first nuclear bomb?
I would take the fame, I suppose, but perhaps the critics are right. Maybe I shouldn’t avoid asking: As an AI expert, what do I fear about artificial intelligence?
Fear of the unforeseen
The HAL 9000 computer, dreamed up by science fiction author Arthur C. Clarke and brought to life by movie director Stanley Kubrick in “2001: A Space Odyssey,” is a good example of a system that fails because of unintended consequences. In many complex systems – the RMS Titanic, NASA’s space shuttle, the Chernobyl nuclear power plant – engineers layer many different components together. The designers may have known well how each element worked individually, but didn’t know enough about how they all worked together.
That resulted in systems that could never be completely understood, and could fail in unpredictable ways. In each disaster – sinking a ship, blowing up two shuttles and spreading radioactive contamination across Europe and Asia – a set of relatively small failures combined together to create a catastrophe.
I can see how we could fall into the same trap in AI research. We look at the latest research from cognitive science, translate that into an algorithm and add it to an existing system. We try to engineer AI without understanding intelligence or cognition first.
Systems like IBM’s Watson and Google’s Alpha equip artificial neural networks with enormous computing power, and accomplish impressive feats. But if these machines make mistakes, they lose on “Jeopardy!” or don’t defeat a Go master. These are not world-changing consequences; indeed, the worst that might happen to a regular person as a result is losing some money betting on their success.
