'We don't yet have sound tools for validating machine learning', says GaryMarcus OpenFuture
To judge from the hype, artificial intelligence is inches away from ripping through the economy and destroying everyone’s jobs—save for the AI scientists who build the technology and the baristas and yoga instructors who minister to them. But one critic of that view comes from within the tent of AI itself: Gary Marcus.
Trustworthy AI has to start with good engineering practices, mandated by laws and industry standards, both of which are currently largely absent. Too much of AI thus far has consisted of short-term solutions, code that gets a system to work immediately, without a critical layer of engineering guarantees that are often taken for granted in other field. The kinds of stress tests that are standard in the development of an automobile , for example, are rarely seen in AI.
The assumption in AI has generally been that if it works often enough to be useful, then that’s good enough, but that casual attitude is not appropriate when the stakes are high. It’s fine if autotagging people in photos turns out to be only 90 percent reliable—if it is just about personal photos that people are posting to Instagram—but it better be much more reliable when the police start using it to find suspects in surveillance photos.
The challenges don’t end there. Once a new technology is deployed, it has to be maintained; and good engineers design their system in advance so that it can easily be maintained. Car engines have to be serviceable; an operating system has to come with some way of installing updates. This is no less true for AI than for any other domain. […]
In the long term, we're going to have to mandate some set of innate values as well. A domestic robot à la Rosie the Robot, for example, will need to recognise the value of human life before it leaves the factory. We can't just leave values to chance, depending on what some system happens to encounter in the world, and what its so-called “training database” is.Current systems are too superficial.
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