Day: 14 September 2018
MIT taught a neural network how to show its work
One of the many concerns about automated decision making is its lack of transparency. Particularly (but by no means only) for government services, accountability requires not just that decisions are well based, but that they can be challenged and explained. AI black boxes may be efficient and accurate, but they are not accountable or transparent.
This is an interesting early indicator that those issues might be reconciled. It’s in the special – and much researched – area of image recognition, so a long way from a general solution, but it’s encouraging to see systematic thought being addressed to the problem.
Table of Disruptive Technologies
Richard Watson and Anna Cupani – Imperial Tech Foresight
Here are a hundred disruptive technologies, set out in waves of innovation, with time to ubiquity on one axis and potential for disruption on the other. On that basis, smart nappies appear in the bottom left corner, as imminent and not particularly disruptive (though perhaps that depends on just how smart they are and on who is being disrupted), while towards the other end of the diagonal we get to transhuman technologies – and then who knows what beyond.
The authors are firm that this is scientific foresight, not idle futurism, though that’s an assertion which doesn’t always stand up to close scrutiny. Planetary colonisation is further into the future than implantable phones, but will apparently be less disruptive when it comes. Dream recording falls in to the distant future category (rather than fringe science, where it might appear more at home), rather oddly on the same time scale but three levels of disruption higher than fusion power.
The table itself demonstrates that dreams are powerful. But perhaps not quite that powerful. And it’s a useful reminder, yet again, that technology change is only ever partly about the technology, and is always about a host of other things as well.