Sikorsky’s autonomous helicopter tech is ready for takeoff
&gt; But SARA doesn’t rely as much on “high-order” functions like AI since those are harder to certify. (“To be FAA certifiable, you need a certain level of determinism in the outcomes,” Van Buiten says.)<p>I wonder whether a hybrid approach is the future, since in some tasks neural networks are just far better than anything we have. If the only tasks of the neural network is to estimate&#x2F;classify some sensor-input and is trained in a purely supervised setting, the &quot;right thing&quot; for the neural network is still pretty well defined and rigorous testing should be possible (simple tasks can be very complex to implement). Then, interpretable, high-level reasoning could be solved by old-school coding (and maybe verifying).<p>This is not possible with end-to-end training.<p>But I am not sure what they mean, normally neural network (and their training) is purely deterministic. It&#x27;s not that they are just very good at rolling a dice.<p>I am not into this stuff (autonomous, &quot;intelligent&quot; systems, more the data-analysis guy), but I would use neural networks for simple to define, hard problems that involve a lot of noisy data (where some kind of accuracy on some test-set is a well-defined metric) and then build a higher-level reasing system by hand.