Solving the problems of image recognition by examining the brain

In one case a baseball that was misclassified as an espresso and in another a 3D-printed turtle was mistaken for a rifle... To Carlini, such adversarial examples “conclusively prove that machine learning has not yet reached human ability even on very simple tasks”.

A truly robust image classifier would replicate what ‘similarity’ means to a human: it would understand that a child’s doodle of a cat represents the same thing as a photo of a cat and a real-life moving cat.. as well as recognising visual features such as edges or objects, our brains also encode the relationships between those features – so, this edge forms part of this object. This enables us to assign meaning to the patterns we see.

In their desire to keep things simple, engineers building artificial neural frameworks have ignored several properties of real neurons – the importance of which is only beginning to become clear. Neurons communicate by sending action potentials or ‘spikes’ down the length of their bodies, which creates a time delay in their transmission. There’s also variability between individual neurons in the rate at which they transmit information – some are quick, some slow. Many neurons seem to pay close attention to the timing of the impulses they receive when deciding whether to fire themselves.

When they recently tweaked their simulations to incorporate this information about the timing and organisation of real neurons, and then trained them on a series of visual images, they spotted a fundamental shift in the way their simulations processed information.Rather than all of the neurons firing at the same time, they began to see the emergence of more complex patterns of activity, including the existence of a subgroup of artificial neurons that appeared to act like gatekeepers: they would only fire if the signals they received from related lower- and higher-level features in a visual scene arrived at the same time.

Stringer thinks that these “binding neurons” may act like the brain’s equivalent of a marriage certificate: they formalise the relationships between neurons and provide a means of fact-checking whether two signals that appear related really are related. In this way, the brain can detect whether two diagonal lines and a curved line appearing in a visual scene, for example, really represent a feature like a cat’s ear, or something entirely unrelated.
http://www.bbc.com/future/story/20181204-why-we-should-worry-when-machines-hallucinate