By: Adam Stanton
May 10, 2019
Numbers figure pretty high up on the list of what a computer can do well. While humans often struggle to split a restaurant bill, a modern computer can make millions of calculations in a mere second. Humans, however, have an innate and intuitive number sense that helped us, among other things, to build computers in the first place.
Unlike a computer, a human knows when looking at four cats, four apples and the symbol 4 that they all have one thing in common – the abstract concept of “four” – without even having to count them. This illustrates the difference between the human mind and the machine, and helps explain why we are not even close to developing AIs with the broad intelligence that humans possess. But now a new study, published in Science Advances, reports that an AI has spontaneously developed a human-like number sense.
For a computer to count, we must clearly define what the thing is we want to count. Once we allocate a bit of memory to maintain the counter, we can set it to zero and then add an item each time we find something we want to record. This means that computers can count time (signals from an electronic clock), words (if stored in the computer’s memory) and even objects in a digital image.
This last task, however, is a bit challenging, as we have to tell the computer exactly what the objects look like before it can count them. But objects don’t always look the same – variation in lighting, position and pose have an impact, as well as any differences in construction between individual examples.
All the successful computational approaches to detecting objects in images work by building up a kind of statistical picture of an object from many individual examples – a type of learning. This allows the computer to recognize new versions of objects with some degree of confidence. The training involves offering examples that do, or do not, contain the object. The computer then makes a guess as to whether it does, and adjusts its statistical model according to the accuracy of the guess – as judged by a human supervising the learning.