Sensors are the basics of perception. Any type of perception. If you need to know what the temperature of this room is, you could use a mercury thermometer. Previously to that, you need to find a material that is sensitive to temperature changes, like liquid mercury. Then, you need to define a scale just to have a sort of a base line and an idea of what it’s lot and what is tiny. Finally, you will need to use a code, or word, say it, express it, process it, and incorporate into other thoughts and more complex measurements.

This is just using technology, that of course, appears to be quite handy. But we, animals, can also perceive temperatures. Sadly, not so accurate than when using mercury, but in a survival way at least. I won’t be able to differentiate that you have a fever, till you are really hot. If I choose to use a thermometer instead, you will be able to explain that weird tiredness you are experiencing and go to bed to rest before you get a crazy high fever. But why? Is it because our sensor is cheap and low quality? Or is it because the differences that we want to differentiate are too tiny for our sensor? Or maybe is that we don’t have installed the proper drivers or trained the algorithm enough to be able to say how much? I wonder if someone can answer conclusively to any of these questions.

But of course, the fun here is to think what could be happening.

In terms of if our sensor is made with good quality materials… we could go one by one and try to make a comparison with other animals with similar sensors, for example. We could say that our vision is lower quality than a mantis shrimp, but much better than a mole. Or maybe, compare it with technology? Wow, that’s a good stat… our eyes are over the fastest high resolution, at different focus and lights, than any photographic camera. But yeah, perceiving temperatures or quantifying electric currents, any cheap instrument or even a platypus, will do better than you.

Let’s think about the fact of receiving enough signal. With temperature, well… it’s true that quantify the temperature of a hot needle is trickier than a hot knife, or the water of a jacuzzi (when being all, of course, at the same temperature), but is also true that the limiting factor here is to not burn the chip. With light is obvious that the amount of it varies a lot inside our range of perception. So if we use these two sensors for concluding, eyes are better sensors measuring light than skin and our nervous system are to measure temperature. But…, what if the temperature is just one of many magnitudes that our skin-sensor can quantify? The comparison now becomes a little bit unfair, since we are talking about one of the best multisensors that I can think about, a multimeter becomes a joke of diversity. Temperature, pressure, wind speed, flavours, light?, a little bit of sound in case you need it, tickles… and not only their magnitude, with many you can even get directionality! It is a vector quantification machine!!

But well… who cares about what is a lot or what is tiny, I just wanted you to think about it with me. But I want to finish with the training of our algorithm and installing drivers, the last part. And in case you were wondering, this is where art, food and science enter in the equation. The world of flavours is one I didn’t know it existed till some years ago. We don’t use many spices in Galicia.. so even we use really delicious and intense flavours, combinations are usually kept quite simple. I’m starting to understand that my sensitivity and flavour detection capabilities are not limited by my sensor, either by the signal, the training of my neural network is what is missing here. I am starting to learn how to identify them, one by one, collecting many data points, trying to focus in limiting and locating them inside some sort of a scale, to then detect and enjoy multiple and weird combinations. Apparently, colours are like this. Colours are complex, but how we visualise them in our brain is an idea, is part of a calibration scale we made. Maybe in a less conscious way than flavours, maybe we come with some preinstalled drivers, but that’s just because colours help us more with natural selection than flavours, but we can learn them too. These are just two interesting examples, but think about what are the limits of this. We can train our algorithm and learn with practice and experiencing becoming a more accurate, optimised, and expert machine. And the science… I will let it to you to figure it out where it fits.