(Originally appeared at: https://korvinag.com/deep_learning_lca_w_digits_april_17_2018.html )
The applications of commercial AI techs are now becoming pretty much widespread and with the tremendous capacity boom regarding processing speed, storage size and sensory sensitiveness these are yielding more and more for the industry. But every time it comes to the age-old question, whether “could machines one day think as humans do?” there is no clear answer yet.
Partly it is because it is yet to be modeled how actually humans thinking in general and how the thinking of given individuals differ from each other. And believe me, that ain’t gonna be a model to be digested easily.
I started to code a very simple logical course of action selector a while back, and I found that even in the field of the machine learning/artificial intelligence field the core design principle will remain the classic ’60s rule:
Keep it Simple, Stupid !
I started with researching the possibilities regarding a car that is traveling among the non-AI cars that are going in every possible directions. What I found is that the flow of the traffic, when seen “from above” is definitely following the rules of hydrodynamics, but it is down to sequences of haphazard events when You see it from the perspective of the individual driver.
The most simple solution would be of course to use the most out of the AI-driven car’s sensors. Watch out for the signs, the surroundings, the other vehicles — and have these permanently synchronized with the on-board (or on-line) geospatial/geological informations database(s).
But this approach is in line somehow with the idea to have someone drive on all the roads of the Earth in order to get a driver’s license — just to learn what is possible when driving. So, here is where the human thinking vs. artificial intelligence thing kicks in.
To cut a long story short, what I found was that the human decision is not necessarily based on sensing (“what one sees”, “what one hears”, etc.) and comparison with the full knowledge (“what one knows”) — but on the possible ensuing logical course of action. (Even though the logic involved might not be ‘logical’ in some instances.)
For this reason I started to collect a set of possible logical courses of action, that can be taught to AI-driven systems. In order to keep it learn-able and simple, these are all have to be represented as processes in order to keep it possible to use them in both feedforward and recurrent digital neural networks.
In the end I was able to use 54 such logical courses of action which finally went into an anti-fraud fintech application. The number of these now reached the 400 mark, but for this writing I will only use the most simple and convincing set: the digits based on the Arabic numerals.
For the purpose of this article letters are representing breakpoints (which can be translated also as interrupts or incidents) in the process of going from one place to another in an autonomous way. The arrows are representing the direction of the process, which equates to the heading of a self-driving vehicle.
Let’s start with the number 1. In my model it is rather like the capital letter ‘I’. It represents the uninterrupted process, going from point ‘A’ to point ‘B’.
The number 2 represents the event when in order to go forward and towards the destination of the self-driving vehicle, it has to get around an obstacle. After circumventing it, the self-driving vehicle rearranges itself at point ‘B’ and begin its uninterrupted journey towards its destination, which is in this case the point ‘C’.
The number 3 represents an event when the self-driving vehicle has to get around an obstacle and immediately after this is done, it has to do so once more again. At point ‘B’ it rearranges itself and decides to circumvent the second obstacle. At point ‘C’ it rearranges itself and can progress towards its initial destination. See below for an example: the DeLorean does it!
There are two different uses of the numeral ‘4’: the open top numeral 4 and the closed top numeral 4. The open top numeral 4 represents the logical course of action, when two processes (‘A’ →’B’) and (‘C’ →’D’) are intersecting each other at one point and there remains a possibility that these processes, sometimes in the future, will intersect each other once more. This is a very prevalent event in the case of highway and speedway traffic for example. The closed top numeral 4 represents the case when the self-driving vehicle has to rearrange itself several times, because the obstacle can not be passed by getting around it. A nice example is when the self-driving vehicle enters a dead-end street (or one that is blocked temporarily). In this case it has to conduct a Y-turn, and in doing so it intersects its previous track.
The current form of the numeral 5, however, is replaced by a sign that resembles a numeral 2 turned upside down. This logical course of action too is the opposite of what we seen in the case of the numeral 2: the self-driving vehicle is progressing in an uninterrupted way, but at point ‘B’ it detects an obstacle and decides to get around it in order to reach its destination. At point ‘C’ it rearranges itself and follows the original travel plan. A single event regarding this logical course of action with the DeLorean:
The numeral 6 could represent many things, but the basic event is when the destination is not accessible to the self-driving vehicle. In order to deliver the cargo it has to decide that instead of circling around the blocked off endpoint (that is breaking the logical loop) it would stop and drop off the cargo.
The numeral 7 I used here is the handwritten form of the number. It represents the case when there are two parallel processes, one of these being an uninterrupted one (‘D’ →’E’). The other process (‘A’ →’C’) is interrupted at a breakpoint (point ‘B’) which forces this process to intersect the ‘D’ →’E’ track of the process. This is what is happening when the self-driving vehicle is overtaking another one, during which event it has to use the oncoming lane. This means that at point ‘B’ the self-driving vehicle starts a maneuver which intersects the track of the opposing traffic. This is a one-time possibility and the AI must use its capabilities to avert an accident by knowing that it could occur. A simple scenario follows with the DeLorean:
The numeral 8 is a representation of any logical loop caused by malfunctions or an inability to find a way out from a situation. Main characteristics are that it continuously performing the same moves (running the same sets of algorithms) and it crosses its own tracks over and over again at approximately the same place (after the same sequences). This could be useful to train an AI/ML-driven system to be able to escape logical loops. And this in itself adds a lot to the capabilities of an AI, since it makes harder to spoof it.
The numeral 9 is the opposite of the numeral 6 and is used here to train the self-driving vehicle to understand the need of other vehicles for purposely circling around a destination. This is what we do all the time when we are circling around in our cars searching for a parking space. We might circle many times around hoping that someone will leave its place. And this logical course of action teaches the AI that there is a relationship between ‘supply’ (e.g. number of parking spaces for example) and ‘demand’ (the number of cars circling around). And of course it makes the AI able to differentiate between logical loops and temporary loops.
The numeral 0 is the way how the AI could relate to its own existence. This logical course of action is the circulation that is the normal way of operation for a self-driving vehicle. This could involve things like departing the zero point, arriving at the endpoint, refueling, performing technical self-checks, departing the zero point, etc. etc.
Of course this is a greatly simplified abstract of the conception, but I hope that some of you will find it useful :)
Thanks for reading and please feel free to share:
Korvin AG
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