What knowledge may actually be about...

I've been cycling in the rain today and suddenly got this awesome thought train. Nothing much to do with rain, but I guess that the cooling characteristics of the cold rain with the cold weather probably caused some superconductivity somewhere. I can't get into too much details what I've come up with to resolve the below general and (as I argue) incorrect assunptions, but I can at least discuss the problems I perceived that might restrict the ability to develop more intelligent behaviours in computer programs. :)

To give you a starting frame of thought, I'm arguing that there are many assumptions about the origin, extent or representation of knowledge in many artificial intelligence reasoning models, in neural networks and whatever else is there. Here is why.

Most computer programs function by taking some parameters from an environment, file or whatever as input and based on that perform some action. A program without input or output is not a computer program. The general idea is thus that by applying some function F(X) on a range of input parameters X, one can derive an action or actions Y that should most logically ensue. The diagram here helps to illustrate this further. It is a depiction of a so-called Markov diagram. These are often used in A.I. to reason about and between states. One can move from state to state along the transitions, but the arrows also restrict movement to just any other state. The diagram need not be statically defined a priori, there are sometimes ways to dynamically extend the diagram at execution time, most of the times determined by a set of rules. M here is the entire world, consisting of possible transitions or relations R, states S (which is more of a placeholder in this case) and measurable parameters T from which you determine the current state S.

The above model works very well for a set of discrete parameters, since they always fit into one single state. For continuous parameters however, it becomes more difficult, because if every single value is considered, the number of states in the model would become infinite. Hence, classification is needed to reduce the number of states, or the development of some kind of 'probability distribution', where one is actually a little bit in all states at once, or one can come up with a fuzzy reasoning method for reasoning between just two states S1 and S2, which are closest to the current 'continuous' state.

The huge assumption made here is that all information leading to some desired action can be derived from a snapshot of the external world, just by inspecting some range of percepts X. Reasoning and intelligence is then reduced to the application of some function F(X), even though this can be quite a complicated affair when there are a possibly very large number of percepts X or a wide range of possible actions Y. The complexity due to the dimensionality of the problem is irrelevant for this conclusion however. Even more worrying here is that there's only one single assignment of intention or purpose in this entire system. The purpose is the definition of the function F(X) in itself, but it'll be difficult to change this as soon as the system has been constructed or trained... Hmmm... :). So one cannot expect this system to generalize a bit and then specialize further into other things.

So, resuming... having a function F(X) implies a direct, causal relationship between (a range of) perception(s) and a most logical (range of) action(s), which only holds if the purpose is fixed and pre-defined and doesn't change. Or worse... the context does not change. Also, knowledge is then only defined as the function that needs to be applied to the percepts in order to derive the action Y, it is not contextual knowledge about what is required in which context. In effect, the only way to make the system more able to deal with situations is to measure more and more in that single snapshot and then trying to fit the function F(X) to suit those measurements.

However, instead of considering that it is the state(s) embedding the information, one should consider that it is the transitions that allow one to collect the real knowledge about events, since transitions embody relationships. The transitions maintain the real information about some state S1, the next state S2, the difference in the percepts, the action(s) that was undertaken and whether S2 is actually a better state to be in when compared to S1, so deriving whether the action was desirable or not, given a certain context. Note that this doesn't yet require much information about a goal state Sg, so one might as easily conclude that there is no direct need to know about the eventual goal state or have a real conception about what it looks like, we can at this point still assume we're slowly and gradually moving towards the goal state and there's still a capability to change direction or impact the environment to reach the goal anywhere (but this is getting too complicated at this point).

This is why I think that discarding the thought that there is a causal relationship between percepts and most likely or desired action should be a first objective in order to develop more intelligent systems. Knowledge is more likely some kind of imagination or dreaming of the effect of actions from any given particular 'general state' and the kind of changes in perceptions how one can measure such progress, all this framed into a particular context. This should allow systems to reason with shorter horizons or reason at different levels with different horizons, but always reason with specific and particular contexts at each level. The definition of how many transitions you allow before making conclusions, or the extent of correlation between events is then part of the game and part of the experience...

Cybernetics

For my work, I've been looking at neural networks in more detail, but specifically how they may be applied to problems with temporal properties. Most artificial neural networks do not store their values or activations as some kind of remanent activation very well. Such neural networks can only work in situations where the input applied to the network is complete enough to derive conclusions from it. In that case, it doesn't matter if there is a known formula for the correlation between input and output values or whether there is one, albeit a complex one, that is being approximated. Such direct correlations after one single frame of observance is the typical neural network that you get today. More recent developments also give you recurrent networks or LSTM cells, but those model patterns or 'data' over time. Thus, they establish correlations or relations between sequences of events. For all these networks, you'd generally expect a mathematical explanation or approach to back up the learning methods applied for it (if they haven't been trained with evolutionary algorithms).

Even in the case of evolutionary algorithms though, there is significant complexity involved in training the network so that it performs well. Unless the input/output correlation between classification or pattern recognition is very strong, which makes any problem quite easy. Basically, this means that in the same way that a formula calculates a set of output value(s) in a deterministic way (and 1+1 is always assumed to equal 2 and never changes), a neural network is just a very deterministic approximator. Given a trained network, any two inputs yield always the same output. This is true, unless you use an RNN. But the RNN is designed to work on temporal problems, so given the same sequence, this yields the same output to classify or reproduce the sequence.

The picture above shows a neuron receptor that is involved in a process called chemo-taxis. This process allows the worm to navigate in an environment where food is available. The worm has no eyes, can feel heat (thermotaxis), and wriggles its way within some environment. If the worm is wriggling towards a food source, it's climbing up an increasing gradient where the chemicals picked up by the neuron increase. When it climbs up an increasing gradient, the worm is less likely to make sharp turns. If the worm is on a decreasing gradient or doesn't detect any food source, it is more likely to make sharper turns.

Such a problem sounds very simple to resolve, but remember that we have eyes. How do you locate a particular source of smell if you only have a nose? You need to move around in a room, being careful not to cause any harm to the body. Depending on the sensitivity of the olfactory sense and the distance of the smelly source, the distance between one sample and another must be larger or may be smaller. The samples also must be taken within a certain amount of time, otherwise no significant difference can be observed.

The above shows how some organism, actor or agent must interact with the environment in order to sense it and make conclusions about the layout or composition of the environment. Without the ability to perform actions in the environment, I don't think there can be any development of intelligence at all.

Such statements are derived from a particular field of knowledge called 'cybernetics'. This sounds like some science fiction term I'm sure, but it's not just related to cyborg, cyberspace, the internetz or what have you.

Cybernetics is a recent term from the 40's and 50's defined by Norbert Wiener. The word is derived from the greek "kybernētēs", which means helmsman, rudder, etc. and has the same root as government. It was initially intended to study self-regulatory systems, but quickly expanded to other areas like management (Stafford Beer), biology and also computer science. The science is quite broad in scope now and poses questions about how systems make sense of their environment by interacting with it. Finding out what an environment is about isn't exactly the same as just perceiving it.

The difference with Artificial Intelligence is quite strong. Cybernetics is more theoretical, whereas A.I. has basically just gone ahead with a lot of assumptions, trying to wrap reality into mathematical models and executing them on a computer. My criticism of A.I. until this point is that it is simply too deterministic for anything 'new' to come out of it. The term A.I. has been a bit bold as well. Sure, we have large-scale systems analyzing emails and other kinds of data and knows how to extract information from it to make predictions, classifications or specific groupings of data. But those capabilities have been intended by designers. There is nothing that such algorithms or machines can ever do different but execute those particular implementations.

In that sense, expecting anything truly intelligent to come out of A.I. as it is currently approached (through mathematical formulas) is very hopeful at best. A better understanding of knowledge, its roots and how it may be represented over time and interacts with new environments is needed.

That is not saying that A.I. is useless. It's saying that A.I. certainly has approaches for doing pseudo-cognitive work by helping out massaging details out of very large mountains of data or finding specific correlations that we cannot even imagine. So there's certainly room for the A.I. as we know it now and some new kind of vision on what truly intelligent machines can bring us. Those kinds of machines however need a different basis than just mathematics and strong correlative behaviour. They need to find out for themselves what is important and have their own ways of interacting with the environment (which is how they find that out :).