I've written the last few posts from a philosophical perspective on how the mind works, with input from some different sources and books and some of my own reasonings and reflections.
It is a very difficult thing to introspect the mind. Reverse-engineering yourself is an absurd idea. A computer might be able to register and find out about how other computers work, but it is very unlikely to gain some kind of consciousness or knowledge about the state of a single transistor in itself, unless it was specifically designed to be so (which therefore generally has a reason).
Artificial intelligence uses "neural networks" to solve some specific types of problems, which in general is a fancy way to express "pattern-seeking". Some pattern exists in some glob of data and the network is being trained to process the information and have, as its output, some new parameters or data. Sounds pretty simple. Many networks like this have 16 neurons only, yet are capable of inferring quite some knowledge. For a good example of such, read the following example on neural networks and download the example executable:
So, in the example you could see that the minesweeper only has as its input the direction to the closest mine. There is no algorithm or other function that changes the direction of the minesweeper directly. Each minesweeper has its own neural network (its brain). The output of the brain is directly used to control the side-tracks (left and right). A very simple physics engine then calculates the rotation of the minesweeper and where it goes next.
The first generation is quite useless, but then winners that picked up at least one mine are promoted to the next generation. It might create children. Eventually, after 50 generations and "natural" selection (of the fittest), the minesweepers exhibit rather intelligent behaviour with regards to the mines and there are many more minesweepers that become very efficient and picking them up, even change direction immediately to the next available mine as soon as the one in front was picked up by another.
A neural network was modeled after the biological brain, which has 10,000,000 neurons, dendrites (the input to the neurons) and axons (their outputs). Something happens inside the neuron that results in the neuron triggering an output charge or not. This only happens after a neuron receives an input charge (or various).
The most interesting questions here is whether 10,000,000 neurons are the human brain, are human thought and allow for reasoning, or are only part of this. Emotions, the way we call them and how we sometimes uncontrollably display our emotional state, suggest that it is not just a computational function. In other blog posts, I reasoned that emotions are the driving forces behind humanity. I also challenge the line of thought that rational thought really is rational in nature (void of emotion), because I recognize that most of our human actions and interactions are based somehow on emotional action or response, where it also uses some reasoning to add to the response or subtract (withheld emotions or exaggerated emotions). I think every one of our decisions, which people sometimes label as rational, are actually emotional decisions with a cover of argument. Only in the case where we reason within a factual model (science, maths, etc, which are generally highly deterministic and consistent) can we state that our reasoning is void of emotion (2+2=4 and will never be 5, whereas our human decisions in similar situations can highly differ through the concept of priorization of emotional importances, which are then argumented further as if these were intellectually considered points).
Should an android like Data from Star Trek exist, then I would not expect him to display emotional state, nor advise anyone on the best course of actions through a lot of reasoning. One could not ask Data if he wanted to go out for dinner, because he wouldn't be able to resolve the question, as he doesn't feel anything and cannot reason within the emotional domain. One could not ask him if he liked the color red, or if he wanted to take care of little androids in his life. To want and to like are unimaginable and unresolveable concepts for the android.
So... what does this mean for us human beings? The brains are a bunch of neurons and outside the microscope we see a hump of meat (humor, sentient meat):
Is it really possible that 10,000,000 neurons connected together can fool us into thinking that we are conscious beings? Is consciousness embedded within these neurons or is consciousness yet another force in our brains that uses the neurons for reasoning and recognizing patterns?
If it is true that the neurons drive us, then we are basically nothing else but pattern-recognizing machines. Everywhere we go, we quickly recognize the world around us and continuously reinforce new patterns when experiencing things never experienced before. It is basically input->processing->output for everything we do, look at, hear and so on. To ourselves, our thoughts claim that we look at absolute objects, have absolute knowledge of the world around us. That is, we recognize that a brick is a brick and it cannot be something else. But maybe the real way *inside* our neurons, how we look at the world is through pattern recognition. If we then consider "thought" as the output of the network, then it is clear that the inner working lies hidden for us. We don't have specific knowledge or awareness of the process within the network, since the output is what we measure.
This might mean that the experience of this world is highly driven through (partial?) neuron activity. You see something, it gets processed, it activates other neurons, and so on, until at the output we recognize it as something.
A mistifying factor here of course is the ability to learn. Not "learning" in the sense like artificial networks do, but learning in the sense to have an interest in understanding something. An ANN is a network that has a very specific purpose and is conditioned to execute its purpose with a training set. It recognizes only that pattern, but don't ask it to reason about something else.
Reasoning can probably also be expressed as the activity of comparing pattern recognition programs with one another in the hope to reuse or pre-initiate a new capability of a new network.
Another limiting thing for androids might be that I do not expect them to start asking questions. Would Data ask anybody else about a particular event? He can be given a goal to execute, the result can be right/wrong or informative, but will Data ask questions from the environment to enrich his own knowledge? Or will he actively seek this information on the Internet?
Looking at a developing baby to a toddler, there are many interactions with the environment. Given a clean slate, the baby starts to develop an interest in the environment. When it is born, it cannot focus the eyes or move the muscles through coordination. These can probably also be seen as "patterns", where the sight and hearing are paired together and then compiled in a network, where the output is directly used to control the muscles. Research here in the area of clock frequency or how fast our movements are adjusted based on changes would be interesting.
In further development, children are shown children's books and we point at pictures and say "zebra" or "tiger". At some point children get it. Then ask a child..."Can lions walk?" and the child uses reasoning to find this out. Uncertainty is a feeling one can have about the validity of a certain answer. This has a direct relationship with the strength of one belief. If a belief is formed by the strength of the trigger of a set of neurons, then you could say that it is also how strongly a pattern has been recognized. If a lion has legs that bend backwards and looks somewhat like a cat or dog, and things that have legs can walk and cats and dogs do walk, then with a good amount of certainty, it can be said that lions walk too. Now consider the parent... there are many different little bits of knowledge that we pair together in our reasoning that the parent uses to teach the child (with that usual mother-kind-of-voice): "The lion has legs and manes and big teeth. It can bite and is dangerous".
This pattern recognition, if it is at the source of intelligence, then also explains why we are so subject to categorization. By forming larger categories, we collect truths in the same basket and then test other things against that rule. That greatly reduces the need for storage space in the brain and makes it possible for us to make assumptions about things never encountered in this world.
Problem solving skills are the next step then. Given a particular goal, we can think and think and come up with a solution to meet that goal. We could say that we have the ability to 'imagine' things we have seen happen in real life and then try to replicate that same event, behaviour or property with other means. Problem-solving highly depends on the ability to create different hypothesis and to recognize (cross-pollinate) ideas from different analogous areas.
It sounds as thus, if the brain has very powerful pattern recognition abilities in the brain, but it is helped and supplemented by additional capabilities that use this network as a tool. Imagery patterns, auditory patterns, behavior patterns and so forth. When studying the child, one can clearly see that it is a lengthy process to get our neurons in order. Learning takes a long time to finish and even then, us humans do not develop in the same way and do not exhibit the same behaviour. Each one of us is unique. Our ability to deal with noise that clouds the pattern is amazing.
An android might actually have some advantages in learning. It should be possible for a computer to demonstrate internal states of neurons and visualize them, or execute "what-if" scenarios (as it is computed and won't have a guaranteed effect on the state or quality of the network), so that we can steer the development of the network or understand it better. One can also imagine a desktop that demonstrates the processed elements with interactive handles to improve the network, the same way a parent would point at things in pictures to explain why certain associations are true and how these same properties in other pictures, which may look somewhat different, also demonstrate the same behavior or constitute the same thing.
New tool in town: KnowledgeGenes.com
7 years ago