Edmund Furse writes about trying to understand learning and memory, particularly of the mathematical variety.#

The problem with human learning, is that so much that we learn is in terms of what we already know. This makes obvious sense. For example, we learn that Paris is the capital of France, but could not really learn this if we did not have some previous idea of what a capital city was, or a country. This, so called learning of facts, is known by psychologists as "declarative learning" to distinguish it from "procedural learning", a distinction made by amongst others the American Cognitive Psychologist, John Anderson.

Anderson built a large model of human learning, memory and problem solving known as ACT (Adaptive Character of Thought), and it has had many different versions. But, he models the way we improve our learning, and do tasks faster, namely how the things we know become proceduralised. For example, when you first learn someone's telephone number, you dial it very deliberatively, one digit at a time. But with practice, this gets faster, until the skill is completely automatic (automatized is the technical term), and then when you think of the name, you can immediately recall the number and dial it.

Defining things based on past experience leads to a very interesting problem...

The difficult problem in trying to understand the nature of the learning of facts, is how can one possibly learn something new? This is a very old problem going back to the Greeks. Meno's paradox, the 'learning paradox' derives from the ancient Greek sophists who argued that truly novel learning was impossible in that "novel knowledge cannot be derived completely from old knowledge, or it would not be new. Yet the transcending part of it cannot be completely new either, for then it could never be understood."

Also, many smart people seem to acknowledge this when they talk of trying to find the right metaphor to describe what something does or what a particularly situation is like. Many times we look at something and it seems unfamiliar but if someone can impress a particularly model of it on it seems to merge with the rest of your second natures.

So the problem that many AI programs have is that they imbue a few core concepts to the memory system and that drastically restricts what is able to emerge. Furse has a better solution though...

This model of learning and memory, the Contextual Memory System, (CMS), starts with no features and no items in memory. It thus starts as a complete tabula rasa. However, it does have built in perceptual MECHANISMS which given an object in the outside world, it can build very large numbers of features of the object. Thus, the ACTUAL features that are built are purely a function of the objects that the agent encounters in the world. If the agent spends a lot of time looking at birds and rabbits, then he will naturally acquire many features relevant to birds and rabbits. In contrast, if he spends his time studying the business news, then he will build many financial features.

So, you get the question of whether when human minds are born, are they completely blank slates or do we inherent a bit of classification? This model suggests that we don't.

The article then talks about the progress that Furse has made with systems to understand mathematics.

The conclusion!

Learning is vital to understanding the human condition. Freud believed that dreams were the royal road to understanding the unconscious. Furse argues that understanding the nature of human learning is the new scientific road to the understanding of the mind. This understanding will, in time, encompass a broad range of human experience, from the mundane to the sublime.

Edmund Furse wrote a paper about "The Theology of Robots."#

Furse, a devout Christian, believes that when intelligent robots (computers with strong AI) are created they will be capable of having religious lives and will be likely to want to take up Christianity.

He talks about arguments for and against the possibility of strong AI with occasional emphasis on the religious opposition:

The fifth argument against strong AI is that God created humans as intelligent persons,

and He made no other such beings. In this view, humans are unique among creation being made in the image and likeness of God, and therefore we will always be different to any artefacts made by us. There are a number of responses to this argument. First of all a distinction needs to be made between humans and persons. Humans are the species homo sapiens on the planet earth; we might also consider other species as humans, e.g. homo erectus. A person is an autonomous agent who is capable of intelligent communication, for example, a human, a Martian, an angel in heaven, and I would argue, an intelligent robot. A number of religious writers in science fiction, for example C.S. Lewis and James Bligh SJ, have suggested that there may be intelligent life forms on other planets - persons in my terminology - and further that these persons will have a religious dimension to their lives. Thus it can be argued that the gospel should be preached on Mars, and throughout the universe. Thus, by considering aliens we can escape the anthropocentric view of persons as only being homo sapiens. Just as when a child is brought up we want it to come to know God, the same argument applies to robots. Why should God not desire the salvation of robots?

He writes about why a robot may want to believe in God and how that would occur,

Clearly, a robot on reading the world's religious literature can come to believe that many humans believe in a divine being known as God. Humans believe that God is all knowing, that He created the universe, and that He loves humanity. Will the robot continue to sit on the agnostic fence talking about the God that people believe in, without attempting to communicate with God himself?

[...]

Let us assume that the robot does believe that God might exist. The next question is why should a robot WANT to believe in God? "See how they love one another" was how the early Christian community was seen by others. Perhaps if the robot had Christian friends, and he had personal experience of their life of love and care, he might want to have something of what they have. Certainly, if the robot had experienced his own failure in attempting to love others, then he might be more predisposed to want to experience this Christian life. But, of course, if all the robots' Christian friends were indistinguishable in their lives from non-Christians, this argument will not cut much ice.

And finally, Edmund wonders if when robots do many things that humans currently do this will create a struggle for employment and a confusion about the nature of meaning for many humans who currently think of their lives as revolving around their jobs.

Meaning and purpose then will have to come from elsewhere than from our employment.

[...]

But there are less ephemeral sources of meaning and purpose. First, and most obviously is prayer and adoration. If there is nothing for us to do, then we can always worship God. Indeed this is the picture we have of our future lives in heaven. But we might get bored with a 10 hour day of praising God. Second then, there would also be plenty of scope for personal development and education. Given a lifetime of 150 years according to the latest scientific research, this would give us plenty of time to visit all sorts of places, to study many cultures and subjects. Of course, this would mean real education, a love of learning for its own sake, rather than in order to achieve wealth or fame. Third, there would be plenty of scope to learn to be better at loving our neighbours, at caring for our families and friends. In a sense love does make the world go round, and it is no accident that stories of self sacrifice for others such as that of Schindler or Jesus himself move us greatly and form an important part of our culture. Jesus taught according to St.Paul that it is better to give than to receive, and loving other people can give a very deep meaning to one's life. And, of course, this would mean in this future society, loving both humans and robots.