Main points:

• Several cognitive functions are transversal in significant ways, including learning and attention
• Many identifiable cognitive functions interact in substantially and important ways, including short-term and long-term memory, inferencing, generalization, prediction and anticipation, to name a few
• Because of this cognitive systems must be studied as a whole; the architecture of mind must be a first-class citizen in those studies

When we say “architecture” we refer to a system's information structural layout and the key principles behind its operation; the constraints that are put on information intake, transfer, storage, and how computations are implemented, performed, controlled, as well as how these may be different over the lifetime of the system and the principles guiding their change over time. Just like people walking around in a building, the building imposing limitations on where they can walk (doors and walls) and what they can look at (windows and glass walls), a software architecture is a blueprint for the components and operations of a software system. The architecture of a system, as implemented in a substrate (hardware) controls how and where information flows, and what computations are performed, their order, and in what manner.

The choice of programming language determines to a significant extent what kinds of systems can (theoretically and practically) be implemented. Programming languages impose constraints on what kinds of architecture is viable – for example, LISP presents very easy ways to write code that writes code, while C++ makes this very difficult. If automatic generation of code, and subsequent runtime-dependent running of such code, is part of the requirements of a system we are planning to construct, we would naturally feel less constrained if we could choose LISP over C++. There are of course other key factors that influence the choice programming language, and we will get to these shortly. Since the vast majority of current programming languages can in theory support any feature encountered in any other language, when we decide which programming language to choose the choice depends primarily on practical factors: how effectively and efficiently does it allow us to implement the data and control structures that we already know we'll be needing.

Prolog seemed at one point to be a good programming language for programming logic-based systems. Logic of some kind seems to be necessary for any AGI if we want it to have some of the logical skills of humans. While it is correct that humans can – and quite often do – engage in activities that most agree to calling “logic” it is not clear that a fundamentally logic-based environment provides the best framework to do AGI research in. One of the problems with most Prolog-based development environments is that the control of its runtime operation is typically out the programmer's hands. Another is that time is not handled well – to do anything involving time, such as move a robot arm from X to Y, can be extremely cumbersome to do, if it can be done reasonably at all. A third problem is a fairly limited extensions beyond first-order logic. A fourth – and very serious problem – has been a lack of thread and parallelization support.

Lisp is another programming language with roots in artificial intelligence. Lisp development frameworks have typically provided more control of a system's behavior than Prolog, and object-oriented extensions such as CLOS (Common Lisp Object System) have been hailed as one of the most powerful commercially available object oriented frameworks. Multithreading has been part of Lisp environments from the early 1990s. Lately both Lisp and Prolog have fallen by the wayside in AI research, being in part replaced by Java (for convenience and library reasons), C++ (for efficiency reasons), and Haskel (for clean design and good support of parallelism).

Many of the cognitive functions that have been studied implement essentially anytime algorithms, in one way or another. It could even be said that intelligence is in itself a form of anytime “algorithm”, because it must be interruptible at all times, and be able to balance how much time it spends on its various goals.

Main points:

• Software/hardware architecture has an effect in mainly two ways: the ability to parallelize processes, and the speed and nature of their interaction
• These features fundamentally affect both the theoretical possibilities and practical implementations of cognitive systems