Article summary of Is a machine realization of truly human-like intelligence achievable? by McClelland - Chapter

Why are people smarter than machines? This used to be a relevant question. Research has been going on for a long time to see if we can mimic human cognitive abilities. However, we have not come very far. For example, Newell and Simon have put together a mathematical system that is generally better at resolving formulas than a human, although, the smooth, adaptive intelligence in 1980 could not yet be imitated. The intelligence to which this refers is the observation of objects in a natural environment, the relationships between them, the understanding of language, the retrieval of relevant information and the implementation of suitable actions. Because this has been a while ago, the relevant question now is: Is it still true that people are smarter than computers, and if so; why?

There has certainly been progress since the 1980s, for example with a chess program (Deep Fritz). This program has defeated the world chess champion. However, the question is whether Deep Fritz has now learned how to play chess. Is it more than just a smart way to program and let a machine look up tables efficiently?

Serre has designed a program based on a feed-forward neural network. The program first learns a certain algorithm. The program is then able to perform a categorization task (assessing whether something is an animal or not). The results correspond to how a person would do this. This is called a natural cognitive task. This kind of progress can also be seen in other natural cognitive tasks, such as the processing of language, memory, planning and choosing actions.

However, there are still a number of questions to be asked about the progress of the intelligence of a machine. According to the author of the article, most systems claiming artificial intelligence do not look broadly enough at intelligence. What people can do, but not a computer, is to think out-of-the-box and reason on this. The conclusions that people can draw come from many different sides, and even the best artificial intelligence systems of the moment cannot.

Why are people smarter than machines?

The problem with machines is that they are unable to think in open ends; they are still dependent on the human programming. The things we miss are fluency, adaptability, creativity, etc. That is why you can say that the "smart" part of the machine is still based on humans. What is this about? Compared to the human neuronal network, a machine needs a week to process the same information that takes a human ten minutes. This requires more power from the computer, but the question is whether that is enough. Progress is needed in several areas, which are discussed below (partly based on known levels of Marr). 

Computational theory

Marr has made a taxonomy with 3 levels, so that it is easy to distinguish between the goal and the core of computer theories on the one hand and the algorithms and their realization on the other. He also emphasizes the level of the computer itself. We must look at what information is in the stimulus.

Various studies have shown that little is yet clear about the relationship between the stimulus characteristics and what the underlying truth is. It is very important to find out more about this cognitive computation .

It is quite difficult to find out how the computational problem works. It would be best if there was a program that could use the data to find out the relationship between situation and consequence. But how can you best explain what needs to be learned in this situation? There are two approaches:

1. The goal must be interpreted as a structured statistical model of the environment. This must find exactly the best format to display the data.

2. The goal must be interpreted in terms of optimum expectation, so that the internal model can remain rudimentary (instead of explicit, as in point 1.)

These two ways must both be further investigated: the first is too restrictive, while the second is too open. We must look at how limitations can lead the search to the optimal solution. We currently have a lot of knowledge about the use of simple solutions (flat solutions), but this does not provide enough information. On the contrary, there must be information about representations from multiple levels.

Algorithm and representation

We need to understand not only what a stimulus makes a stimulus and what the best strategy is to use it, but also how a computer mechanism can apply this as efficiently as possible.

The systematic structure (computational basis) of the characteristics of brain representations is currently being examined. This makes it possible to visualize how lower-level representations in the visual and auditory system are natural solutions in response to the structure of natural visual and auditory stimuli. This approach can be used to understand higher-level representations.

Architecture

Some architectures are intended to map human cognition, others are intended to build modern artificial cognitive systems.

The literature emphasizes a combination between explicit symbols on the one hand and implicit sub-symbols on the other (paying more attention to the connection between each other). An example of this is SAL (a combination of the ACT-R model and the LEABRA architecture). According to the author, the best outcome would be a system that is mainly sub-symbolic, but that the cognitive processes (which are now seen as symbolic) are the output of the calculations of the sub-symbolic processes.

The von Neumann computer is currently used as the underlying computer architecture. Although there is a need for a more brain-like system, the von Neumann architecture underlies most computer programs of human cognition.

Nurturance, culture and education

The fourth and final part to understand cognitive computation is the role of nurturance, culture and education in structuring human cognitive capacities. The human’s mental capacity is formed by experience, and experience is formed by culture and social and governmental influences. Understanding how cognitive capacity arises will also give us more information about how we can achieve artificial intelligence.