Does the Turing Test really tests intelligence?

The Turning Test has a solid stand in the Artificial Intelligence research community as the ultimate test for intelligent machines. That solid stand may be the result of historical reasons. Alan Turing visionary paper and predictions make the Turing Test at the heart of any discussion on machine intelligence. Perhaps because there is no machine can pass that test without cheating. A full intelligent machine cannot pass the test but a well programmed machine within a time limit can fool a human examiner and pass the test mechanism but not in its spirit. There may be other reasons but there are few, it seems, who would question the test itself. The declared aim of the Turing Test is to test intelligence and to provide a benchmark by which we can tell a machine is intelligent. But does it do that?

Let us examine the Turing Test closely. The test requires that a human examiner to have a conversation with two unseen entities. One of these entities is a human whilst the other is the machine to be tested. There is an agreed time limit of 5 minutes but that is often argued against. The key here is that the human could not tell which is the machine and which is the human through the conversation. Now, one of the conversation topics that we can trap a pretending machine is the weather.

Assume we asked how is the weather outside and we got some of the following for an answer:

* It is 21 degrees with northerly wind at speed of 5 knots
* It is a lovely weather today [do not you like it sunny?] (the actual weather outside is heavy rain)
* I do not like the weather in England, how do you cope with it?

Now which one do we think is a human answer and which one is a machine's? The first answer gives an impression of a machine with good weather sensors but could not be a human with a weather station who is mentally lazy and read it as it is? The last one could be a human who is foreigner to England but could not be a machine who just has a preset answers to divert the topics in directions in which it can converse? The answer in the middle is the interesting one. The first part of the answer, which can be a genuine answer by an intelligent being be it a human or a machine, gives the impression of a machine. However, when the optional part is added, which could again be a preset answer for a machine, it gives the sense of cynicism that one likely to connect with a human rather than with pattern matching machine. These cases show the flaws in the Turing Test argument and return us to the question, what does it test?

For an ultimate intelligent machine to pass the test, the machine has to be able to pretend to be human. This requires that the machine is conscious of itself that it is a machine. It is conscious of the fact that the test requires it to come cross as human. It is conscious of time and visual limitation. And finally it is conscious of what makes a human comes cross as human, i.e. all non-intelligent human quirkiness. After all we would be much quick to accept a robot to be intelligent if it can hold a conversation with a good laugh about football!

In my opinion, Turing Test does not test intelligence, or at least not solely so. It tests consciousness, self-awareness, and the ability to lie. The last is the most important because the ability to lie is distinctively a human characteristic associated with our ability to create from imagination.

Our complex cognition makes it difficult for us to distinguish between awareness, consciousness, thinking, intelligence, and recognition of cognitive processes. The latest is a good example of the complexity and the level of interweaving of our abilities. When we remember an experiences we recognize that we remembered after the memory have been recalled; but that recognition in itself make us aware of the process of memorizing; this often leads us to analyze the memory, the memorization process and the reasons why it was triggered; in other words, we become conscious of our existence in time, the existence of the memories associated with an experience and the stimulus that triggered these memories. This leaves us wonder where is intelligence in all of this and how can we quantify it for measurement?

The thoughts provoked by this article is not completely new. Similar notions of wonderment has been expressed over the Turing test and some attempts are being made to find a quantifiable test of intelligence. The advances in cognitive systems make the need to such test, or even better metrics, the greater and more urgent. Many of these alternatives, however, fail because of their focus on one element of intelligence or cognition, often focusing on learning and rational deduction. In most cases, intelligence is the result of integration of abilities, simple they may be, but together demonstrate the various facades of cognition and intelligence. For example, survival is an important ability but not necessary rational; social relations are important element of thinking but may not lead to rational decisions, e.g. parents staying with their children in a burning building.

Integrative (artificial) intelligence would require quantifiable metrics by itself measuring the different factors in ratios proportioned to their impact on behaviour. For example, learning can be form of categorization, but categorization is in itself can be form of thinking and decision making, though it may lead to stereo type based perception. Equally, categorization can be viewed as a form of memory organization to enable associative memorization. Thus, learning, thinking, memory, perception are all necessary in defining intelligence. In addition, embodiment is as important. Studies in animal intelligence gave us and could give us more insight in the separation between intelligence and the other aspects of mind and indeed of being a human.

These are some thoughts on what is needed in building metrics to test intelligent systems that are more coherent, unconfused, and measurable to truly test intelligence; but this is not an attempt by any means to set such metrics for doing so comprehensive discussion between psychologists, sociologists, AI researchers, neurologists and philosophers is needed to extract the components of intelligence from the mesh of the human mind and identify their weights in defining an intelligent being (be it a machine!).

A good reading list on the Turing list and associated topics can be found at: http://www.aisb.org.uk/publicunderstanding/turing_test.shtml

Cognition, Serious Gaming, and Behaviourism: The Emotional Dimension

Speaker: Dr. Aladdin Ayesh, De Montfort University
Place: IOCT, DMU
Date and Time: 2pm, 1st December 2008

Abstract:
Video games have advanced greatly in the last few years. Their advance and great popularity can be contributed to a number of reasons. Some of these reasons are very obvious such as the advances of hardware both in increased capability and reduced cost. Another important reason is graphics and visualisation. But there are other reasons that are less noticeable but as important if not more so. Cognitive studies application in developing games can make huge difference and allow a graphic inferior platform such as Wii by pass far superior platforms such as Playstation in terms of overall experience and user attraction.
Applying cognitive studies to develop interfaces is not particularly new. However, games gave a new dimension because of new set of user requirements and expectations. Thus, the cognition we are talking about here relates strongly to the behaviour of the platform and the games played. It also relates to means of interaction and user presence within the game that creates an attachment. In this case, emotions play a big role in developing platforms, games scenarios, games avatars and so on.
Emotion modelling is attracting more researchers in the last two years. There are still only few formal models in computing whilst a great literature in psychology and sociology. In this talk we will look at the current developments in emotion modelling, emotion-based inference, emotion expression and classification. A particular attention will be paid to behaviourist theories of emotions, which are often used in developing the computational models. Serious gaming, which is the use of game technology in developing serious applications such as simulators and training suites (e.g. some companies setup presence and deliver training and advertising services through Second Life), will be used as a context to show the importance of computational models of emotions in an era of parallel living avatars and domestic robots.
The talk will draw on existing projects. One particular project relates to crowd management, especially during disasters and emergencies (e.g. war zones). There are soldiers/police avatars, who are clearly identified, but then there are men, women and children within the crowd. Within that crowd there are also troublemakers. Each one of these characters has different levels of emotions derived from their motivations and perception of the situation and their social relations to others within the crowd. Now, a player or a trainee can become a part of this virtual world as an avatar. It will be shown how through using emotions modelling and social relations rules the avatars can exhibit panic and curiosity as an emergent property similar to what we may observe in the real world.
The talk will conclude with the launch of www.computational-emotions.org web site, which aims to provide a point of reference to researchers in this area and the basis of a new book by the speaker.


If you would like to book a place at this event, please contact Lisa McNicoll on lmcnicoll@dmu.ac.uk

Swarm Intelligence from Particle to Human

I am due to give a talk at Le Havre Universitie tomorrow 24th Jan 2008 on Swarm Intelligence with video link to Rouen University. Here is the abstract, which I hope will initiate some discussions. Slides are to follow.
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Vous êtes cordialement invité au
Prochain séminaire du LITIS (axe ISC)
Jeudi 24 Janvier 2008
14h Salle de visioconférence de l'UFRST du Havre
et Salle de
visioconférence de l'Université de Rouen - Madrillet


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======================
Swarm Intelligence and Applications
Optimization, Nano Technology, Social Simulation, and Art
Par
Aladdin Ayesh,
Co-ordinator of Intelligent Mobile Robots and Creative Computing
Research Group, De Montfort University, UK




Swarm intelligence is the result of observing nature where we see
swarms in the form of bee hives, ant colonies, chemical structures,
physical particles. In all these cases, these small tiny entities
interact and work together producing an intelligent behavior from
simple rules.

In recent years, there has been an expansion in using swarm
intelligence in a number of ways. First, swarms used as a simulation
tool for sociological and ecological studies. This perhaps their first
real application. Second, a number of optimization algorithms based on
swarms emerged, e.g. Ant Colony Optimization and Particle Swarm
Optimization. Third, nano-technology advances increased the interest
in hardware swarms, i.e. nano-robots, and software swarms, e.g. to
simulate bio-chemical structures and their physical properties before
attempting to create them in the labs. Finally, swarms are social
structures that fascinated artists, e.g. cellular automata and pixel
hives.

This seminar will explore swarm intelligence as a current and active
trend in artificial intelligence community. A brief history and themes
of swarm intelligence will be covered, then the main application areas
will be discussed. Some on-going projects at De Montfort University
Intelligent Mobile Robots group will be discussed. The seminar will
conclude by identifying some areas of growth in swarm intelligence
especially those related to complex systems.
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Emotion Modeling for Reasoning and Interaction

I am due to give a talk at Le Havre University on Tuesday 15th, 2008 on the subject of emotions. As I was writing the abstract it evolved into a short article. Whilst a shorter version is used to publicize the talk, here is the extended abstract of the talk.
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Emotions have been recognized as an important factor of the human composition since the early human thoughts and scientifically studied as early as 1872 with Darwin's book 'The Expression of the Emotions in Man and Animals'. This followed by a number of theories and schools, e.g. Lang (1880), Watson (1919), and Ekman (1978) to mention but few. However, early researchers working on formalizing knowledge and reasoning ignored the impact of emotions and depended on rational logical systems (e.g. propositional and predicate logics, modal logics, etc.).

Nowadays, cognitive processes have been understood to go beyond the logical reasoning. The connections between memory, perception, and desires are being recognized as important factor in human reasoning and decision making. These ideas filtered through to Artificial intelligence research, e.g. Belief-Desire-Intention (BDI, 1987). However this filtering remained within the boundaries of computational solution dressed into psychological and sociological analogies tagged as Folk Psychology.

The recent advances in cognitive psychology, e.g. the work of Anderson, Miller and Piaget, helped by advances in neuro psychology and advances in neurological computational models started to give stronger scientific basis to studying human cognition. As a result, a growing research community on cognitive systems evolved looking at scientific approaches to research cognition and to develop cognitive systems. This growing interest is also reflected in the EU and national research councils funding. Cognitive systems is now a main stream theme in EU FP7.

These advances in cognitive systems gave a greater importance to emotion modeling research. The more we try to make a cognitive agent, and often fail, the more that emotion effects are recognized. The relation between emotions, perception and action selection, especially in dynamic uncertain environments, have been the primary study of a number of projects, e.g. Flame project (2000). There are several approaches to modeling emotions. Cognitive maps, fuzzy logic, multi-valued logics and neural networks are few examples of techniques used to model emotions. The interesting aspect about these new modeling approaches is that they attempt to capture the essence of emotions beyond the early computational attempts that used emotions as mere elaborated thresholds.

In this seminar, we will explore the psychology of emotions, discuss emotion modeling for cognitive agents with particular focus on two areas. The first is the effects of emotions on reasoning that includes action selection, decision making and problem solving. The second focus will be the effects of emotions on social interaction between cognitive agents within rational or irrational situations, e.g. crowd simulation in normal and under pressure situations.

The talk will conclude with areas of interest that is still open to investigation. In particular, the effects of emotions on memory and association between experience, perception, emotions and evolving personality. The applications of emotional agents in games and other applications will be discussed.