Abstract
Moving from a behavioral definition of intelligence, which describes it as the ability to adapt to the surrounding environment and deal effectively with new situations (Anastasi, 1986), this paper explains to what extent the performance obtained by ChatGPT in the linguistic domain can be considered as intelligent behavior and to what extent they cannot. It also explains in what sense the hypothesis of decoupling between cognitive and problem-solving abilities, proposed by Floridi (2017) and Floridi and Chiriatti (2020) should be interpreted. The problem of symbolic grounding (Harnad, 1990) is then addressed to show the problematic relationship between ChatGPT and the natural environment, and thus the impossibility for it to understand the symbols it manipulates. To explain the reasons why ChatGPT does not succeed in this task, an investigation is carried out and a possible solution to the problem in the artificial domain is proposed by making a comparison with the natural ability of living beings to ground their own meanings from some basic cognitive-sensory aspects, which, it is explained, are directly related to the emergence of self-awareness in humans. Thus, the question is raised whether a possible and concrete solution to the Symbol Grounding Problem would involve in the artificial domain the development of cognitive abilities fully comparable to those of humans. Finally, I explain the difficulties that would have to be overcome before such a level could be reached, since human cognitive capacities are intimately linked to intersubjectivity and intercorporeality.
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Notes
When asked “Do you know what time it is?“ or “Do you know what day it is today?“ it did not simply answer “yes” or “no”, but immediately “understood” that it had to give the time or day (except that in this case it was unable to do so and thus gave an incorrect answer). Or again, in response to the open statement “I’m going out for dinner tonight”, the answer was intended to provide suggestions for restaurants in the area, i.e. it was relevant to the speaker’s intentions, who did not want to be encouraged or discouraged in his intentions but wanted useful information about where to go for dinner.
Bubeck et al. (2023) have shown how the performance of GPT-4 has improved significantly after its introduction over a period of one month.
In contrast, for von Uexküll (2020), animals have an optimal relationship with their environment and a pessimal one with its limits. The optimality of this relationship is what guarantees the survival of the species. Pessimality, on the other hand, is what ensures that the species, precisely because it has an optimal relationship with its environment, does not prevail over all other species.
As shown by Sobieszek and Price (2022), calculating conditional probabilities for each subsequent word of a given preceding group of words is not sufficient to justify the relevance achieved by GPT-3, since the possible outcomes for each individual sentence are enormous, in most of the cases exceeding the number of atoms of the universe.
The question of where to place the lower limit of cognition in living systems is widely debated. Some place it at the level of insect-like organisms, i.e., at the animal level (Beer, 2003; Brooks, 1999), others at the plant level (Castiello & Guerra, 2020; Castiello, 2019; Alpi et al., 2007; Baluška & Levin, 2016; Baluška & Mancuso, 2009a, b; Brenner et al., 2006; Calvo, 2007, 2018; Carruthers, 2004; Castiellu, 2019; Cvrcková et al., 2016; Gruntman et al., 2017; Karban, 2008; Mancuso and Viola, 2013; Segundo-Ortin and Calvo, 2019; Trewavas, 2009, 2014, 2016, 2017), and others at the unicellular level (Di Primio et al., 2000; Lengeler et al., 2000; Van Duijn et al., 2006).
As explained by Rovelli (2017, p. 34), such general law, also known as Clausius’ Law, “is the only general law of physics that distinguishes the past from the future”. The difference between heat and all other physical bodies is precisely that heat is the only thing that cannot return to a previous state on its own: “A ball can fall, but it can also bounce back up by itself. Heat cannot”. And again, “the connection between time and heat is profound: whenever there is a difference between past and future, heat is involved”.
Warm-blooded animals maintain a constant body temperature (through a homeostatic mechanism) despite heat dissipation by consuming energy that must be replenished to keep the process going. This means that energy dissipation is experienced not only directly but also indirectly through hunger.
Strictly speaking, if the organism is immersed in an environment rich in nutrients that are constantly and immediately available, it does not need to operate cognitively. Cognitive faculties begin to emerge when these nutrients are no longer constantly and immediately available to the organism. It could be said that this observation does not apply to the human fetus, which is already cognitively operating even though it unconditionally receives from the mother all the nutrients it needs, by starting to structure an attachment bond with her (Cranley, 1981; Salisbury et al., 2003). However, the human fetus is the result of evolution, which most likely would not have occurred in an environment like the one just described.
Excluding quantum mechanics and relativity theory which in any case do not affect our time perception.
Through his studies, Craig has identified a sense capable of bringing unity to the body: interoception. The ‘interoceptive cortex’ includes “somatotopic representations of the activity in small-diameter primary afferent sensory fibers that generate numerous individually mapped and distinct ‘feelings’ from the body, such as first ‘pricking’ pain, second ‘burning’ pain, cool, warm, itch, muscle ache, sensual touch, thirst, hunger, taste, ‘air hunger’ and other visceral sensations” (Craig, 2009, p. 1933). This shows how cold and warm feelings, but also hunger and thirst contribute to the ability to feel the body as a whole.
Although I consider this information to be important for the study and further investigation of questions concerning the subject of my inquiry, I still think it is preferable not to fixate on it, since brain and brain imaging studies provide us with just a picture, a true snapshot of a millennia-old evolutionary process. In any case, it is of great interest considering that many studies reported by Craig confirm the specific activation of the AIC while humans perceive time intervals in the range of seconds or subseconds (Craig, 2009).
Li and Mao’s (2022) proposal, which is based on evolutionary robotics research and utilizes the behavior-based robotics experiment made by Floreano and Mondada (1996), also violates the Z condition. The two experiments performed by Floreano and Mondada, indeed, provides a sort of “pre-training” of the robots through a “pre-designed fitness function” engineered to obtain increasingly complex behaviors. This means that meanings like “escaping from a room” or “avoiding obstacles” are already externally assigned to the robots through the fitness functions.
As noted by Floreano and Mondada (1996), the recharging was simply simulated to save time, since performing the same experiment with real charges would have taken about 6 years instead of 10 days.
Such a perspective must be adopted if you don’t want to solve the same problem over and over again, which means having the possibility to make progress in the field.
The work of Man and Damasio (2019), highlights the potential benefits that could come from making an embodied AA vulnerable and in search of homeostasis. In a later work, Man et al. (2022) have demonstrated the benefits arising from applying such an approach for a non-embodied AA operative in a digital environment. This could be a possible solution to determine a certain degree of intrinsic motivation in the AAs in artificial environments devoid of corporeality. Nevertheless, a purely digital environment, as far as it can simulate the basic mechanisms of the real environment, remains radically different from it, and it also lacks its two basic elements, extension, and duration. It is therefore difficult to see how such an approach can lead, as a next step, to to the capacity of understanding human language and developing human-like intelligence, since symbolic grounding is not operated in the real environment anyway.
For human beings, I do not necessarily intend such an experience to be immediate after birth. It requires, indeed, a certain degree of learning which can be intended, with Pavlov, as a sort of reaction learning, i.e. the ability to generate conditioned reflexes to new environmental stimuli. Obviously, this ability is cognitively pregnant, but it does not require the transfer of any meaning from outside, since it is derived directly from the relationship between the environment and the body. The motive well represents such an intermediate state that precedes what is usually called “intentional” state and that requires a more developed cognitive level, since it represents an oriented action that seeks information in the environment in order to be directed and confirmed. Thus, while the motive is what directs the baby’s first actions in its search for food and care, the intention is what can direct my decision to go out to dinner with a friend, which can imply a wide range of meanings for me.
Like von Humboldt (1991, p.36), I consider human language as the “forming organ of thought”.
I prefer to speak of quasi-omniformative semantic capacity of human language rather than omniformative, because human language cannot function as a perfect equivalent of a Fellini movie or a Van Gogh painting. Therefore, the term quasi-omniformative refers to the fact that there are no limits to what a natural language can say, even though it cannot express all possible meanings.
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Abbate, F. Natural and Artificial Intelligence: A Comparative Analysis of Cognitive Aspects. Minds & Machines 33, 791–815 (2023). https://doi.org/10.1007/s11023-023-09646-w
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DOI: https://doi.org/10.1007/s11023-023-09646-w