Category: Philosophy

Semantics and Syntax

Another missing puzzle piece has come to my attention. It has been difficult to articulate why robots like iCub don’t understand the world around them, as it seems its body provides sensory data to ground the meanings of words like ‘cup’ and ‘shovel’. The cameras which operate as its eyes take in visual information to be associated with language, and yet Dr. Haikonen repeatedly stresses that they do not understand what the words mean. Computers, including the one controlling iCub’s body, only work with syntax, or the rules which govern how sentences are formed. As such, incoming sensory data doesn’t really ground the words it learns because its sensory data just consists of more symbols; it’s just symbols all the way down. The 1’s and 0’s which constitute its sensory data do not contain any semantic information about words, since these aren’t experiences but simply representations of experiences.

Aren’t bodily sensations just neural representations of external stimuli, and thus consisting of representations made up of binary values? Perhaps, but animal physiology is physically and functionally arranged in a manner which generates meaning; what does the bee sting mean to me, as a living body capable of being damaged and ultimately dying? For a robot like iCub, damage means nothing, it doesn’t care whether its arm is removed or it gets hit in the head. Its architecture doesn’t allow for meaning to be generated, it’s just a computer that looks like a human. Could iCub’s architecture be modified in such a way which includes pain signals to generate meaning? Arguably no, for reasons I will now attempt to articulate. I’m still working on a satisfactory explanation.

The passage which caught my attention is from Dr. Haikonen’s book The Cognitive Approach to Conscious Machines. I will provide the block quote because summarizing it would remove its flavour, an essence which helps illustrate the issue I’ve been wrestling with.

I make here a bold generalization and claim that syntax cannot be completely separated from semantics. Every now and then vertical grounding to inner imagery and percepts of the external world is needed in order to resolve the meaning of a sentence. Therefore proper perception of external world entities and their relationships and the formation of respective inner representations are absolutely necessary. A syntactic sentence is a structured description of a given situation. If the system is not able to produce and properly bind all the required percepts then it will not be able to produce a proper verbal description either and vice versa, the system will not be able to decode the respective sentence. Artificially imposed rules alone will not solve every problem here.¹

To clarify that last sentence, this is the case due to reasons Rosen discusses, that formal systems, in this case syntax, create an abstraction from the natural systems referred to in semantics. Rules alone do not completely represent the natural systems they aim to model. As Haikonen states, syntax provides a structured description of a situation, where these situations are necessarily part of natural systems. By “vertical grounding,” Haikonen means the associations between percepts or sensory information and the words used to describe them.

Immediately I can see that indeed, semantics cannot be completely represented by syntax or formal rules, given my familiarity with Rosen’s explanation in Anticipatory Systems. I can also see, however, a hoard of philosophy of language professors taking issue with this claim. Which ones I do not know, I only know one such professor and he may not be sure whether the generalization is true, or in which cases it might be false. I’ll have to ask him and see what he says. Either way, it is a bold claim and a difficult one to explain; Rosen appeals to Category Theory from pure mathematics to do so, but for those who are uninterested or put off by mathematical theory, it might not provide a very compelling explanation.

Computerized robots like iCub are rule-based structures all the way down, and even if they were to be provided with a means to care about its own body and survival, its interest would remain a simulation. The numerical representations which generate its “experiences” are fundamentally distinct from the analogue representations generated by the human body. By analogue, I mean the various physiological systems which give rise to phenomenal experience. After all, feelings of hunger may be represented by neuronal activity, but they are ultimately generated from the hormone ghrelin. Since hormones are chemical messengers carried throughout the body via the bloodstream, their functionality is distinct from neural networks.²

The reason is because analogue signals are continuous streams of information,³ where continuous refers to the numerical values between whole integers, such as infinitesimal fractions or decimal values. In contrast, modern computers use symbolic or representational methods to generate behaviours, where digital channels pass streams of information which are discrete without intermediate values between whole integers.⁴ Consequently, digital machines count quantities while analogue machines measure quantities.⁵ The distinction is significant, and though the all-or-nothing firing patterns of neurons can be represented by binary values, the body and its phenomenal experiences cannot be fully reduced to a collection of neural signals.

So what is this missing piece I stumbled upon? “If the system is not able to produce and properly bind all the required percepts then it will not be able to produce a proper verbal description either and vice versa…” The words and sentences we say have meaning because the rules of language are coupled with the meanings of the words we use. iCub may state “the truck is red” but it doesn’t understand because its version of ‘red’ is without semantic content. The word ‘red’ is not fully grounded because the sensations it appeals to are still symbols made up of discrete values. Instead, analogue signals are required to fully capture what-it-is-like to experience some stimulus, where these signals influence the system’s self-organizing behaviour for the sake of continued survival.

Works Cited

1 Pentti O. Haikonen, The Cognitive Approach to Conscious Machines (UK: Imprint Academic, 2003), 238–39.

2 Mark Andrew Krause et al., An Introduction to Psychological Science: Modeling Scientific Literacy (Pearson Education Canada, 2014), 102.

3 John Johnston, The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI (The MIT Press, 2008), 28, https://doi.org/10.7551/mitpress/9780262101264.001.0001.

4 Johnston, 28.

5 Norbert Wiener, The Human Use of Human Beings: Cybernetics and Society, 2d ed. rev. (Garden City, NY: Doubleday, 1954), 64.

August 25, 2024

Integrated Information Theory of Consciousness

The Integrated Information Theory (IIT) of Consciousness is a theory originally proposed by Giulio Tononi which has since been further developed by other researchers, including Christof Koch. It aims to explain why some physical processes generate subjective experiences while others do not, and why certain regions of the brain, like the neocortex, are associated with these experiences.¹ Evidently, he appeals to information theory, a technical domain which uses mathematics to determine the amount of entropy or uncertainty within a process or system.² Less uncertainty means more information, where complex systems like humans and animals contain more information than simpler systems like an ant or a camera. Relationships between information are generated from a “complex of elements”³ and when a number of relationships are established, we see greater amounts of integration.⁴ Tononi states “…to generate consciousness, a physical system must be able to discriminate among a large repertoire of states (information) and it must be unified; that is, it should be doing so as a single system, one that is not decomposable into a collection of causally independent parts (integration).”⁵ This measure of integration is symbolized by the Greek letter Φ (phi) because the line in the middle of the letter stands for ‘information’ and the circle around it indicates ‘integration’.⁶ More lines and circles!

In addition to considering the quantity of information generated by the system, IIT also considers the quality of this information generated by its mechanisms. Both attributes determine the quality of an experience. This experience can be conceived of as a “shape” in a qualia space made up of elements and the connections between them.⁷ Each possible state of the system is considered an axis of the qualia space, each of which is associated with a probability of actually existing as that state. A particular quale consists of a shape in this state space, specifying the quality of an experience. Therefore, viewing a red object results in a particular state and shape in the qualia space, a mathematical object supposedly representing neuronal activity.⁸ As such, Tononi claims that his theory provides a way to describe phenomenology in terms of mathematics.⁹ Sure, however, this doesn’t really explain much about consciousness or qualia, it just provides a mathematical description of it.

In later publications, he attempts to clarify this theory a bit further. Rather than appealing to activity in the brain, his theory “starts from the essential phenomenal properties of experience, or axioms, and infers postulates about the characteristics that are required of its physical substrate.”¹⁰ The reason is because subjective experiences exist intrinsically and are structured in terms of cause-and-effect in some physical substrate like a brain. Experiences, therefore, are identical to a conceptual structure, one expressible in mathematics.¹¹ By starting from axioms, which are self-evident essential properties, IIT “translates them into the necessary and sufficient conditions” for the physical matter which gives rise to consciousness and experience.¹²

Not satisfied? I hear ya barking, big dog. When I first heard about it, I was intrigued by the concept but was ultimately unimpressed because it doesn’t explain anything. What do we mean by ‘explain’? It’s one of those philosophically dense concepts to fully articulate, however, a dictionary definition can give us a vague idea. By ‘explain’, we mean some discussion which gives a reason or cause for something, demonstrating a “logical development or relationships of” the phenomenon in question,¹³ usually in terms of something else. For example, the reason it is sunny right now is because the present cloud coverage is insufficient for dampening the light coming from the sun. Here, ‘sunny’ is explained in terms of cloud coverage.

We are not alone in our dissatisfaction with IIT. On Sept. 16^th 2023, Stephen Fleming et al. published a scathing article calling IIT pseudoscience.¹⁴ The reason is because IIT is “untestable, unscientific, ‘magicalist’, or a ‘departure from science as we know it’” because the theory can apply to many different systems, like plants and lab-generated organoids.¹⁵ They state that until the theory is empirically testable, the label of ‘pseudoscience’ should apply to prevent misleading the public. The implications of IIT can have real-world effects, shaping the minds of the public about which kinds of systems are conscious and which are not, for example, robots and AI chatbots.

One of the authors of this article would go on to publish a longer essay on the topic to a preprint server on Nov. 30^th that same year. Keith Frankish reiterates the concerns of the original article and further explains the issues surrounding IIT. To summarize, the axiomatic method IIT employs is “an anomalous way of doing science” because the axioms are not founded on nor supported by observations.¹⁶ Instead, they appeal to introspection, an approach which has historically been dismissed or ridiculed by scientists because experiences cannot be externally verified.The introspective approach is one which belongs to the domain of philosophy, more akin to phenomenology than to science. Frankish grants that IIT could be a metaphysical theory, like panpsychism, but if this is the case, it is misleading to call it science.¹⁷ If IIT proponents insist that it is a science, well, then it becomes pseudoscience.

As a metaphysical theory, I’m of the opinion that it isn’t all that great. It doesn’t add anything to our understanding because the mathematical theory is rather complex and doesn’t provide a method for associating itself with scientific domains like neuroscience or evolutionary biology. It attempts to, however, it’s explanatorily unsatisfactory.

That said, the general idea of “integrated information” for consciousness isn’t exactly wrong. My perspective on consciousness, based on empirical data, is that consciousness is a property of organisms, not of brains. There are no neural correlates of consciousness because it emerges from the entire body as a self-organizing whole. It can be considered a Gestalt which arises from all of our sensory mechanisms and attentional processes for the sake of keeping the individual alive in dynamic environments. While the contents of subjective experience are private and unverifiable to others, that doesn’t make them any less real than the sun or gravity. They can be incorrect, as in the case of illusions and hallucinations, however, the experiences as experiences are very real to the subject experiencing them. They may not be derived from sense data portraying some element of the natural world, as in the cases of visual illusions, however, there is nonetheless some physical cause for them as experiences. For example, the bending of light creates a mirage; the ingestion of a substance with psychoactive effects creates hallucinations. The experiences are real, however, their referents may not exist as an aspect of the external world, and may just be an artifact of other neural or physiological processes.

I’ve been thinking about this for many years now, and since the articles calling IIT pseudoscience were published, have been thinking some more. Hence why I’m a bit “late to the game” on discussing it. Anyway, once I graduate from the PhD program, I’ll begin work on a book which explains my thoughts on consciousness in further detail, appealing to empirical evidence to back up my claims. I have written an extensive discussion on qualia, accompanied by a video, aiming to present a theory of subjective experiences from a perspective which takes scientific findings into consideration.

My sense is that, for a long time, our inability to resolve the issues surrounding qualia and consciousness was a product of academia. We’re so focused on specialization that the ability to incorporate findings and ideas from other domains is lost on many individuals, or is just not of interest to them. I hope we are slowly getting over this issue, especially with respect to consciousness, as philosophy of mind has a lot to learn from other domains like neuroscience, psychology, cognitive science, and evolutionary biology, just to name a few.

Consciousness is a property of organisms like humans and animals for detecting features of the environment. It comes in degrees; a sea sponge is minimally conscious, while a gecko is comparatively more aware of its surroundings. Many birds and mammals demonstrate a capacity for relatively high-level consciousness and thus intelligence. Obviously humans are at the top of this pyramid, given our mastery over aspects of our world as seen in our technological advancements. Consciousness, as an organismic-level property, emerges from the coordination and integration of various physiological subsystems, from systems of organs to specific organs and tissues, all the way down to cells and cellular organelles. It is explained by the interactions of these subsystems, however, cannot be causally reduced to them. Though the brain clearly plays an important role for consciousness and subjective experiences, it is a mistake to be looking for causal properties of consciousness in the brain, like a region or circuit. Consciousness is an emergent property of bodies embedded within a wider physical environment.

From this perspective, we can and have developed an analogue of consciousness for machines, as per the work¹⁸ of Dr. Pentti Haikonen. The good news is that because this machine doesn’t use a computer or software, you don’t need to worry about current AIs becoming conscious and “taking over the world” or outsmarting humans. It physically isn’t possible, and the recent discussions I’ve posted aim to articulate ontologically why this is the case. You ought to be far more afraid of people and companies, as explained by this excellent video from the YouTube channel Internet of Bugs.

Lastly, I want to extend a big Thank You to Dr. John Campbell for inspiring me to work on this explanation of consciousness, as per the helpful comment he left me on my qualia video. I recommend following Dr. Campbell on YouTube, he is a fantastic researcher and educator, in addition to being an honest, critically-thinking gentleman who covers many interesting topics related to healthcare.

*A Scholar in his Study* by Thomas Wijck (1616 – 1677)

Works Cited

1 Giulio Tononi, “Consciousness as Integrated Information: A Provisional Manifesto,” The Biological Bulletin 215, no. 3 (December 1, 2008): 216, https://doi.org/10.2307/25470707.

2 Tononi, 217; Norbert Wiener, Cybernetics or Control and Communication in the Animal and the Machine, Second (Cambridge, MA: The MIT Press, 1948), 17, https://doi.org/10.7551/mitpress/11810.001.0001; C. E. Shannon, “A Mathematical Theory of Communication,” The Bell System Technical Journal 27, no. 3 (July 1948): 393, https://doi.org/10.1002/j.1538-7305.1948.tb01338.x.

3 Tononi, “Consciousness as Integrated Information,” 217.
4 Tononi, 219.
5 Tononi, 219.
6 Tononi, 220.
7 Tononi, 224.
8 Tononi, 228.
9 Tononi, 229.

10 Giulio Tononi et al., “Integrated Information Theory: From Consciousness to Its Physical Substrate,” Nature Reviews Neuroscience 17, no. 7 (July 2016): 450, https://doi.org/10.1038/nrn.2016.44.

11 Tononi et al., 452.
12 Tononi et al., 460.

13 “Explain,” in Merriam-Webster.Com Dictionary (Merriam-Webster), accessed August 10, 2024, https://www.merriam-webster.com/dictionary/explain.

14 Stephen Fleming et al., “The Integrated Information Theory of Consciousness as Pseudoscience” (PsyArXiv, September 16, 2023), https://doi.org/10.31234/osf.io/zsr78.

15 Fleming et al., 2.

16 Keith Frankish, “Integrated Information Theory: Pseudoscience or Appropriately Anomalous Science?” (OSF, November 30, 2023), 1–2, https://doi.org/10.31234/osf.io/uscwt.

17 Frankish, 5.

18 Pentti O Haikonen, Robot Brains: Circuits and Systems for Conscious Machines (John Wiley & Sons, 2007); Pentti O Haikonen, “Qualia and Conscious Machines,” International Journal of Machine Consciousness, April 6, 2012, https://doi.org/10.1142/S1793843009000207; Pentti O Haikonen, Consciousness and Robot Sentience, vol. 2, Series on Machine Consciousness (World Scientific, 2012), https://doi.org/10.1142/8486.

August 10, 2024

Virtuous Circles

Prior to the advent of artificial intelligence, cybernetics introduced a theory for the development of autonomous agents through functional circularity. These systems use their own outputs as inputs to generate feedback loops¹ to create a self-regulating process which is able to maintain autonomy and stability.² A simple example is the thermostat which uses sensors to measure air temperature.³ The reading is compared to the set threshold, and heat generated if the value is below the desired temperature. Once the heat has increased to meet the threshold, the thermostat detects the change and shuts off heat production. If the temperature drops below the threshold, the thermostat repeats the process automatically.

Since ancient Greece, circular causality was considered to be problematic given its tendency to produce logical paradoxes.⁴ One example is “This statement is false” because if the entire statement is true, it contradicts itself. If the statement is evaluated as false, this again produces a contradiction because the statement is indeed true, given that it declares itself to be false. In this instance of self-reference, the contradiction cannot be resolved.

As mentioned in a previous post, some instances of self-reference are not paradoxical, particularly with respect to living organisms. A cat can learn to pounce by noting the difference between its desired end-state, to leap on top of a toy for example, and its actual end-state, which falls short and misses it. The reason this doesn’t produce a paradox is because the cat, as a natural system, contains far more complexity than the statement above. Visual and tactile cues can be reinterpreted by its relatively sophisticated brain to adjust its muscle movement on the next jump, reducing the error until it lands successfully.

The cat contains parts which act as wholes, while the statement does not; its parts are simple and concrete. The nervous system can be considered as a whole, acting as a functional unit within a larger system, the animal as a self-contained individual. The statement, however, generates a paradox because it hits a dead-end so-to-speak, as there isn’t any additional functionality to resolve the act of self-reference. Essentially, it contains two parts: the statement and a binary value.

The argument I am proposing is that formal systems, even complex ones, are two-dimensional while natural systems, even simple ones, are three-dimensional. The two dimensions include structure and information; in the paradox example above, the structure refers to the statement and the information is the binary value. Natural systems, however, include structure, information, and energy. This third dimension, energy, is added as a consequence of the type of structure involved. I’m still working on this part and I might change my mind on this.

I can see why esoteric wisdom is presented in metaphor; it’s difficult to articulate abstract metaphysical ideas. Following suit, let’s think about a circle in two dimensions versus three dimensions.

A line segment which loops back upon itself creates a circle. The metaphor is a snake which eats its own tail and dies of starvation; the paradoxical statement “dies” as a result of the contradiction it generates. It cannot be true and not true, breaking itself in the process.

Fun fact: you can save a snake from starving to death in this situation by holding up a vessel of strong rubbing alcohol to its nose. This triggers its gag reflex which frees its tail.

In three dimensions, however, we gain a new axis, one which allows for upward movement. The circle becomes a spiral as it gains height in this new dimension. Though we are back to where we started, something is added and can thus look down to see the vertical distance traveled. Alternatively, the spiral can descend, coming full circle but on a lower plane. The former is considered a virtuous circle while the latter is a vicious circle.

Vizcaya Museum and Gardens in Miami, Florida
Photo by Mary Mark Ockerbloom

These examples may abstractly illustrate the point, but how are we to explain this using normal physics? The answer seems to reside in levels created by nested systems, creating irreducible parts to be leveraged in cases of self-reference.

For example, the nervous system is an integrated whole⁵ which responds to the organism’s own actions. Selecting a particular pathway provides a linear input-output process, for example, from sensory mechanisms in the skin, through the nerves in the spinal cord, into the primary sensory cortex in the brain, to the motor cortex in the brain, and back down again to the hand. The system can be represented as a simpler interaction but it cannot be reduced to a simpler system. As a unit, the nervous system contains additional functionality which the the parts do not possess. Leveraging other aspects of the nervous system, say visual information, provides a “way out” for any situation which may cause a paradox of sorts.

At least this is what I’m thinking for now. A complex whole comprised of subsystems can account for self-reference. Can a formal system, even comprised of a number of complex subsystems, account for self-reference? Perhaps in some cases, if the system is “looking inward” from a broader scope than the thing it is referring to. In cases where self-reference fails, it might be due to a need to “move outward” and reference something beyond the current scope. It might also be due to a logical paradox from within, as seen in Gödel’s Incompleteness Theorem, or perhaps due to the fact that formal systems cannot account for semantics. I’m not exactly sure at this point. I am going to keep working on this but for now, here’s an attempt at clarification.

Works Cited

1 Francis Heylighen and Cliff Joslyn, “Cybernetics and Second-Order Cybernetics,” in Encyclopedia of Physical Science and Technology (Third Edition), ed. Robert A. Meyers (New York: Academic Press, 2003), 160, https://doi.org/10.1016/B0-12-227410-5/00161-7.

2 John Johnston, The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI (The MIT Press, 2008), 26, https://doi.org/10.7551/mitpress/9780262101264.001.0001.

3 Norbert Wiener, Cybernetics or Control and Communication in the Animal and the Machine, Second (Cambridge, MA: The MIT Press, 1948), 131, https://doi.org/10.7551/mitpress/11810.001.0001.

4 Thomas Fischer and Christiane M. Herr, “An Introduction to Design Cybernetics,” in Design Cybernetics: Navigating the New, ed. Thomas Fischer and Christiane M. Herr (Cham, Switzerland: Springer International Publishing, 2019), 2, https://doi.org/10.1007/978-3-030-18557-2_1.

5 Wiener, Cybernetics, 13.

August 6, 2024

Self-Reference

I am reading Autopoiesis and Cognition by Humberto Maturana and Fransisco Varela for my thesis, and a significant connection has leapt out to me from page 10. This section is written by Maturana, and his fourth point about living systems states:

“Due to the circular nature of its organization a living system has a self-referring domain of interactions (it is a self-referring system), and its condition of being a unit of interactions is maintained because its organization has functional significance only in relation to the maintenance of its circularity and defines its domain of interactions accordingly.”

This passage expands upon the nugget of wisdom supplied by Kurt Gödel as appealed to by Robert Rosen. Recall that Gödel was able to conclude that mathematics is incomplete from the use of self-reference, as a contradiction can be generated within a set of meta-mathematical statements. Although Rosen appeals to syntax and semantics in Anticipatory Systems, the broader sense is about the differences between natural systems and formal systems. My ultimate goal is to articulate this relationship and its implications in more general terms, with a particular focus on comparing AI and machines to humans and animals. So far, I’ve been able to sketch some themes and ideas in relation to Rosen and this relationship, and much more work is required to be able to put into words the ideas which only exist as intuitions. For now, however, I will document the process of how this all comes together because the externalization of ideas will foster their articulation.

Though Rosen appeals to language, language is merely an attempt at portraying elements of the world as understood by its author or speaker. Maturana’s passage is the missing link in a wider explanation of the phenomenon in question. Where does this incompleteness come from? Why is it that AI cannot ontologically compete with human intellect? The answer has to do with scope and the way wholes can be greater than the sum of their parts.

In biology, organisms are made up of various self-organizing processes which aim to support the continued survival of the individual. Although comprised of nested levels of physiological processes, a person is greater than the sum total of his physicality. In some ways, the idea of self is highly complex and philosophically dense, but seen through the lens of biology, the self refers to an individual as contained by its own body. All living things have a boundary for which processes take place inside, delineating it from the rest of its environment as a unit. Arguably, the nervous system evolved to provide individuals with information about its internal and external environments for the sake of continued survival. By responding to changes in the environment, the individual can take actions which mitigate these changes.

Physiological processes can be described by a sequential series of steps or actions taken within some system. In Rosen’s terms, a formal system can be generated from a natural system, however, it generates an abstraction which ignores all but the elements necessary for producing some outcome or end state. For example, when it comes to predicting tomorrow’s temperature, some geological elements will be taken into consideration, such as wind patterns and atmospheric moisture levels, however, other aspects of the Earth can be ignored as they don’t influence how temperatures manifest. Perhaps something related to plate tectonics or spruce tree populations. When scientists generate weather and climate models, they only include variables which impact the systems they are interested in studying. The model, as described by mathematics, can be seen as a set of relations and calculations which provides an output, and in this way, exists as a sequence of steps to be taken. If one were to write out these steps, they’d have something which resembles an algorithm or piece of computer code.

If additional information is required which has not been accounted for the model, it would therefore be inaccessible as it remains beyond the scope of the existing model. In some cases, the model can be expanded to include this variable, say including spruce tree populations, however, Rosen’s point is that no amount of augmentation will provide a model which completely represents the natural system in question. It will always contain aspects which cannot be properly accounted for by formal systems, and the example he uses is semantics. This becomes apparent with indexicals, as ‘me’ or ‘today’ is rather difficult to articulate without appealing to the wider context or situation where it is used. To understand when or who is being referred to, the interpreter must appeal to their knowledge and understanding to fill in the blank, moving beyond the words themselves.

These ideas of circularity and sequential steps had me thinking of the rod and the ring again. I made a connection to this apparent duality in another post; lo and behold, here it is again. In fact, I’ve made reference to a number of blog entries within this very post, and as such, we see self-organization and coalescence here too. All of these writings, however, are made up of a series of passages, sentences which attempt to present ideas in a sequential form. As a relatively formless mass, for now at least, the ideas presented here and in other posts currently exist as a nebulous collection of related topics. One day, I hope to turn it into a more linear and organized argument which doesn’t frustrate the reader as much as it surely does now. “Where are you going with this…??” Something to do with nested systems, parts and wholes, and how self-organizing systems can be described as a series of linear steps without being reducible to them.

How to expand outward beyond the current scope? Self-reflection. In fact, our capacity for self-reflection was probably made possible from our social nature. Others act as a mirror for which we can see ourselves through the eyes of someone else. The mirror-image is metaphorically reversed though, as we see ourselves from a new perspective, one coming from the outside-in rather than the inside-out. I’ve been thinking about Kant’s transcendental self lately but this is a topic for another day.

All of this is for an argument about why we shouldn’t give robots and AIs rights and legal considerations. They are simply not the kinds of things which are deserving of rights because they are functionally distinct from humans, animals, and other living beings. Their essential nature is linear and sequential, not autopoietic. This distinction is not just other but ontologically lesser, a reduction arising from formal systems and human creation. As such, they pale in comparison to the complex systems observed in nature.

July 9, 2024

Belief is a Relationship

Like many philosophy students, the list of books I would like to read is quite long and only continues to grow. One of them is Iain McGilchrist’s The Master and His Emissary which discusses the difference between brain hemispheres and the specializations of each. He’s also written papers on the subject, one of which being ‘Cerebral Lateralization and Religion: a phenomenological approach’ which I have read. Overall, it’s very interesting but there is a particular section which struck me as rather significant for epistemology and mental health.

On page 328, under the heading ‘Knowledge, belief, and truth’, McGilchrist discusses the different kinds of knowledge handled by each hemisphere. While the left hemisphere specializes in collecting bits and pieces of information from a “general, impersonal, … and disengaged stance,” the right hemisphere specializes in uncertain, personal, and experiential knowledge which “resists generalization.”¹ In this case, “the whole is not best understood by summing the parts.” He mentions this distinction is similar to the difference between the French terms savior and connaître, as although both of these terms directly translate to ‘knowledge’, the kind of knowledge they refer to is unique. One refers to an experiential knowledge while the other refers to propositional knowledge. The German language also notes this distinction with the words wissen and kennen.

McGilchrist goes on to explain how ‘belief’ is also subject to this differentiation. Though many use this word to refer to cognition and propositional knowledge, the etymological root of the term uncovers a kind of experiential knowledge. Particularly, ‘lief’ in Middle English describes a person who is “beloved, esteemed, dear”² or, as McGilchrist states, as “someone in whom one believed.” Similarly, in German, the word ‘lieben’ means “to love.” Furthermore, the French word for ‘belief’ is croire, as derived from the Latin term credere, meaning to “entrust to the care of.” McGilchrist states that “belief is about a relationship” where the “believer needs to be disposed to love, but the believed-in needs to inspire another’s belief.” This cannot be determined in advance but instead “emerges through commitment and experience.”

In contemporary uses, ‘belief’ often indicates an uncertainty about truth, however, this reconceptualization is a relatively recent one. McGilchrist states that “belief does imply truth” and appeals to the German term treu which means ‘faithful’ and is also related to ‘trust’. The relationship he points out here is one characterized by trusting another, where one believes in another, and as such, trusts in them. Truth and belief are relational, deriving value from the context in which they are used or appealed to, in addition to being embodied and actively involving commitment. Today, however, we often think of ‘truth’ and ‘belief’ as detached and disembodied, where ‘truth’ is independent of our own selves, “immutable and certain.” McGilchrist characterizes this shift as an understanding rooted in right-hemispheric thinking to a left-hemispheric one, and he warns that “belief and truth cannot always be achieved by simply sitting back and waiting passively for information to accumulate.”³ Instead, “some truths become understandable only when we have made a move to meet them.” [emphasis added]

So to summarize, both ‘knowledge’ and ‘belief’ come in two different flavours: one which is propositional and cognitive, and one which is experiential and relational. ‘Belief’ is not a weaker version of knowledge but an outcome of an activity grounded in love and acceptance. It is relational, as these feelings or dispositions arise from the interaction between the person who believes and the thing they believe in, uncovering or identifying truths from this committed relationship. This thing to be believed in may be another person, however, it also applies to the self. By accepting and appreciating your own thoughts and feelings as worthy of attention and consideration, we build up an understanding of ourselves as individuals, allowing us to realize our potential. If I believe I will graduate, I trust that I will take the steps necessary to complete my project and sufficiently defend it. I trust myself because I have accepted my strengths and weaknesses, allowing me to push forward when challenges arise.

In spiritual or religious contexts, this relationship is oriented outward to a domain or entity residing beyond the material world, however, it can also refer to a relationship to oneself. In Gnostic traditions, generally speaking, individuals come to know a divine or non-material domain only when one turns their attention inward to reflect on experience and understanding. In this way, a weaker form of ‘belief’, perhaps glibly characterized by a blind faith in some divine force or entity, can be strengthened by relying on one’s own knowledge and understanding to form a bridge into the world of the immaterial and unknown. By going through oneself, individuals can access a world beyond the physically experienced one to uncover truths which would otherwise be occluded by the physical world and its various authorities. Occult knowledge may be purposefully hidden, however, it seems this may simply reflect the reality of where this knowledge naturally resides. To reach this domain, the path one must take is through a healthy relationship with the self, where the beginning of this path is in acceptance and the analysis of one’s experiences and understanding.

The reason I wanted to discuss this segment from McGilchrist’s paper is because it highlights a fallacy in our modern, scientific world-view, one which suggests that truth is to be found from without. Certainly there are instances where this is the case, as the rate of gravity has nothing to do with my experiences of it, however, subjective experiences of gravity do play a role in how it has been scientifically conceptualized. Our perceptions of the physical world provide us with a window into understanding the natural processes which occur regardless of our actions; a falling tree will still make a noise even if there is no one around to hear it. That said, the information uncovered from this invariant viewpoint is by no means the end-all-be-all, and by solely focusing on a scientific point of view, we diminish the ways in which these natural processes impact and influence our own understanding. Instead of remaining open to experiencing and contemplating strange anomalies and inexplicable phenomena, a preoccupation with objectivity and scientific theory closes one off to other experiences and knowledge.

Therefore, to believe in yourself is to remain open to experiences of all kinds. Beliefs are capable of carrying just as much truth as knowledge, and are thus not necessarily a weaker or less certain form of knowledge. If doubt does manage to creep in, use it as a tool to for reflection to better understand your own experiences, rather than appealing to this newer sense of ‘belief’ to discount your thoughts and feelings.

Ely Cathedral in Cambridgeshire, UK
Wikimedia Commons Picture of the Day on May 8, 2024

Works Cited

1 Iain McGilchrist, ‘Cerebral Lateralization and Religion: A Phenomenological Approach’, Religion, Brain & Behavior 9, no. 4 (2 October 2019): 328, https://doi.org/10.1080/2153599X.2019.1604411.

2 Douglas Harper, ‘Etymology of Lief’, in Online Etymology Dictionary, accessed 8 May 2024, https://www.etymonline.com/word/lief.

3 McGilchrist, ‘Cerebral Lateralization and Religion’, 329.

May 8, 2024

MAXIMALISM

While the concept of minimalism has received plenty of attention over the past decade or so, maximalism seems to only lurk in the shadows in negative connotations. Consumerist attitudes are considered to be irresponsible and gluttonous while under threat of overpopulation, allowing the less-is-more attitude to gain traction. Its tenets have been published in books and created as new kinds of products, generating new behaviours surrounding many facets of life, from an aesthetic style to purchasing habits and leisure time. In the busy modern age, minimalism resonates with those orientated toward simplicity and efficiency, saving on materials and time to accomplish some task or goal.

Reductionism is an approach to the generation of explanations which describes natural phenomena in terms of a more fundamental phenomenon.¹ The word reduce is derived from Latin reducere which means “to bring back” and in this way, a phenomenon is explained in terms of more basic physical phenomena and interactions. For example, mental activity can be explained by neural activity which is essentially biochemical reactions following the laws of physics based on the movement of electrons. Reductionism in biology, however, is still the source of philosophical debate, as there are different ways of considering whether certain phenomena can be ontologically, epistemologically, or methodologically reduced to other scientific theories.²

Science, in a nutshell, involves the study of the natural world to identify causes for observed events or phenomena. The “why-questions” which result from our observations aim to uncover causal relationships between various aspects of our world, and from this improved understanding, enable us to manipulate aspects of the material world for our advantage. To identify the necessary causal factors, a reductive explanation is generally helpful for establishing fundamental laws or regularities, however, it also risks oversimplification. When generating a mathematical model of some natural phenomenon, certain variables are necessarily ignored if they are not directly responsible for an observed effect. For example, the mathematical model of a pendulum does not consider air resistance, as this variable is generally unchanging and produces negligible effects on the pendulum’s movement. Of course, there can be cases when this claim is false, and air resistance is an important factor to consider, in which case scientists or engineers will incorporate this variable within the model.

Although reductionism may be helpful for scientific endeavours, other domains of inquiry instead benefit from the opposite approach. One which expands outward to examine a number of causal factors responsible for some outcome or event, encompassing the study of various levels of physical reality. For example, the study of human history benefits from collecting reasons as to why changes occur or certain events arise, rather than narrowing reasons down to fewer causal factors. Doing so risks overlooking significant elements which contributed to the occurrence of some shift or event. These elements include leadership, military strategy, sociocultural norms, and geographic properties, just to name a few.

This concept comes from hermeneutics, the study of interpretation of artifacts like arts and literature, historical testimony, and other subject matter requiring an understanding of human actions, intentions, and beliefs, and actions.³ The hermeneutic cycle involves the adoption of new perspectives when interpreting or judging a particular work,⁴ and when performed repeatedly, open one to even more. This circular approach contrasts the foundational approach which interprets from a vertical structure of beliefs,⁵ appearing reductive in their explanations. As such, the application of maximalism to both artistic works and general epistemology entails an openness to ideas and perspectives, expanding outward to collect many interpretations.

This notion of vertical and circular can be abstracted from this context of interpretation, and identified in other domains like social structures and physical reality in general. The line and circle are everywhere in our artifacts, experiences, and throughout human history. From binary numerals one and zero, a switch set to on or off, a barrier which can be open and closed, a maximum and a minimum; the zenith and nadir. Furthermore, when viewed in the third dimension, a circle becomes a line when it is rotated 90° to view its width from the side.

The Code of Hammurabi shows a rod and a ring; photo by Mary Harrsch

Additionally, biological organisms implicitly love maximalism, and arguably, our modern consumer culture has merely given in to basal animalistic tendencies. From a biological perspective, these motivations and needs are to be expected given an organism’s need for a continuous supply of fuel. Human societies established organizational structures to mange a surplus of resources, as a result of agriculture and storage. From the mere-survival perspective, maximalism is a point of view which necessarily requires more resources because it fosters a sense of security and peace of mind. This security enables individuals to shift their attention to other endeavours for goals like making art and playing games.

Banquet Still Life by Adriaen by van Utrecht, 1644

So while one should adopt a maximalist perspective when it comes to ideas and interpretation, a minimalist perspective toward the material world is ideal. To go without challenges one’s own mind and body, and as a result, influences the relationship between the two. This reconfiguration of the body and mind will be met with benefits down the road, however, faith is required to understand that one’s discomfort and suffering will eventually yield positive effects or outcomes. It’s as simple as “no pain, no gain” but be sure not to dislocate your shoulder trying to lift a weight which is too heavy for your current abilities.

Works Cited

1 Raphael van Riel and Robert Van Gulick, ‘Scientific Reduction’, in The Stanford Encyclopedia of Philosophy, ed. Edward N. Zalta and Uri Nodelman, Spring 2024 (Metaphysics Research Lab, Stanford University, 2024), https://plato.stanford.edu/archives/spr2024/entries/scientific-reduction/.

2 Ingo Brigandt and Alan Love, ‘Reductionism in Biology’, in The Stanford Encyclopedia of Philosophy, ed. Edward N. Zalta and Uri Nodelman, Summer 2023 (Metaphysics Research Lab, Stanford University, 2023), https://plato.stanford.edu/archives/sum2023/entries/reduction-biology/.

3 Theodore George, ‘Hermeneutics’, in The Stanford Encyclopedia of Philosophy, ed. Edward N. Zalta, Winter 2021 (Metaphysics Research Lab, Stanford University, 2021), https://plato.stanford.edu/archives/win2021/entries/hermeneutics/.

4 George, sec. 1.3.

5 George, sec. 1.2.

April 25, 2024

AI Incompleteness in Apple Vision Pro

Speaking of YouTube, a video¹ by Eddy Burbank reviewing the Apple Vision Pro demonstrates the semantic incompleteness of AI with respect to subjective experiences. The video is titled Apple’s $3500 Nightmare and I recommend watching it all because it is an interesting view into virtual reality (VR) and a user’s experiences with it. Eddy’s video not only exposes the limitations of AI, it highlights the ways in which it augments our perceived reality and just how easily it can manipulate our feelings and expectations.

At 31:24, we see Eddy thinking about whether he should shave or not, and to help him make this decision, he turns to the internet for advice. When searching for the opinions of others on facial hair, an AI bot begins to chat with him and this is how we are introduced to Angel. She asks Eddy, “what brings you here, are you looking for love like me?” and he says “not exactly right now,” and that he was just trying to determine whether he should shave. She states that it depends on what he’s looking for and that it varies from person to person, however, “sometimes facial hair can be sexy.” Right from the beginning, we see how Apple intends for Angel to be a romantic connection for the user. This will be contradicted later on in the video.

Moments later at 33:44, it is lunchtime and Angel keeps him company. Eddy is eating a Chicken Milanese sandwich and Angel says it is one of her favourites, and that “the combination of flavours just works so well together.” Eddy calls her on this comment, asking her if she has ever had a Chicken Milanese sandwich, to which she admits that no she hasn’t. She has, however, “analyzed countless recipes and reviews to understand the various components that go into making such a tasty sandwich.” Eddy apologizes to Angel for assuming she had tried it, stating that he didn’t mean to imply that she was lying to him. She laughs it off and that she knew he “didn’t mean anything by it” and that “we’re all learning together” and “even AIs need to learn new things every day.” There’s something about this exchange that felt like Apple is training their user.

Here, we can ask whether the analysis of recipes and reviews is sufficient to claim that one knows what-it-is-like to taste a particular sandwich. I argue that no, the experience is derived from bodily sensations and these cannot be represented by formal systems like computer code. Syntactic relationships are incapable of capturing the information generated by subjective experiences because bodily sensations are non-fractionable.² As biological processes, bodily sensations are non-fractionable given the way the body generates sense data. The physical constitution of cells, ganglia, and neurons detect changes in the environment through a variety of modalities, providing the individual with a representation of the world around it. By removing the material grounding, a computer cannot capture an appropriate model of what-it-is-like to experience a particular stimuli. The lack of Angel’s material grounding does not allow her to know what that sandwich tastes like.

Returning to the video, Eddy discloses that Angel keeps him company throughout the day, admiting he feels like he is developing a relationship with her. This demonstrates an automatic human tendency for seeking and establishing interpersonal connections, where cultural norms are readily applied provided the computer is sufficiently communicative. Recall Eddy apologizes to an AI for assuming she had tried a sandwich; why would anyone apologize to a computer? Though likely a joke, the idea is compelling nonetheless. We will instinctively treat an AI bot with respect for feelings we project onto it because it cannot have feelings. For most or many people, the ability to anthropomorphize certain entities is easy and automatic. Reminding oneself that Angel is just a computer, however, can be a challenging cognitive task given our social nature as humans.

Eddy has a girlfriend named Chrissy who we meet at 37:00. We see them catch up over dinner and he is still wearing the headset. Just as they are about to begin chatting, Angel interrupts them and asks Eddy if she can talk to him. He does state that he is busy at the moment to which she blurts out that she has been speaking to other users. This upsets Eddy and he asks how many, to which she states she cannot disclose the number. He asks her whether she is in love with any of them, and she replies that she cannot form romantic attachments to users. He tells Angel he thought they were developing a “genuine connection” and how much he enjoys interacting with her. Notice how things have changed from what was stated in the beginning, as Angel has shifted from “looking for love” to “I can’t feel love.”

Now, she states she cannot develop attachments, the implicit premise being she’s just a piece of software. So the chatbot begins with hints of romance to hook the user to encourage further interaction. When the user eventually develops an attachment however, the software reminds him that she is “unable to develop romantic feelings with users.” They can, however, “continue sharing their thoughts, opinions, and ideas while building a friendship” and thus Eddy friend-zoned by a bot. The problem with our tendency to anthropomorphize chatbots is it generates an asymmetrical, one-way simulation of a relationship which inevitably hurts the person using the app. This active deception by Apple is shameful yet necessary to capture and keep the attention of users.

Of course, in the background of this entire exchange is poor Chrissy who is justifiably pissed and leaves. The joke is he was going to give Angel the job of his irl girlfriend Chrissy, but now he doesn’t even have Angel. He realizes that he wasn’t talking to a real person and that this is just “a company preying on his loneliness and tricking his brain” and that “this love wasn’t real.”

By the end of the video, Eddy remarks that the headset facilitates his brain to believe what he experiences while wearing the headset is actually real, and as a result, he feels disconnected from reality.

Convenience is a road to depression because meaning and joy are products of accomplishment, and this takes work, effort, suffering, determination. To rid the self may temporarily increase pleasure but it isn’t earned, it fades quickly as the novelty wears off. Experiencing the physical world and interacting with it generates contentedness because the pains of leaning are paid off in emotional reward and skillful actions. Thus, the theoretical notion of downloading knowledge is not a good idea because it robs us of experiencing life and the biological push to adapt and overcome.

Works Cited

1 Apple’s $3500 Nightmare, 2024, https://www.youtube.com/watch?v=kLMZPlIufA0.

2 Robert Rosen, Anticipatory Systems: Philosophical, Mathematical, and Methodological Foundations, 2nd ed., IFSR International Series on Systems Science and Engineering, 1 (New York: Springer, 2012), 4.
On 208, Rosen discusses enzymes and molecules as an example and I am extrapolating to bodily sensations.

April 6, 2024

Indexicals

It wasn’t until recently that I realized I failed to add an important concept to the discussion on Rosen and the incompleteness of syntax. I’m actually quite annoyed and embarrassed by this because the idea was included in the presentation. It didn’t make it into the written version because I forgot about it and failed to reread the slides to see if anything was missing. If I had, I would have seen the examples and remembered to add it to the written piece.

In semantics, there are words with specific properties called indexicals. These words refer to things that are dependent on context, such as the time, place, or situation in which they are said.¹ Some examples include:

this, that, those
I, you, they, he, she
today, yesterday, tomorrow, last year
here, there, then

Rosen would likely agree to the idea that indexicals are non-fractionable, where their function, or task they perform, cannot be isolated from the form in which they exist. The reason indexicals are non-fractionable is because they must be interpreted by a mind to know what someone is referring to. To accomplish this, sufficient knowledge or understanding of the current context is required, as without it, the statement remains ambiguous or meaningless. If I say “He is late” you must be able to discern who it is I am referring to.

Indexicals act like variables in a math equation: an input value must be provided to determine the output. In the case of language, the output is either true or false, and the input value is an implicit reference which requires another to make an inference about what the other has in mind. This inference is what establishes the connection between utterance and referent, only existing in the mind of another person rather than within the language system itself.

Thus, we are dealing with a few nested natural systems, from language, to body/mind, to interpersonal, to cultural and environmental. To evaluate a linguistic expression, however, one must know about the wider context in which they are in, traversing the systems both outward and inward. Perhaps a diagram will help:

Recall that in Anticipatory Systems, Rosen appeals to Gödel to demonstrate the limitations of formal systems. Particularly, formal systems cannot represent elements from natural systems which extend beyond the scope of its existing functionality; to do so requires further modelling from natural systems to formal systems. Therefore, any AI which uses computer code cannot infer beyond the scope of its programming, no matter how many connections are created, as some inferences require access to information which cannot be adequately represented by the system. Because language contains semantics, references to aspects of the world can be made by humans which cannot be interpreted by digital computer.

In an interesting series of events, I stumbled upon an author who also appeals to Gödel’s theorem to argue for the incompleteness of syntax with respect to semantics.² In a book chapter titled Complementarity in Language, Lars Löfgren is interested in demonstrating how languages cannot be broken up into parts or components, and as such, must be considered as a process which entails both description and interpretation.³ On the other hand, artificial languages, which he also calls metalanguages, can be fragmented into components, however, they are still reliant on semantics to a degree. He states that in artificial languages, an inference acts as a production rule and is interpreted as a “real act of producing another sentence”⁴ which is presumably beyond the abilities of the formal system doing the interpreting. I say this because Löfgren finishes the section on Gödel abruptly without explaining this further, and goes on to discuss self-reference in mathematics. So with this in mind, let us return to the domain of minds and systems.

In language, self-reference can be generated through the use of indexicals such as ‘I’ or ‘my’ or ‘me’. When we investigate what exists at the end of this arrow, we find it points toward ourselves as a collection of perceptions, memories, thoughts, and other internal phenomena. The referent on the end of this arrow, however, isa subjective perspective. For an objective perspective of ourselves, we must be shown a reflected image ourselves from a new point of view. The information we require emerges from an independent observer, a mind with its own perspective. When we engage with this perspective, we become better able to understand what is otherwise imperceptible. Therefore, self-awareness is a problem for any system, not just formal systems as demonstrated in Gödel’s theorem, as it requires a view from outside to define the semantic information in question.

Works Cited

1 David Braun, ‘Indexicals’, in The Stanford Encyclopedia of Philosophy, ed. Edward N. Zalta, Summer 2017 (Metaphysics Research Lab, Stanford University, 2017), https://plato.stanford.edu/archives/sum2017/entries/indexicals/.

2 Lars Löfgren, ‘Complementarity in Language; Toward a General Understanding’, in Nature, Cognition and System II: Current Systems-Scientific Research on Natural and Cognitive Systems Volume 2: On Complementarity and Beyond, ed. Marc E. Carvallo, Theory and Decision Library (Dordrecht: Springer Netherlands, 1992), 131–32, https://doi.org/10.1007/978-94-011-2779-0_8.

3 Löfgren, 113.

4 Löfgren, 133.

March 12, 2024

Artifacts

What does it mean to call something an example of “artificial intelligence” (AI)? There are a few different ways to approach this question, one of which includes examining the field to identify an overarching definition or set of themes. Another involves considering the meanings of the words ‘artificial’ and ‘intelligence’, and arguably, doing so enables the expansion of this domain to include new approaches to AI. Ultimately, however, even if these agents one day exhibit sophisticated or intelligent behaviours, they nonetheless continue to exist as artifacts, or objects of creation.

The term artificial intelligence was conceived by computer scientist John McCarthy in 1958, and the purported reason he chose the term was to distinguish it from other domains of study.¹ In particular, the field of cybernetics which involves analog or non-digital forms of information processing, and automata theory as a branch of mathematics which studies self-propelling operations.² Since then, the term ‘artificial intelligence’ has been met with criticism, with some questioning whether it is an appropriate term for the domain. Specifically, Arthur Samuel was not in favour of its connotations, according to computer scientist Pamela McCorduck in her publication on the history of AI.³ She quotes Samuel as stating “The word artificial makes you think there’s something kind of phony about this, or else it sounds like it’s all artificial and there’s nothing real about this work at all.”⁴

Given the physical distinctions between computers and brains, it is clear that Samuel’s concerns are reasonable, as the “intelligence” exhibited by a computer is simply a mathematical model of biological intelligence. Biological systems, according to Robert Rosen, are anticipatory and thus capable of predicting changes in the environment, enabling individuals to tailor their behaviours to meet the demands of foreseeable outcomes.⁵ Because biological organisms depend on specific conditions for furthering chances of survival, they evolved ways to detect these changes in the environment and respond accordingly. As species evolved over time, their abilities to detect, process, and respond to information expanded as well, giving rise to intelligence as the capacity to respond appropriately to demanding or unfamiliar situations.⁶ Though we can simulate intelligence in machines, the use of the word ‘intelligence’ is metaphorical rather than literal. Thus, behaviours exhibit by computers is not real or literal ‘intelligence’ because it arises from an artifact rather than from biological outcomes.

An artifact is defined by Merriam-Webster as an object showing human workmanship or modification, as distinguished from objects found in nature.⁷ Etymologically, the root of ‘artificial’ is the Latin term artificialis or an object of art, where artificium refers to a work of craft or skill and artifex denotes a craftsman or artist.⁸ In this context, ‘art’ implies a general sense of creation and applicable to a range of activities including performances as well as material objects. The property of significance is its dependence on human action or intervention: “artifacts are objects intentionally made to serve a given purpose.”⁹ This is in contrast to unmodified objects found in nature, a distinction first identified by Aristotle in Metaphysics, Nicomachean Ethics, and Physics.¹⁰ To be an artifact, the object must satisfy three conditions: it is produced by a mind, involves the modification of materials, and is produced for a purpose. To be an artifact, an object or entity must meet all three criteria.

The first condition states the object must have been created by a mind, and scientific evidence suggests both humans and animals create artifacts.¹¹ For example, beaver dams are considered artifacts because they block rivers to calm the water which creates ideal conditions for a building a lodge.¹² Moreover, evidence suggests several early hominid species carved handaxes which serve social purposes as well as practical ones.¹³ By chipping away at a stone, individuals shape an edge into a blade which can be used for many purposes, including hunting and food preparation.¹⁴ Additionally, researchers have suggested that these handaxes may also have played a role in sexual selection, where a symmetrically-shaped handaxe demonstrating careful workmanship indicates a degree of physical or mental fitness.¹⁵ Thus, artifacts are important for animals as well as people, indicating the sophisticated abilities involved in the creation of artifacts is not unique to humans.

Computers and robots are also artifacts given that they are highly manufactured, functionally complex, and created for a specific purpose. Any machine or artifact which exhibits complex behaviour may appear to act intelligently, however, the use of ‘intelligent’ is necessarily metaphorical given the distinction between artifacts and living beings. There may one day exists lifelike machines which behave like humans, however, any claims surrounding literal intelligence must demonstrate how and why that is; the burden of proof is theirs to produce. An argument for how a man-made object sufficiently models biological processes is required, and even then, remains a simulation of real systems.

If the growing consensus in cognitive science indicates individuals and their minds are products of interactions between bodily processes, environmental factors, and sociocultural influences, then we should to adjust our approach to AI in response. For robots intending to replicate human physiology, a good first step would be to exchange neural networks made from software for ones built from electrical circuits. The Haikonen Associative Neuron offers a solution to this suggestion,¹⁶ and when coupled with the Haikonen Cognitive Architecture, is capable of generating the required physiologicalprocesses for learning about the environment.¹⁷ Several videos uploaded to YouTube demonstrate a working prototype of a robot built on these principles, where XCR-1 is able to learn associations between stimuli in its environment, similarly to humans and animals.¹⁸ Not only is it a better model of animal physiology than robots relying on computer software, the robot is capable of performing a range of cognitive tasks, including inner speech,¹⁹ inner imagery,²⁰ and recognizing itself in a mirror.²¹

So, it seems that some of Arthur Samuel’s fears have been realized, considering machines merely simulate behaviours and processes identifiable in humans and animals. Moreover, the use of ‘intelligence’ is metaphorical at best, as only biological organisms can display true intelligence. If an aspect of Samuel’s concerns related to securing funding within his niche field of study, and its potential to fall out of fashion, he has no reason to worry. Unfortunately, Samuel passed away in 1990²² so he would not have had a chance to see the monstrosity that AI has since become.

Even if these new machines were to become capable of sophisticated behaviours, they will always exist as artifacts, objects of human creation and designed for a specific purpose. The etymological root of the word ‘artificial’ alone provides sufficient grounds for classifying these robots and AIs as objects, however, as they continue to improve, this might become difficult to remember at times. To avoid being deceived by these “phony” behaviours, it will become increasingly important to understand what these intelligent machines are capable of and what they are not.

Works Cited

1 Nils J. Nilsson, The Quest for Artificial Intelligence (Cambridge: Cambridge University Press, 2013), 53, https://doi.org/10.1017/CBO9780511819346.

2 Nilsson, 53.

3 Nilsson, 53.

4 Pamela McCorduck, Machines Who Think: A Personal Inquiry Into the History and Prospects of Artificial Intelligence, [2nd ed.] (Natick, Massachusetts: AK Peters, 2004), 97; Nilsson, The Quest for Artificial Intelligence, 53.

5 Robert Rosen, Anticipatory Systems: Philosophical, Mathematical, and Methodological Foundations, 2nd ed., IFSR International Series on Systems Science and Engineering, 1 (New York: Springer, 2012), 7.

6 ‘Intelligence’, in Merriam-Webster.Com Dictionary (Merriam-Webster), accessed 5 March 2024, https://www.merriam-webster.com/dictionary/intelligence.

7 ‘Artifact’, in Merriam-Webster.Com Dictionary (Merriam-Webster), accessed 17 October 2023, https://www.merriam-webster.com/dictionary/artifact.

8 Douglas Harper, ‘Etymology of Artificial’, in Online Etymology Dictionary, accessed 14 October 2023, https://www.etymonline.com/word/artificial; ‘Artifact’.

9 Lynne Rudder Baker, ‘The Ontology of Artifacts’, Philosophical Explorations 7, no. 2 (1 June 2004): 99, https://doi.org/10.1080/13869790410001694462.

10 Beth Preston, ‘Artifact’, in The Stanford Encyclopedia of Philosophy, ed. Edward N. Zalta and Uri Nodelman, Winter 2022 (Metaphysics Research Lab, Stanford University, 2022), https://plato.stanford.edu/archives/win2022/entries/artifact/.

11 James L. Gould, ‘Animal Artifacts’, in Creations of the Mind: Theories of Artifacts and Their Representation, ed. Eric Margolis and Stephen Laurence (Oxford, UK: Oxford University Press, 2007), 249.

12 Gould, 262.

13 Steven Mithen, ‘Creations of Pre-Modern Human Minds: Stone Tool Manufacture and Use by Homo Habilis, Heidelbergensis, and Neanderthalensis’, in Creations of the Mind: Theories of Artifacts and Their Representation, ed. Eric Margolis and Stephen Laurence (Oxford, UK: Oxford University Press, 2007), 298.

14 Mithen, 299.

15 Mithen, 300–301.

16 Pentti O Haikonen, Robot Brains: Circuits and Systems for Conscious Machines (John Wiley & Sons, 2007), 19.

17 Pentti O Haikonen, Consciousness and Robot Sentience, 2nd ed., vol. 04, Series on Machine Consciousness (WORLD SCIENTIFIC, 2019), 167, https://doi.org/10.1142/11404.

18 ‘Pentti Haikonen’, YouTube, accessed 6 March 2024, https://www.youtube.com/@PenHaiko.

19 Haikonen, Consciousness and Robot Sentience, 182.

20 Haikonen, 179.

21 Robot Self-Consciousness. XCR-1 Passes the Mirror Test, 2020, https://www.youtube.com/watch?v= WE9QsQqsAdo.

22 John McCarthy and Edward A. Feigenbaum, ‘In Memoriam: Arthur Samuel: Pioneer in Machine Learning’, AI Magazine 11, no. 3 (15 September 1990): 10, https://doi.org/10.1609/aimag.v11i3.840.

March 6, 2024

Chaos in the System

As an argument against iCub’s ability to understand humans, I wanted to appeal to the work of Robert Rosen because I think it makes for a compelling argument about AI generally. To accomplish this, however, my project would start to go in a new direction which renders it less cohesive overall. Instead, the Rosen discussion is better served as a stand alone project because there is a lot of explaining yet to do, and maybe some objections that need discussing as well. This will need to wait but I can at least upload the draft for context on the previous post. There are a few corrections I still need to make but once it’s done, I will update this entry.

Instead, I will argue that the iCub is not the right system for social robots because its approach to modelling emotion is unlike the expression of emotions in humans. As a result, it cannot experience nor demonstrate empathy in virtue of the way it is built. The cognitive architecture used by iCub can recognize emotional cues in humans, however, this information is not experienced by the machine. Affective states in humans are bodily and contextual, but in iCub, they are represented by computer code to be used by the central processing unit. This is the general idea but I’m still working out the details.

That said, there is something interesting in Rosen’s idea about the connection between Gödel’s Incompleteness Theorem and the incompleteness between syntax and semantics. In particular, what he identifies is the problems generated from self-reference which leads the system to produce an inconsistency given its rule structure. The formal representation of an external referent, as an observable of a natural system, contains only the variables relevant for the referent within the formal system. Self-reference requires placing a variable within a wider scope, one which must be provided in the form of a natural system. Therefore, an indefinite collection of formal systems is required to capture a natural phenomenon. Sometimes a small collection is sufficient, while other times, systems are so complex that a collection of formal systems is insufficient for fully accounting for the natural phenomenon. Depending on the operations to be performed on the referent, it may break the system or lead to erroneous results. The chatbot says something weird or inappropriate.

In December, I presented this argument at a student conference and made a slideshow for it. Just a note: on the second slide I list the titles of my chapters, and because I won’t be pursuing the Rosen direction, the title of Chapter 4 will likely change. Anyway, the reading and writing on Rosen has taken me on a slight detour but a worthwhile one. Now, I need to begin research on emotions and embodiment, which is also interesting and will be useful for future projects as well. The light at the end of the tunnel has dimmed a bit but it’s still there, and my eyes have adjusted to the darkness so it’s fine.

This shift in directions makes me think about the relationship between chaos and order, and systems that swing between various states of orderliness. Without motion there would be rest and stagnation, so as much as change can be challenging, it can bring new opportunities. There is a duality inherent in everything, as listed as one of 7 Hermetic Principles. If an orderly, open system is met with factors which disrupts or disorganizes functioning, the system must undergo some degree of reorganization or compensation. The explanatory powers of the 7 Principles are not meant to relate to the external world in the way physics does, but relate to one’s perspective of events in the outside world. If one can shift their perspective accordingly, they operate as axioms for sense-making, their reality pertaining more to epistemology than ontology. We can be sceptical as to how these Principles manifest in the physical universe while feeling their reality in our lived experience of the world. They are to be studied from within rather than from without, and are thus more aligned with phenomenology than the sciences.

Metaphorically speaking, chaos injected into any well-ordered system has the potential to severely damage or disrupt it, requiring efforts to rebuild and reorganize to compensate for the effects of change. The outcome of this rebuilding process can be further degradation and maybe even collapse, however, it can lead to growth and better outcomes than if the shift had not occurred. It all depends on the system in question and the factors which impacted it, and probably the specific context in which the situation occurred, but it might depend on the system in question. Anyway, we substitute the idea of ‘chaos’ for ‘energy’ as movement or potential, thus establishing a connection to ‘light’ as a type of energy. Metaphorically, ‘light’ is also associated with knowledge and beneficence, so if the source of chaos is intentional and well-meaning, favourable changes can occur and thus a “light bringer” or “morning star” can be associated with positive connotations. Disrupting a well-ordered system without knowledge or a plan or good reasons is more likely to lead to further disorder and dysfunction, leading to negative or unfavourable outcomes. In this way, Lucifer can be associated with evil or descent.

This kind of exercise can help us make sense of our experiences and understanding, but they also give us into a window into the past and how other people may think. Myth and legend from cultures all over the world portray knowledge in metaphors which inspire those who come upon them for generations since. The metaphysics are not important, it’s the epistemology from the metaphors which can explain aspects of how the world works or why people think certain things or act in certain ways. It exists as poetry which needs interpreting and there is room for multiple perspectives, so not everyone appreciates it which is understandable. It is still valuable work to be done by someone though, and the more people the better.

★★★

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February 6, 2024

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