Levels of Convergence
We are living in an unique time in the history of humanity. This moment, this turning point, is unprecedented. In order to face the new possibilities of our future it is of utmost importance to be prepared to make wise choices about how will we shape our future as a new technologically enhanced and driven species. Science has taken us this far, but complexity has shown us that science alone will not take us much further. Technology has pervaded each and every aspect of our lives, to a point in which it has gotten literally beneath our skins. Many edifices are falling apart.
It is not only about a paradigm shift. It is about a much more profound kind of shift, one that alters dramatically our ideas, our values, our bodies, our perceptions, everything and every aspect of human life. In these times of transition, we must try to bridge the gap between disciplines, find a common language that fosters cooperation, and most importantly, open our minds to fresh and daring perspectives. As we do so, trying to catch up with the overwhelming speed of the winds of change, hopefully we will contribute to a better understanding of the challenges that lie ahead.
Recent advances in nanotechnology, and its subsequent application in a variety of academic fields, have given rise to an unprecedented phenomenon of technological convergence. The importance of NBIC (Nano-Bio-Info-Cogno) convergence comes from the fact that â€œall matter â€“ living and non-living â€“ originates at the nanoscale. The impact of technologies controlling this realm cannot be overestimated: control of nanoscale matter is control of natureâ€™s elementsâ€ (ETC Group, 2003, p.6). NBIC convergence integrates three main levels of material reality through nanotechnology; namely biology, computing, and neuroscience.
The distinction between different levels of reality can be done according to various types of criteria. Poli (1998) has skillfully drawn on Chwistek, Brouwer, Husserl, Hartmann, and Luhmann to arrive at the classification of three main strata of reality, each originating different levels: social (history â†” art â†” law, etc), psychological, and material (biology, chemistry, physics). In the light of NBIC convergence, however, a more fundamental dichotomy between levels starts to emerge around the dimensions of mind and matter. For the first time in the history of science, there is a technology-mediated convergence between material levels of reality and cognitive levels of human experience. The â€œunity of nature on the nanoscaleâ€ provides brand new grounds for this new kind of interface:
NBIC convergence requires, and is made possible by, the radically new capabilities to understand and to manipulate matter that are associated with nanoscience and nanotechnology. The integration of technology will be based on the unity of nature at the nanoscale, as well as an information system that would cross disciplines and fields of relevance. (Bainbridge & Roco 2006, p.2).
Unity of matter through NBIC makes possible the integration of biological and neural systems to artificial systems, including systems of Artificial Intelligence (AI). The unity of matter at the nanoscale makes possible the systemic integration between biological and non-biological entities; in order to perform a task or to enhance a human capacity, for example. Future prospects are overwhelming. These prospects are not solely related to enhancing the human species biologically. They include the enhancement of human cognitive states as well. Organism becomes artifact, and artifact becomes organism:
Nanotechnology enables one to engineer at the nanoscale and thereby perhaps to reconfigure everything molecular. From the point of view of nanotechnology, what used to be separate domains of biomedicine, information technology, chemistry, photonics, electronics, robotics, and materials science come together in a single engineering paradigm (Nordmann, 2004, p.12).
The main concept behind NBIC convergence is the universal dimension of information. Such accelerating rate of human-machine hybridization brings about serious ethical and philosophical implications. New ontological questions arise from the radical concept of technological unity of matter, such as questions regarding the very meaning of what is the essence of being human. Understanding biological processes as codes, as sign systems, scientists are now codifying and programming their artificial replications. Decodification of micro bio-systems serves as a road map for the codification of artificial agents that will mirror and interact with them. Because it is now possible to understand matter in terms of information, scientists have become able to re-configure and re-engineer all kinds of matter through nano technology.
Nanotechnology enables the integration between all material structures, be they biological or non-biological. The ways in which such total integration becomes possible are similar to the ways information flows through all kinds of systems. Much in the same way in which information pervades everything, nano technologies could also pervade every level of material reality. Converging technologies share four main characteristics:
From the four characteristics named above, the third is perhaps the most complex and interesting one. Converging technologiesÂ´s greatest achievement is the advancement being done on the development of new kinds of interface between body and mind, or between intelligence and its material platform, the brain. Matter and mind are two levels of reality that are inextricably together, and still their interface remains largely uncharted.
So far there have been many attempts made on the conquest of AI, all of which fall short of coming close to replicating consciousness. However, when it comes to NBIC convergence, the question is a bit different. It is not about creating independent artificial minds, but about enhancing human minds artificially to an extent in which the categories of human intelligence and artificial intelligence could blend in. Converging technologies are making possible the emergence of hybrid forms of intelligence through the technological enhancement of our own.
Levels of convergence among different realities begin to emerge. These levels of convergence occur at the interfaces between physical and mental states. Where one reality touches another, interacts with another – and therefore transforms another â€“ we find convergence taking place.
2 Neurotechnology and Artificial Intelligence
By engineering and programming nano robots which possess certain amounts of AI, scientists are becoming able to introduce intelligent agents into all types of material and molecular structures. When these nano intelligent artifacts enter within the neural networks of a brain, they become part of the conscious experience of that brain. This is the new field of neurotechnology (Khushf, 2006). Intelligent nano agents are now close to being able to interfere directly within the consciousness experiences of a brain.
An example of this kind of matter/mind hybrid interface which is emerging as a result of NBIC convergence is the research currently being done on Biologically-Inspired Robotic Cellular Architectures (Bernstein et al., 2006, p.134-135). Through the mapping of the neural circuits within the brain, nano artifacts are being produced that simulate the behavior of a neuron, being able to interact with and be integrated to systems of cells. In this case, the neurons are those on the visual cortex, specifically those responsible for image-formation. Nano devices are being developed that could interfere directly with image-formation within the brain.
Our understanding of brain functioning begins to transcend biology when nano robotic neurons have been taught how to speak the language of biological neurons. Cognitive processes are then understood as natural language processes. If these nano agents can successfully interact with and perform the functions of biological cognitive agents on a physical level, could they also perform mental activities? This is a question that no one has answered satisfactorily yet.
Brain scanning technologies are being developed which make feasible what is being called the reverse engineering of the human brain (Bostrom, 2000; Kurzweil, 2006). The human brain is the result of thousands of years of adaptive processes of biological evolution. AI, on the other hand, has been developed over less than 50 years. Brain scanning is even more recent.
The pace of the biological evolution of intelligence lags far behind that of the technological evolution of intelligence. This exponential increase in the velocity of technological evolution is explained by the law of accelerating returns, which represents â€œthe inherent acceleration of the rate of evolution, with technological evolution as a continuation of biological evolutionâ€ (Kurzweil 2005, p. 7). Ray Kurzweil has achieved his notoriety due to his incredible skill at making accurate predictions of future technologies. What he is saying now is that, in his own words:
…biological intelligence, while it could be better educated and better organized, is not going to significantly change. Nonbiological intelligence, however, is multiplying by over 1,000 per decade in less than a decade. So once we can achieve the software of intelligence, which we will achieve through reverse-engineering the human brain, non-biological intelligence will soar past biological intelligence. But this isnâ€™t an alien invasion, it is something that will literally be deeply integrated in our bodies and brains. (Kurzweil, 2006).
Converging technologies are indeed getting inside the human brain in applications that are deeply integrated to cognitive systems. However, this does not mean that any significant change in the inner levels of human consciousness is likely to occur. The terms and expressions used by Ray Kurzweil are packed with metaphors comparing the human mind to a computer. Software of intelligence. Reverse-engineering the human brain. It is clear, given KurzweilÂ´s terminology, that his approach is based on materialism and reductionism. Intelligence is some kind of biological software that will be replicated once we reverse-engineer the human brain. According to this view, the human brain is a biological computer. It follows that if consciousness seems to be a property of a biological computer then any other kind of computer able to fully replicate the functioning of the brain could be capable of consciousness.
Those who believe in this possibility are defenders of a technology-based new era of evolution: machines will not only be able to replicate all human qualities, but will merge with humans and generate a new species of super-intelligent beings. This merger, together with the exponential speed of technological advancement, will eventually alter the very nature of reality, resulting in a technological Singularity (Kurzweil, 2005).
There are many advocates of Strong AI. Marvin Minsky (1990) and Ray Kurzweil (1999, 2005, 2006) stand as two of its most prominent representatives. According to John Searle (1980), Strong AI refers to â€œthe claim that the appropriately programmed computer literally has cognitive states and that the programs thereby explain human cognitionâ€ (Searle, 1980, p. 417). There are two main ways in which AI could achieve this goal. The first is based on programming that tries to represent the symbolic structures of human minds; the second is based on the study and artificial replication of neural networks within the brain.
Minsky (1990) refers to the effort of trying to achieve Strong AI through symbolic research the â€œtop-down approachâ€, and of trying to achieve Strong AI through connectionist research the â€œbottom-upâ€ approach. The first strategy would depend highly on interpretation, context and self. The second depends on nothing but decoding the functions of neural networks and programming artificial ones. Therefore, the symbolic approach in AI has been vanishing, while the connectionist approach has continued to prosper.
Bringing attention to the symbolic limitations of AI, Searle (2002) has compared the Chinese Room Argument to what happened with Deep Blue. When beating Kasparov, Deep Blue was not playing chess, because the concept of chess has symbolic layers of meaning attached to it. A computer couldnÂ´t possibly have access to the symbolic level of a chess game given that â€œthe symbols in the computer mean nothing at all to the computerâ€ (Searle, 2002). So while Kasparov had an understanding of chess based on its symbolic meaning, Deep Blue was merely performing a function which was programmed to arrive at decisions based on calculations regarding possibilities.
SearleÂ´s view represents the Weak AI approach, which relies on the uniqueness of our aesthetic, religious, philosophical and deep symbolic/archetypical levels to rebuke the possibility of all-mighty programming and nano-engineering. Roger Penrose (1989, 1994) and William Dembsky (2002a, 2002b, 2007) are also defenders of Weak AI, although in different manners.
The controversy surrounding this debate is so wide that even within the same approach there are important epistemological differences. Searle and Penrose are both connectionists, and would represent the equivalent, in neuroscience, to proponents of the â€œbottom-upâ€ approach in AI. So while Searle and Penrose both stand up against the possibility that machines could fully replicate mind, they seem to believe that consciousness is an emergent property of biological neural networks within the brain.
According to this view, the mind is a property of a biological system. The main argument against Strong AI would then be that only biological systems can possess emergent properties of consciousness. Connectionists understand that consciousness is a property of a certain level of biological complexity. Strong AI proponents understand that once computation achieves this certain level of complexity, artificial consciousness will emerge. There are similarities in both approaches.
Dembsky, on the other hand, believes that reducing consciousness to a complex property of a biological neural network would be equivalent to the reductionism practiced by proponents of Strong AI. He states that â€œ…nothing I’ve seen to date leads me to believe that intelligence can properly be subsumed under complexity or computationâ€ (Dembsky, 2002a). In DembskyÂ´s perspective, wherever there is a first person, there is a non-reducible entity. The uniqueness of this subjective first person can not be artificially replicated. Mind can not be a property of matter according to Dembsky (2007), because all properties of matter would have to be material, since they come from matter in the first place. David Jakobsen (2005) has commented on the differences between the approaches of Kurzweil, Searle and Dembsky:
Ray Kurzweilâ€™s strongest argument … is to point out the arbitrariness present in the distinctions of John Searle between silicon and biology. Thus the question is thrown back into another domain â€“ the old mind/matter debate. A debate where the physicalist has the upper hand these days and views like the one of William Dembski can be defeated by calling it old fashionedâ€(Jakobsen, 2005).
The main difference between symbolists (such as Dembsky) and connectionists (such as Kurzweil and Minsky) is that the first approach is centered on levels of meaning, and the second is centered on levels of information NBIC convergence adds something to this debate. Converging technologies might change the grounds of this debate by enabling direct interference from artificial intelligent agents within the systems underlying conscious states of a first person, in DembskyÂ´s sense.
The main focus of the debate might change from determining the possibility of Strong AI, to establishing the possibility of hybrid forms of intelligence, whose sense of self awareness is either established through or mediated by artificial agents. AI is a product of the biological evolution of human intelligence, however through NBIC convergence it will most certainly enhance human intelligence in a new sort of hybrid bio-technological evolutionary process.
Consciousness remains grounded and limited to a biological platform; however, cognitive nano applications have the potential to artificially enhance and alter conscious states. Given the fact that these nano agents are endowed with artificial degrees of intelligence, a principle of hybridization is directly established between mental processes and artificial intelligence. This hybrid interface would simultaneously pervade mind and matter.
The ways in which nano artifacts and neural cells interact are informational. A shared continuum of information and meaning thus represent the framework in which structures of hybrid systems of intelligence could be formed. All cognitive and mental processes have to do with the processing of information and the attribution of meaning. Consciousness is always about perception, perception is always about interpretation, and interpretation always refers to information. Matter is not only a vehicle of information, but also does, in itself, embody physical patterns of information. Within the context of NBIC convergence, the informational nature of reality becomes evident (Floridi, 2007a).
The reason why we have been experiencing recently the rise of soft-materialism (Dembsky, 2007), is explained by the emergence of a revised materialism based on information. According to the soft-materialist view, if we can decode reality, we can recode ourselves. And since mind has an informational relation to matter, thereby it follows that if we decode matter, it will eventually lead us into mind. NBIC convergence is being heralded as the knight-in-shining-armor that will lead us in the conquest of mind by unlocking all â€œprogrammingâ€ secrets of matter. This is the â€œbottom-upâ€ approach to AI.
Aside from all differences between the approaches, stands the relationship between mind and matter as being informational at its core. In this context, symbolists such as Dembsky and connectionists such as Searle, Penrose – and even Kurzweil and Minsky â€“ find a common ground. Biological evolution could possibly be converging with technological evolution because if in its essence all matter is informational, and if information determines the structural designs of matter in all its forms, biology and technology are therefore information-based processes which share a common semiotic nature. There seems to be a level of convergence between symbolic and material levels of reality, based on intersemiosis.
3 Digital Levels
There are other levels of convergence between biological and digital realities. AI is behind the development of FloridiÂ´s philosophy of information (Floridi, 2002), which interprets NBIC technologies as forming elements of an information-based, all-encompassing environment: the infosphere. Within such an environment, permeated by intelligent processes, all beings and things acquire an informational ITentity. Philosophy of information interprets the ontological impact of AI and the â€œintelligentificationâ€ of external reality (Floridi, 2007b).
Advances in RFID (radio frequency identification) technologies allow any physical object to acquire an informational identity, called ITentityby Floridi (2007b). These very smallRFID tags are microchips that can be incorporated into living and non-living beings and objects, and provide Wi-Fiaccessto the Internet. This type of technology makespossible a newexpanded hybrid networkof digital and biological informational entities, one that is not restricted to any computational platform, but expands intothe surrounding environment, configuring an infosphere. In this infospheric network, human consciousnessrelates and interactswith AI agents, forming new hybrid networks of collective intelligence. This combination between human intelligence andAIis expressed by the concept of inforg, informational organism(Floridi, 2007b).
Assuming that by applying RFID technologies to objects it is then possible to confer to each object an ITentity (and that this digital inforg possesses a certain degree of AI being able to communicate and interact over the Net), then an â€œintelligentificationâ€ of things occurs. Beings acquire properties of electronic devices (digital expansion of human cognition) and electronic devices acquire properties of living creatures (intelligence and communication). NBICdevelopments aremaking theboundaries between on-line and off-line, digital and non-digital,to becomeless and lessclear. FloridiÂ´s ideas point to a convergence between multiple levels of reality in terms of theconvergence between online and offline: be it digital or genetic, everything is code, everything is information – and if everything is information, everything communicates. Multiple levels of reality are being digitally connected and expanded.
The development of information technologiesliterally creates new levels of reality, when modifying and expanding the cognitive reach of human consciousness. Cyberspace and Virtual Reality (VR) are digital immersion environments which can also be interpreted as parallel realities in the expression and flow of human consciousness. The complex interactions connecting AI agents and human agents modify the structure of reality itself, which seems to be constituted more and more by a technological mix between ever more integrated levels of reality. Digital becomes the common language uniting organic to non-organic.
The digital expansion of human cognition is analyzed by Floridi (2007b) in its external aspects, such as the establishment of an infosphere. Ascott (2003) will also address this issue; however, his analysis is centered on the internal realm of human experience, by placing consciousness at the core of his research. Ascott (2003) presents the idea of convergence between levels of reality through the concept of Moist Reality: an inorganic, digital, DryReality vs. an organic, biological, Wet Reality.He grants to cyberspace the status of a level of reality of its own. In this cyber level of reality, human cognition is augmented digitally. To this electronically enhanced cognition he calls cyberception. Cyberception is about the convergence of new conceptual and cognitive aspects of human consciousness, triggered by the hyper connectivity of cyberspace (Ascott, 1994). The concept of Moist Reality, formed by the coupling of the “wetâ€ dimension of biology to the â€œdryâ€ dimension of digital technologies, is very close to the concept of infosphere. Ascott also identifies new forms of â€œartificial consciousnessâ€ emerging from of these new forms of interaction between man and machine.
Another important point of contact between Floridi and Ascott is that it is becoming more and more difficult to distinguish, in the universe as a whole, man from non-man. Hybrid cognitive interfaces between human and artificial intelligence are internal (neural) and external (infosphere). The basic differences in the essence of organic and inorganicattributes start to be effaced by NBIC, giving rise toa new ontological perspective of unity in diversity. This perspective is transdisciplinary and portrays information technology as the main element of a new philosophical ontology based on dynamics of information and meaning.
4 Information vs. Meaning
Information pervades every level of reality, be it material, mental or emotional. It can be considered as being embodied in any type of pattern that could be perceived by, interpreted and transformed in other patterns (LIP, 2005). Patterns are only patterns in relation to an observer. While symbolists focus on the observer and on interpretation, connectionists focus on information structures, processes and dynamics. The cognitive hybrid interface between neuron and nano artifact is informational at its core. When it comes to hybrid systems of intelligence, the focus shifts toward the interface connecting the symbolic level of the first person to the material level of information processes, be they biological, cognitive or digital.
Informational Realism and the theory of LoA (Levels of Abstraction) inform us that reality might be understood in terms of information structures (Floridi, 2007a). Epistemic Structural Realism (ESR) and Ontic Structural Realism (OSR) both refer to informational structures as the main instruments of knowledge acquisition. ESR implies that only through observing the informational interfaces between structures and systems can we understand reality. OSR, by the same token, states that we can only reach the essence of a given object via its informational structure. According to OSR, all structures are informational. Therefore, reality is about structure and structure is about information.
However, Informational Realism does not account either for meaning or interpretation. The analysis made by philosophy of information is strictly constructionist, and could be aligned with the connectionist approach to mind. It is centered on the material aspects of reality: matter is information, so all information is material. According to philosophy of information, mind is an â€“ informational â€“ property of an â€“ informational â€“ system. It follows that if matter is information and all information is material â€“ mind would also be material (?). This approach seems to fall into DembskyÂ´s category of soft-materialism, representing a new kind of soft-reductionism based on Informational Realism. Meaning is left out of this equation because it cannot be reduced to an object with an external independent existence. Meaning is always formulated by a first person. Meaning is always relative to a first person.
The interface between matter and mind has data, information and meaning as its main elements. Mind only achieves knowledge (meaning) through the processing of information, information only gets to mind through perception, and perception interprets data in order to deliver information to the mind. As we can observe in NiteckiÂ´s (1993) elucidative representation on Fig. 01, what connects matter to mind is a continuous flow of data, information and knowledge (meaning):
The flow of information, being intrinsically connected to the flow of knowledge, still is not responsible for it. So while information is intrinsic to intelligence it does not account for intelligence. While meaning is always achieved through information, it is not reducible to information. The concept of infosphere does not encompass the dimension of meaning. Theories of information, however useful to the study of information processes, are not sufficient to the study of meaning. Having thus recognized the limitations of mathematical theories of information such as ShannonÂ´s, and also of philosophy of information in the analysis of hybrid cognitive interfaces between mind and matter, we move on to exploring wider theoretical perspectives.
5 Semiosis & Meaning
The concept of semiosis was developed by C. S. Peirce in the context of his semiotics, the general theory of signs, where it was defined as follows:
All dynamical action, or action of brute force, physical or psychical, either takes place between two subjects [whether they react equally upon each other, or one is agent and the other patient, entirely or partially] or at any rate is a resultant of such actions between pairs. But by ‘semiosis’ I mean, on the contrary, an action, or influence, which is, or involves, a cooperation of three subjects, such as a sign, its object, and its interpretant, this tri-relative influence not being in any way resolvable into actions between pairs (CP 5.484).
Semiosis, the action of the sign, is the action of being interpreted in another sign. Perception is the door through which signs reach mind, being transformed into meaning, by means of the translation of one sign into another. This movement of sign as it goes from perception to interpretation is implied in semiosis. Although it is possible to visualize the mechanisms of perception, it is not so easy to visualize semiosis. While perception is about recognizing patterns of information, semiosis is about the symbolic meaning which will be attributed to them. There is no semiosis without transformation. Pattern recognition is transformed into meaning through semiosis â€“ however, exactly where and how does it happen? Santaella (1998, p. 22) calls this question â€œthe problem of perceptionâ€ which goes beyond the mere reproducing and copying of patterns of information, it is mainly about continuous interpretation.
As the interpreting process does not necessarily imply its embodiment in a human mind but may be performed by any subject with the capability of translating one sign or any signal into another, the concept of semiosis was incorporated by biologists. For these, semiosis can help us to answer several questions in biology, especially those concerning interpretation and meaning with which the quantitatively oriented mathematical theory of information cannot cope (Emmeche, 1991), Emmeche and Hoffmeyer (1991), Hoffmeyer and Emmeche (1991, 1999). Hence, semiosis fills the gap between information and meaning, by encompassing the first person and also intentionality. Brier (2006) describes semiosis in living systems in the following way:
Molecules are composed of sequences of atoms and make three-dimensional shapes. They interact informationally through formal causality. Macromolecules are composed of minor molecules often put in sequences. Cells interpret the molecules as coded signs and interact with them through final causation in semiosis (Brier, 2006, p. 35).
Concerned with the relations between life and meaning and the symbolic structures of living semiotic systems, biosemiotics considers that â€œthe evolution of life is not only based on physical, chemical and even informational processes but on the development of semiotic possibilitiesâ€ (Brier, 2006, p.35). Kull (1998) states that semiosis:
…could be defined as the appearance of a connection between things, which do not have a priori anything in common, in the sense that they do not interact or convert each other through direct physical or chemical processes. However, as far as the relation between them, once established (by a subject), is nevertheless intermediated by physical or chemical processes, this infers that the relation is semiotic as long as it is established through learning (Kull, 1998, p. 6).
In sum: semiosis is the general technical term to cover the semantic field of terms such as intelligence, mind, thought â€“ which can no longer be considered as privileges of the human kind. Whenever there may be a tendency to learn, toward self-correction processes, changes of habit, wherever there may be goal directed actions, there will be intelligence, wherever it may occur: in the pollen-grain which fertilizes the ovule of a plant, in the flight of a bird, in the immunological system, or in human reason. Thus it is that semiosis has to be understood