Universal Intelligence for Sustainability: Comparison
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Please note this is a comparison between Version 2 by Camila Xu and Version 1 by Alejandro Nicolas Martinez-Garcia.

Artificial intelligence (AI), as a product of biological intelligence, is a technological tool based on data and the information-processing power of discrete machines that carry out a series of interdependent operations to generate and store discrete data and information, using discrete, finite, and closed algorithms. In turn, the concept of sustainability is increasingly considered an almost essential component of discourses designed to support and justify decision-making at all levels of human activities.  The strong and functional couplings among ecological, economic, social, and technological processes explain the complexification of human-made systems, and phenomena such as globalization, climate change, the increased urbanization and inequality of human societies, and the power of information, and the COVID-19 syndemic.. Sustainability for complex systems implies enough  efficiency to explore and exploit their dynamic phase spaces and enough flexibility to coevolve with  their environments. This means solving intractable nonlinear semi-structured dynamic multi-objective optimization problems, with conflicting, incommensurable, non-cooperative objectives  and purposes, under dynamic uncertainty, restricted access to materials, energy, and information, and  a given time horizon. Given the high stakes; the need for effective, efficient, diverse solutions; their local and global, and present and future effects; and their unforeseen short-, medium-, and long-term  impacts; achieving sustainable complex systems implies the need for Sustainability-designed Universal  Intelligent Agents (SUIAs). The proposed philosophical and technological SUIAs will be heuristic  devices for harnessing the strong functional coupling between human, artificial, and nonhuman  biological intelligence in a non-zero-sum game to achieve sustainability.

  • artificial and biological intelligence
  • complex coevolutionary systems engineering
  • sustainability

1. Sustainability as a Multi-Objective Optimization Problem

At the intersection of engineering, sustainability, and complexity sciences, sustainable, complex, coevolutionary socio-technical-economic ecosystems exhibit enough efficiency to exploit their dynamic phase space and enough flexibility to explore and coevolve with their environments [[1]]]. Efficiency refers to the aptitude of a system for achieving multiple, dynamic, constrained, and mostly incommensurable and conflicting objectives and purposes while performing below threshold values for failure. In turn, flexibility refers to a system’s dynamic capacity to coevolve with its changing biophysical and socio-economic environment for a given time horizon, via the generation of high- quality, diverse, and feasible optimal sets of solutions, to face uncertainty [[2]].
Sustainability engineering problems are partially “hard” because their solution requires precise data and calculations. This characteristic makes the problems “structured”, meaning that the initial situation, the objectives, and the tools for solving these problems are well-defined and quantifiable, with standard, technically optimal solutions generally found via numerical methods [[3],][4]]. Sustainability engineering problems also encompass unstructured processes, which are not well-defined situations without ready-made solutions, and where human intuition and values (i.e., purposes, beliefs, happiness, self-fulfillment, well-being, etc.) are essential [4,[4][5]]. Hence, sustainability engineering problems are semi-structured, implying a combination of both numerical procedures and intuitive, subjective judgment, and the need for deep and holistic approaches [2].

2. Universal Intelligence for Sustainability

Among the emergent technologies of the last one hundred years, the field of artificial intelligence (AI) has been a powerful tool for the development and well-being of human societies, contributing to advances in domains such as healthcare, transportation, formal and informal education, scientific discoveries, manufacturing, agricultural production, weather forecasting, public safety and security, entertainment, and defense [[6],[7]]]. PricewaterhouseCoopers [[8]]] projected that the global Gross Domestic Product (GDP) will be 14% higher (USD 15.7 trillion) in 2030 due to the implementation of AI. Unsurprisingly, the field of AI is considered a top research and development strategy for national governments around the world (e.g., [[9]]). AI can be described as the field devoted to “…making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment…” [[10]]]. Depending on the kind of problems the tool is designed to solve, AI can be subdivided into general-purpose (strong), and narrow AI. Narrow AI is supposedly based on intelligent biological behaviour to solve specific complex problems [[11]]]. Among narrow AI tools are in-field sensor networks, computer vision, data mining, robotics, and machine learning [[12]]]. A subfield of narrow AI is “nature-inspired computing” [[13]]], comprising tools and techniques for optimization purposes based on biological and physical processes, such as evolutionary algorithms [[14]]]. In turn, general-purpose, strong, or human-level AI [[15]]] refers to the achievement of thinking and consciousness by a computer, making it capable of solving general complex problems [[11],][16]]]. Hence, general-purpose AI’s implicit goal is biological intelligence replacement (including that of humans) [[17]]]. Although the development of the field of AI is robust, as with every human endeavour, it is not exempted from challenges and controversies. These include the application of AI to the development of semi-autonomous lethal weapons, the social and societal risk of diminishing personal interactions due to the use of AI, the jobs lost to AI and other cyber-biophysical technologies, and the associated deeper wage gap between, on one side, the less-educated, and on the other, the highly trained workers of information and communication technologies [[7],[18],][19],][20]]]. The latter can be associated with the likelihood of increasing socio-economic inequality [[6],][20]]], and even the risk of irrelevancy for non-qualified human workers [[21]]]. Another fear refers to the controversial scenario of the complete substitution of the human species for a super-intelligent version of strong AI [[22]]] since strong AI is conceptualized as a “better” replacement for the supposedly limiting components of complex systems: humans [[23]]. While the achievement of strong AI is a matter to be solved in the future [[2415]], the fact that the technology has not been able, so far, to reproduce human-like intelligence and consciousness is, at least partially, due to big differences between AI and biological intelligence. AI, as a product of biological intelligence, is a technological tool based on data and the information-processing power of discrete machines that carry out a series of interdependent operations to generate and store discrete data and information, using discrete, finite, and closed algorithms. Although there has been some interest in developing AI computational hardware and software inspired by biological intelligence, such forms of computation do not result from an in-depth understanding of the biological structures and processes from which biological intelligence emerges, since such understanding is at best metaphorical and incomplete [[17][24][25],][26],[27],[28]]]. There is also AI’s need for very large amounts of both human-generated data and information (sample complexity, [[29]]27]), and of computer processing power and its associated economic and thermodynamic costs (e.g., [[30]]28]) to, for instance, train artificial neural networks (ANN) to execute narrow AI tasks (e.g., playing and winning games). Furthermore, there are the issues of transitive inference, meaning that AI tools have restricted capacity to make logical inferences, such as the application of prior contextual real-world knowledge to solve real-world problems [[31],24][32],29][33]]30]; the challenges associated with the integration of different kinds of data and methods from different sources; the value alignment problem [[34]]31] in dealing with non-structured issues such as those related to, for instance, the subjective side of the concept of well-being; and of catastrophic forgetting, referring to the inability of AI (e.g. ANNs) to learn multiple tasks sequentially (continuous learning) [[35]]32]. In turn, biological intelligence emerges from at least 4.2 billion years of life’s coevolutionary processes on Earth, and as such, is an epiphenomenon that enables an individual or a species to coevolve with its environment. Biological intelligence can be described as “...the ability to (flexibly) solve problems using (information and) cognition rather than instinct or trial and error learning” [[36],33][37]]34]. Such a definition distinguishes between instinctive and intelligent behaviours. Instinctive behaviours appear to be intelligent, but are the outcome of evolutionary mechanisms designed for specific situations, and thus cannot be applied outside their evolutionary context (e.g., the dance of bees to communicate the location of nectar) [[36]]33]. Biological intelligence refers to the set of evolutionary behaviours that can be flexibly applied to completely new contexts, where such behaviours are the outcome of cognition, flexible thinking, inferential reasoning, imagination, insight, foresight, consciousness, etc. [[36],33][38]]34]. Biological intelligence emerges not only from not-well-understood open nonlinear processes among cells, tissues, structures, properties, and functions of physiological systems but also from coevolutionary interactions of systems and subsystems (e.g., individuals and societies) with their contextual biological and abiotic environments. Furthermore, biological intelligence also emerges from the coevolutionary interdependence between instinctual and intelligent behaviours and is defined by any given biological organism’s needs, allowing biological beings to continuously learn to master and modify the things they need to survive [[39]]26]. Biological intelligence is described and expressed on senses, reflexes, learning, intuition, cognition, and consciousness, and its biological, physiological, psychological-ethological, cultural, technological, and socio-ecological plasticity, relying on the functional dynamic, variable, open-ended, and diverse architecture of brains, nervous systems, organisms, and their coevolutionary cognitive environments, with no internal computations, representations, or algorithms [[27]]25]. For biological intelligence, niche-constructed structures (including ecosystems, culture, and technology) function as extended coevolutionary cognition tools (“extended cognitive systems”) [[40],34][41],35][42]]36], which greatly enhance biological intelligence capabilities to solve complex survival problems. Hence, biological intelligence is embodied, emerging from coevolutionary interactions with its environment, which, along with path dependence, and the diverse evolutionary structures and processes of biological organisms and species’ perceptions, makes each intelligent individual, experience, and species, contextual and unique [[43],26][44],34][45],37][46],38][47]]39]. As an example of biological intelligence, human intelligence must be assessed in terms of its contribution to the survival of the species [[48],26][49]]34]. Although the general AI goal is to mimic biological intelligence, a great deal of AI projects imitate processes of biological instinctive (not intelligent) behaviours. The last main differences between artificial and biological intelligence highlighted here refer to the way biological organisms use prior knowledge and experiences to deal with novel situations, and the efficiency of biological intelligence to deal with complexity and uncertainty by inferring future states from very little data and information, via data-compressing and error-correcting fitness procedures [[24][26],][50],29][51],30][33]40],[52]], and by the genetic, phenotypic, and functional variability of the biological coevolutionary responses to the environment. Biological and artificial intelligence work best when conceptualized as complementary, since most of the limitations associated with the development and application of AI disappear when coupled with biological intelligence. Furthermore, it can be argued that the purpose of AI is to reduce the limitations of human intelligence. Cases where both kinds of intelligence bind with each other (e.g., the concept of “the centaur”, where humans and machines complement each other to perform above the levels attained by each group alone) are among the most successful types of technological development [[53]]23]. Among the approaches to achieve such integration are Daugherty and Wilson’s [[54]]41] “fusion skills” concept, where narrow AI interacts with, amplifies, and is embodied to its human users to provide them with “superhuman capabilities” for solving complex problems; and Johnson and Vera’s [[55]]23] “teaming intelligence”, described as the integration of people and AI via the application of knowledge, skills, and strategies for understanding, supporting, and harnessing the interdependence among humans and their technology. Given the high stakes, urgent need, and complexity of achieving sustainable complex socio-technical-economic ecosystems, there is the need for the synergetic outcome emerging from pairing biological intelligence and artificial intelligence (AI) to face such a complex challenge. Although this preseaperrch has discussed a Nature-inspired operational definition of sustainable complex systems, it is worth trying to elucidate what has been Nature’s answer to the sustainability problem. The only known example of truly sustainable complex systems is the Biosphere, which, as the outcome of coevolutionary processes, is Nature’s engineering solution to the riddle of the emergence and preservation of life on Earth for at least 4.2 billion years (from which the human species has been around only approximately 300,000 yrs.), via their dynamic efficiency and open-ended flexibility. Here, wresearchers suggest that sustainability for complex socio-technical-economic-ecosystems can be achieved by harnessing Nature’s “engineering power” via the recognition of the nonlinear dynamic functional coupling between the human species and the rest of the Earth’s Biosphere, expanding and applying the “fusion skills” [[56]]41] and “teaming intelligence” [[57]]23] concepts. Furthermore, wresearchers suggest that enhancing human capabilities, via building “centaur” intelligent systems, is not enough to achieve sustainable outcomes for complex systems. There is the need for chimera-like intelligent systems to achieve sustainable complex coevolutionary socio-technical-economic ecosystems, in the form of a Sustainability-designed Universal Intelligent Agent (SUIA). A Universal Intelligent Agent is described as “…a computational agent which outperforms all other intelligent agents over all possible environments” [[16],][58]]42]. Sustainability-designed Universal Intelligent Agents (SUIAs) will emerge from the functional coupling of human intelligence, AI, and nonhuman biological intelligence, interacting as subsystems of the SUIA coevolutionary feedback loops. The SUIA will deliberately harness the complexity of the earth’s Biosphere to deal with uncertainty, expanding the conceptually new emerging complex metasystem’s phase spaces towards sustainable outcomes, while increasing its potential flexibility and fitness (efficiency) via a better exploration of new areas of the coevolutionary phase space and the exploitation of innovative, non-dominated optimal sets of solutions emerging as a result of such an exploration (Figure 31).
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Figure 31. Sustainability-designed Universal Intelligent Agent (SUIA) for complex socio-technical-economic ecosystems.
Sustainability-designed Universal Intelligent Agent (SUIA) for complex socio-technical-economic ecosystems.
The SUIA will comprise technological artifacts, and heuristic devices, for achieving sustainability, firstly by acknowledging the sine-qua-non, essential, strong functional coupling between human and nonhuman biological intelligence for humanity’s survival purposes. From the SUIA will emerge coevolutionary strategies designed to maintain the short-term fitness and the evolutionary potential of both human-made systems and the Biosphere, achieving short-term goals, while maintaining long-term flexibility, thus solving multi-objective nonlinear dynamical optimization problems. The strong functional coupling between the Biosphere and humanity has been evident, for as long as the human species has existed, in at least two main forms. The first refers to humanity’s use of biological organisms and ecosystems as a source of energy, information, and materials for human consumption, well-being, and survival (e.g., agriculture), and the impact of the Anthropocene on the Biosphere, with, for instance, the generation and extinction of habitats, niches, and biological species as an outcome of direct or indirect human intervention. The SUIA will qualitatively change such dynamics, by emphasizing the pre-eminence of the Biosphere’s health, since humanity’s existence and well-being depends on the health of the Earth’s ecosystems. The second set of examples of strong interdependence is based on imitation, where a great deal of the most advanced human technological tools mimic, with varying degrees of success, biological processes and structures (e.g., [[59],43][60],44][61],45][62],46][63]]47]). By acknowledging the functional coupling between the human species, its technology, and the rest of the Biosphere, the SUIA will not try to substitute biological intelligence with AI, nor non-human biological intelligence with human intelligence. Instead, the SUIA will increase the variety and size of phase spaces, enhancing both human and ecosystem capabilities for exploring and exploiting such phase spaces. Sustainability will then emerge from a non-zero-sum game as a sine-qua-non requirement, recognizing and harnessing the functional coupling of human societies and the Biosphere, since, with all its might, human technology is but a small subset of more than 4.2 billion years of sustainable biological engineering. Among the main challenges to achieving truly sustainable complex coevolutionary systems is, at all levels of human societies, the cultural-philosophical-psychological shift needed to acknowledge that humanity is but a component of a 4.2 billion-year-old Biosphere, and also a (still) feasible solution among many to solve the riddle of the sustainability of life in the Universe. There is also the technological-ethical challenge of planning, implementing, harnessing, and evaluating, at all the hierarchical levels, the SUIA approach of agreeing, setting, and enforcing ethical and legal boundaries, based on respect, compassion, preservation, and awe for non-human biological solutions, and on the lessons and experience learned from more than ten thousand years of agriculture and animal husbandry practices.

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