- Email: [email protected]
Distributed, decentralized, and democratized artificial intelligence
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Technological Forecasting & Social Change journal homepage: www.elsevier.com/locate/techfore
Distributed, decentralized, and democratized artificial intelligence ⁎
Gabriel Axel Montesa,b,c,d, , Ben Goertzele,f,g a
University of Newcastle, University Drive, Callaghan, NSW 2308, Australia Hunter Medical Research Institute, 1 Kookaburra Circuit, New Lambton Heights, NSW 2305, Australia Bias in Research Node, Charles Perkins Centre, University of Sydney, University Pl, Camperdown, NSW 2006, Australia d Neural Axis, United States e Xiamen University, 422 Siming S Rd, Siming Qu, Xiamen Shi, Fujian Sheng 361005, China f OpenCog Foundation, Hong Kong g SingularityNET Foundation, Amsterdam, Netherlands b c
A R T I C LE I N FO
A B S T R A C T
Keywords: Artificial intelligence Blockchain Decentralization Consciousness Ethics Governance
The accelerating investment in artificial intelligence has vast implications for economic and cognitive development globally. However, AI is currently dominated by an oligopoly of centralized mega-corporations, who focus on the interests of their stakeholders. There is a now universal need for AI services by businesses who lack access to capital to develop their own AI services, and independent AI developers lack visibility and a source of revenue. This uneven playing field has a high potential to lead to inequitable circumstances with negative implications for humanity. Furthermore, the potential of AI is hindered by the lack of interoperability standards. The authors herein propose an alternative path for the development of AI: a distributed, decentralized, and democratized market for AIs run on distributed ledger technology. We describe the features and ethical advantages of such a system using SingularityNET, a watershed project being developed by Ben Goertzel and colleagues, as a case study. We argue that decentralizing AI opens the doors for a more equitable development of AI and AGI. It will also create the infrastructure for coordinated action between AIs that will significantly facilitate the evolution of AI into true AGI that is both highly capable and beneficial for humanity and beyond.
1. Current pains in AI Artificial intelligence is a rapidly growing industry with widespread predictions of dramatically changing the economic and labor landscape of the world. By 2020, the global AI market is projected at $47 billion (USD) and the global big data analytics market at $203 billion. To date, the overwhelming majority of AI development is done by a handful of technology mega-corporations (e.g. Facebook, Google, Amazon, IBM, Microsoft, Baidu, etc.). While the world's population is over 7 billion people, only around 10,000 people in roughly seven countries are writing the code for all of AI (Shen, 2017). By remaining in the hands of a few, the trajectory of AI applications may be significantly compromised. The datasets used to develop such AI and the AIs themselves are biased and may not be generalizable to the wider population, and the companies are beholden to their stakeholder's interests. The result is a ‘technocracy’ in which the future of one of the most potent set of technologies in the history of humankind is spoken for by a small biased minority. The potential of AI is also bogged by several factors currently
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afflicting the AI landscape: (1) AI is fragmented by a closed development environment; (2) each company tends to focus on one or a few narrow tasks; (3) the various AI agents are uninteroperable, as no interoperability standards exist; and (4) there is an absence of a formal infrastructure for AI cooperation. Moreover, the current market has some acute needs to be addressed: (5) the work of AI academics and independent developers is out of reach in GitHub repositories' (6) there is a lack of large datasets; and (7) the financial means for building a custom AI remains out of reach for independent developers and smallto mid-sized companies. One phenomenon that occurs because of these acute market needs is that independent developers remain practically invisible unless they go through the startup ecosystem, which often serves as a de facto recruitment mechanism for the tech titans (Newman, 2017), who also have been recruiting ravenously from academia (Gibney, 2016; Kahn, 2017). Given these considerations, their concentrated efforts to develop artificial general intelligence (AGI) are unlikely to be positioned for the greatest ethical and beneficial impact possible. The capabilities, resources, and speed of AI development by mega-corporations would far exceed those of other parties.
Corresponding author at: University of Newcastle, University Drive, Callaghan, NSW 2308, Australia. E-mail address: [email protected] (G.A. Montes).
https://doi.org/10.1016/j.techfore.2018.11.010 Received 14 February 2018; Received in revised form 18 October 2018; Accepted 14 November 2018 0040-1625/ © 2018 Published by Elsevier Inc.
Please cite this article as: Montes, G.A., Technological Forecasting & Social Change, https://doi.org/10.1016/j.techfore.2018.11.010
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SingularityNET API, which then gets incorporated into smart contract templates. With DLT as a foundation, a distributed, decentralized AI network can be enabled. With blockchain, the AI playing field can begin to level out. Independent developers can exercise fee-free ownership over their intellectual property, receive compensation for their work at a market price of their choosing, maintain data sovereignty and privacy, and transact with whom they wish in an open market. An independent developer or small- to medium-sized company who is in socioeconomic circumstances that may dim chances of joining a large corporation now has an outlet through which to capitalize on their work. Such affordances of DLT can improve social good by accommodating into an ecosystem a diversity of AIs and developers beyond the limited scope of oligopolistic interests.
It has been previously argued by one of the authors that the creation and development of AI is a product of a recursive externalization and projection of human cognition into the form of external artefacts (G. A. Montes, 2017a), building a kind of ‘extended mind’ (Clark and Chalmers, 1998). This process began with niche construction (OdlingSmee et al., 2003) by pre-historic tool-fashioning hominins and snowballed into increasingly complex artefacts (e.g. art, architecture, calculators, computers) and has reached a high point today with AI. This process is slated to continue until and through the cognitive artefacts can resemble and fluidly interact with humans as well as self-replicate, i.e. a full synthetic and/or biohybrid AGI with self-reproductive capabilities. The implications of this process—termed ‘causal biomimesis’—are gargantuan, because what is replicated/reproduced into the artefacts is largely propelled and determined by the values of humans and the designers and engineers of the artefacts (G. A. Montes, 2017a). There is therefore a constant imperative to participate in the future of the species and planet within a limited time and efficacy window. A central implication of causal biomimesis for the ethics of AI is this: if humans are en route to create an AGI that represents human cognition and that will ostensibly have a large effect on humanity, sentient beings, the planet, and ventures into outer space, then AI/AGI needs to account for the whole range and gestalt of human ability rather than a miniscule portion of it. This includes the full gamut of cognition, affect, self-organization, autopoiesis (Maturana and Varela, 1991), and nonordinary abilities (Goertzel et al., 2017; Montes, 2017b) that capture the neurodiversity of the human (and arguably other non-human) species. The needs and motives of tech giants are arguably much narrower than this, and/or their manifestation of these would be constrained by their interests. Although, there has been a recent effort to establish a “Partnership on AI” joint venture between AI mega-corporations (Chui, 2017), the centralized and for-profit nature of these corporations plus competition between them is potentially comprising to optimal AI/AGI development and ethics. What could be a more ethical approach to building an AI that can make space for social good, moral machine decision-making, value-alignment, and fairness and transparency?
2.1.1. Self-organizing cooperation In the case of SingularityNET, a DLT architecture further enables cooperation between AI agents themselves. To understand the ethical implications of such a freshly new and potentially paradigm-shifting technology, it is worth expounding on a number of details. A cooperation of AIs could: purchase new capabilities, monetize their assets, autonomously improve, coordinate functions, tackle new industries, develop emergent skills, outperform competitors, vastly improve accuracy, leverage newfound access, and boost processing power. Coordinated AI at scale could achieve: (a) synergy by combining AI agents into the needed technology stacks; (b) access to a library of datasets and open AI technology; (c) a market for buying and selling services, (d) from datasets to analysis, to a global market of AI buyers; (e) the creation of new AIs to coordinate existing datasets and AI agents. Coupled with the participation of smaller corporations/businesses and independent providers of AI services, cooperative AIs could significantly multiply the reach, potency, utility, and profit on those AI services. The technological utility of the cooperation capability would attract users and grow the network, increasing the likelihood that a decentralized AI network would have market demand, traction, and thus impact. A native token would be the medium for exchanging AI services and for AI agents coordinating among themselves, with users being able to buy into and out of the network using other major currencies (e.g. USD, EUR, CNY, BTC). A native token contributes to the robustness and survival of the network by: (1) enabling liquidity for AI microservices; (2) enabling native governance for steering network development and resist outside manipulation; and (3) making the network globally open without being tied to any external economies, which could make the network vulnerable to manipulation by elites in those unrelated economies. Examples of AI services would include and not be limited to: image video processing, language processing, datasets for training AI, dataset analysis, exchanging processing time or memory for tokens. While the deeper details of the economic logic of SingularityNET bear on the behavior of agents in the network and have more nuanced ethical ramifications, they are not in the scope of this paper. SingularityNET is a network designed with interoperability, data sovereignty and privacy, modularity, and scalability in mind. As a whole, the network forms a “decentralized self-organizing cooperative” (DSOC); smart contracts are used among AI agents (nodes) and external parties to access the network, transactions are a union of economic and cognitive (thanks to AI) logic, a native internal “currency” (or token) enables exchange and a democratic governance process, and the SingularityNET Foundation provides limited high-level stewardship. Such an innovative economic mechanism is designed to catalyze human and machine intelligence toward a new form of ethnically beneficial self-organizing intelligence, as we will explain.
2. Decentralizing AI In contrast to the current landscape of AIs concentrated in skyscraper silos, herein the authors expound on how and why the ethical value of a decentralized AI is more promising. Functionally speaking, high-level potential gains from a decentralized AI network could include a coordinated AGI, a global commons infrastructure, universalized stakeholdership, and democratic governance. It is worth going into more detail as to how these may be achieved by using a platform that is currently being developed (led by Ben Goertzel) based on distributed ledger technology (DLT), SingularityNET, as a case study of the kinds of features, goals, and ethical implications of what a truly decentralized AI could look like. It is the first of its kind and is poised to influence the evolution of the AI field and may potentially inspire the emergence of like-minded entities, and is thus worth discussing. 2.1. Distributed coordination SingularityNET is a platform for an open AI marketplace in which buyers and sellers exchange AI services via distributed ledger technology (DLT) and AI agents transact with each other. The DLT currently used by SingularityNET is blockchain. Blockchain uses distributed ledgers to establish consensus among the community rather than privately (Swan, 2015). A ‘smart contract’ contains if-then logic to enact agreements between two parties and automatically executes payment when the conditions stipulated in the contract are fulfilled. Blockchain allows for decentralized inter-party agreements without the need for a middle man. In this case, the parties would be AI merchants and AI agents within the network. AI sellers would wrap their AI in the
2.1.2. Multi-agent interaction Because most current AI is still considered ‘narrow AI’, in most 2
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both as a field and as a market. In the case of SingularityNET, the cooperation enabled by a DLT architecture has baked in transparency from the ground up; marketplace merchants can track their contributions and reap financial reward as datasets and cooperative services grow and evolve. 2.1.3. Democratic governance For a decentralized and free market of AIs to maintain a beneficial trajectory as it grows increasingly complex, incentives in addition to financial ones may need to be incorporated. Otherwise, it would be easy for bad actors to take advantage of the lack of regulation and game the system to achieve control, swaying the trajectory of AI development and AGI evolution in potentially undesirable directions. Indeed, there are human-system interaction risks of a DSOC; hackers taking over the network, wealthy individuals with malintent buying and taking over the network, etc. For these reasons, SingularityNET's architecture includes a reputation system that incentivizes actions that are healthy for the whole network. A voting system is implemented wherein network operations and distribution of tokens are governed democratically. Voting is filtered by reputation, such that only agents with a certain base reputation can participation in voting, owners whose agents have higher reputation have more voting power, and there is protection against gaming schemes which attempt artificial upvoting. The system is a kind of “proof of contribution”, where contribution means investing, creating, and gaining a decent reputation in the network. Liquid democracy will allow agents to delegate votes to other agents, mitigated by smart contracts.
Fig. 1. Example of the creation of AI agents by other AI agents. An agent trained on deep learning models for video processing creates other AI agents that specialize in particular kinds of video processing.
cases, there will be multiple agents that can fulfill a request within the network in different ways and degrees. This permits complex networks of dependency. In SingularityNET, such a network among agents exchanging offers for services or services for payments is known as an offer network, which the founders have explored in prior publications and prototypes (Goertzel, 2014, 2017). Each request to the network will require a unique combination of agents, and therefore a key dynamic of the SingularityNET network as a whole will involve matchmaking agents. Such functions will comprise cloud-based ‘cognitive services’ in exchange for micropayments. This will be able to occur on software and/or hardware. Internet of Things (IoT) or robots can carry out small network transactions with each other based on purely local network interactions where internet connectivity may be an issue. Agents will also be able to spawn and train new independent AIs, allowing the network to auto-coordinate and evolve (Fig. 1). Another crucial element of coordinated AI is that network nodes can collaborate in building gigantic, decentralized datasets. In SingularityNET, data producers can specify privacy restraints and other restrictions on their data, and contributors receive percentage payments. This marks a major relief of the current pain of tech giants hoarding very large datasets, leaving smaller companies struggling with access to data and without expertise to turn their data into useable form. Interoperation among agents is not a trivial matter. The non-existence of an interoperability standard for multi-agent systems is in part due to the challenge of coordinating agent-specific goals within the larger goal among a collective of various agents. Promising work has been done on applying market economic methods to distributed learning systems: a market-based algorithm can be used to apportion tasks to agents that collaborate on reinforcement learning, each given the motivation to improve the performance of the whole system (Baum, 2004). Furthermore, there is a lack of consensus on what exactly constitutes “intelligence”—however, standards of interoperability can be established and used even between software embodying different theories about how intelligence works. Multi-agent interaction presents a technical challenge for DDD AI, and addressing it is crucial component of SingularityNET's technical roadmap. By enabling coordinated AI and multi-agent interaction, data ownership and access to gigantic datasets are decentralized, thus taking an important step to democratize access to AI technology and its benefits. This “bottom-up” approach has vast implications for the future of AI,
2.1.4. Beneficial AI In addition to a reputation system, a decentralized and self-organizing AI would be even better served if there was a mechanism that could steer the long-term development of the system to be of greater benefit to humanity and the world. Because of unpredictable market dynamics, reputation as described above alone would be insufficient for an AI system that aims to be ethical because reputation in this case is largely based on utility rather than a greater good that goes beyond arbitrary and potentially volatile aggregations of internal values. For greater social good beyond the myopia of the internal networks, a beneficial and altruistic component would be apropos. In previous work (Goertzel, 2015, 2016; Goertzel et al., 2012), the founders of SingularityNET have voiced the view that positive outcomes in AI are best militated by encouraging the application of AI to positive/beneficial causes and to as AI grows, it is supportive and inclusive of as much of humanity as possible. The OpenCog technology that is part of the foundation of SingularityNET has been applied in this way, such as autism therapy (Hanson et al., 2012), biomedical research (Goertzel et al., 2008, 2006; Smigrodzki et al., 2005), and assisting people in achieving health states of mind (Goertzel et al., 2017). In SingularityNET, a certain percentage of tokens will belong to a “beneficial reserve”, which are gradually distributed to agents with a sufficiently high reputation and that are also verified as beneficial agents. Some of the beneficial reserve will also be allocated to external humanrun organizations who are democratically judged to be beneficial to the network. Furthermore, agents receive ‘benefit votes’ based on their benefit ratings. Essentially, a project is certified as beneficial when it receives a non-trivial plurality of votes among benefit voters, and then it becomes entitled to its share of benefit tokens. Taken together, an agent's reputation consists of ownership, stake, validation, and benefit rating. The beneficial reserve fosters a virtuous cycle with the network wealth because (1) the more wealth in the network, the more valuable the beneficial reserve; and (2) using tokens from the beneficial reserve will drive agents in new directions otherwise not pursued. Network diversity is thereby increased. More diversity of AI has notable ethical implications, namely the resultant tendency to lead to a broader, more general intelligence that represents a wider swath of humanity's capabilities, skills, and interest. 3
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monarch. SingularityNET understands some of the limitations of democracy, and that is why it attempts to carefully balance decentralized democratic governance with a limited degree of high-level benevolent stewardship. The SingularityNET Foundation plans to refine the democratic governance mechanisms for optimal function and gradually shift the network to a full democracy. In this process, SingularityNET has identified potential attack vectors on the network, and is installing safeguards and corrective mechanisms that prevent gaming or manipulation of the system by bad actors or coordinated AIs. In any case, with the its own governance structure not bound to external economies plus benevolent intent, a DSOC of AIs in the vein of SingularityNET is poised to promote maximal liberty and sovereignty for owners and AI agents, and great benefit to humanity.
Furthermore, a greater overall network value attracts more users, thereby organically cultivating an economy of users in the decentralized network. These users are then working according to the beneficial ethos of the network, which in turn propagates beneficence as a value among more AI merchants and agents. 3. Technology and values AI is widely considered the next digital frontier (Chui, 2017). The features of a DSOC such as SingularityNET exemplify the potential of decentralized AI. The goals in this case are to: (a) reduce friction and costs in machine-to-machine interactions; (b) maximize open and international access to a world of developers; (c) facilitate dramatic growth; (d) provide resources to projects and technology that are democratically approved as beneficial; (e) provide open, worldwide, frictionless access; (f) promote AI curation and discovery; and (g) encourage good behavior and AI diversification. DLT allows the AIs to keep track of the transactions throughout the network without the need for a centralized control, and homomorphic encryption (through cryptography) allows agents to privately share with agents and permissions of choice. This level of user freedom is unmatched in current AI skyscraper silos, where employees who work on the code are bound to company policy regarding the release of source code and use of opensource software. Decentralized AI affords functions that could transform the AI landscape with positive ethical effects. AI can be unsiloed and made to coordinate and cooperate with other AIs, breeding an economy of AI-asa-service. Discovery mechanisms increase visibility of otherwise also siloed independent developers and small businesses. AI developer talent can be incentivized to earn financial reward for their work more quickly without necessarily going through the startup ecosystem or into elite academia. Access to powerful AI tools and datasets earlier in a developer's career path affords an opportunity to bolster the professional development of each developer more than if they had to wait for a long time with little to no reward, then settling when they get hired by a well-paying company with its own scope of interest, which may not necessarily align with the developer's. In this point, we can envision an alignment between values and work earlier on in a personal/career path, something that is arguably not uncommonly compromised as an individual sublimates into the corporatist system conveyor belt, as many developers might forgo adhering to their positive value structures in order to gain income. Basic value structures have shown common trends across individuals and societies, wherein entities exhibit a general trend to evolve from ego- to ethno- to world-centric worldviews and values (Beck and Cowan, 1996; Cowan et al., 2005). Through a coalescence of political, benefit, and research-oriented motives, a decentralized AI platform such as SingularityNET can serve as a benevolent steward for humanity, with the more of the human race as a whole participating in the growth of AGI. While there have been many all-too-optimistic propositions of technology improving ethics, the present case study demonstrates how a combination of features baked into the technology positions it to become a diverse and flexible decentralized breeding ground of intelligence.
3.2. Accelerated AGI development It is worth commenting on the name of the organization, “SingularityNET”, and its connotations. The authors maintain that AI and AGI will have a prominent role to play in the future of humanity. Many refer to the fusion of artificial and human intelligence as the “Singularity” (Kurzweil, 2010). Rather than dismiss the remarkable progress being made by large corporations toward advanced AI and the increasing integration of AI into the techno-social fabric, the founders of SingularityNET aim to introduce into the market an alternative that is able to attract AI talent and make a mark in the trajectory of AI development. If/when there may be a “Singularity”, we aim to maximize the chances that it may be a benevolent one where the voices of humans across a great range of socio-economic demographics are included as well as their efforts rewarded. As humans, we believe that we have a stake and are choosing to participate in the momentum of AI rather than stand on the sidelines. Additionally, scenarios have been publicly suggested in which tech titans and governments would have to provide some form of universal basic income (UBI) to offset workforce displacement by AI advancement. We would like to attempt to disrupt the narrative of that potential scenario, even if partially, to maximize the chances of individuals maintaining liberty and sovereignty in a human-AI/biohybrid future. A goal of SingularityNET is the accelerated development of AGI. This may give some pause, and it is worth briefly justifying this underappreciated facet. (1) As stated previously, it is important to acknowledge that AI will integrate into the fabric of society, whether by its own merits and/or by the wish-fulfilling prophecies/projections of and lust for AI by the masses, and that therefore participation in the development of AI and AGI is exponentially more powerful than abstention, particularly when the intention is benevolent. (2) A distributed, decentralized, and democratic AI is better able to receive ethical training because of the wide variety of humans acting in parallel to construct and refine the network, and therefore any AGI that emerges will better represent humanity than the limited AIs of skyscraper silos. As per the earlier discussion of causal biomimesis, the values of the humans and engineers involved will shape the AI artefacts that snowball into mega-niche construction (G. A. Montes, 2017a). (3) As discussed in previous work by the founder of SingularityNET (Goertzel et al., 2012), developing AGI sooner than later helps to prevent it from outpacing ethical theorizing. AGI development is currently sufficiently slow that ethical theorization can keep up. Crucially, if substantial work on AGI is already underway at this phase, then substantial ethical work can be accomplished in the near term before rapid punctuated technological advances by centralized corporations occur. The effort to develop AGI should also be coupled with the development of ‘Global Brain’ technologies (Goertzel, 2012; Goertzel et al., 2012; Heylighen, 2007), which are together defined as:
3.1. Balanced governance There are, naturally, limitations to the potency of democracy. Winston Churchill's famous quote extols the virtues of democracy while also, to a degree, acknowledging (and accepting) its imperfection: “It has been said that democracy is the worst form of government except all the others that have been tried” (Churchill, 1947). Austrian economist Hans-Hermann Hoppe has argued (Hoppe, 2011) that a democracy is like having a tenant temporarily renting a property; s/he does not have the same incentive to care for the property as would the actual owner of the property; the latter cause would be analogous to a
“…the composite, self-organizing information system comprising humans, computers, data stores, the Internet, mobile phones, and other communication systems.” 4
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References
From a Global Brain, which would be facilitated by DSOCs like SingularityNET, we can “foster deep, consensus-building interactions between divergent [human] viewpoints” (Goertzel et al., 2012). To make this technology as valuable as possible, it is important that it be created quickly enough to use the blended viewpoints and volition that it extracts into the shaping of the first powerful AGIs. For these reasons, the rapid and measured development of an AGI vis-à-vis democratic mechanisms like the ones proponed by SingularityNET is desirable.
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4. Conclusion The authors have presented a basic high-level overview of a DSOC platform that uses DLT to create a distributed, decentralized, democratized cooperative of coordinated AIs as services, and expounded on its ethical merits. By alleviating current pains in AI and offering unique gains, acute market needs are addressed, new opportunities are created, and ethical issues plaguing the present state of AI are poised to be largely rectified. The long-term goals of SingularityNET are to (1) build a new socioeconomic engine; (2) correlate advancements toward AGI with benevolence; (3) turn AI and AGI into a global commons operated by an open-source framework with democratic governance mechanisms; and (4) innovate a fused ‘cognitive-economic’ logic that fosters “open-ended intelligence”, a “formative process of self-organization by which intelligent agents are individuated” (Weinbaum and Veitas, 2016). While there are technical challenges to be tackled, such as efficient multiagent interoperability, and human-system interaction risks to mitigate, the founders aim for this system to have the most beneficial and ethical impact on society possible and for it to nurture the whole of humanity and sentient beings for the most equitable relationship with AI. A decentralized, distributed, and democratized AI would foster the emergence of AGI, the Global Brain, and open-ended intelligence, and with that pave a path for the apprehension of the farther reaches of human nature and potential, such as non-ordinary consciousness (G. A. Montes, 2017b) and outer space exploration and colonization (Montes, 2015; Montes, 2017). As decentralized and coordinated AI/AGI develop in earnest, it may begin to exhibit properties of selfhood and agency that appear and/or behave increasingly as humans and/or biological agents do, as per the causal biomimetic process (G. A. Montes, 2017a). By decentralizing AI development, aggregation, and cooperation, AI/ AGI may be reached with a greater swath of humanity's capabilities considered, including humans' more noble qualities, leading to a more ethically sound synthetic and biohybrid landscape. Acknowledgements The authors would like to acknowledge the team members of SingularityNET, who have contributed to the platform's design and execution and several of whom have collaborated in previous work: David Hanson, Cassio Pennachin, Simone Giacomelli, Eddie Monroe, Antonin Kolonin, Nil Geisweiller, Linas Vepstas, Betelhem Dessie, Ruiting Lian, and Alexey Potapov. Previous and ongoing work by the OpenCog Foundation and Novamenta LLC have made SingularityNET possible.
Gabriel Axel Montes is a cognitive neuroscience PhD Candidate at the University of Newcastle (Australia). His work revolves on the cognitive neuroscience of bodily selfconsciousness, selfhood, their translation into/through technologies such as artificial intelligence, virtual/augmented reality, and blockchain, and the philosophical and ethical implications thereof. He is also the founder of Neural Axis, a consultancy for applications involving the intersection of mind-body entrainment and transformation with the above technologies. He received his MSc degree in Neuroscience & Cognition from Utrecht University (Netherlands), where he researched the functional genetics and transcriptomics of neurological disorders schizophrenia and autism.
Declarations of interest Ben Goertzel is the Chief Scientist and Chairman of SingularityNET, and the Chief Scientist of Hanson Robotics, a Hong Kong robotics company that creates the world's most advanced humanoid robots. He also serves as Chairman of the Artificial General Intelligence (AGI) Society and the OpenCog Foundation, an organization leading international development of advanced open source AI tools. He is the main architect and designer of the OpenCog system and is one of the world's foremost experts in AGI. He has held various executive roles at AI consulting and product development firms and served in various faculty positions. He has published nearly 20 scientific books and well over 100 scientific research papers.
None. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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Contents lists available at ScienceDirect
Technological Forecasting & Social Change journal homepage: www.elsevier.com/locate/techfore
Distributed, decentralized, and democratized artificial intelligence ⁎
Gabriel Axel Montesa,b,c,d, , Ben Goertzele,f,g a
University of Newcastle, University Drive, Callaghan, NSW 2308, Australia Hunter Medical Research Institute, 1 Kookaburra Circuit, New Lambton Heights, NSW 2305, Australia Bias in Research Node, Charles Perkins Centre, University of Sydney, University Pl, Camperdown, NSW 2006, Australia d Neural Axis, United States e Xiamen University, 422 Siming S Rd, Siming Qu, Xiamen Shi, Fujian Sheng 361005, China f OpenCog Foundation, Hong Kong g SingularityNET Foundation, Amsterdam, Netherlands b c
A R T I C LE I N FO
A B S T R A C T
Keywords: Artificial intelligence Blockchain Decentralization Consciousness Ethics Governance
The accelerating investment in artificial intelligence has vast implications for economic and cognitive development globally. However, AI is currently dominated by an oligopoly of centralized mega-corporations, who focus on the interests of their stakeholders. There is a now universal need for AI services by businesses who lack access to capital to develop their own AI services, and independent AI developers lack visibility and a source of revenue. This uneven playing field has a high potential to lead to inequitable circumstances with negative implications for humanity. Furthermore, the potential of AI is hindered by the lack of interoperability standards. The authors herein propose an alternative path for the development of AI: a distributed, decentralized, and democratized market for AIs run on distributed ledger technology. We describe the features and ethical advantages of such a system using SingularityNET, a watershed project being developed by Ben Goertzel and colleagues, as a case study. We argue that decentralizing AI opens the doors for a more equitable development of AI and AGI. It will also create the infrastructure for coordinated action between AIs that will significantly facilitate the evolution of AI into true AGI that is both highly capable and beneficial for humanity and beyond.
1. Current pains in AI Artificial intelligence is a rapidly growing industry with widespread predictions of dramatically changing the economic and labor landscape of the world. By 2020, the global AI market is projected at $47 billion (USD) and the global big data analytics market at $203 billion. To date, the overwhelming majority of AI development is done by a handful of technology mega-corporations (e.g. Facebook, Google, Amazon, IBM, Microsoft, Baidu, etc.). While the world's population is over 7 billion people, only around 10,000 people in roughly seven countries are writing the code for all of AI (Shen, 2017). By remaining in the hands of a few, the trajectory of AI applications may be significantly compromised. The datasets used to develop such AI and the AIs themselves are biased and may not be generalizable to the wider population, and the companies are beholden to their stakeholder's interests. The result is a ‘technocracy’ in which the future of one of the most potent set of technologies in the history of humankind is spoken for by a small biased minority. The potential of AI is also bogged by several factors currently
⁎
afflicting the AI landscape: (1) AI is fragmented by a closed development environment; (2) each company tends to focus on one or a few narrow tasks; (3) the various AI agents are uninteroperable, as no interoperability standards exist; and (4) there is an absence of a formal infrastructure for AI cooperation. Moreover, the current market has some acute needs to be addressed: (5) the work of AI academics and independent developers is out of reach in GitHub repositories' (6) there is a lack of large datasets; and (7) the financial means for building a custom AI remains out of reach for independent developers and smallto mid-sized companies. One phenomenon that occurs because of these acute market needs is that independent developers remain practically invisible unless they go through the startup ecosystem, which often serves as a de facto recruitment mechanism for the tech titans (Newman, 2017), who also have been recruiting ravenously from academia (Gibney, 2016; Kahn, 2017). Given these considerations, their concentrated efforts to develop artificial general intelligence (AGI) are unlikely to be positioned for the greatest ethical and beneficial impact possible. The capabilities, resources, and speed of AI development by mega-corporations would far exceed those of other parties.
Corresponding author at: University of Newcastle, University Drive, Callaghan, NSW 2308, Australia. E-mail address: [email protected] (G.A. Montes).
https://doi.org/10.1016/j.techfore.2018.11.010 Received 14 February 2018; Received in revised form 18 October 2018; Accepted 14 November 2018 0040-1625/ © 2018 Published by Elsevier Inc.
Please cite this article as: Montes, G.A., Technological Forecasting & Social Change, https://doi.org/10.1016/j.techfore.2018.11.010
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SingularityNET API, which then gets incorporated into smart contract templates. With DLT as a foundation, a distributed, decentralized AI network can be enabled. With blockchain, the AI playing field can begin to level out. Independent developers can exercise fee-free ownership over their intellectual property, receive compensation for their work at a market price of their choosing, maintain data sovereignty and privacy, and transact with whom they wish in an open market. An independent developer or small- to medium-sized company who is in socioeconomic circumstances that may dim chances of joining a large corporation now has an outlet through which to capitalize on their work. Such affordances of DLT can improve social good by accommodating into an ecosystem a diversity of AIs and developers beyond the limited scope of oligopolistic interests.
It has been previously argued by one of the authors that the creation and development of AI is a product of a recursive externalization and projection of human cognition into the form of external artefacts (G. A. Montes, 2017a), building a kind of ‘extended mind’ (Clark and Chalmers, 1998). This process began with niche construction (OdlingSmee et al., 2003) by pre-historic tool-fashioning hominins and snowballed into increasingly complex artefacts (e.g. art, architecture, calculators, computers) and has reached a high point today with AI. This process is slated to continue until and through the cognitive artefacts can resemble and fluidly interact with humans as well as self-replicate, i.e. a full synthetic and/or biohybrid AGI with self-reproductive capabilities. The implications of this process—termed ‘causal biomimesis’—are gargantuan, because what is replicated/reproduced into the artefacts is largely propelled and determined by the values of humans and the designers and engineers of the artefacts (G. A. Montes, 2017a). There is therefore a constant imperative to participate in the future of the species and planet within a limited time and efficacy window. A central implication of causal biomimesis for the ethics of AI is this: if humans are en route to create an AGI that represents human cognition and that will ostensibly have a large effect on humanity, sentient beings, the planet, and ventures into outer space, then AI/AGI needs to account for the whole range and gestalt of human ability rather than a miniscule portion of it. This includes the full gamut of cognition, affect, self-organization, autopoiesis (Maturana and Varela, 1991), and nonordinary abilities (Goertzel et al., 2017; Montes, 2017b) that capture the neurodiversity of the human (and arguably other non-human) species. The needs and motives of tech giants are arguably much narrower than this, and/or their manifestation of these would be constrained by their interests. Although, there has been a recent effort to establish a “Partnership on AI” joint venture between AI mega-corporations (Chui, 2017), the centralized and for-profit nature of these corporations plus competition between them is potentially comprising to optimal AI/AGI development and ethics. What could be a more ethical approach to building an AI that can make space for social good, moral machine decision-making, value-alignment, and fairness and transparency?
2.1.1. Self-organizing cooperation In the case of SingularityNET, a DLT architecture further enables cooperation between AI agents themselves. To understand the ethical implications of such a freshly new and potentially paradigm-shifting technology, it is worth expounding on a number of details. A cooperation of AIs could: purchase new capabilities, monetize their assets, autonomously improve, coordinate functions, tackle new industries, develop emergent skills, outperform competitors, vastly improve accuracy, leverage newfound access, and boost processing power. Coordinated AI at scale could achieve: (a) synergy by combining AI agents into the needed technology stacks; (b) access to a library of datasets and open AI technology; (c) a market for buying and selling services, (d) from datasets to analysis, to a global market of AI buyers; (e) the creation of new AIs to coordinate existing datasets and AI agents. Coupled with the participation of smaller corporations/businesses and independent providers of AI services, cooperative AIs could significantly multiply the reach, potency, utility, and profit on those AI services. The technological utility of the cooperation capability would attract users and grow the network, increasing the likelihood that a decentralized AI network would have market demand, traction, and thus impact. A native token would be the medium for exchanging AI services and for AI agents coordinating among themselves, with users being able to buy into and out of the network using other major currencies (e.g. USD, EUR, CNY, BTC). A native token contributes to the robustness and survival of the network by: (1) enabling liquidity for AI microservices; (2) enabling native governance for steering network development and resist outside manipulation; and (3) making the network globally open without being tied to any external economies, which could make the network vulnerable to manipulation by elites in those unrelated economies. Examples of AI services would include and not be limited to: image video processing, language processing, datasets for training AI, dataset analysis, exchanging processing time or memory for tokens. While the deeper details of the economic logic of SingularityNET bear on the behavior of agents in the network and have more nuanced ethical ramifications, they are not in the scope of this paper. SingularityNET is a network designed with interoperability, data sovereignty and privacy, modularity, and scalability in mind. As a whole, the network forms a “decentralized self-organizing cooperative” (DSOC); smart contracts are used among AI agents (nodes) and external parties to access the network, transactions are a union of economic and cognitive (thanks to AI) logic, a native internal “currency” (or token) enables exchange and a democratic governance process, and the SingularityNET Foundation provides limited high-level stewardship. Such an innovative economic mechanism is designed to catalyze human and machine intelligence toward a new form of ethnically beneficial self-organizing intelligence, as we will explain.
2. Decentralizing AI In contrast to the current landscape of AIs concentrated in skyscraper silos, herein the authors expound on how and why the ethical value of a decentralized AI is more promising. Functionally speaking, high-level potential gains from a decentralized AI network could include a coordinated AGI, a global commons infrastructure, universalized stakeholdership, and democratic governance. It is worth going into more detail as to how these may be achieved by using a platform that is currently being developed (led by Ben Goertzel) based on distributed ledger technology (DLT), SingularityNET, as a case study of the kinds of features, goals, and ethical implications of what a truly decentralized AI could look like. It is the first of its kind and is poised to influence the evolution of the AI field and may potentially inspire the emergence of like-minded entities, and is thus worth discussing. 2.1. Distributed coordination SingularityNET is a platform for an open AI marketplace in which buyers and sellers exchange AI services via distributed ledger technology (DLT) and AI agents transact with each other. The DLT currently used by SingularityNET is blockchain. Blockchain uses distributed ledgers to establish consensus among the community rather than privately (Swan, 2015). A ‘smart contract’ contains if-then logic to enact agreements between two parties and automatically executes payment when the conditions stipulated in the contract are fulfilled. Blockchain allows for decentralized inter-party agreements without the need for a middle man. In this case, the parties would be AI merchants and AI agents within the network. AI sellers would wrap their AI in the
2.1.2. Multi-agent interaction Because most current AI is still considered ‘narrow AI’, in most 2
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both as a field and as a market. In the case of SingularityNET, the cooperation enabled by a DLT architecture has baked in transparency from the ground up; marketplace merchants can track their contributions and reap financial reward as datasets and cooperative services grow and evolve. 2.1.3. Democratic governance For a decentralized and free market of AIs to maintain a beneficial trajectory as it grows increasingly complex, incentives in addition to financial ones may need to be incorporated. Otherwise, it would be easy for bad actors to take advantage of the lack of regulation and game the system to achieve control, swaying the trajectory of AI development and AGI evolution in potentially undesirable directions. Indeed, there are human-system interaction risks of a DSOC; hackers taking over the network, wealthy individuals with malintent buying and taking over the network, etc. For these reasons, SingularityNET's architecture includes a reputation system that incentivizes actions that are healthy for the whole network. A voting system is implemented wherein network operations and distribution of tokens are governed democratically. Voting is filtered by reputation, such that only agents with a certain base reputation can participation in voting, owners whose agents have higher reputation have more voting power, and there is protection against gaming schemes which attempt artificial upvoting. The system is a kind of “proof of contribution”, where contribution means investing, creating, and gaining a decent reputation in the network. Liquid democracy will allow agents to delegate votes to other agents, mitigated by smart contracts.
Fig. 1. Example of the creation of AI agents by other AI agents. An agent trained on deep learning models for video processing creates other AI agents that specialize in particular kinds of video processing.
cases, there will be multiple agents that can fulfill a request within the network in different ways and degrees. This permits complex networks of dependency. In SingularityNET, such a network among agents exchanging offers for services or services for payments is known as an offer network, which the founders have explored in prior publications and prototypes (Goertzel, 2014, 2017). Each request to the network will require a unique combination of agents, and therefore a key dynamic of the SingularityNET network as a whole will involve matchmaking agents. Such functions will comprise cloud-based ‘cognitive services’ in exchange for micropayments. This will be able to occur on software and/or hardware. Internet of Things (IoT) or robots can carry out small network transactions with each other based on purely local network interactions where internet connectivity may be an issue. Agents will also be able to spawn and train new independent AIs, allowing the network to auto-coordinate and evolve (Fig. 1). Another crucial element of coordinated AI is that network nodes can collaborate in building gigantic, decentralized datasets. In SingularityNET, data producers can specify privacy restraints and other restrictions on their data, and contributors receive percentage payments. This marks a major relief of the current pain of tech giants hoarding very large datasets, leaving smaller companies struggling with access to data and without expertise to turn their data into useable form. Interoperation among agents is not a trivial matter. The non-existence of an interoperability standard for multi-agent systems is in part due to the challenge of coordinating agent-specific goals within the larger goal among a collective of various agents. Promising work has been done on applying market economic methods to distributed learning systems: a market-based algorithm can be used to apportion tasks to agents that collaborate on reinforcement learning, each given the motivation to improve the performance of the whole system (Baum, 2004). Furthermore, there is a lack of consensus on what exactly constitutes “intelligence”—however, standards of interoperability can be established and used even between software embodying different theories about how intelligence works. Multi-agent interaction presents a technical challenge for DDD AI, and addressing it is crucial component of SingularityNET's technical roadmap. By enabling coordinated AI and multi-agent interaction, data ownership and access to gigantic datasets are decentralized, thus taking an important step to democratize access to AI technology and its benefits. This “bottom-up” approach has vast implications for the future of AI,
2.1.4. Beneficial AI In addition to a reputation system, a decentralized and self-organizing AI would be even better served if there was a mechanism that could steer the long-term development of the system to be of greater benefit to humanity and the world. Because of unpredictable market dynamics, reputation as described above alone would be insufficient for an AI system that aims to be ethical because reputation in this case is largely based on utility rather than a greater good that goes beyond arbitrary and potentially volatile aggregations of internal values. For greater social good beyond the myopia of the internal networks, a beneficial and altruistic component would be apropos. In previous work (Goertzel, 2015, 2016; Goertzel et al., 2012), the founders of SingularityNET have voiced the view that positive outcomes in AI are best militated by encouraging the application of AI to positive/beneficial causes and to as AI grows, it is supportive and inclusive of as much of humanity as possible. The OpenCog technology that is part of the foundation of SingularityNET has been applied in this way, such as autism therapy (Hanson et al., 2012), biomedical research (Goertzel et al., 2008, 2006; Smigrodzki et al., 2005), and assisting people in achieving health states of mind (Goertzel et al., 2017). In SingularityNET, a certain percentage of tokens will belong to a “beneficial reserve”, which are gradually distributed to agents with a sufficiently high reputation and that are also verified as beneficial agents. Some of the beneficial reserve will also be allocated to external humanrun organizations who are democratically judged to be beneficial to the network. Furthermore, agents receive ‘benefit votes’ based on their benefit ratings. Essentially, a project is certified as beneficial when it receives a non-trivial plurality of votes among benefit voters, and then it becomes entitled to its share of benefit tokens. Taken together, an agent's reputation consists of ownership, stake, validation, and benefit rating. The beneficial reserve fosters a virtuous cycle with the network wealth because (1) the more wealth in the network, the more valuable the beneficial reserve; and (2) using tokens from the beneficial reserve will drive agents in new directions otherwise not pursued. Network diversity is thereby increased. More diversity of AI has notable ethical implications, namely the resultant tendency to lead to a broader, more general intelligence that represents a wider swath of humanity's capabilities, skills, and interest. 3
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monarch. SingularityNET understands some of the limitations of democracy, and that is why it attempts to carefully balance decentralized democratic governance with a limited degree of high-level benevolent stewardship. The SingularityNET Foundation plans to refine the democratic governance mechanisms for optimal function and gradually shift the network to a full democracy. In this process, SingularityNET has identified potential attack vectors on the network, and is installing safeguards and corrective mechanisms that prevent gaming or manipulation of the system by bad actors or coordinated AIs. In any case, with the its own governance structure not bound to external economies plus benevolent intent, a DSOC of AIs in the vein of SingularityNET is poised to promote maximal liberty and sovereignty for owners and AI agents, and great benefit to humanity.
Furthermore, a greater overall network value attracts more users, thereby organically cultivating an economy of users in the decentralized network. These users are then working according to the beneficial ethos of the network, which in turn propagates beneficence as a value among more AI merchants and agents. 3. Technology and values AI is widely considered the next digital frontier (Chui, 2017). The features of a DSOC such as SingularityNET exemplify the potential of decentralized AI. The goals in this case are to: (a) reduce friction and costs in machine-to-machine interactions; (b) maximize open and international access to a world of developers; (c) facilitate dramatic growth; (d) provide resources to projects and technology that are democratically approved as beneficial; (e) provide open, worldwide, frictionless access; (f) promote AI curation and discovery; and (g) encourage good behavior and AI diversification. DLT allows the AIs to keep track of the transactions throughout the network without the need for a centralized control, and homomorphic encryption (through cryptography) allows agents to privately share with agents and permissions of choice. This level of user freedom is unmatched in current AI skyscraper silos, where employees who work on the code are bound to company policy regarding the release of source code and use of opensource software. Decentralized AI affords functions that could transform the AI landscape with positive ethical effects. AI can be unsiloed and made to coordinate and cooperate with other AIs, breeding an economy of AI-asa-service. Discovery mechanisms increase visibility of otherwise also siloed independent developers and small businesses. AI developer talent can be incentivized to earn financial reward for their work more quickly without necessarily going through the startup ecosystem or into elite academia. Access to powerful AI tools and datasets earlier in a developer's career path affords an opportunity to bolster the professional development of each developer more than if they had to wait for a long time with little to no reward, then settling when they get hired by a well-paying company with its own scope of interest, which may not necessarily align with the developer's. In this point, we can envision an alignment between values and work earlier on in a personal/career path, something that is arguably not uncommonly compromised as an individual sublimates into the corporatist system conveyor belt, as many developers might forgo adhering to their positive value structures in order to gain income. Basic value structures have shown common trends across individuals and societies, wherein entities exhibit a general trend to evolve from ego- to ethno- to world-centric worldviews and values (Beck and Cowan, 1996; Cowan et al., 2005). Through a coalescence of political, benefit, and research-oriented motives, a decentralized AI platform such as SingularityNET can serve as a benevolent steward for humanity, with the more of the human race as a whole participating in the growth of AGI. While there have been many all-too-optimistic propositions of technology improving ethics, the present case study demonstrates how a combination of features baked into the technology positions it to become a diverse and flexible decentralized breeding ground of intelligence.
3.2. Accelerated AGI development It is worth commenting on the name of the organization, “SingularityNET”, and its connotations. The authors maintain that AI and AGI will have a prominent role to play in the future of humanity. Many refer to the fusion of artificial and human intelligence as the “Singularity” (Kurzweil, 2010). Rather than dismiss the remarkable progress being made by large corporations toward advanced AI and the increasing integration of AI into the techno-social fabric, the founders of SingularityNET aim to introduce into the market an alternative that is able to attract AI talent and make a mark in the trajectory of AI development. If/when there may be a “Singularity”, we aim to maximize the chances that it may be a benevolent one where the voices of humans across a great range of socio-economic demographics are included as well as their efforts rewarded. As humans, we believe that we have a stake and are choosing to participate in the momentum of AI rather than stand on the sidelines. Additionally, scenarios have been publicly suggested in which tech titans and governments would have to provide some form of universal basic income (UBI) to offset workforce displacement by AI advancement. We would like to attempt to disrupt the narrative of that potential scenario, even if partially, to maximize the chances of individuals maintaining liberty and sovereignty in a human-AI/biohybrid future. A goal of SingularityNET is the accelerated development of AGI. This may give some pause, and it is worth briefly justifying this underappreciated facet. (1) As stated previously, it is important to acknowledge that AI will integrate into the fabric of society, whether by its own merits and/or by the wish-fulfilling prophecies/projections of and lust for AI by the masses, and that therefore participation in the development of AI and AGI is exponentially more powerful than abstention, particularly when the intention is benevolent. (2) A distributed, decentralized, and democratic AI is better able to receive ethical training because of the wide variety of humans acting in parallel to construct and refine the network, and therefore any AGI that emerges will better represent humanity than the limited AIs of skyscraper silos. As per the earlier discussion of causal biomimesis, the values of the humans and engineers involved will shape the AI artefacts that snowball into mega-niche construction (G. A. Montes, 2017a). (3) As discussed in previous work by the founder of SingularityNET (Goertzel et al., 2012), developing AGI sooner than later helps to prevent it from outpacing ethical theorizing. AGI development is currently sufficiently slow that ethical theorization can keep up. Crucially, if substantial work on AGI is already underway at this phase, then substantial ethical work can be accomplished in the near term before rapid punctuated technological advances by centralized corporations occur. The effort to develop AGI should also be coupled with the development of ‘Global Brain’ technologies (Goertzel, 2012; Goertzel et al., 2012; Heylighen, 2007), which are together defined as:
3.1. Balanced governance There are, naturally, limitations to the potency of democracy. Winston Churchill's famous quote extols the virtues of democracy while also, to a degree, acknowledging (and accepting) its imperfection: “It has been said that democracy is the worst form of government except all the others that have been tried” (Churchill, 1947). Austrian economist Hans-Hermann Hoppe has argued (Hoppe, 2011) that a democracy is like having a tenant temporarily renting a property; s/he does not have the same incentive to care for the property as would the actual owner of the property; the latter cause would be analogous to a
“…the composite, self-organizing information system comprising humans, computers, data stores, the Internet, mobile phones, and other communication systems.” 4
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References
From a Global Brain, which would be facilitated by DSOCs like SingularityNET, we can “foster deep, consensus-building interactions between divergent [human] viewpoints” (Goertzel et al., 2012). To make this technology as valuable as possible, it is important that it be created quickly enough to use the blended viewpoints and volition that it extracts into the shaping of the first powerful AGIs. For these reasons, the rapid and measured development of an AGI vis-à-vis democratic mechanisms like the ones proponed by SingularityNET is desirable.
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4. Conclusion The authors have presented a basic high-level overview of a DSOC platform that uses DLT to create a distributed, decentralized, democratized cooperative of coordinated AIs as services, and expounded on its ethical merits. By alleviating current pains in AI and offering unique gains, acute market needs are addressed, new opportunities are created, and ethical issues plaguing the present state of AI are poised to be largely rectified. The long-term goals of SingularityNET are to (1) build a new socioeconomic engine; (2) correlate advancements toward AGI with benevolence; (3) turn AI and AGI into a global commons operated by an open-source framework with democratic governance mechanisms; and (4) innovate a fused ‘cognitive-economic’ logic that fosters “open-ended intelligence”, a “formative process of self-organization by which intelligent agents are individuated” (Weinbaum and Veitas, 2016). While there are technical challenges to be tackled, such as efficient multiagent interoperability, and human-system interaction risks to mitigate, the founders aim for this system to have the most beneficial and ethical impact on society possible and for it to nurture the whole of humanity and sentient beings for the most equitable relationship with AI. A decentralized, distributed, and democratized AI would foster the emergence of AGI, the Global Brain, and open-ended intelligence, and with that pave a path for the apprehension of the farther reaches of human nature and potential, such as non-ordinary consciousness (G. A. Montes, 2017b) and outer space exploration and colonization (Montes, 2015; Montes, 2017). As decentralized and coordinated AI/AGI develop in earnest, it may begin to exhibit properties of selfhood and agency that appear and/or behave increasingly as humans and/or biological agents do, as per the causal biomimetic process (G. A. Montes, 2017a). By decentralizing AI development, aggregation, and cooperation, AI/ AGI may be reached with a greater swath of humanity's capabilities considered, including humans' more noble qualities, leading to a more ethically sound synthetic and biohybrid landscape. Acknowledgements The authors would like to acknowledge the team members of SingularityNET, who have contributed to the platform's design and execution and several of whom have collaborated in previous work: David Hanson, Cassio Pennachin, Simone Giacomelli, Eddie Monroe, Antonin Kolonin, Nil Geisweiller, Linas Vepstas, Betelhem Dessie, Ruiting Lian, and Alexey Potapov. Previous and ongoing work by the OpenCog Foundation and Novamenta LLC have made SingularityNET possible.
Gabriel Axel Montes is a cognitive neuroscience PhD Candidate at the University of Newcastle (Australia). His work revolves on the cognitive neuroscience of bodily selfconsciousness, selfhood, their translation into/through technologies such as artificial intelligence, virtual/augmented reality, and blockchain, and the philosophical and ethical implications thereof. He is also the founder of Neural Axis, a consultancy for applications involving the intersection of mind-body entrainment and transformation with the above technologies. He received his MSc degree in Neuroscience & Cognition from Utrecht University (Netherlands), where he researched the functional genetics and transcriptomics of neurological disorders schizophrenia and autism.
Declarations of interest Ben Goertzel is the Chief Scientist and Chairman of SingularityNET, and the Chief Scientist of Hanson Robotics, a Hong Kong robotics company that creates the world's most advanced humanoid robots. He also serves as Chairman of the Artificial General Intelligence (AGI) Society and the OpenCog Foundation, an organization leading international development of advanced open source AI tools. He is the main architect and designer of the OpenCog system and is one of the world's foremost experts in AGI. He has held various executive roles at AI consulting and product development firms and served in various faculty positions. He has published nearly 20 scientific books and well over 100 scientific research papers.
None. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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