The emergence of enhanced intelligence in a brain-inspired cognitive architecture.

The Causal Cognitive Architecture is a brain-inspired cognitive architecture developed from the hypothesis that the navigation circuits in the ancestors of mammals duplicated to eventually form the neocortex. Thus, millions of neocortical minicolumns are functionally modeled in the architecture as millions of "navigation maps." An investigation of a cognitive architecture based on these navigation maps has previously shown that modest changes in the architecture allow the ready emergence of human cognitive abilities such as grounded, full causal decision-making, full analogical reasoning, and near-full compositional language abilities. In this study, additional biologically plausible modest changes to the architecture are considered and show the emergence of super-human planning abilities. The architecture should be considered as a viable alternative pathway toward the development of more advanced artificial intelligence, as well as to give insight into the emergence of natural human intelligence.

An intelligence coordination system toward creating the super-intelligent law firm.

A large law firm typically exhibits a collective intelligence comprised of hundreds or thousands of legal minds aimed at simultaneously engaging thousands of active matters across scores of industries and dozens of practice specialties with distinct doctrinal and procedural characteristics. The firm is challenged not only to achieve successful, cost-effective outcomes for its clients, but must also simultaneously, in competition with other firms and alternative service providers, attract and cultivate talent, develop and coordinate capabilities across multiple evolving areas of practice and continually improve a robust collective intelligence to gain a competitive edge. As various types of machine intelligences and tools are introduced, firms must also groom these into the collective. In this paper we explore a human-machine hybrid system for addressing this large scale, multi-dimensional, dynamic optimization challenge to coordinate a collective intelligence of humans and machines. Machine intelligence is needed to handle the computational complexity and it is complemented by human intelligence to help handle exceptions and novel situations. We believe this approach has potential for transforming the collective intelligence that is the large law firm.

Optimising peace through a Universal Global Peace Treaty to constrain the risk of war from a militarised artificial superintelligence.

This article argues that an artificial superintelligence (ASI) emerging in a world where war is still normalised constitutes a catastrophic existential risk, either because the ASI might be employed by a nation-state to war for global domination, i.e., ASI-enabled warfare, or because the ASI wars on behalf of itself to establish global domination, i.e., ASI-directed warfare. Presently, few states declare war or even war on each other, in part due to the 1945 UN Charter, which states Member States should "refrain in their international relations from the threat or use of force", while allowing for UN Security Council-endorsed military measures and self-defense. As UN Member States no longer declare war on each other, instead, only 'international armed conflicts' occur. However, costly interstate conflicts, both hot and cold and tantamount to wars, still take place. Further, a New Cold War between AI superpowers looms. An ASI-directed/enabled future conflict could trigger total war, including nuclear conflict, and is therefore high risk. Via conforming instrumentalism, an international relations theory, we advocate risk reduction by optimising peace through a Universal Global Peace Treaty (UGPT), contributing towards the ending of existing wars and prevention of future wars, as well as a Cyberweapons and Artificial Intelligence Convention. This strategy could influence state actors, including those developing ASIs, or an agential ASI, particularly if it values conforming instrumentalism and peace. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00146-021-01382-y.

Superethics Instead of Superintelligence: Know Thyself, and Apply Science Accordingly.

The human species is combining an increased understanding of our cognitive machinery with the development of a technology that can profoundly influence our lives and our ways of living together. Our sciences enable us to see our strengths and weaknesses, and build technology accordingly. What would future historians think of our current attempts to build increasingly smart systems, the purposes for which we employ them, the almost unstoppable goldrush toward ever more commercially relevant implementations, and the risk of superintelligence? We need a more profound reflection on what our science shows us about ourselves, what our technology allows us to do with that, and what, apparently, we aim to do with those insights and applications. As the smartest species on the planet, we don't need more intelligence. Since we appear to possess an underdeveloped capacity to act ethically and empathically, we rather require the kind of technology that enables us to act more consistently upon ethical principles. The problem is not to formulate ethical rules, it's to put them into practice. Cognitive neuroscience and AI provide the knowledge and the tools to develop the moral crutches we so clearly require. Why aren't we building them? We don't need superintelligence, we need superethics.

A Motivational Model of BCI-Controlled Heuristic Search.

Several researchers have proposed a new application for human augmentation, which is to provide human supervision to autonomous artificial intelligence (AI) systems. In this paper, we introduce a framework to implement this proposal, which consists of using Brain⁻Computer Interfaces (BCI) to influence AI computation via some of their core algorithmic components, such as heuristic search. Our framework is based on a joint analysis of philosophical proposals characterising the behaviour of autonomous AI systems and recent research in cognitive neuroscience that support the design of appropriate BCI. Our framework is defined as a motivational approach, which, on the AI side, influences the shape of the solution produced by heuristic search using a BCI motivational signal reflecting the user's disposition towards the anticipated result. The actual mapping is based on a measure of prefrontal asymmetry, which is translated into a non-admissible variant of the heuristic function. Finally, we discuss results from a proof-of-concept experiment using functional near-infrared spectroscopy (fNIRS) to capture prefrontal asymmetry and control the progression of AI computation of traditional heuristic search problems.

Artificial intelligence and synthetic biology: A tri-temporal contribution.

Artificial intelligence can make numerous contributions to synthetic biology. I would like to suggest three that are related to the past, present and future of artificial intelligence. From the past, works in biology and artificial systems by Turing and von Neumann prove highly interesting to explore within the new framework of synthetic biology, especially with regard to the notions of self-modification and self-replication and their links to emergence and the bottom-up approach. The current epistemological inquiry into emergence and research on swarm intelligence, superorganisms and biologically inspired cognitive architecture may lead to new achievements on the possibilities of synthetic biology in explaining cognitive processes. Finally, the present-day discussion on the future of artificial intelligence and the rise of superintelligence may point to some research trends for the future of synthetic biology and help to better define the boundary of notions such as "life", "cognition", "artificial" and "natural", as well as their interconnections in theoretical synthetic biology.