Research Agenda into Human-Intelligence/Machine-Intelligence Governance

Since the birth of modern artificial intelligence (AI) at the 1956 Dartmouth Conference, the AI community has pursued modeling and coding of human intelligence into AI reasoning processes (HI Þ MI). The Dartmouth Conference\u27s fundamental assertion was that every aspect of human learning and intelligence could be so precisely described that it could be simulated in AI. With the exception of knowledge specific areas (such as IBM\u27s Big Blue and a few others), sixty years later the AI community is not close to coding global human intelligence into AI. In parallel, the knowledge management (KM) community has pursued understanding of organizational knowledge creation, transfer, and management (HI Þ HI) over the last 40 years. Knowledge management evolved into an organized discipline in the early 1990\u27s through formal university courses and creation of the first chief knowledge officer organizational positions. Correspondingly, over the last 25 years there has been growing research into the transfer of intelligence and cooperation among computing systems and automated machines (MI Þ MI). In stark contrast to the AI community effort, there has been little research into transferring AI knowledge and machine intelligence into human intelligence (MI Þ HI) with a goal of improving human decision making. Most important, there has been no research into human-intelligence/machine-intelligence decision governance; that is, the policies and processes governing human-machine decision making toward systemic mission accomplishment. To address this gap, this paper reports on a research initiative and framework toward developing an HI-MI decision governance body of knowledge and discipline

Computable Rationality, NUTS, and the Nuclear Leviathan

This paper explores how the Leviathan that projects power through nuclear arms exercises a unique nuclearized sovereignty. In the case of nuclear superpowers, this sovereignty extends to wielding the power to destroy human civilization as we know it across the globe. Nuclearized sovereignty depends on a hybrid form of power encompassing human decision-makers in a hierarchical chain of command, and all of the technical and computerized functions necessary to maintain command and control at every moment of the sovereign's existence: this sovereign power cannot sleep. This article analyzes how the form of rationality that informs this hybrid exercise of power historically developed to be computable. By definition, computable rationality must be able to function without any intelligible grasp of the context or the comprehensive significance of decision-making outcomes. Thus, maintaining nuclearized sovereignty necessarily must be able to execute momentous life and death decisions without the type of sentience we usually associate with ethical individual and collective decisions

Next Generation M2M Cellular Networks: Challenges and Practical Considerations

In this article, we present the major challenges of future machine-to-machine (M2M) cellular networks such as spectrum scarcity problem, support for low-power, low-cost, and numerous number of devices. As being an integral part of the future Internet-of-Things (IoT), the true vision of M2M communications cannot be reached with conventional solutions that are typically cost inefficient. Cognitive radio concept has emerged to significantly tackle the spectrum under-utilization or scarcity problem. Heterogeneous network model is another alternative to relax the number of covered users. To this extent, we present a complete fundamental understanding and engineering knowledge of cognitive radios, heterogeneous network model, and power and cost challenges in the context of future M2M cellular networks

Interpersonal and Ideological Kindness: A Biocultural Approach

In accordance with Richard Dawkins’ materialist “selfish gene” theory of human behavior, altruism is a subject matter that is treated conservatively by biologists, whose understanding of the human version of altruism tends toward mutualistic and sometimes reputation-based explanations of charity, kindness, and helping. Trivers (1971) first stated that non-kin altruism could evolve if altruistic behavior is balanced between partners over time, implicating a strictly mutualistic domain for kindness. But kindness herein is defined, beyond mere mutualism or reciprocity, as “the quality of being friendly, generous, and considerate.” Further, kindness tends to have an action-oriented dimension, as in Goetz et al.’s (2010) definition of compassion, denoting helpfulness, the reduction of another’s suffering, or self-sacrifice. In this paper, I will employ a biocultural approach in exploring the psychological and neuroscientific data on the evolutionary aspect of social behavior as it pertains to kindness. First, I will draw on evolutionary theories of cooperation in suggesting that an individual and ideological ethos of kindness could have evolved as an adaptive orientation that, in a Durkheimian sense, preempted ostracism and cemented alliances as a beneficial balance to the fitness risks inherent in altruism. Then, consulting data on the neurochemical profiles of dopamine and oxytocin, I will describe the sort of human psychological variation that would reveal a complimentary continuum of evolved social proclivities, from selfish to giving. In proposing that non-reciprocal kindness indeed exists, however, I argue that its presence in human societies is statistically rare, as assumptions about human biology suggest. This study thus concludes with a cautious message about the human condition: while the rareness of kindness should have a profoundly fundamental explanatory value in social analysis, scientific confirmation of its fragility would recommend further scholarship designed to highlight its exceptional biological position vis-à-vis the selfish gene

Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework

In this paper, we argue that the future of Artificial Intelligence research resides in two keywords: integration and embodiment. We support this claim by analyzing the recent advances of the field. Regarding integration, we note that the most impactful recent contributions have been made possible through the integration of recent Machine Learning methods (based in particular on Deep Learning and Recurrent Neural Networks) with more traditional ones (e.g. Monte-Carlo tree search, goal babbling exploration or addressable memory systems). Regarding embodiment, we note that the traditional benchmark tasks (e.g. visual classification or board games) are becoming obsolete as state-of-the-art learning algorithms approach or even surpass human performance in most of them, having recently encouraged the development of first-person 3D game platforms embedding realistic physics. Building upon this analysis, we first propose an embodied cognitive architecture integrating heterogenous sub-fields of Artificial Intelligence into a unified framework. We demonstrate the utility of our approach by showing how major contributions of the field can be expressed within the proposed framework. We then claim that benchmarking environments need to reproduce ecologically-valid conditions for bootstrapping the acquisition of increasingly complex cognitive skills through the concept of a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017 conference (Lisbon, Portugal