The Nexus between Artificial Intelligence and Economics

This book is organized as follows. Section 2 introduces the notion of the Singularity, a stage in development in which technological progress and economic growth increase at a near-infinite rate. Section 3 describes what artificial intelligence is and how it has been applied. Section 4 considers artificial happiness and the likelihood that artificial intelligence might increase human happiness. Section 5 discusses some prominent related concepts and issues. Section 6 describes the use of artificial agents in economic modeling, and section 7 considers some ways in which economic analysis can offer some hints about what the advent of artificial intelligence might bring. Chapter 8 presents some thoughts about the current state of AI and its future prospects.

Robot Consciousness: Physics and Metaphysics Here and Abroad

Interest has been renewed in the study of consciousness, both theoretical and applied, following developments in 20th and early 21st-century logic, metamathematics, computer science, and the brain sciences. In this evolving narrative, I explore several theoretical questions about the types of artificial intelligence and offer several conjectures about how they affect possible future developments in this exceptionally transformative field of research. I also address the practical significance of the advances in artificial intelligence in view of the cautions issued by prominent scientists, politicians, and ethicists about the possible dangers of such sufficiently advanced general intelligence, including by implication the search for extraterrestrial intelligence

Synthetic cell research: Is technical progress leaving theoretical and epistemological investigations one step behind?

Advancements in the research on so-called “synthetic (artificial) cells” have been mainly characterized by an important acceleration in all sorts of experimental approaches, providing a growing amount of knowledge and techniques that will shape future successful developments. Synthetic cell technology, indeed, shows potential in driving a revolution in science and technology. On the other hand, theoretical and epistemological investigations related to what synthetic cells “are,” how they behave, and what their role is in generating knowledge have not received sufficient attention. Open questions about these less explored subjects range from the analysis of the organizational theories applied to synthetic cells to the study of the “relevance” of synthetic cells as scientific tools to investigate life and cognition; and from the recognition and the cultural reappraisal of cybernetic inheritance in synthetic biology to the need for developing concepts on synthetic cells and to the exploration, in a novel perspective, of information theories, complexity, and artificial intelligence applied in this novel field. In these contributions, we will briefly sketch some crucial aspects related to the aforementioned issues, based on our ongoing studies. An important take-home message will result: together with their impactful experimental results and potential applications, synthetic cells can play a major role in the exploration of theoretical questions as well

Towards a unified list of ethical principles for emerging technologies. An analysis of four European reports on molecular biotechnology and artificial intelligence

Artificial intelligence (AI) and molecular biotechnologies (MB) are among the most promising, but also ethically hotly debated emerging technologies. In both fields, several ethics reports, which invoke lists of ethics principles, have been put forward. These reports and the principles lists are technology specific. This article aims to contribute to the ongoing debate on ethics of emerging technologies by comparatively analysing four European ethics reports from the two technology fields. Adopting a qualitative and in-depth approach, the article highlights how ethics principles from MB can inform AI ethics and vice versa. By synthesizing the respective ethical cores of the principles included in the analysed reports, the article derives, moreover, a unified list of principles for assessing emerging technologies. The suggested list consists of nine principles: autonomy; individual and social well-being and prevention of harm; reliability, safety and security; informational privacy; transparency; accountability; communication, participation and democracy; justice, fairness, and non-discrimination; sustainability

Quo Vadis, Artificial Intelligence?

Since its conception in the mid 1950s, artificial intelligence with its great ambition to understand and emulate intelligence in natural and artificial environments alike is now a truly multidisciplinary field that reaches out and is inspired by a great diversity of other fields. Rapid advances in research and technology in various fields have created environments into which artificial intelligence could embed itself naturally and comfortably. Neuroscience with its desire to understand nervous systems of biological organisms and systems biology with its longing to comprehend, holistically, the multitude of complex interactions in biological systems are two such fields. They target ideals artificial intelligence has dreamt about for a long time including the computer simulation of an entire biological brain or the creation of new life forms from manipulations of cellular and genetic information in the laboratory. The scope for artificial intelligence in neuroscience and systems biology is extremely wide. This article investigates the standing of artificial intelligence in relation to neuroscience and systems biology and provides an outlook at new and exciting challenges for artificial intelligence in these fields. These challenges include, but are not necessarily limited to, the ability to learn from other projects and to be inventive, to understand the potential and exploit novel computing paradigms and environments, to specify and adhere to stringent standards and robust statistical frameworks, to be integrative, and to embrace openness principles

JRC horizon scanning on dual-use civil and military research

This report presents the results of a horizon scanning exercise carried out by the JRC, aiming at identifying “emerging” issues displaying potentiality for dual-use research and further applications, i.e. use of civilian research outcomes for defence purposes and vice versa. The exercise allowed the identification of 14 issues, seven of which being further commented: six perceived as having very high relevance for dual-use applications in short- to mid-term (between now and ca. 10 years), plus one assessed as emerging at the longer-term. Interestingly four issues out of these seven ones fall entirely or partially under the realm of biology: “Multifunctional materials”, “CRISPR and genetic manipulations”, “Enhancing humans” and “Synthetic biology”. All together, these thematic issues, actually interrelated, show that the importance of the manipulation of the living, including that of human bodies, with all its aspects, positive and negative, bright and dark, defensive and offensive, is expected to increase in time and impact.JRC.E.7-Knowledge for Security and Migratio

Opening the Black-Box of AI: Challenging Pattern Robustness and Improving Theorizing through Explainable AI Methods

Machine Learning (ML) algorithms, as approach to Artificial Intelligence (AI), show unprecedented analytical capabilities and tremendous potential for pattern detection in large data sets. Despite researchers showing great interest in these methodologies, ML remains largely underutilized, because the algorithms are a black-box, preventing the interpretation of learned models. Recent research on explainable artificial intelligence (XAI) sheds light on these models by allowing researchers to identify the main determinants of a prediction through post-hoc analyses. Thereby, XAI affords the opportunity to critically reflect on identified patterns, offering the opportunity to enhance decision making and theorizing based on these patterns. Based on two large and publicly available data sets, we show that different variables within the same data set can generate models with similar predictive accuracy. In exploring this issue, we develop guidelines and recommendations for the effective use of XAI in research and particularly for theorizing from identified patterns

Application of Software Engineering Principles to Synthetic Biology and Emerging Regulatory Concerns

As the science of synthetic biology matures, engineers have begun to deliver real-world applications which are the beginning of what could radically transform our lives. Recent progress indicates synthetic biology will produce transformative breakthroughs. Examples include: 1) synthesizing chemicals for medicines which are expensive and difficult to produce; 2) producing protein alternatives; 3) altering genomes to combat deadly diseases; 4) killing antibiotic-resistant pathogens; and 5) speeding up vaccine production. Although synthetic biology promises great benefits, many stakeholders have expressed concerns over safety and security risks from creating biological behavior never seen before in nature. As with any emerging technology, there is the risk of malicious use known as the dual-use problem. The technology is becoming democratized and de-skilled, and people in do-it-yourself communities can tinker with genetic code, similar to how programming has become prevalent through the ease of using macros in spreadsheets. While easy to program, it may be non-trivial to validate novel biological behavior. Nevertheless, we must be able to certify synthetically engineered organisms behave as expected, and be confident they will not harm natural life or the environment. Synthetic biology is an interdisciplinary engineering domain, and interdisciplinary problems require interdisciplinary solutions. Using an interdisciplinary approach, this dissertation lays foundations for verifying, validating, and certifying safety and security of synthetic biology applications through traditional software engineering concepts about safety, security, and reliability of systems. These techniques can help stakeholders navigate what is currently a confusing regulatory process. The contributions of this dissertation are: 1) creation of domain-specific patterns to help synthetic biologists develop assurance cases using evidence and arguments to validate safety and security of designs; 2) application of software product lines and feature models to the modular DNA parts of synthetic biology commonly known as BioBricks, making it easier to find safety features during design; 3) a technique for analyzing DNA sequence motifs to help characterize proteins as toxins or non-toxins; 4) a legal investigation regarding what makes regulating synthetic biology challenging; and 5) a repeatable workflow for leveraging safety and security artifacts to develop assurance cases for synthetic biology systems. Advisers: Myra B. Cohen and Brittany A. Dunca

Interim Report and Third Quarter Recommendations, October 2020

A report containing the National Security Commission on Artificial Intelligence (NSCAI)'s third quarterly memo and second interim report with 66 recommendations in key areas. They are organized under five tabs: strengthening the triangular alliance for AI research, applying AI for national security missions, training and recruiting AI talent, protecting and building upon U.S. technology advances, and marshaling global AI cooperation & ethics