Machine Learning and Gene Editing at the Helm of a Societal Evolution

The integration of artificial intelligence (AI) and biotechnology, whilst in its infancy, presents significant opportunities and risks, and proactive policy is needed to manage these emerging technologies. Whilst AI continues to have significant and broad impact, its relevance and complexity magnify when integrated with other emerging technologies. The confluence of Machine Learning (ML), a subset of AI, with gene editing (GE) in particular can foster substantial benefits as well as daunting risks that range from ethics to national security. These complex technologies have implications for multiple sectors, ranging from agriculture and medicine to economic competition and national security. Consideration of technology advancements and policies in different geographic regions, and involvement of multiple organisations further confound this complexity. As the impact of ML and GE expands, forward looking policy is needed to mitigate risks and leverage opportunities. Thus, this study explores the technological and policy implications of the intersection of ML and GE, with a focus on the United States (US), the United Kingdom (UK), China, and the European Union (EU). Analysis of technical and policy developments over time and an assessment of their current state have informed policy recommendations that can help manage beneficial use of technology advancements and their convergence, which can be applied to other sectors. This study is intended for policymakers to prompt reflection on how to best approach the convergence of the two technologies. Technical practitioners may also find it valuable as a resource to consider the type of information and policy stakeholders engage with.

A Brief Overview of Emerging Vaccine Technologies for Pandemic Preparedness

Over the past two decades, pandemic preparedness has proven to be critical to health, national, and economic security. Now, countries are investing billions of dollars in various pandemic preparedness tools, such as vaccines and broad-spectrum medical countermeasures (MCM), to address the threats arising from outbreaks. These tools not only offer protection against naturally occurring and accidental biological incidents but can also help provide some protection against deliberate biological attacks. Furthermore, pandemic preparedness has substantial economic implications for both the public and private sectors because of its connection with the biotechnology industry, an important component of the worldwide economy. With so many aspects of pandemic preparedness tied to public health, national security, and economic competition, understanding the key technology and policy trends of the major country stakeholders in this space provides valuable insights into pandemic preparedness gaps and ways of addressing them. This study provides a brief characterization of the trends and strategic implications associated with specific aspects of pandemic preparedness in the United States, China, and Russia. The authors discuss both technical and policy aspects of vaccine concepts and technologies, broad-spectrum MCM, and immunization facilitation.

Syndromic Surveillance 2.0: Emerging Global Surveillance Strategies for Infectious Disease Epidemics

The U.S. Army has a long history of preventing, detecting, and treating infectious diseases. Like other organizations and agencies involved in public health, the Army is increasingly interested in syndromic surveillance strategies-those designed to identify outbreaks before clinical data are available. Researchers use various methods to identify surveillance strategies across the globe, investigate these strategies' benefits and limitations, and recommend actions to aid the Army in their efforts to detect emerging epidemics and pandemics.

Potential impact of artificial intelligence on the emerging world order

The fast-paced diffusion of technologies broadly falling under the umbrella of artificial intelligence (AI) is said to be shaping the emerging world order in international relations (IR). It is expected that the global AI race will pave the way for another rise and fall of great powers in the international system, similar to the impact caused by the three industrial revolutions of the past. The literature in IR identifies three major powers - namely, the United States of America (USA), China, and Russia, as the leading contenders in this AI race. The ongoing AI-enabled fourth industrial revolution is all the more unique due to the markedly different approaches these three powers have adopted for integrating AI into their military, political, and socio-economic spheres. The AI strategies of these countries further reflect their capabilities and intentions towards how they plan on employing the technology to elevate their prestige and power status in the international system. This paper draws from a historiography of the First, Second, and Third Industrial Revolutions to study how technological innovations have altered relative power capabilities of nations, triggering a re-ordering of power hierarchies at a systemic level. Drawing from this understanding, it analyses the nature of AI as an emerging technology and assesses whether it can cause systemic alterations. It critically examines and compares the AI strategies of the USA, China, and Russia as leading contenders in the global AI race and outlines their strengths and weaknesses. It further draws from the Adoption Capacity Theory to argue that the AI race may well be determined by the relative capacity of the major institutions in each of these countries to manage and adapt to the disruptions this technology is bound to bring to the fore.

A Collaborative Metaverse based A-La-Carte Framework for Tertiary Education (CO-MATE)

The paper aims to propose a futuristic educational and learning framework called CO-MATE (Collaborative Metaverse-based A-La-Carte Framework for Tertiary Education). The architectural framework of CO-MATE was conceptualized in a four-layered approach which depicts various infrastructure and service layer functionalities. CO-MATE is a technologically driven educational metaverse environment involving loosely coupled building blocks to provide an a-la-carte model for platform designers. For this, the authors had undertaken a systematic mapping study of the pre/post-COVID period to review the application of various emerging technologies. Further, the paper also discusses the core attributes and component offerings of CO-MATE for a technology-driven and automated immersive-learning environment and exemplifies the same through various use cases.

Has Covid-19 accelerated opportunities for digital entrepreneurship? An Indian perspective

Covid-19 has challenged many businesses to orient themselves towards digital solutions for their survival. Due to the rising digital wave during Covid-19, there has been a plethora of opportunities for aspiring entrepreneurs to enter the market. Hence, this study focuses on understanding emerging areas and technologies for digital entrepreneurship. This study adopted a qualitative approach with semi-structured interviews through the lens of the diffusion of innovations theory. A total of 23 entrepreneurs responded and presented their views on Covid-19-induced opportunities for digital entrepreneurship. A structured process of open, axial, and selective coding was adopted for the thematic analysis. The study presents a framework based on four promising propositions. Results of the thematic analysis indicate the emergence of digital entrepreneurship opportunities in technology (EdTech, FinTech, cybersecurity), healthcare (diagnostics, virtual care, fitness), entertainment (over the top, gaming, social media), and e-commerce (contactless delivery, payment methods, augmented reality). In this study, entrepreneurs presented their views based on their experience with the platform or technology they operated. To this end, the present study offers implications both for scholars and entrepreneurs working in and aspiring to digital entrepreneurship along with future scope of research.

Pilot-scale field demonstration of a hybrid nanofiltration and UV-sulfite treatment train for groundwater contaminated by per- and polyfluoroalkyl substances (PFASs)

Previous laboratory scale studies indicate nanofiltration (NF) and UV-sulfite photochemical treatments as promising technologies for the removal and destruction, respectively, of per- and polyfluoroalkyl substances (PFASs) from contaminated water. This study reports on a field demonstration of a pilot-scale hybrid NF and UV-sulfite treatment train for the remediation of 12 PFASs detected in groundwater impacted by aqueous film-forming foam (AFFF) at a U.S. Department of Defense installation. For most of the detected PFASs, NF rejection was consistently ≥ 95% over a 30-day field trial when operating at 90% total permeate recovery. Rejection of short-chain perfluorosulfonic acids (PFSAs) by NF decreased when recoveries increased from 90 to 97%; tests with a reverse osmosis (RO) membrane showed ≥ 99% rejection of all PFASs regardless of increasing recovery. UV treatment of the NF reject following 90% permeate recovery resulted in variable destruction of individual PFASs, with rates also being dependent on pH and the identity and concentration of UV photosensitizer. Rates of perfluorocarboxylic acid (PFCA) degradation were greater than those measured for PFSAs and perfluoroalkyl acid (PFAA) precursors and were independent of perfluoroalkyl chain length. In contrast, rates of PFSA degradation increased with increasing chain length. Consistent levels of PFAS degradation by UV-sulfite were observed during a 30-day demonstration experiment in NF reject water amended with 10 mM sulfite and adjusted to pH 11.2. Collectively, > 75% of the detected PFAS mass in the NF reject was destroyed after 4 h of UV treatment, increasing to > 90% after 8 h of treatment. An analysis of electrical energy inputs for the hybrid NF/UV-sulfite treatment train showed energy per order magnitude (EE/O) requirements ranging from ≤ 13.1 kWh/m³ for PFCAs and 14.1 kWh/m³ for PFOS to values > 100 kWh/m³ for more recalcitrant short-chain PFSA analogues. The UV reactor and water-cooling system were the major contributors to overall energy requirements and represent the greatest opportunities for improving efficiency of the technology.