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The recent acceleration and ongoing development in Artificial Intelligence (AI) and its related (and/or enabling) digital technologies presents new challenges and considerable opportunity on which businesses and individuals may capitalise. In the era of BD - and with increasing societal value being placed on sustainable business to minimise or mitigate the impacts of climate change - customers and regulators alike are turning to organizations to tackle large and complex sustainable development goals. AI and BD can help interpret and monitor the environment, identify which problems need attention, design strategies, generate decisions, and action the tactics. A key challenge in sustainable entrepreneurship is a failure to integrate 'systems thinking' beyond a limited number of issues, rather than taking the time to understand the relationship between business processes, macro ecological processes, boundary conditions, and tipping points. The recent and substantial increase in data availability simultaneously advances the potential for AI and BD to enhance ecological sustainability through validation and testing of beliefs and hunches, offering empirical guidance to every stage involved in decision making, and comparing inputs against the outcomes - particularly in fast-changing and highly uncertain environments. To prepare, we must strategize by looking to and engaging with the market, our clients, and our customers for guidance. Only then can we then proceed to develop viable and sustainable business models and plans. To reap the rewards of progress in AI, BD, and related technologies, we need to find ways to race with the emerging technologies while also identifying ways to act in symbiosis with them. The demands of adapting to AI and BD are no different from the situation with past disruptive technologies such as the automobile, radio, and the Internet.

Additional file 1 Fig. S1. Country-specific timeline for adoption of travel policy restrictions. Diamond markers with black outlines represent the first travel restriction implemented. Countries are ranked according to the Globalization measure. ^Countries with no travel restriction records (n = 32). *Countries without KOF index (n = 24). Five countries do not have any confirmed COVID case at time of study. Fig. S2. Correlation between timing of first confirmed COVID case and globalization. Pearson’s correlation (ρ) is − 0.543 (p

As artificial intelligence (AI) thrives and propagates through modern life, a key question to ask is how to include humans in future AI? Despite humaninvolvement at every stage of the production process from conception and design through to implementation, modern AI is still often criticized for its "black box" characteristics. Sometimes, we do not know what really goes on inside or how and why certain conclusions are met. Future AI will face many dilemmas and ethical issues unforeseen by their creators beyond those commonly discussed (e.g., trolley problems and variants of it) and to which solutions cannot be hard-coded and are often still up for debate. Given the sensitivity of such social and ethical dilemmas and the implications of these for human society at large, when and if our AI make the "wrong" choice we need to understand how they got there in order to make corrections and prevent recurrences. This is particularly true in situations where human livelihoods are at stake (e.g., health, well-being, finance, law) or when major individual or household decisions are taken. Doing so requires opening up the "black box" of AI; especially as they act, interact, and adapt in a human world and how they interact with other AI in this world. In this article, we argue for the application of cognitive architectures for ethical AI. In particular, for their potential contributions to AI transparency, explainability, and accountability. We need to understand how our AI get to the solutions they do, and we should seek to do this on a deeper level in terms of the machine-equivalents of motivations, attitudes, values, and so on. The path to future AI is long and winding but it could arrive faster than we think. In order to harness the positive potential outcomes of AI for humans and society (and avoid the negatives), we need to understand AI more fully in the first place and we expect this will simultaneously contribute towards greater understanding of their human counterparts also.

Despite the great promises and potential of quantum computing, the full range of possibilities and practical applications is not yet clear. In this contribution, we highlight the potential for quantum theories and computation to reignite the art and science of expert systems and knowledge engineering. With their grounding in uncertainty and unpredictability, quantum concepts are able to expand theoretical and practical boundaries of research and exploration. We demonstrate the advantages of using quantum logic and computation when applying expert knowledge such as the ability to ask imprecise (inherently probabalistic) queries and do so e complicated computational spaces by leveraging quantum concepts such as superposition, entanglement, parallelism, and interference to sustain and manipulate networks of quantum qubits. This, we argue, can provide non-insignificant computational speedups and enable more complex data representations and analysis. We then explore three practical applications and provide evidence for their quantum advantages - Scientific Discovery, Creativity and Breakthrough Thinking, Complex Systems, and Future of Sports - that we regard as largely unexplored in popular accounts of quantum theory; as such, we suggest these are low hanging fruit for the more-immediately feasible applications of quantum technologies.

AI and Big Data provide opportunities and challenges with respect to how we achieve safety in livable smart cities. In this contribution, we look at set of aspects that are important at the city level; namely, how urban analytics and digital technologies can be used; how crime safety is influenced by predictive policing; how city planning and urban development can use realtime data; how complexity is connected to traffic safety; how AI offers opportunities for public health; and what are the societal implications of using, applying, or implementing new technologies. A core argument of the paper is the significance of acknowledging the "human factor" when using smart technologies to design a safe and livable smart city.

This paper examines the effects of globalisation on the pace of governments implementing international travel restrictions during the recent coronavirus pandemic. We find that more globalised countries experienced a longer delay in implementing international travel restriction policies with respect to the date of the first confirmed COVID-19 case. We also find that informational (a subcomponent of social globalisation) and political globalisation have the strongest effects on the observed delays in implementing international travel restriction policies in more globalised countries. Lastly, we do not find evidence that more globalised countries are more likely to adopt a more restrictive international travel policy as the first response to the pandemic. These findings highlight the dynamic relationship between globalisation and protectionism when governments respond to significant global events such as a public health crisis.

In this article, we argue for application of the Human Factors Analysis and Classification System (HFACS) to proactive incidence prevention in the public health system response to COVID-19. HFACS is a framework of causal categories of human errors typically applied for systematic retrospective incident analysis in high-risk domains. By leveraging this approach proactively, appropriate, and targeted measures can be quickly identified and established to mitigate potential errors at different levels within the public health system (from frontline healthcare workers to regulatory and statutory decision makers).

In this contribution, we highlight the potential for quantum theories to reignite the art and science of expert systems and knowledge engineering. With their fundamental grounding in uncertainty and unpredictability, quantum concepts are able to expand theoretical and practical boundaries of research and exploration. We demonstrate the similarities between quantum concepts and expert systems, outlining five domains (Scientific Discovery, Creativity and Breakthrough Thinking; Complex Systems; Medical Sciences and Advice; Strategic Business Management; and Future of Sports) we regard as largely unexplored in popular accounts of quantum theory; as such, these are low hanging fruit for the application of quantum technologies.

We describe a case of a 24-year-old male presenting urgently with a juvenile nasopharyngeal angiofibroma (JNA) with difficulty breathing, inability to swallow, and respiratory distress following throat swelling. The swelling was reduced with administration of dexamethasone and the JNA was surgically resected within 48 hours. This presentation was atypical given the acuity of presentation and the patient’s older age.