Artificial Intelligence in Engineering Risk Analytics

Risks exist in every aspect of our lives, and can mean different things to different people. While negative in general they always cause a great deal of potential damage and inconvenience for stakeholders. Recent engineering risks include the Fukushima nuclear plant disaster from the 2011 tsunami, a year that also saw earthquakes in New Zealand, tornados in the US, and floods in both Australia and Thailand. Earthquakes, tornados (not to mention hurricanes) and floods are repetitive natural phenomenon. But the October 2011 floods in Thailand were the worst in 50 years, impacting supply chains including those of Honda, Toyota, Lenovo, Fujitsu, Nippon Steel, Tesco, and Canon. Human-induced tragedies included a clothing factory fire in Bangladesh in 2012 that left over 100 dead. Wal-Mart and Sears supply chains were downstream customers. The events of Bhopal in 1984, Chernobyl in 1986, Exxon Valdez in 1989, and the Gulf oil spill of 2010 were tragic accidents. There are also malicious events such as the Tokyo Sarin attach in 1995, The World Trade Center and Pentagon attacks in 2001, and terrorist attacks on subways in Madrid (2004), London (2005), and Moscow (2010). The news brings us reports of such events all too often. The next step up in intensity is war, which seems to always be with us in some form somewhere in the world. Complex human systems also cause problems. The financial crisis resulted in recession in all aspects of the economy. Risk and analytics has become an important topic in today’s more complex, interrelated global environment, replete with threats from natural, engineering, economic, and technical sources (Olson and Wu, 2015)

Avoiding the Basilisk: An Evaluation of Top-Down, Bottom-Up, and Hybrid Ethical Approaches to Artificial Intelligence

This thesis focuses on three specific approaches to implementing morality into artificial superintelligence (ASI) systems: top-down, bottom-up, and hybrid approaches. Each approach defines both the mechanical and moral functions an AI would attain if implemented. While research on machine ethics is already scarce, even less attention has been directed to which of these three prominent approaches would be most optimal in producing a moral ASI and avoiding a malevolent AI. Thus, this paper argues of the three machine ethics approaches, a hybrid model would best avoid the problems of superintelligent AI because it minimizes the problems of bottom-up and top-down approaches while maximizing their advantages. After detailing the importance of discussing morality in ASI’s and outlining some necessary conditions, the problems of machine ethics will be considered, and arguments for and against the three approaches will be responded to

Dynamic Cognition Applied to Value Learning in Artificial Intelligence

Experts in Artificial Intelligence (AI) development predict that advances in the dvelopment of intelligent systems and agents will reshape vital areas in our society. Nevertheless, if such an advance isn't done with prudence, it can result in negative outcomes for humanity. For this reason, several researchers in the area are trying to develop a robust, beneficial, and safe concept of artificial intelligence. Currently, several of the open problems in the field of AI research arise from the difficulty of avoiding unwanted behaviors of intelligent agents, and at the same time specifying what we want such systems to do. It is of utmost importance that artificial intelligent agents have their values aligned with human values, given the fact that we cannot expect an AI to develop our moral preferences simply because of its intelligence, as discussed in the Orthogonality Thesis. Perhaps this difficulty comes from the way we are addressing the problem of expressing objectives, values, and ends, using representational cognitive methods. A solution to this problem would be the dynamic cognitive approach proposed by Dreyfus, whose phenomenological philosophy defends that the human experience of being-in-the-world cannot be represented by the symbolic or connectionist cognitive methods. A possible approach to this problem would be to use theoretical models such as SED (situated embodied dynamics) to address the values learning problem in AI

Development and validation of the AI attitude scale (AIAS-4): a brief measure of general attitude toward artificial intelligence

The rapid advancement of artificial intelligence (AI) has generated an increasing demand for tools that can assess public attitudes toward AI. This study proposes the development and the validation of the AI Attitude Scale (AIAS), a concise self-report instrument designed to evaluate public perceptions of AI technology. The first version of the AIAS that the present manuscript proposes comprises five items, including one reverse-scored item, which aims to gauge individuals’ beliefs about AI’s influence on their lives, careers, and humanity overall. The scale is designed to capture attitudes toward AI, focusing on the perceived utility and potential impact of technology on society and humanity. The psychometric properties of the scale were investigated using diverse samples in two separate studies. An exploratory factor analysis was initially conducted on a preliminary 5-item version of the scale. Such exploratory validation study revealed the need to divide the scale into two factors. While the results demonstrated satisfactory internal consistency for the overall scale and its correlation with related psychometric measures, separate analyses for each factor showed robust internal consistency for Factor 1 but insufficient internal consistency for Factor 2. As a result, a second version of the scale is developed and validated, omitting the item that displayed weak correlation with the remaining items in the questionnaire. The refined final 1-factor, 4-item AIAS demonstrated superior overall internal consistency compared to the initial 5-item scale and the proposed factors. Further confirmatory factor analyses, performed on a different sample of participants, confirmed that the 1-factor model (4-items) of the AIAS exhibited an adequate fit to the data, providing additional evidence for the scale’s structural validity and generalizability across diverse populations. In conclusion, the analyses reported in this article suggest that the developed and validated 4-items AIAS can be a valuable instrument for researchers and professionals working on AI development who seek to understand and study users’ general attitudes toward AIpublishedVersio

Singularity and Coordination Problems: Pandemic Lessons from 2020

One of the strands of the Transhumanist movement, Singulitarianism, studies the possibility that high-level artificial intelligence may be created in the future, debating ways to ensure that the interaction between human society and advanced artificial intelligence can occur safely and beneficially. But how can we guarantee this safe interaction? Are there any indications that a Singularity may be on the horizon? In trying to answer these questions, We'll make a small introduction to the area of security research in artificial intelligence. We'll review some of the current paradigms in the development of autonomous intelligent systems and evidence that we can use to prospect the coming of a possible technological Singularity. Finally, we will present a reflection using the COVID-19 pandemic, something that showed that our biggest problem in managing existential risks is our lack of coordination skills as a global society

Conceptualizing possibilities of artificial intelligence in furtherance of the banking sector : an effective tool for improving customer relationship, customer service and public relations

Purpose: In many developing countries, the agricultural sector has been seen as a major sector that should drive economic development and industrialization because of its importance in the provision of food for the increasing population, the supply of raw material to the growing industrial sector, generation of foreign exchange earnings, creation of employment opportunities, and provision of market for the product of the industrial sector. This study therefore investigates the causal linkage between agricultural financing and agricultural output growth in Nigeria. Design/Methodology/Approach: The data were mainly sourced from Central Bank of Nigeria statistical bulletins and World Bank Economic Indicators and the study adopted the Pairwise Granger Causality test. Findings: The result showed that there was no causal linkage between agricultural financing and agricultural output growth within the period under review. Practical Implications: With these findings it is therefore imperative for Nigeria to take more careful look into why agricultural financing has not made significant impact on agricultural output growth. There should exist massive education and enlightenment of farmers to know the different sources of agricultural financing available. When such funds are accessed, it should be properly monitored to ensure efficient utilization in order to increase agricultural output. Originality/Value: The study adds to literature on agricultural financing in Nigeria and it has serious implications for agricultural output growth and other areas of the economy. The findings of this study is novel and it is a pointer to the government to more proactive in ensuring that the agricultural sector is well financed and monitored in order to increase agricultural productivity.peer-reviewe