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Should We Be Afraid of the Current State of AI?
Published in Catriona Campbell, AI by Design, 2022
There will be a difference between previous industrial revolutions and the coming AGI revolution. The Industrial Revolution began in the UK, and from 1760 to 1840 many “old” jobs were replaced by professions in the newly invented technologies in factories, trains, mines and dockyards. People moved from villages to new jobs in towns and cities. Urban centres mushroomed; shops and entertainment blossomed. The industrial revolution saw the world’s first increase in population, accompanied by the rise in per capita wealth. Admittedly, the wealth generated was concentrated in the hands of factory owners, landowners and entrepreneurs who built the new technologies. Pockets of poverty remained and just shifted from rural to urban centres, where civil unrest led to rioting and early unionisation to protect workers in this new world. Innovation created new jobs that replaced ones lost by innovation. The difference in the upcoming AGI industrial revolution is that AGI has the potential to remove or dramatically reduce almost every job role we currently understand. In the next 10 years, AI has been forecast to replace up to 30% of current jobs. Ultimately, AGI could replace the vast majority of the current workforce with quicker, more reliable and cheaper AI labour.
AI Emerging Communication and Computing
Published in S. Kanimozhi Suguna, M. Dhivya, Sara Paiva, Artificial Intelligence (AI), 2021
The AGI model is precisely defined as it will act appropriately like human-level AI. The other terms used to scientifically describe AGI model are “computational intelligence,” “natural intelligence,” “cognitive architecture,” and “biologically inspired cognitive architecture” (BICA). AGI can accomplish a variety of objectives and carry out a diversification of tasks in various circumstances and surroundings like a human being. The person who claims to know diverse fields. For example, Isaac Newton was an English mathematician, prominent physicist, astronomer, theologian, and scientist. In the AGI community, the prominent researcher Goertzel (2014) expressed about the core AGI hypothesis as given below: Core AGI hypothesis: the creation and study of synthetic intelligence with sufficiently broad (e.g. human-level) scope and strong generalization capability are at the bottom qualitatively different from the creation and study of synthetic intelligence with a significantly narrower scope and weaker generalization capability.
Designing for DSA in Future Road Transport Systems and Beyond
Published in Paul M. Salmon, Gemma J. M. Read, Guy H. Walker, Michael G. Lenné, Neville A. Stanton, Distributed Situation Awareness in Road Transport, 2019
Paul M. Salmon, Gemma J. M. Read, Guy H. Walker, Michael G. Lenné, Neville A. Stanton
As with advanced automation, AI is upon us and it is likely that it will play an increasing role in society. Despite this, it is widely acknowledged that we do not fully understand the emergent risks associated with the use of AI in different contexts. As well as the risks posed by inadequately designed or poorly performing AI systems, there is a concern that advanced AI systems could also pose an existential threat to humanity. For example, Artificial General Intelligence (AGI) systems are those that perform any task that a human can, including intellectual tasks and the capacity to learn. The use of AGI in areas such as defence therefore carries with it significant risks associated with the potential for AGI to be used maliciously or indeed for AGI itself to turn on humans.
A ladder to human-comparable intelligence: an empirical metric
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2018
The ability to perform any intellectual task a human being can is a much desired property of Artificial General Intelligence (AGI). Although prominent philosophers argue that strong1 AI is not achievable using symbolic programming (Searle, 1980), most AI researchers ignore this (Russell & Norvig, 2010, p. 1020). The latest achievements in the AI field are astounding, and support the assumption that consciousness is not necessary for intelligence. Despite this progress, however, the race to AGI is not over yet, but has only just begun.
The risks associated with Artificial General Intelligence: A systematic review
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2023
Scott McLean, Gemma J. M. Read, Jason Thompson, Chris Baber, Neville A. Stanton, Paul M. Salmon
Although ANI systems such as Uber’s automated vehicles can create safety risks (Stanton et al., 2019), they do not, at present, pose a significant threat to humanity (Bentley, 2018). This is not the case with AGI, with many scholars discussing potential existential threats (Salmon et al., 2021). The risks associated with AGI are generated by the challenge of controlling an agent that is substantially more intelligent than us (Baum, 2017). The exponential rate at which technology is advancing, such as in the areas of computing power, data science, neuroscience, and bioengineering, has led many scholars to believe that an intelligence explosion will be reached in the near future (Kurzweil, 2005; Naudé & Dimitri, 2020). An intelligence explosion would see AI exceed human-level intelligence (Chalmers, 2009). At this point, which is estimated to occur between 2040 to 2070 (Baum et al., 2011; Müller & Bostrom, 2016), it is hypothesised that an AGI will have the capability to recursively self-improve by creating more intelligent versions of itself, as well as altering their pre-programmed goals (Tegmark, 2017). The emergence of AGI could bring about numerous societal challenges, from AGI’s replacing the workforce, manipulation of political and military systems, through to the extinction of humans (Bostrom, 2002, 2014; Salmon et al., 2021; Sotala & Yampolskiy, 2015). Given the many known and unknown risks regarding AGI, the scientific community holds concerns regarding the threats that an AGI may have on humanity (Bradley, 2020; Yampolskiy, 2012). These concerns include malevolent groups creating AGI for malicious use, as well as catastrophic unintended consequences brought about by apparently well-meaning AGI’s (Salmon et al., 2021). There is much scepticism among experts as to whether AGI will ever eventuate, and responses to the AGI debate are broad and range from doing nothing, as an AGI may never be created (Bringsjord et al., 2012), to the extremes of allowing AGI to destroy humanity and take our place in an evolutionary process (Garis, 2005).
The role of artificial intelligence in supply chain management: mapping the territory
Published in International Journal of Production Research, 2022
Rohit Sharma, Anjali Shishodia, Angappa Gunasekaran, Hokey Min, Ziaul Haque Munim
Practitioners often classify AI into two broad areas viz. artificial general intelligence (AGI) and artificial narrow intelligence (ANI). AGI can perform tasks that match or surpass human intelligence and ANI performs specific tasks (Bawack, Fosso Wamba, and Carillo 2019). AI has brought biggest advancements in data science through perception and cognition, which are the distinguished features of AI. The application areas of AI include machine learning, deep learning, computer vision, robotic process automation, speech and voice recognition, and neural networks as depicted in Figure 1. The recent COVID-19 pandemic has highlighted how fragile global supply chains are (with longer physical flows and dependency on multiple tier suppliers across geographies). Thereby, the demand for resiliency, agility, and flexibility from the stakeholders has drastically increased and this calls in for AI enabled solutions for SCM. Almost all application areas of AI find their use across one supply chain decision-making area or the other (for example, predicting supply chain risks using machine learning (Baryannis et al. 2019b); trend forecasting in the fashion supply chain using logistic regression (Chakraborty, Hoque, and Kabir 2020); prediction of backorder scenarios in the supply chain using gradient boosting machine learning techniques (Islam and Amin 2020); forecasting (Nguyen et al. 2021); and machine failure mode analysis (Okabe and Otsuka 2021)). The wide adoption of AI in SCM is because of AIs ability to improve the decision-making capabilities, reduce cycle-times, and improve the overall operational efficiency. As the supply chains are becoming global, the level of complexity and uncertainty increases therefore, organizations worldwide are investing in autonomous AI enabled systems for improving their supply chain processes (Kohtamäki et al. 2019). The advantages of AI in SCM are powerful optimization capabilities, accurate forecasting capabilities, improved quality, lower supply chain costs, and safe working conditions. Studies in the recent past (see Toorajipour et al. 2021) have highlighted that AI in SCM can lend unprecedented value and competitiveness through effective forecasting and efficient risk management techniques.