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Why This Book?
Published in James Luke, David Porter, Padmanabhan Santhanam, Beyond Algorithms, 2022
James Luke, David Porter, Padmanabhan Santhanam
In short, with all the progress made in the past decade, another AI Winter is extremely unlikely. However, just the rush to exploit the new machine learning techniques in pretty much every domain conceivable, has already exposed some serious cracks in the current AI technology [9–11]. The fact remains that far too many AI projects in business still fail past the prototyping stage. Not only do they fail, but the reasons for their failure are often misdiagnosed. There is a huge tendency to attribute failure of a project to the failure of the AI. It is too easy to declare that the AI isn’t intelligent enough. The reality is that often projects are not chosen well to meet the current capabilities of AI technology and the practical aspects of engineering the application are not adequately addressed to meet the business challenge. The current excitement about AI provides us with a massive opportunity. For the first time in decades, there is a genuine desire to embrace AI and to make the systems we interact with every day more intelligent and more responsive to our needs. We simply cannot afford to miss this opportunity. We can deliver on the current promise of AI, only if we understand how to engineer and deliver AI solutions. As the title of this book suggests, there is more to building a successful AI application beyond the shiny algorithms of the day.
Nomadic Artificial Intelligence and Royal Research Councils
Published in Maurizio Tinnirello, The Global Politics of Artificial Intelligence, 2022
Hubert Dreyfus, known for his early critiques against AI imaginary promises,24 spoke more recently of “first-step fallacies” (cf. Turing's 1950 well-cited future views and the most famous 1958 predictions by Simon and Newell25). The unrealisability of such early promises, after a big expenditure of government money, has led twice to the phenomenon of AI winters, that is, “[…] pessimism in the AI community, followed by pessimism in the press, followed by a severe cutback in funding, followed by the end of serious research.”26 Two such AI winters have been observed so far: the first is considered to be the period between 1974–1980 and the second from 1987 to 1993, while some specialists have expressed concerns for a third AI winter.27 The first winter was marked by the document Artificial Intelligence: A General Survey, authored by Sir James Lighthill,28 ordered by the then Science Research Council in the UK, and offered a particularly pessimistic view on the promises given by AI researchers until that time. Crevier points out that the report resulted in severe funding cuts not only in the UK but also in the US. Moreover, it led to a nomadic movement of certain AI researchers from the UK to the US:As a result, his 1973 report called for a virtual halt to all AI research in Britain. This recommendation led to the quasi-dismantling of top-flight research groups, such as that at the University of Edinburgh, and to the emigration of eminent British AI workers to the United States.29
An Overnight Sensation, after 60 Years
Published in Chace Calum, Artificial Intelligence and the Two Singularities, 2018
From 1974 until around 1980, it was very hard for AI researchers to obtain funding, and this period of relative inactivity became known as the first AI winter. This bust was followed in the 1980s by another boom, thanks to the advent of expert systems, and the Japanese fifth generation computer initiative. Expert systems limit themselves to solving narrowly defined problems from single domains of expertise (for instance, litigation) using vast data banks. They avoid the messy complications of everyday life, and do not tackle the perennial problem of trying to inculcate common sense.
Automating versus augmenting intelligence
Published in Journal of Enterprise Transformation, 2018
William B. Rouse, James C. Spohrer
The 1970s saw applications of AI to enhance medical diagnosis and treatment, starting perhaps with MYCIN (Shortliffe & Buchanan, 1975). However, a report by James Lighthill (1973) criticized AI for articulating and then failing in its pursuit of grandiose objectives. This report, and other forces, led to the First AI Winter, with substantial DARPA funding cuts.
The future of the eHighway system: a vision of a sustainable, climate-resilient, and artificially intelligent megaproject
Published in Journal of Mega Infrastructure & Sustainable Development, 2022
Regina Linke, Jürgen K. Wilke, Özgür Öztürk, Ferdinand Schöpp, Eva Kassens-Noor
Data-driven systems evolved parallel to improvements in AI, from the preliminary application of data systems through the most recent deep learning techniques. First time AI terms came up with a project proposal by McCarthy et al. in 1955 (John McCarthy et al. 2006). McCarthy and Hayes stated philosophical AI questions regarding observations, representation of the physical world, and knowledge (McCarthy and Hayes 1981). In the late 1960s, Herbert Simon and Marvin Minsky emphasized separately that the high probability of representing human ability by artificial intelligence exists after a generation (Simon 1965; Minsky 1967). In the early 1970s, Chamberlin and Boyce developed Structured Query Language to access and handle data efficiently (Chamberlin and Boyce 1976; Chamberlin 2012). In the 1980s, the development of ‘expert systems’, a computer system with a capability of imitation in decision making, was an important milestone since it is ‘the first truly successful’ application of AI technology (Jackson 1986; Russell and Norvig 1995). Data-driven solution studies couldn’t keep a high interest between the 1970s and the 2000s. AI researchers used the term ‘AI Winter’ for a pessimistic period in the 1970s with low funds and low interest. Disappointment and criticism related to high expectations resulted in funding cuts. Failure of a machine translation project, the collapse of the LISP machine market or the unsuccessful ending of the Fifth Generation Project had an adverse impact on AI studies. Against negative myths about AI, Rodney Brooks (Kurzweil 2005) stated the importance of AI as “AI is around you every second of the day.” AI term is known in the world by AI-based solutions in different areas. Since AI technology is data-dependent, a high amount of data collection started from different sources. Big data was mentioned first time in the literature in 2001 and an asymptotic interest in research was observed by improvement in technology (Tang et al. 2022). To handle and analyse such an amount of data, supercomputers and new techniques were developed.