SYNOPSIS: The AI Winters and Summers

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In the beginning.   

While it is presumed that the advent of data science and AI is a rather new phenomenon, some do not realize modern AI has its roots in the early 1950s (Schuchmann, 2019a). AI has experienced several historical highs and lows described as Artificial Intelligence (AI) Winters and Summers, respectively. The beginnings of modern-day AI emerged dated to Alan Turing’s test in 1950 to determine whether a machine can demonstrate human-like intelligent behavior (Nield, 2019; Schuchmann, 2019a). The history of AI has proven volatile but has also resulted in evolutionary capabilities to support decision-making and predictive insights. Over the past several decades, AI has proven more vital to an ever-fast paced world demanding more information and knowledge about risks to the global community leveraging these technical advancements (Nield, 2019).

In 1954, one of the earliest experiments in machine translation occurred that could be characterized as the beginnings of modern-day AI development. The experiment included a word-for-word automated translator from Russian to English languages using simplistic machine conversions between the two languages (Schuchmann, 2019a). The use of AI to translate information better than human-beings was a key AI focus area. This research afforded a significant academic attempt in the creation of human-like neural network design and the supporting mathematics that highlights early progress (Cummins, 2018; Schuchmann, 2019a). It would become an early benchmark of the AI developmental effort for the period.

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TOP 7: “Cyber-Analytics” Journal Articles (2019)

Specifically, the U.S. government had a great interest in 1960 regarding AI’s development and began investing heavily in it to support DOD technical efforts and objectives. (Schuchmann, 2019a). DOD’s early investments spurred this phase of growth, and the government’s role in Research and Development (R&D) became the central funding mechanism of AI for the period. Regrettably, AI successes were seen as limited and would soon result in the first AI Winter (Schuchmann, 2019a).

The first AI Winter began because many within the academic community noted “that computers would need too much information about the world for correct [and successful Machine Learning (ML)-based] translation” results (Schuchmann, 2019a, para. 12). The wide-ranging consensus that greater amounts of data was needed to accomplish the complexities of AI, and this would prove to be the then-failing root problem for AI.  Existing technologies could not effectively store or process the complexity of the data or algorithms to assure AI’s continuance for the period (Schuchmann, 2019a). This recognition led to reduced and central funding by the Department of Defense (DOD) in 1966 of vital and supportive R&D endeavors (Schuchmann, 2019a); the final event closing this phase was the issuance of the 1973 Lighthill Report from the British Science Research Council (Lighthill, 1972; Schuchmann, 2019a).

The Lighthill report criticized the excessive waste of time and money afforded to the then early AI efforts. The findings were especially concerned that there were “enormous sums … spent with very little useful result[s]” (Lighthill, 1972, para. 40). These findings led to the first AI Winter in 1973 (Cummins, 2018; Nield, 2019; Schuchmann, 2019a).

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In the 1980s, advances in AI reemerged from the AI community as the second AI Summer developed (Nield, 2019; Schuchmann, 2019b). It was more a period of the advent of the concept of expert systems where human experts provided the backbone information of an if-then rule approach for making predictions in the fields of “financial planning, medical diagnosis, geological exploration, and microelectronic circuit design” (Schuchmann, 2019b, para. 2). While not wholly reliant upon software coding and advanced algorithmic design, the 1980s proved to be a crucial resurgence in ML and AI.

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The AI historical timeline. Reprinted from Toward Data Science, by. S. Schuchmann, 2019a, Retrieved from https://towardsdatascience.com/history-of-the-first-ai-winter-6f8c2186f80b.

This AI Summer also proved fleeting.  Highlighted particularly by AI critics of the expert system approach to AI led to the next Winter in 1988 (Schuchmann, 2019b). This form of AI was too restrictive in such areas as medical diagnosis, for example, and lacked the complexity to anticipate secondary and tertiary aspects of medical findings that might result in actual harm or death to a patient (Schuchmann, 2019b). The concluding verdict by DARPA, the lead R&D and funding agent agency for the DOD, was that there was “very limited success in particular [in the area of AI];” this determination by DARPA led to the second instance of an AI Winter (para. 8).

The 21^st Century AI Summer emerged in approximately 2012 and is rapidly exploding the demands for data science tools, solutions, and expertise (Peasland, 2017; Ray, 2019; Schuchmann, 2019). Peasland (2017) addresses the myriad of capabilities and challenges of modern data science.  She staunchly believes in ML and AI leading to needed future technical and academic progress for the global community. However, she also recognizes this is an ongoing and open discussion about the usefulness and viability of AI. Peasland (2017) states that “if the language isn’t exactly correct, is it not worth the ambiguity to start the conversation” (para. 12). The current pace of data science in problem-solving and decision-making processes is vital to human progress (Peasland, 2017).  Data science is rapidly proving to be a powerful tool destined to expand for the foreseeable future.

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Honeypot Data to Improve Threat Detection

While there is an expectation from the academic data science and ML communities of the next AI Winter, Ray (2019) does not see that as likely.  He suggests that it is improbable for one primary reason: “it [data science] has become industrialized” (para. 2). Data science now is under the development and predominant control of the private sector. It no longer relies upon government R&D funding to drive its innovation. It is now determined by the commercial sector to include major technological leaders to include, for example, Google and Amazon (Levy, 2019; Shankland, 2019). Data science does not appear to be losing its interest, and the demand for technical specialists dedicated to its reemergence remain in high demand for the immediate future (Columbus, 2019c).

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