Issue link: https://www.e-digitaleditions.com/i/1081695
42 I ICT TODAY • With the exception of MYCIN, a system identifying bacteria and recommending antibiotics for patient treatments and the Proceedings of the IEEE in furthering natural language processing, the late 1970s marked the beginning of the slow-down of AI investment, interest, and research that culminated in the bursting of the AI bubble in the 80s. • The AI bubble burst continued into the 90s, caused by a lack of adequate basic theories, a myriad of accident-prone autonomous vehicle attempts, and the failure to make the major impact that AI promised to deliver to society. There were still some advancements, such as the development of Chabot A.L.I.C.E (artificial linguistic internet computer entity) in 1995 enabling the Web; the emergence of the term Internet of Things (IoT) in 1999, LG's announcement of the first Internet refrigerator, the introduction of real-time locating systems (RTLS) in 1998, as well as the ongoing research of IBM's Watson Center. AI debuted its merits in the 2000s when Watson competed on Jeopardy and defeated two of the game's best champions in 2011. Furthermore, AI reascended as the enabling technology for the 4th Industrial Revolution (Industry 4.0), catapulted in 2011 by IoT, digitalization, and the IP-enabled smart applications being implemented today and envisioned for tomorrow in response to 5G and other yet to be defined emerging technologies. ICT'S SUBTLE CONTRIBUTIONS TO AI ADVANCEMENTS Most historic advancements in AI are attributed to university researchers in conjunction with leading software giant companies. However, one of the most successful AI applications was ICT specific when AT&T, using artificial intelligence, developed its Automated Cable Expert (ACE) system in the late 1970s; it was later implemented within the Bell System in 1982. This telephone cable maintenance system provided timely troubleshooting reports, management analyses, and repair of high capital cost equipment once performed by expert maintenance personnel. 10 When considering innovation in machine automation and AI, ICT field technicians and installers often do not realize that many optical fiber fusion splicers have evolved into amazing computers. Evolving from large, clunky machines into IP-enabled hand-held splicing devices, today's fusion splicers have eliminated much of the manual processes and human error in optical fiber termination. Working behind the scenes in some of the higher-end core alignment and V-groove mass (ribbon) splicers are sensors, lab quality lenses, and a lot of intelligent back-end software programming that incorporates geometry, the laws of physics (e.g., Marcuse's equation for core alignment) with imaging processes and AI algorithms to account for the multitude of factors that can cause a bad splice. To obtain precise splice loss estimations, the fusion splicer integrates lab-grade lenses with sensors in order to extract successfully the vital information (e.g., curves) from the fiber image. Once curves are derived, the result is compared by an algorithm to hundreds of images stored in the splicer's memory. Stored loss estimates are compared to those that were calculated to obtain the most accurate splice loss possible to avoid network failures and unnecessary and costly downtime before OTDR testing. Clearly, many of today's fusion splicers through its evolution are Research forecasts predominantly agree that global AI uses will grow at a CAGR between 50 to 63 percent over the next three years.