As Krishna Jadhav says in this article, Artificial intelligence (AI) will soon be at the heart of every significant global technology framework for managing and accessing strategic data (Figure 1). Cybersecurity and homeland security, anti-money laundering, payments, financial markets, biotechnology, healthcare, marketing, natural language processing (NLP), computer vision, electrical grids, nuclear power plants, air traffic control, and the Internet of Things are just a few applications (IoT) (Saravalle, 2022). Artificial Intelligence is rapidly becoming a critical component of innovation; moreover, only some people appreciate the benefits and drawbacks of AI and Machine Learning advances. In contrast, machine intelligence is sure to play an essential part in developing cutting-edge frameworks in a wide range of industry domains sooner rather than later.
Figure 1: Data mining cross-industry standard process CRISP-DM
(Source: Nguyen et al., 2019)
5G
Mobile operators worldwide are ready to install the fifth generation of 3GPP mobile wireless networks (5G) (Gallagher and DeVein, 2019). Compared to the current mobile foundation, 5G will provide higher throughput, lower latency, more effective signalling, support for more range groups, improved programmability, and other sophisticated processes to increase utilisation and optimise costs (Figure 2). Because of this enhanced performance, the number of linked devices will skyrocket: sensors will benefit from more inexpensive internet bandwidth, and heavy consumers of uplink traffic, such as video cameras, will be able to exchange more data.
Figure 3: Machine Learning Frameworks and Libraries Overview
(Source: Nguyen et al., 2019)
Stylist Virtual
A few shops currently direct AI/ML-based solutions that recognise customers’ looks and attire to provide recommendations (Bulusu and Abellera, 2020). Guess developed a prototype FashionAI concept shop at Hong Kong Polytechnic University in Hong Kong. Machine learning and computer vision are used at the idea store to “learn” from customers and designers within the framework. Customers entered the store using face recognition technology. RFID-enabled dress rack options were then displayed on the smart mirror, which provided style advice (Figure 3). Other AI/ML-based styling assistants provide data to sales associates so that they may directly provide clients with recommendations, making the shopping process more consistent and successful.
Figure 3: Frameworks and libraries for deep learning layering
(Source: Nguyen et al., 2019)
Conclusion
In Krishna Jadhav’s conclusion, Transportation Systems (ITS) advancements are pushing the deployment of an increasing number of cars with self-driving capabilities (Guevara and Auat Cheein, 2020). Nonetheless, intelligent automation in ITS is not limited to autonomous cars. There are efforts underway to improve the vitality of traffic systems, such as the structure of streets and relics (signal lights, traffic islands, bus stops, vehicle parking, and so on), the regulation of traffic signals, and the setup of directions based on mobility pattern forecasts.
Reference List
Bulusu, L., and Abellera, R. (2020). AI Meets BI: Artificial Intelligence and Business Intelligence. CRC Press. https://books.google.com/books?hl=en&lr=&id=-goHEAAAQBAJ&oi=fnd&pg=PP1&dq=A+few+shops+are+currently+directing+AI/ML-based+solutions+that+recognise+customers%27+looks+and+attire+to+provide+recommendations.+&ots=fVc0WUWKPk&sig=AnSLs8h7MmZaHaEjWqqfPsdbV_Q
Gallagher, J. C., and DeVine, M. E. (2019). Fifth-generation (5G) telecommunications technologies: Issues for congress. Congressional Research Service, 1(30), 1–39. https://www.everycrsreport.com/files/20190130_R45485_137f498cc8cdc0cfba1e98e09646420028e326f0.pdf
Guevara, L., and Auat Cheein, F. (2020). The role of 5G technologies: Challenges in smart cities and intelligent transportation systems. Sustainability, 12(16), 6469. https://www.mdpi.com/794798
Nguyen, G., Dlugolinsky, S., Bobák, M., Tran, V., López García, Á., Heredia, I., … and Hluchý, L. (2019). Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey. Artificial Intelligence Review, 52(1), 77–124. https://link.springer.com/article/10.1007/s10462-018-09679-z
Saravalle, E. (2022). Recasting Sanctions and Anti-Money Laundering: From National Security to Unilateral Financial Regulation. Colum. Bus. L. Rev., 550. https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/colb2022§ion=13
