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Introduction
The development of the field of computer science has intensified the use of online platforms like the social media, websites, blogs, and emails to promote the products of various entities. In this case, technical and software solutions have been fostered through the online platforms since the services can be accessed by clicking the ads (Bangia 76).
This paper focus on the description of advertising technology, the insights gained in its development, and the interpretation of machine learning coupled with how tech ads contributed to the development of machine learning and other technological advancements.
Advertising technology (ad tech)
Advertising technology, which is also referred to as ad tech, implies the integration of software or technical solutions and services that focus on the delivery, display, target, and control of online advertisements. Thus, numerous technology vendors have emerged to seize the opportunities available in the digital advertising platforms that have seen increased traffic from the tech-savvy generations (Ko, Cho, and Roberts 65). Consequently, the development of ad tech has elicited new insights among the vendors, online platforms, and the users.
The essence of ad tech has been monetized as clicking the ads interprets to financial gains for the vendors of the technical services and solutions. Besides, ad tech supports the growth of communication and social media platforms like Gmail and Facebook as it boosts revenues. Additionally, the development of ad tech has been instrumental for the career growth of personalities like Sheryl Sandberg and Gokul Rajaram, who found their way to the helm of Google and Facebook (Mims par. 2).
Machine Learning
The concept of machine learning as pioneered by Rajaram infers to the aspect of computer intelligence that facilitates every Internet system to respond to the needs of the users (Mims par. 3). In this case, ads can be viewed as systems that integrate machine learning to respond to the unique needs of the Internet users through software and technical solutions. Further, the use of machine learning today is revealed by the useful search results, voice recognition, and enhanced interpretation of the human genome.
The pervasive use of the machine learning today has enabled the solving of the problems faced by humanity. This assertion holds because the growth of computer science has made it easy to obtain results without explicit programming. Thus, vast advertising platforms have emerged due to the advancements in machine learning that interprets the unique needs of the online networks users (Sethuraman, Tellis, and Briesch 468).
Why ad tech triggered the development of machine learning and other technologies
The growth of the machine learning technology was pioneered by the development of the ad tech as online-based corporations needed to foster product and services sales through advertisements (Smith 112). Therefore, academic programs were introduced to enhance knowledge concerning the creation of machine learning to facilitate the growth of ads in the online platforms.
Consequently, startups have emerged to provide technical and software solutions in various sectors leading to a variety of machine learning uses. For instance, SigOpt integrated the ads to develop the machine learning system that could optimize the world (Mims par. 5).
Economic dynamics of the contemporary world also triggered machine learning advancements, as the two-sided markets had to be fulfilled. For instance, Uber and Airbnb adopted the technology that Google AdSense had previously integrated into its business and clients service delivery (Mims par. 4). Moreover, the ad tech industry has portrayed its presence in the military, construction, and transport sectors as the tech ecosystem embraces machine learning in providing solutions to the interested parties (Berke, Fulton, and Vaccarello 84).
Works Cited
Bangia, Ramesh. Comprehensive Multimedia and Web Technology Xi, New Delhi, Firewall Media, 2006. Print.
Berke, Adam, Gregory Fulton, and Lauren Vaccarello. The Retargeting Playbook: How to Turn Web-Window Shoppers Into Customers, Hoboken: Wiley, 2014. Print.
Ko, Hanjun, Chang-Hoan Cho, and Marilyn Roberts. Internet uses and gratifications: A structural equation model of interactive advertising. Journal of Advertising 34.2 (2005): 57-70. Print.
Mims, Christopher. Hats Off to Web Advertising. No, Really 2015. Web.
Sethuraman, Raj, Gerard Tellis, and Richard Briesch. How well does advertising work? Generalizations from meta-analysis of brand advertising elasticities. Journal of Marketing Research 48.3 (2011): 457-471. Print.
Smith, Mike. Targeted: How Technology is revolutionizing Advertising and the Way Companies Reach Consumers, New York: AMACOM, 2014. Print.
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