By Emma Bailey, Joshua O’Grady, Andrew Panneton, and Matt Stevens (AKA EGMOSAM)
*All authors contributed equally to this work
Technology is at the heart of modern society, especially in the form of AI, machine learning, and big data. This is an ever-growing field and becomes even more important to humans’ lives with every new technology that arises. Due to the large-scale effects of technology, developers must be cognizant of the history of computing. When envisioning the future of technologies such as AI, machine learning, and big data, technology developers should keep in mind implications of “big data” and maintain the line between human and machine.
The computing industry has evolved in such a way that in today’s world, vast amounts of data are available pertaining to every person in society. This data is a reflection of their lifestyles, relationships, and role in society, and can be used in conjunction with data from others to find patterns, trends, and associations in the way people live. However, “while browsers, cell phone companies, corporate and software companies, and, as recently revealed, the U.S. government, accumulate extensive information about individuals, the depth and the scale of the accumulated data remains opaque and inaccessible to the ordinary person” (Tufecki). Despite the fact that there are databanks filled with information pertaining to an individual or group, they are mostly inaccessible to those people. In addition, “the advent of digital and networked technologies has caused an explosion in the amount and variety of data available on each individual, as well as the velocity with which such data becomes available” (Bryant and Raja, 2014; U.S. Federal Trade Commission, 2014). It is important for developers in this field to keep in mind that as technology progresses, this data will become even more readily available, and that it is critical to not only avoid unnecessary conclusions from the data, but also to understand the speed at which the availability of big data is increasing as technology develops.
When advancing technology, the line distinguishing human from machine must be kept in check. This is becoming an increasingly relevant issue with modern technologies developing well past where they had been conceived to be just decades ago. However, this issue was considered even in 1960, as J.C.R. Licklider theorized “man-computer symbiosis” in which machines would aid people in real time thinking. The idea of man to machine communication is ever present in the modern age and typically goes unnoticed. The potential for issues arises when people become so dependent on the technologies they create that the line between what is human and what is machine becomes indistinguishable. Especially with the creation of the internet, as “the boundaries between human and mechanical continue to blur and evolve as the online world takes on an order and reality of its own.” (Mindell, 4). The blurry line between humans and machines is a problem because the AI machines that are being made “essentially program themselves” (Knight). An example of this being a real issue is “if we were to create robot tanks and other killing machines, it is important that their decision-making be consistent with our ethical judgements” (Knight). If we are to keep machines separate from humans, then they must be able to be controlled by humans. Otherwise, if machines are to run on their own intelligence, like humans, then they should have to follow human rights and matters of ethics. This is something that should be considered when developing new technology to ensure that this interweaving does not accelerate too far.
There are several things that developers need to consider when envisioning the future of AI, machine learning, and big data. As always it is important for technology developers to learn from the past, specifically from the history of computing, to be able to learn how to properly collect, use, and interpret big data for the betterment of society. The future of technology is bright, but there are points that need to be taken into consideration, such as the usage of big data and the line between humans and machines.
Works Cited
David A. Mindell. 2002. Between Human and Machine. Feedback, Control, and Computing
before Cybernetics. [Introduction: A History of Control Systems]
Knight, Will. “There’s a Big Problem with AI: Even Its Creators Can’t Explain How It
Works.” MIT Technology Review, MIT Technology Review, 12 May 2017,
www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai/.
Zeynep Tufekci. 2014. “Engineering the public: Big data, surveillance and computational
politics.” First Monday, Volume 19, Number 7.
WC: 654 (not including title and works cited)