The Knowledge Race between Humans and Machines (Week 3)

Both science and art are cumulative processes of knowledge production, with scientists attempting to explain the physical universe and artists attempting to display the cultural world [1]. "Everything we know and do is rendered through shared and transforming intelligence," states the author.

In this week's lecture, we observed the scope of progress become increasingly narrow over time as information-processing capabilities have grown exponentially efficient. In supplement, I unapolegetically drew from my favorite YouTube channel, "Crash Course". In video 6 of the series titled Big History, I was first drawn to the notion of collective learning, the ability of a species to transfer knowledge across generations [2]. In the innovative, interdependent social world that we reside, knowledge is power, power is money, and capital has driven every industrial revolution thus far, with post-industrial societies appearing to replace human labor with computing power [3]. The likely result, I believe, is a globalized economy supplanted by machinery that think- and even feel- with greater maturity than humans.

As noted in my first blog, I had, for some time, considered transitioning my minor coursework from the humanities to the "hard" sciences, yet I found some remediation in my research this week. Maybe no discipline meets the creative intersection of art, science, and technology like cognitive science, and the idea of creating and testing artificial life is arguably the greatest leap in collective learning.

[4] I took this opportunity to inform myself of the most applaudable achievement of combined human intelligence, the general purpose computer. Apparently, humans have been creating computing devices since ancient Mesopotamia, and it wasn't until the mid 1940s that we had an electronic, programmable, and Turing-complete device!

[5] Since their proliferation following World War II, computers and their constituent parts have become faster and longer-lasting to keep up with the pace of data production.

[6] The Church-Turing Hypothesis for defending computer intelligence is simple: Computation power is computation power; it doesn’t matter if that power comes from electrical circuitry or a human brain. Specifically, Turing redirected the challenge of creating a computer with infinite memory to creating a computer that could learn on its own, thereby establishing the pursuit of artificial intelligence.

Honoring the subtitle of his most acclaimed essay [7], the film The Imitation Game first revealed to me the heroism of Alan Turing with meaningful quotes about thinking differently and doing the unimaginable [8]. The ideas proposed by Turing are celebrated benchmarks in the fields of computer and cognitive science [9]. He offered advanced theories, such as the cortex of the brain simply being an information-processor and that demonstrating emotion is essential to human-like intelligence [10], both of which are being realized today [11]. 

Another movie that I have watched regarding machine intelligence, Ex Machina, pits the main character, Caleb, as the Turing Test by which the cyborg, Ava, must use emotionality to win his favor [12]. Further, my Monday lecture in social psychology covered how the evolutionary brain codes both real and fake facial movements involved in genuine emotional expression! While artificial intelligence can equally perform such operations through abstract programming, time will tell whether AI could ever parallel our pace of cultural learning and production.

                                                                

[13] Artificial neural networks and deep learning have succeeded in tasks like facial recognition and speech processing to a similar degree as humans, but does this make them more intelligent than humans? My education from this week suggests that true learning involves observation and manipulation of both the real (physical) and imaginary (cultural) worlds. 

[1] Knox, Gordon. “Art/Science & Big Data: Parts 1, 2 and 3.” Leonardo, The MIT Press, 14 Oct. 2016, https://muse.jhu.edu/article/632467

[2] Greene, John, and Hank Greene. “Human Evolution: Crash Course Big History #6 .” Youtube, Crash Course: Big History, 5 Nov. 2014, https://www.youtube.com/watch?v=UPggkvB9_dc.

[3] Florida, Richard. “The New Industrial Revolution.” Futures, Elsevier, 26 Apr. 2002, https://doi.org/10.1016/0016-3287(91)90079-H.

[4] Philbin, Carrie Anne. “Early Computing: Crash Course Computer Science #1.” YouTube, 22 Feb. 2017, https://www.youtube.com/watch?v=O5nskjZ_GoI&list=RDLVO5nskjZ_GoI&start_radio=1&t=2s.

[5] Philbin, Carrie Anne. “Electronic Computing: Crash Course Computer Science #2.” YouTube, YouTube, 1 Mar. 2017, https://www.youtube.com/watch?v=LN0ucKNX0hc&list=RDLVO5nskjZ_GoI&index=2.

[6] Green, Hank. “The Computer and Turing: Crash Course History of Science #36.” YouTube, 11 Feb. 2019, https://www.youtube.com/watch?v=3xdmEwTIsd0.

[7] Turing, A. M. “I. The Imitation Game.—Computing Machinery and Intelligence.” Mind, LIX, no. 236, 1950, pp. 433–460., https://doi.org/10.1093/mind/lix.236.433.

[8] Tyldum, Morten. The Imitation Game. The Weinstein Company, 2014.

[9] A.M. Turing Award, 2019, https://amturing.acm.org/.

[10] Turing, A.M. "Intelligent Machinery." London: National Physical Laboratory, 1948. Ed. B. Jack Copeland. The Essential Turing. Oxford: Clarendon Press, 2004. 411-432

[11] Hanson, David. “Robots That ‘Show Emotion.’” David Hanson: Robots That "Show Emotion" | TED Talk, Feb. 2009, https://www.ted.com/talks/david_hanson_robots_that_show_emotion?language=en.

[12] Garland, Alex. Ex Machina. A24, 2014.

[13] Green, Hank. “The Computer and Turing: Crash Course History of Science #36.” YouTube, 11 Feb. 2019, https://www.youtube.com/watch?v=3xdmEwTIsd0.