Discuss the Advent of Artificial Intelligence and Its Threats to Mankind
Lim YongKean, Wong YiSuen, Phang JianWei
Artificial intelligence (AI) like United States military robotic systems (Dave Gershgorn,2016) and self-driving taxis in Pittsburgh (Mark Harris,2016), has had significant impact on the modern world since the its field founded in 1956, when John McCarthy introduced the term, AI. (Avneet Pannu,2015). Despite the great benefits from intelligent machines, many scientists and tech giants including Stephen Hawking, Elon Musk and Bill Gates warned about its dangerous outcomes(Charles Arthur,2015). Disastrous events like Microsoft Twitter public relation chat bot, Tay (John West,2016) and financial market crash by Knight Capital Group (Matthew Philips,2012) proves the vulnerability of AI. In this paper, I am going to discuss the advent of AI and its threats to mankind.
Artificial intelligence level will surpass human organic intelligence and threaten mankind’s status quo in the world. According to Avneet Pannu (2015), artificial intelligence is “the study and developments of intelligent machines and software that can reason, learn, gather knowledge, communicate, manipulate and perceive the objects.” According to Tim Urban(2015), there are 3 AI caliber categories, artificial narrow intelligence (develop to perform specific task), artificial general intelligence (computer that can perform intellectual tasks when done by human) and artificial superintelligence (“an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills.”; Nick Bostrom,1998). Kurzweil (2015) asserts that the creation of artificial superintelligence (ASI) and artificial general intelligence (AGI) from current widely used artificial narrow intelligence (ANI) will happen in the near future with the reference to Moore’s Law1 and The Law of Accelerating2, and entails an intelligent explosion. I.J.Good (1963) wrote about the consequences of developing an ASI:
1Moere’s Law is an observation made by Intel co-founder Gordon Moore in 1965, and used to describe computer processing power doubles every two years.
2 The Law of Accelerating Returns states that “fundamental measures of information technology follow predictable and exponential trajectories. (Kurzweil, 2015) 1
Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultra- intelligent machine could design even better machines; there would then unquestionably be an "intelligence explosion," and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control. (Sec.2)
Humans are dominant species in the world due to wide cognitive gap between human and other animals (Marc Hauser,2008). With the presence of ASI, with intelligence level that exceeds human intelligence level, human will inevitably be ruled and in the current place of animal to the new ruler.
AI will eliminate labour force and produce mass unemployment. During the last few decades, machines started to perform routine jobs and today, automation is integrated in society and economy. Martin Ford (2010) suggests that “technologies such as artificial intelligence, machine learning and software automation applications will eventually vaporise vast job opportunities that are fundamentally repetitive and routine.” In addition, current technological advancements may cause what Keynes (1931) imagined:
“For the moment the very rapidity of these changes is hurting us and bringing difficult problems to solve. Those countries are suffering relatively which are not in the vanguard of progress. We are being afflicted with a new disease of which some readers may not yet have heard the name, but of which they will hear a great deal in the years to come – namely, technological unemployment. This means unemployment
due to our discovery of means of economising the use of labour outrunning the pace
at which we can find new uses for labour.”
Unlike Industrial Revolution in 1800s when machines were used to replace physical labour has made the entire workforce more productive and wealthy, Digital Revolution in 2000s has made computers that can accomplish cognitive task - which will put the low and mid classes of workers out of place permanently (Drum,2013). Krugman (2012) reveals that in this era, capital-biased technological change will be the main cause of income inequality between workers and the owner of the capital. The development of AI and robots will accelerate the pace of automation - which will not consume the goods and services it manufactures (Drum, 2013), will lead to mass unemployment, civil unrest and mass incarceration like Luddites in 1810 and Detroit in 1970.
In conclusion, AI can bring enormous benefits to mankind as well as existential crisis - when the intelligent machine does not act in accord to human ethical and moral values. Michio Kaku (2010) proposes that by the end of the century human will create “machines that are as smart as monkey, that can be potentially dangerous”. Guidelines and precautions must be outlined to regulate the trajectory of AI development, similar to the establishment of National Institute of Health (NIH) guidelines to regulate biotechnology research in 1946 and Partial Test Ban Treaty (PTBT) which prohibits all nuclear detonations unless conducted underground in 1963.
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Mark Harris. (2016). Self-driving taxis roll out in Singapore - beating Uber to it. Retrieved from https://www.theguardian.com/technology/2016/aug/24/self-driving-taxis-roll-out-in- singapore-beating-uber-to-it
Avneet Pannu. (2015) Artificial Intelligence and its Application in Different Areas.
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John West. (2016). Microsoft’s disastrous Tay experiment shows the hidden dangers of AI. Retrieved from http://qz.com/653084/microsofts-disastrous-tay-experiment-shows-the- hidden-dangers-of-ai/
Matthew Philips. (2016). Knight Shows How to Lose $440 Million in 30 Minutes. Retrieved from http://www.bloomberg.com/news/articles/2012-08-02/knight-shows-how-to-lose-440- million-in-30-minutes
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