Artificial Intelligence
Kade Brannon, Leonel Chavez, Lydia Johnson, Daniel Larson, Benjamin Lindahl
History and Core Components
AI’s history isn’t a very long one. According to Chris Smith, the term Artificial Intelligence was first coined by John McCarthy in 1956 in an academic conference he held (Smith, 2006, 4). This came about from the invention by Allen Newell, Cliff Shaw, and Herbert Simon that they called “The Logic Theorist”. This was supposed to, “mimic the problem-solving skills of a human and was funded by Research and Development (RAND) Corporation. It’s considered by many to be the first artificial intelligence program…” (Anyoha, 2017).
A little bit before this, Alan Turing discussed the idea of a “thinking machine.” He developed the Turing Test. According to Quan-Haase, a Turing Test is defined as, “a machine’s ability to showcase human intelligence” (Quan-Haase, 2016, 279). The goal is for the interrogator to discriminate between the machine and the human by a series of queries using “natural language.” Although there are claims that a computer passed the test in 2014, we are still looking for that first computer to completely pass the Turing Test.
AI had a big jumpstart from the mid 50’s all the way into the mid 70’s where computers started to become faster, more accessible, and store more information. Even in 1970, Marvin Minsky was quoted in Life Magazine saying, “from three to eight years we will have a machine with the general intelligence of an average human being” (Anyoha, 2017). That wasn’t the case though. That only helped AI for so long before there started to become hang ups. The computers were faster, but not fast enough and they could store more, but couldn’t store enough to help AI become what they wanted it to.
The 80’s was a time that AI saw a big boost in funds and algorithmic toolkit. This charge was led by the likes of John Hopefield, David Rumelhart, Edward Feigenbaum, among others. From 1982–90, the Japanese government invested more than $400 million dollars into the Fifth Generation Computer Project (FGCP) to help with AI developments. Ultimately, it is looked at as a failure, but “indirect” effects from the FGCP could have helped inspire young engineers and scientists. After this project, AI wasn’t highly thought of or sought out in the public for a while, but was still thriving though.
By 1997, IBM’s Deep Blue beat Gary Kasparov, a world class chess player, in a game of chess. In the same year, Dragon Systems implemented a speech recognition software onto Windows. Since then, AI has just gained more and more ground. Thanks to the increase in computer storage that has surpassed our needs, it has helped us expand on AI more and more every single year. It has now found its way into banking, marketing, and entertainment. Nelito has said this about the finance industry, “Over time, AI is not only going to revolutionize the financial industry but become the industry itself” (Nelito, 2017). That is quite a statement about a technology that we thought was stunned in growth just 25 years ago.
Now, what is Artificial Intelligence and how exactly does it work? Quan-Haase defines AI as, “a branch of computer science dedicated to designing machines capable of resembling or outperforming human intelligence” (Quan-Haase, 2016, 258). It is basically supposed to help human’s lives get easier. A great example would be Amazon’s “Alexa” that can play your music, turn off and on your lights and TV, and set alarms and reminders for you all without you leaving your couch.
Concerning how it works, SAS says, “AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data” (SAS, 2018). In AI there are different subfields that go with it. Deep Learning for example, “uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data.” One of the main applications for this is voice and speech recognition. Another would be Machine Learning which, “automates analytical model building.”
This diagram above by Semantic Software shows how a basic AI technology works. From a computer, watch, phone, or voice, it gathers the message. It goes into a “Big Data” and goes into syntactic transformation. From there it goes into the Interoperability and into semantic transformation; making its way into the stage of solution and where it tries to enable the solution. Finally, it reaches the Cognitive Computing Environment where it achieves the task and then goes into action.
Diffusion
When John McCarthy coined the term “Artificial Intelligence”(AI) in 1955 and then presented it in 1956 (Childs, 2011), he predicted that machines would one day be able to replicate human thinking and intelligence. However, he believed that ”the breakthrough might come in ‘five to 500 years’” (Childs, 2011). Since then, Artificial Intelligence (AI) diffusion rates slowly rose in society through the late 1900s and early 2000s. Before 2000, society’s use of AI was limited. However, with the frequent use of computers and smartphones, Artificial Intelligence’s use in society’s daily life has grown. Quan-Haase defines diffusion as “the process, by which, over time, an innovation becomes adopted by a social group” (2016, p. 106).
One strong example of an AI innovation that has shown a great rate of diffusion is the smart speaker, such as the Amazon Echo, Google Home, and the Apple HomePod. Adopters in the United States quickly took to the Amazon Echo when it arrived on the scene in 2014. As you see in the chart below, the virtually non existent smart speaker market in the United States started to come to life at the very end of 2014 and the beginning of 2015 (Hollander, 2018). Once it began to take off, the rate of diffusion has been staggering in the United States, and it is expected to continue to grow with millions owning them for personal or home use.
The rate of diffusion is also impressive for smart speakers in the world wide market. The diagram below shows the massive amount of product that is expected to be used in homes around the world over the next three years. Considering these devices were not on the market until 2014, the diffusion rate is quite high. In fact, Amazon Echo devices were not available anywhere aside from the United States until late September, 2016, where they were launched in the UK (Heathman, 2016). As you can see in the chart, the diffusion rate is expected to climb to over 300 million units by the year 2022 (O’Brien, 2018).
It is not hard to understand why this is the case. One of Amazon Echo’s early adopters explains that “unlike any other voice-recognition technology I’d tried, Alexa understood what I was saying at least 80 percent of the time, and very often offered logical, informative replies” (Ackerman, 2015). He goes on to say that the Echo was the best technology of the year.
According to Hodge (2018), 10% of American homes have an Amazon Echo (Hodge 52). Further, he says 13% of American homes have a smart speaker. Projection for these speakers in homes is expected to rise to 55% by 2023 (Hodge 52). The author goes on to discuss the reason for such huge diffusion rates in the recent past and the near future. While referencing technology leader Scott Amyx, Hodge says “we are at a fascinating period in history with regards to AI user technologies and interfaces as they now begin to become more widely adopted on a mass scale” (Hodge 54). He goes on to explain the reason for this is the accessibility of these AI enabled speakers and the ease of speech-based human-machine interface.
Artificial Intelligence has entered the daily lives of all Americans. The ambition behind the creation of AI is to create an easier environment to operate our lives. AI is entered into our phones, watches, TVs, computers and now home devices. These home devices like the Amazon Echo are meant to make our daily routines easier. An echo can help set your schedule, reminders, make purchases, read a book, play music and so much more. These are just some of the basics that an Echo and other home devices offer.
Societal Effects
As a result of the high diffusion rates of AI Smart Speaker, it’s developers have an interest in how it can affect society’s relational ability. Current generations are extremely soaked into their phones constantly throughout the day which has become a concern in some homes. According to an article by D’Onfro (2018), AI devices like the Amazon Echo are helping keep kids and family off of their phones. The article states, “Amazon’s Echo and Google’s Home devices are meant to transform the way you manage your life and control your home, using artificial intelligence to put an ever-increasing range of capabilities at your command. But at their most basic, they simply allow you to spend less time tapping away on a screen.” These home devices can actually make us have more face to face interaction, rather than staring at our phones. “Playing a game with an adult or another child using a voice-enabled device, you’re not focused on a screen, so the interaction encourages you to look at each other and pay attention to each other,” researcher from Cornell Solace Shen was quoted saying in the article. “That’s a unique advantage of these voice enabled systems. If they’re designed right, they can be unobtrusive, but speak up when needed.” While you can also get this same interaction from a simple board game, these devices offer quick and easy access to in home entertainment. Amazon Echos offer tons of games such as simple ones like Bingo and Tic Tac Toe, to more complex games like Jeopardy, BOSCH: A Detective’s Case, or even Escape the Room, a virtual escape room with puzzles to solve and items to find to navigate through a room (Martin, 2018). There is no need to go to the store and purchase a board game, bring it home and set it up when you can play one of the many games offered by simply saying, “Alexa, open Bingo.” These are some of the numerous ways Echos and other home devices have impacted families.
Artificial Intelligence is clearly the reality of the future. With the advancements that AI has already made, it has been implemented into education. According to an article from Marr (2018), teachers have collaborated with AI in the education industry to help develop learning skills in students. This as a result will make the roles of teachers more efficient. They work together to provide the best learning environment for students that have not been possible before. Marr writes, “AI can drive efficiency, personalization and streamline admin tasks to allow teachers the time and freedom to provide understanding and adaptability — uniquely human capabilities where machines would struggle.” AI is capable of making tasks for teachers like grading tests and homework much less time consuming, allowing them time to focus on other aspects of teaching. Not only does AI help teachers give a better learning experience in the classroom, it also offers resources to help the students learn the material in different ways. AI is able get students simpler access to learning outside of the classroom. Marr stated, “Tutoring and studying programs are becoming more advanced thanks to artificial intelligence.” Students also have easy access to all material presented in class whether they need it to review for a test, or learn something they might have missed due to an absence. “Presentation Translator is a free plug-in for PowerPoint that creates subtitles in real time for what the teacher is saying.” Marr also expresses the importance of students using and learning AI devices and software in school, as AI will be what today’s students will be using in their careers. An article written by Levesque (2018) claims, “In addition, an increasing demand for technologically skilled workers likely means that proficiency in education in science, technology, engineering, and mathematics (STEM) subjects can position students to be competitive in the workforce.” Students will be using and learning AI as it continues to have growing affects on our society. The education system is just getting started in using AI and they are expected to keep making advances to make both teaching and learning more efficient.
Forecasted Effects
Artificial Intelligence affects many areas of life, and in researching some of these areas more in depth, there are typically two sides. Some people think that Artificial Intelligence will be used for good, while others think that there are bigger problems in the future by implementing AI into these areas. Some, but not all, of the areas that AI touches are: weapons, robotics, medical science and technologies, and computer science. However each of these areas, should be taken into consideration of how AI looks socially, economically and politically. Each come with a need for caution and effective stopping mechanisms if, or when, things get out of hand; but also a realization for the benefits of AI throughout these areas.
Stuart Russel, in “The Ethics of Artificial Intelligence” talks about the reasons for caution with AI. Specifically in the area of war and weapons. Although there are many examples of how AI is used, this is the example used because it touches on a variety of areas that can be seen with the many uses of other AI. The use of AI in weapons can find a target, fix on it, track it, engage and then assess the damages after. This is important because it can make procedures easier if enemies are trying to jam signals, because an autonomous weapon can do this on it’s own. However, something like this could also get out of hand if not closely monitored. Politically, legalities have been discussed about using something like this against humans in war. It could reduce the number of civilian casualties, while making it easier to attack without having any pertinent risk. There are benefits and risks with each technology that AI is put into.
Max Tegmark touches on this idea as well, emphasizing how AI can be dangerous if it is let out of hand. He sees this in two ways. First, he sees that AI can be dangerous if it is programmed to do something devastating. This can be seen in the weapons debate like previously mentioned. He thinks in an area like this, it could be beneficial in the ways previously stated, but that it could be difficult to create a simple “turn-off” switch, and therefore could get out of control easily. Secondly, he thinks that AI can be dangerous if it is programmed to do a good thing but goes about it in a destructive way. The AI program is intelligent, but it completes a command literally, allowing for the possibility to hurt someone. It is important to monitor these systems in these areas as well to keep things like this from happening.
Socially there is concern about AI taking over the world. As Pedro Domingos says, “People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.” He emphasizes that if we just let AI do everything for us,it will take over the world, but not because it wants to, but because humans let it. There is no real concern with AI taking over the world like movies have done, however there is real concern that it will take over jobs. This could be seen as an economic benefit, but to some it will result in unemployment. In the Literature Review entitled “The Potential Economic Impacts of AI”, we see multiple ways that AI has and will affect the economy. For example, there is less cost and more precise production when processes are done with a computer, especially when it comes to manufacturing. This puts low skill workers at risk of their jobs being eliminated. This brings to discussion the idea that there is a need for social and creative intelligence in society over jobs that can be automated. This means more man power can be put into the creative elements of jobs, while the mundane side will be eliminated almost entirely. In turn, this would benefit the economy as a whole, despite some workers needing to find new jobs, which could be in the field of AI programming, creation and maintenance.
As stated, there are specific areas of concern both politically and economically with the power of Artificial Intelligence. However, there are also serious social concerns as well that should be addressed. AI is in many places, from doctor’s offices, to law firms, to households, but there is no way to truly look at how these things affect the human population in the long run. Crawford and Ryan narrow these effects down to three most important areas that should be considered when looking at the social and ethical impacts of AI. They are compliance, ‘values in design’ and thought experiments. Despite all being important, they state that neither individually or collectively are these areas completely effective in assessing the impacts of AI on society. Compliance takes into account the idea of being socially and legally correct as much as possible within the practices that currently reside in that area. Values in design looks at the values of the perspective of users and puts them into the design of the technology using AI to continue to socially benefit the user. Thought experiments are used to create hypothetical situations or risk analysis in specifically hospitals or courtrooms. All of these areas take into account a variety of ways that AI affects the population, yet they are not completely comprehensive, since that is impossible. Yet if these areas are looked at it is easy to see the social benefits and concerns of artificial intelligence.
References
10 Ways Artificial Intelligence (AI) will Impact the Finance Industry
Ackerman, Dan (2015). Why I think Amazon’s Echo is the breakthrough product of
2015. CNet. Retrieved from
https://www.cnet.com/news/amazon-echo-and-alexa-the-most-impressive-new-technology-of-2015/#
Anyoha, R. (2017, August 28). The History of Artificial Intelligence.
Artificial Intelligence — What it is and why it matters. (n.d.).
Childs, Martin (2011). John McCarthy: Computer scientist known as the father of AI.
Independent. Retrieved from https://www.independent.co.uk/news/obituaries/john-mccarthy-computer-scientist-known-as-the-father-of-ai-6255307.html
Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature,538(7625),
311–313.
D’Onfro, J. (2019). Experts are hopeful that Google Home and Amazon Echo won’t addict kids
like Smartphones. CNBC.
Ethics of artificial intelligence: Four leading researchers share their concerns and
solutions for reducing societal risks from intelligent machines. (2015). Nature,
521(7553), 415–418.
Heathman, Amelia (2016). Amazon Echo arrives in the UK: What is it and how does it work?
Wired. Retrieved from https://www.wired.co.uk/article/amazon-echo-dot-feature
Hodge, Stuart (2018). The virtual desktop. Gigabit Magazine. 50–60. Retrieved from
https://www.gigabitmagazine.com/magazine/gigabit/may-2018?id=5097
Hollander, Rayna (2018). Amazon just launched two new Alexa-enabled devices. Business
Insider. Retrieved from
https://www.businessinsider.com/amazon-fire-tv-cube-echo-look-2018-6
Levesque, E. (2019). The role of AI in education and the changing US workforce. Brookings Institution.
Marr, B. (2019). How Is AI Used In Education — Real World Examples Of Today And A Peek Into The Future. Forbes.
Martin, T. (2019). 25 fun games to play with your Alexa speaker. CNET.
O’Brien, Chris (2018). Smart speaker adoption expected to grow 6 times by 2022, with
Apple trailing. Venture Beat. Retrieved from
Quan-Haase, Anabel (2016). Technology & society. Canada: Oxford University Press.
Schiff, A., & Glass, H. (n.d.). The Potential Economic Impacts of AI. Literature Review.
Smith, C. (2006, December). The History of Artificial Intelligence.
Tegmark, M. (n.d.). Benefits & Risks of Artificial Intelligence.
Wood, G. (2017, March 28). AI and the Financial Service Segment.
