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Week 1 Assignment
Student
University
Course
Professor
Date
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Chapter 1
The latest developments in AI technology do not provide true intelligence but rather a
key aspect: the ability to predict. Prediction involves using available information, often data, to
infer unknown information. Prediction plays a major role in decision-making, and economics has
a strong framework for understanding decisions (Agrawal et al., 2018). By combining the new
and not yet fully understood capabilities of prediction technology with the established principles
of decision theory from economics, we can better understand how to approach AI in an
organization.
Chapter 2
As the cost of prediction decreases, more predictions will be made. This is a basic
principle of economics; more is consumed when something becomes less expensive. Predictions
are used for traditional tasks such as inventory management and demand forecasting.
Additionally, as prediction becomes cheaper, it is being used for problems not traditionally seen
as prediction problems (Agrawal et al., 2018). Importantly, when the cost of a certain input, such
as prediction, decreases, it can increase the value of other things. When the prediction is cheap, it
will lead to more predictions and complementary uses of prediction. These economic forces
drive new opportunities in the field of prediction machines. At lower levels, prediction machines
can take over tasks from humans and save costs. As the machine's performance increases, it can
also improve the quality of decision-making.
Companies can take advantage of prediction machines by using AI tools to help
implement their current strategies. As these tools become more advanced, it may lead to changes
in the design itself. For example, suppose a company like Amazon can predict what customers
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want to buy. In that case, they may shift from a model where goods are shipped after orders are
placed to a model where goods are shipped before orders are placed, which would greatly change
how the company operates. Risks AI poses to businesses include some prediction machines that
have been trained on human-generated data and have acquired harmful biases and stereotypes.
#1
Regarding the concern about jobs, A common consequence of AI is that it automates
many repetitive tasks through the use of bots. This results in a decrease in the need for human
labor and a reduction in employment opportunities for people(Soni, 2021). People often talk
about how machines and humans can collaborate to improve the precision of predictions.
#2
When we talk about inequality, AI can potentially increase existing inequalities. For
example, Suppose AI systems are mostly created and utilized by a select few with access to
ample resources. In that case, they may have an even greater advantage over those who do not
have access to the technology. (Manyika et al., 2019).A more diverse group of people working in
AI would help to anticipate and address any potential biases in the technology and involve the
communities that may be affected by it.
#3
As AI continues to be more useful and profitable, the risks of companies using it for
monopolistic practices will increase (Stacey, 2020). Regulators and those in the AI industry need
to know the capability for monopolies designed by the advancements in AI.
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#4
Countries are exploring how AI can improve their respective industries; for instance,
Japan has set a plan to compete with China and the US in robotics by creating regulations and
goals for robotics development and implementation in specific industries. (Rao, 2019). Countries
strive to boost the competitiveness of domestic companies and foster innovation while regulating
the influence of new technologies on society. AI's promising economic benefits clarify why
countries are keen to participate in its advancement.
#5
AI can be superintelligent, outshining human intelligence. It may lead to major risks,
including the extinction of the human race knowingly or unknowingly (Walsh, 2019). It's crucial
to be advised and concerned about the potential dangers of AI and to work towards responsible
development and deployment of the technology.
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References
Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: the simple economics of
artificial intelligence. Harvard Business Press.
Rao, A. (2019, January 22). Is AI the next frontier for national competitive advantage?
Strategy+Business. Retrieved January 14, 2023, from https://www.strategybusiness.com/blog/Is-AI-the-Next-Frontier-for-National-Competitive-Advantage
Walsh, B. (2019, August 22). AI. Is the Cause Of — And Solution To — the End of the World.
Medium. Retrieved January 14, 2023, from https://onezero.medium.com/a-i-is-the-causeof-and-solution-to-the-end-of-the-world-2d247d37eccb
Manyika, Silberg, & Presten. (2019, October 25). What Do We Do About the Biases in AI?
Harvard Business Review. Retrieved January 14, 2023, from
https://hbr.org/2019/10/what-do-we-do-about-the-biases-in-ai
Stacey, E. (2020, October 28). Emerging AI Will Drive The Next Wave Of Big Tech Monopolies.
Forbes. Retrieved January 14, 2023, from
https://www.forbes.com/sites/edstacey/2020/10/28/emerging-ai-will-drive-the-nextwave-of-big-tech-monopolies/
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Soni, P., & A. (2021, November 4). How is AI impacting the workplace? | Analytics Steps. How
Is AI Impacting the Workplace? | Analytics Steps. Retrieved January 14, 2023, from
https://www.analyticssteps.com/blogs/how-ai-impacting-workplace