Considering the information below, state a BUSINESS CASE (NOT A LEGAL COURT CASE) that is against AI/Machine Learning:

• What is learning in general?

Human learning is simply learning from experience. There are many tricks that we would fall for when we were younger that we could never fall for today. The process of learning for humans is remembering, adapting, and understanding. If we understand the last time we were placed in a certain circumstance and we had tried to adapt with a certain action, it either worked or it did not. If it did not, then we will try to adapt with a different action, but if it worked then we will repeat our action. As it relates to Machine Learning, systems learn from experience similarly, but from data that they have collected. They collect data, they recognize that it gave a specific output, and if it was correct then it will repeat the action, if not then it will require more data to formulate a new action.

• What exactly is AI?

AI stands for Artificial Intelligence. Specifically, it refers to any sort of technological system that mimics intelligent behaviour to achieve set goals. These goals could vary drastically from system-to-system, but the main focus is a certain degree of autonomy/automation.

How does Machine Learning relate to AI?

Machine Learning is a term that AI uses to refer to an AIs' ability to learn and adapt to their situation. If the AI can do those two things with a certain degree of accuracy, achieving its goals becomes more certain. Modification and adaptation are two important factors when it comes to accuracy. Accuracy in Machine Learning is the measurement of how well the AI chooses certain actions compared to the known, correct actions. To put it into perspective, imagine that you are playing a Chess game against a computer. The computer, if using machine learning, will lose a few times in the beginning, but it will progressively get better as it modifies its' play-style and adapts. Eventually, you may end up losing to the AI, and hopefully, if its machine learning is successful, every time.

• What is the Machine Learning process?

The Machine Learning process can be further broken down into five steps. The first step beingdata collection. Data can typically be hard to collect, and even once it is collected, can be hard to sort between relevant and irrelevant data. Data that is completely relevant is considered to be clean data, meaning that it is free of errors and/or missing data. Once clean data has been collected, a set offeatures will be chosen for the AI. The features will be chosen based on the problem that is being evaluated. The next step is to determine thealgorithm and itsparameters to identify the values that will be used in training. Once that step is completed, training can begin.Training is the step when the data model, consisting of many previous data sets, is used for output prediction when new sets of data arrive. Lastly, anunderstanding and conclusive step is carried out to measure the system's accuracy regarding data that it was not trained on.

• Who are the competitors in AI?

Many competitors in the AI industry are made up of cities. In these cities, AI development has skyrocketed. The technological advancements in AI development can be broken down into five components: top-level design, algorithmic understanding, factor standard, integration ability, and application ability. The current leading cities in these areas are: San Francisco, London, Boston, and Tokyo. In the United States, the AI market tops two trillion dollars and is expected to top six trillion dollars by the year 2025.

• What ethical dilemmas present themselves?

Privacy and human rights are an area of concern when considering artificial intelligence (AI). A form of AI that many consumers use in their homes are called Intelligent Personal Assistants (IPA). Examples of APIs are Siri from Apple, Google Home from Google, and Amazon Echo from Amazon. These devices mimic the learning pattern that is discussed in this paper and they focus on adapting to the interests of their users by collecting personal data and rationalizing the correct output. When it comes to personal data collection, these AI should be capable of erasing themselves and any data they have collected from the user. Another area of concern is the impact on human psychology. When considering artificial intelligence, there is a heavy risk of losing an ability to think for ourselves and to make our own decisions. Introducing even more technology into our lives only creates more insecurities. For example, if a hacker gained control of a personal robot by carrying out an exploit, they could potentially trick the owner into buying things mistakenly. This is a form of deceit and manipulation that draws a clear disconnect and vulnerability between the lacking empathy and guilt in robots and our humanistic behavior to naturally express these feelings.