The planning fallacy occurs when we underestimate how long it will take us to complete a future task. Despite knowing that similar tasks have generally taken longer than planned, we hold overly optimistic expectations and believe that next time will be different. Because we make unrealistic plans, we often end up running out of time, money, or energy.
Planning fallacy exampleYou are preparing for your upcoming midterm paper and set aside just one week to finish it. Although similar assignments in the past took you more time than expected, you feel confident that one week is enough time.
However, as the days pass, you realize that the topic is rather complex and demands more research than anticipated. On top of that, you catch a cold and are less productive. With the deadline approaching, you struggle to finish your paper, realizing that your optimism caused you to miscalculate the time and effort needed for this assignment.
The planning fallacy can impact any type of task and lead to several issues, including missed deadlines, increased costs, and frustration for both individuals and organizations.
An algorithm is a set of steps for accomplishing a task or solving a problem. Typically, algorithms are executed by computers, but we also rely on algorithms in our daily lives. Each time we follow a particular step-by-step process, like making coffee in the morning or tying our shoelaces, we are in fact following an algorithm.
In the context of computer science, an algorithm is a mathematical process for solving a problem using a finite number of steps. Algorithms are a key component of any computer program and are the driving force behind various systems and applications, such as navigation systems, search engines, and music streaming services.
The appeal to pity fallacy occurs when someone attempts to persuade others by provoking feelings of guilt or pity. Instead of presenting factual information and evidence to support an argument, one may try to play on people’s feelings. However, this is a manipulative tactic because feelings of pity are usually irrelevant to the point being made.
Appeal to pity fallacy example“Could you please change my grade from D to C? I worked really hard for this assignment. I even pulled an all-nighter to finish on time, and my parents will be so disappointed!”
The appeal to pity fallacy is also known as argumentum ad misericordiam or argument from pity and can be observed in various contexts like marketing, political campaigns, and law.
Appeal to emotion fallacy occurs when someone tries to convince another person by evoking their feelings rather than providing evidence.
With the appeal to emotion fallacy, people accept a claim as true because they react emotionally to it. As a result, they focus on factors irrelevant to the question at hand, ignoring facts and logical reasoning.
Appeal to emotion fallacy exampleYour sibling is trying to convince you to let them eat the last piece of the dessert:
“Can I have the last piece of cake? You know how much I love it, and it’s been a tough day for me. I’ve had such a bad day, and this cake would just make me feel so much better. You don’t want me to be sad, right?”
Appeal to emotion fallacy (also known as argument from passion, argumentum ad passiones, or argument from emotion) is common in various contexts like advertising, law, and politics.
An either-or fallacy occurs when someone claims there are only two possible options or sides in an argument when there are actually more. This is a manipulative method that forces others to accept the speaker’s viewpoint as legitimate, feasible, or ethical. This type of black-and-white thinking often appears in political speeches, advertising, and everyday conversations.
Either-or fallacy example“You can either go with me to the party tonight or sit at home alone and be bored all night.”
The either-or fallacy is also known as the false dilemma fallacy, false dichotomy, or false binary.
Data mining is the process of extracting meaningful information from vast amounts of data. With data mining methods, organizations can discover hidden patterns, relationships, and trends in data, which they can use to solve business problems, make predictions, and increase their profits or efficiency.
The term “data mining” is actually a misnomer because the goal is not to extract the data itself, but rather meaningful information from the data .
Reinforcement learning (RL) is a branch of machine learning that focuses on training computers to make optimal decisions by interacting with their environment. Instead of being given explicit instructions, the computer learns through trial and error: by exploring the environment and receiving rewards or punishments for its actions.
Together with supervised and unsupervised learning, reinforcement learning is one of three basic machine learning approaches. Reinforcement learning has a wide range of real-world applications, including robotics, game playing, and diagnosing rare diseases.
There are two main approaches to machine learning: supervised and unsupervised learning. The main difference between the two is the type of data used to train the computer. However, there are also more subtle differences.
Machine learning is the process of training computers using large amounts of data so that they can learn how to independently complete tasks associated with human intelligence (e.g., translating, making recommendations).
Two key aspects of machine learning are data and algorithms. Any type of information that can be used as an input by a computer (text, images, audio etc.) is data. An algorithm is a set of instructions given to a computer so that it processes the data and learns from it. Data and algorithms (combined through training) make up the machine learning model.
A false cause fallacy occurs when someone incorrectly assumes that a causal relation exists between two things or events. This is an improper conclusion because either such a relationship does not exist or the evidence in support of it is insufficient.
False cause fallacy example“Every time I bring my umbrella with me, it rains. Clearly, if I leave it at home, there will be sunshine!”
This type of reasoning error can lead to superstitious beliefs about the causes of various phenomena and events, a poor understanding of reality, and an inability to address root causes of problems.
Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on developing methods for computers to learn and improve their performance. It aims to replicate human learning processes, leading to gradual improvements in accuracy for specific tasks. The main goals of ML are:
classifying data based on models that have been developed (e.g., detecting spam emails).
making predictions regarding some future outcome on the basis of these models (e.g., predicting house prices in a city.)
Machine learning has a wide range of applications, including language translation, consumer preference predictions, and medical diagnoses
