Issues That Arise When Using AI Examples in Real Life

People confuse AI with some artificial brain and recall images from Sci-fi robot movies. They couldn't be farther from the truth in the world today. While AI functions more like the human brain, it does not need to be likened to a biological system.

Before we delve deeper into real-life examples of AI, let's examine some basic concepts about engaging with an AI model. Multiple AI models or algorithms exist, e.g., Neural networks, Bayesian networks, Hidden Markov Models and Support Vector Machines.

The Difference between Human and Artificial Intelligence

AI allows a computer to function the same as a human brain. AI, however, doesn't seek to copy every brain functioning aspect. Biological plausibility is how AI models relate to actual human brain functions. The 'what' is more important than the 'how.' At an advanced level, the human brain is similar to most AI models.

The Human Brain and its Reactions to Real-Life Situations

While we know very little about the brain's interior workings, we know quite a bit about its exterior workings. The brain is simply a black box with nerves connecting it. These nerves transmit messages from the brain to the entire body. A certain collection of inputs results in a particular output.

In the same way, outputs from our nerves to our muscles are the only way to interact with the world. The human brain's output is a product of inputs and the brain's internal state. The brain's internal state will change to yield output in response to specific inputs. This internal state handles the essence of the input order.

Unlike the human brain, computer neural networks are not for general purposes. They perform trivial but unique tasks. The reality experienced by an AI model is based on giving output based on the input it is currently feeding and the model's internal state. The algorithm's attached reality may change as experiments continue with the algorithm.

While some algorithms are more sophisticated, the concept of inputs, outputs and internal states holds for most AI models, whether for stock pickers or robots. These models are explained bellow:

Data Classification

Classification tries to subdivide input data into specific classes. It occurs in supervised training after a user feeds data and awaits results from an ML (machine learning) algorithm. The data class is identified with this method.

Supervised training always works with pre-existing data. Machine-learning algorithms are estimated correctly throughout the training stage to determine how successfully they categorize data. The algorithm, once trained, is expected to be able to categorize unknown data as well.

Why Do We Use Regression Analysis?

Regression analysis is a predictive modeling method examining the relationship between dependent and independent variables. The formula is essential for forecasting and determining the relationship between variables and time series modeling. For example, the relationship between drunk driving and a driver's road carnage count can be determined using regression analysis. This formula is essential for data analysis and modeling.

The following are a few advantages of regression analysis:


● It validates critical relationships between variables.
● It shows how strongly many independent variables can impact a dependent variable.

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