An algorithm uses mathematics and logic input to process information and produce remarkable output. Machine learning or AI algorithms, use a specific set of instructions to combine inputs and outputs to enable machines to learn, analyze data and make decisions. 

The general process of how AI algorithms work

The inherently complex nature of AI algorithms is not a limitation but a strength. They learn and grow by incorporating training data, whether labeled or unlabeled, provided by developers or acquired by the program. This adaptability allows them to complete tasks and solve complex problems using training data sets. 

Step-by-step guide or flowchart

The general process is as follows:

Input data to learn from → Training data to teach the AI → Model selection to select the best machine learning algorithm for your specific dataset or task → Model training to teach the AI how to use the data and allow it to learn patterns → Model evaluation to analyze how well the AI learned → Model prediction using new data to make predictions 

Real-world applications 

AI systems are practical tools that are continuously trained to improve their performance by absorbing and learning from new data, receiving data for training and applying it to enhance their understanding, accomplish tasks and increase accuracy. This constant evolution instills a sense of optimism and hope for a better future. Some of the real-world applications of AI include: 

• When Alexa, a popular AI system, is asked to play your favorite music, it's not just a simple command. The AI system detects and recalls your voice, associating you with your music choices. It then recalls and plays your most streamed music playlist. This is just one example of how AI algorithms make a real difference in our lives. 

• AI algorithms are used in self-driving cars to process sensor data and make real-time decisions on the road.

• AI algorithms in healthcare are used to analyze medical images to detect diseases. 

With these real-life applications and the potential to deliver significant benefits, the impact of AI is undeniably astonishing!

Types of AI algorithms 

The three most popular types of AI algorithms are supervised, unsupervised and reinforcement learning. Each category has a unique way of training and functioning. Understanding each of them will give you a deeper insight into AI: 

1. Supervised learning algorithm

Supervised learning, the most widely used type of algorithm, is a complex process that involves teaching the AI using examples already labeled with the correct answers, similar to a math test with an answer key. Developing this type of algorithm is challenging and requires that a team of professionals analyze the findings meticulously. 

Data scientists painstakingly test the models generated by the algorithm to ensure their accuracy against the original data and to identify any potential AI flaws. 

• Examples/methods: Support vector machines, decision trees and neural networks.

• Applications: Understanding emotions in text, identifying objects in images and speech recognition. 

• Tasks: Regression such as linear, time-series and logistics, and classification problems.

2. Unsupervised learning algorithm

Algorithms in unsupervised learning functions train using unlabeled data sets by identifying correlations, patterns or data clusters. They are not supplied with any answers; it's up to the algorithm to identify similar data and understand it. Unsupervised learning doesn't involve a skilled team of humans for supervision; it uses raw data to create patterns and connections. 

• Examples/methods: Autoencoders, k-means clustering and principal component analysis (PCA). 

• Applications: Image compensation, customer segmentation and feature extraction. 

• Tasks: Anomaly detection, clustering and dimensionality reduction. 

3. Reinforcement learning algorithm

In the third class, the AI keeps trying different actions, learns from the results and updates its strategy to arrive at the best outcome. It's similar to training a dog to learn tricks by providing rewards. After training, the AI uses what it has learned to make decisions and can adjust its strategy based on feedback.

The algorithm receives a reward after every correct prediction, which serves to increase its accuracy. 

• Examples/methods: Policy gradient, Q-learning, Asynchronous actor-critic agents (A3C) and State-action-reward-state-action (SARSA).

• Applications and tasks: Autonomous vehicles, robotics and game playing. 

Impact of AI algorithms 

AI algorithms are game-changing technology that are transforming society for the better. They have been able to enhance mental health diagnoses, automate tasks and boost productivity for less experienced industry workers. However, they also present challenges, such as racial or socioeconomic biases, lack of transparency and data privacy issues. Using AI responsibly and ethically is crucial to address these challenges.