Ai And Machine Learning Seminar

Explanation
Unsupervised Machine Learning is a type of machine learning where the input data does not have any corresponding output variables. In this approach, the algorithm is tasked with finding patterns or relationships within the data without being given any specific labels or targets to predict. The absence of corresponding output variables allows the algorithm to explore and discover hidden structures in the data on its own, making it useful for tasks such as clustering, anomaly detection, and dimensionality reduction.

Explanation
AI has the ability to make decisions based on the information it has processed and learned, while machine learning only allows the system to learn new things from the available data. Machine learning algorithms analyze and identify patterns in data to make predictions or decisions, but they do not have the capability to autonomously make decisions without the input of data. AI, on the other hand, can use the knowledge gained from machine learning to make informed decisions based on the data it has processed.

Explanation
The types of AI are Reactive Machines, Limited Memory, Theory of Mind, and Self-Awareness. Limited awareness refers to AI systems that have a partial understanding of their environment and lack the ability to fully comprehend or anticipate certain aspects. These AI systems may have some level of awareness or knowledge about their surroundings, but it is limited in scope and depth.

Explanation
AI can help us reduce costs in the medical field by automating certain tasks and processes, leading to increased efficiency and decreased expenses. Additionally, AI can assist in solving crimes by analyzing large amounts of data and identifying patterns that humans may overlook. Lastly, AI can contribute to the development of automated transportation systems, such as self-driving cars, which can improve safety, reduce congestion, and enhance overall transportation efficiency.