4.17 million queries per minute on the internet :
a treasure trove of business opportunities.
Data collection: To function effectively, an AI requires data. This data can be collected from various sources
Data preprocessing: Before being used by the AI, raw data often needs to be cleaned and preprocessed. This may involve operations such as filtering irrelevant data, normalizing data, error correction,
AI training: Once the data is prepared, the AI is trained using machine learning or deep learning algorithms. The goal of training is to enable the AI to learn from the data and recognize specific patterns, relationships, and features.
Machine Learning: In machine learning, the AI uses algorithms to analyze training data and find patterns. These models are then used to make decisions or predictions on new data.
Deep Learning: Deep learning is an approach to machine learning that uses artificial neural networks with multiple layers. These networks can autonomously learn from large amounts of unstructured data to perform complex tasks, such as image or speech recognition.
Decision-making or result generation: Once trained, the AI can be used to make decisions, generate results, or make predictions on new data. The AI applies the learned models during training to solve problems or accomplish specific tasks.
Feedback and continuous improvement: The performance of the AI can be improved by providing feedback on its results. The AI can be periodically adjusted or retrained to adapt to new data or improve its performance.
