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Artificial Intelligence (AI) is the technology that enables machines to replicate human learning, human intelligence, problem-solving, decision-making, and many other capabilities.
This means that computational systems can perform all the tasks that are typically performed by human minds such as perceiving, reasoning, and interacting with surroundings.
Artificial intelligence enables computer systems to perform advance-level tasks such as language understanding and translation, visual recognition, data analysis, personalized recommendations, and a lot more.
Artificial Intelligence Examples
Some simple examples of AI are customer support chatbots on business websites, virtual assistants like Siri, Alexa, and Google Assistant, recommendation systems like Netflix and Amazon, and self-driving cars.
Types of Artificial Intelligence
The AI ecosystem is vast. It includes branches like agentic AI, generative AI, predictive AI, conversational AI, cognitive AI, autonomous AI, multi-modal AI, retrieval augmented AI (RAG), federated learning AI, explainable AI, and responsible AI.
How is Data Used for AI?
It’s rightly said – data is the new oil. Data is at heart of all artificial intelligence and machine learning initiatives. There are different types of data used for AI development – structured data, semi-structured data, unstructured data, textual data, image data, sensory data, audio data, and time-series data.
Quick breakdown of how data is used for AI:
Large sets of labeled/unlabeled data are leveraged to train AI models. Example: If you were to train you model to recognize horses, you’ll have to train it with image data – maybe thousands of horse images.
Historical data can be used by AI systems to make business predictions, recommendations, and decisions. Example: AI systems analyze customer behavior data to accurately predict which customers are at risk of leaving.
Recommendation systems like Netflix keep on learning from new data. Recommendation engines refine their suggestions in real time based on a user’s browsing behavior.
How do you Prepare Data for AI?
Tip #1: Define your Objective
Figure out what you want to do with your data – are you using it for generative AI or machine learning? Collaborate with stakeholders on the types of datasets you require for your AI model.
#Tip 2: Clean your Data
Remove duplicate, poorly formatted, and incorrect data within your dataset. AI algorithms relying on good data will yield good results!
#Tip 3: Leverage Metadata
It adds more context to your data, explains what data means, and how different data points connect. By providing context about your data, metadata enhances its overall quality. Quality prompts, in turn, enhance artificial intelligence.
Read More Here: How to Prepare Your Data for AI
