Final answer:
Unsupervised learning in AI refers to algorithms that learn without labeled data, as they must identify patterns in the data on their own. It is different from supervised learning, which uses labeled data, and is used for various tasks such as clustering and determining associations in data.
Step-by-step explanation:
The correct description of unsupervised learning in artificial intelligence (AI) is Option 2: The algorithm learns without labeled data. In unsupervised learning, the AI system is not provided with pre-categorized or labeled examples. Instead, it must find patterns and relationships within the data on its own. This is distinct from supervised learning (Option 1), which operates with labeled data and is often used in classification and regression tasks. Unsupervised learning is used for clustering, association, and dimensionality reduction among other applications.
Another critical aspect of AI is the set of algorithms it uses to process and analyze data. Algorithms provide step-by-step instructions for solving problems and achieving outcomes, like search engine rankings or targeted news feeds. While unsupervised learning can seem unpredictable and potentially biased due to the absence of labeled training data, developers and researchers in the field are constantly working to improve transparency and control over these algorithms to address such challenges.
In summary, unsupervised learning represents systems capable of analyzing and understanding data without external guidance, forming the basis of adaptive technologies that can decipher complex and unstructured datasets, thereby advancing the field of AI.