Legal reviews are becoming increasingly complex and time-consuming, requiring sophisticated tools and techniques to manage and analyze large and diverse datasets. This trend has been driven by the growth of cloud computing, which allows businesses and organizations to store vast amounts of data online, as well as the growing use of social media, which generates large amounts of data in real-time. As a result, legal professionals are increasingly turning to technology solutions, such as e-discovery software, to help them manage the complexity and volume of data in modern legal cases.
Supervised and unsupervised learning are two different approaches to training artificial intelligence (AI) systems. Supervised learning involves training the AI using labeled data, which means that human annotators have manually labeled data with the correct answer or category. This labeled data is then used to train the AI model to make predictions on new, unlabeled data.
In contrast, unsupervised learning is a type of AI training where the AI system is not given labeled data. Instead, it is fed with large amounts of unstructured data, and it must find patterns and relationships on its own. In other words, unsupervised learning allows an AI system to learn from data without human intervention.
At Anzyz Technologies we use a combination of the two techniques, a so-called hybrid AI approach, by first applying unsupervised learning techniques to identify patterns and relationships in text data, and then applying supervised learning techniques to classify text data into categories.
A hybrid AI approach allows AI systems to take advantage of the strengths of both supervised and unsupervised learning, which can lead to more accurate predictions and classifications, particularly for complex and large-scale datasets.
With its hybrid AI system, Anzyz’ e-discovery removes the mundane work of having to manually train the AI, resulting in faster and more accurate reviews for legal professionals.
