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Generative AI holds great promise for legal work, with 82% of legal professionals acknowledging its value. Despite this, concerns about handling confidential data persist. Generative AI's ability to draft documents and contracts can improve efficiency and reduce human error, but the risk of "hallucinations" or inaccurate outputs is a concern. To ensure safe and effective use, generative AI tools should have built-in accuracy, reliability, and hallucination protections. Key features to look for include ethical walls, traceability, enterprise-grade security, deployment flexibility, and siloed algorithms. These safeguards ensure AI outputs are useful, reliable, and secure, while protecting client confidentiality.

Legal work relies heavily on accessing information quickly. However, navigating through knowledge bases can be challenging due to complex systems and sub-par search tools. Generative AI offers a solution by leveraging natural language search to boost productivity and make better use of existing insights. Traditional search methods like Boolean require precise language, making the process time-consuming. Large Language Models (LLMs), however, can interpret queries in natural, conversational language, simplifying access to information and enhancing research efficiency. To ensure effectiveness, LLMs should be securely connected to a firm's databases, providing accurate and relevant results. Implementing AI safety measures is crucial to protect data and maintain client trust.

The automation of jobs and tasks by algorithms like GPT-4 is becoming increasingly prevalent. If a task can be 80% completed by the algorithm, it's likely it will be fully automated in 1-3 years. This is especially relevant to companies in the legal and insurance sectors. Automation is no longer just affecting blue-collar jobs, but white-collar professions too, such as law, finance, and software development. Companies that invest in automation technology early will likely gain a competitive edge, while those who don't could lose out. It's essential for organisations to recognise this trend and strategise accordingly.

Recent advancements in machine learning, particularly deep learning, have enhanced Intelligent Document Processing (IDP), which aims to extract relevant information from documents and convert it into structured data. This technology, combined with Optical Character Recognition (OCR), improves productivity, accelerates processes, and reduces errors. However, the lack of performance comparisons among the Big 3 cloud platforms - Microsoft Azure, Google Cloud Platform, and Amazon Web Services - makes it challenging to choose the best OCR service. This post aims to provide a comparison across these major OCR services based on experiments conducted over three different datasets.