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By now, you may have heard the story of the lawyers who filed a legal brief using AI-generated work product. The problem for the lawyers, of course, was that the AI software had completely made up the legal precedent cited within the brief - that is, it engaged in AI hallucination. When the opposing partys lawyers could not locate the precedent using traditional legal databases (because it was completely made up), this caused quite a ruckus in the legal community.
Thats the bad news.
The good news is, with reliable input, proper supervision and quality control measures, AI can handle large volumes of data and repetitive tasks across an organization so that employees can focus on creative solutions, complex problemsolving and impactful work. When used properly, it can increase efficiencies, streamline workflows and make life a lot easier.
When we say AI, we are referring to artificial intelligence. The type of AI that has been at issue recently is generative AI-particularly generative AI using machine learning that is trained on vast amounts of data and natural language processing or large language models. At a high level, generative AI uses algorithms to generate content based upon the data set on which the generative AI model was trained. Generative AI takes input and instructions from a user and provides data as the output. That output is based on the data and natural language patterns that the generative AI has learned. The output is not the result of specific research being done in real-time by the AI platform. Rather, it is based on data that the platform had previously been fed (sometimes from years prior and therefore largely outdated). Since large language models are trained on a specific data set, the output is...