By now, you may have heard the story of the lawyers who filed a legal brief using AI-generated work product.[1] The problem for the lawyers, of course, was that the AI software had completely made up the legal precedent cited within the brief —“AI hallucination” is the term. When the opposing party’s 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.
That’s the bad news.
The good news is, with proper supervision and quality control measures, AI can handle repetitive tasks across an organization so that employees can focus on creative solutions, complex problem-solving 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 large language models. As a high-level explanation, generative AI uses algorithms to generate content based upon the data set on which the generative AI model was trained. Through multiple layers of processing, the generative AI takes input from a user and provides new data as the output. The output is based on various 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. Since large language models are trained on a specific data set, the output...
The Kremlin is reviving false claims about Ukrainian bioweapons programs as part of a broader propaganda campaign aimed at justifying the war, Ukraine’s Center for Countering Disinformation (CCD) ...