Can AI Models Really Revolutionize Healthcare and Finance?

The recent launch of two specialized large language models (LLMs) by Writer, a San Francisco-based AI company, has sent shockwaves through the healthcare and financial services industries. Palmyra-Med-70b and Palmyra-Fin-70b are designed to tackle domain-specific tasks, boasting impressive accuracy rates and the potential to reshape how these highly regulated sectors adopt artificial intelligence. But what if these models are not as revolutionary as they seem? What if their success is built on shaky assumptions, or what if they're just a band-aid solution for deeper problems? Let's dive deeper into the claims made by Writer and examine the potential implications of their domain-specific approach. Writer's Palmyra-Med-70b model has achieved an average accuracy of 85. 9% across all medical benchmarks in zero-shot attempts, outperforming competitors like Med-PaLM-2. However, this achievement is not without its limitations. For instance, what if the model is only accurate in a specific subset of medical scenarios? What if it struggles with ambiguous or unclear diagnoses? And what if it's not even trained on real-world clinical data, but rather simulated scenarios? Similarly, Palmyra-Fin-70b's ability to pass the CFA Level III exam is impressive, but what if it's just a means to an end? What if the real challenge lies in implementing these models in real-world financial scenarios, where human judgment and expertise are still crucial? And what if the model's performance is solely based on its ability to regurgitate existing knowledge, rather than truly understanding the underlying principles? Writer's open-source strategy is an interesting development, as it allows customers to scale and customize the models to their specific needs. However, this approach also raises questions about the potential for bias and errors in the data used to train the models. What if the data is incomplete, inaccurate, or reflects systemic biases? How will these biases be addressed, and what guarantees can Writer provide that their models will not perpetuate harmful stereotypes or discrimination? As the AI industry continues to evolve, Writer's launch represents a significant development that could reshape how enterprises, particularly in regulated industries, approach AI adoption. But before we get too excited, let's take a step back and examine the assumptions underlying these models. Are they truly revolutionary, or just the latest iteration of an old problem?