Please join the Princeton Precision Health (PPH) Initiative for the next talk of our spring semester series on Friday, March 14, at 12:00 pm, at 252 Nassau Street.

Niraj Jha, PhD, Princeton University, will present a talk, “Bottom-up Medical Superintelligence.” 

The artificial intelligence (AI) industry is currently focused on achieving superintelligence in a top-down fashion by training a very large (hundreds of billions to trillions of parameters) omniscient multimodal model using a large language model (LLM) as the base. This approach is insatiably thirsty for data during training, leading to unsustainable CO2 emissions. Even after incurring such huge computational and energy costs, these models are known to hallucinate. This makes it difficult to employ such models in domains where accuracy is important, e.g., smart healthcare. We propose to take the opposite tack – build medical superintelligence bottom-up, modeled after how superintelligence is achieved in the human society. Each of us just has human intelligence, but a society of humans achieves superintelligence in a bottom-up fashion by looking at the problem from diverse angles. Could we build medical superintelligence in the same bottom-up fashion through a society of medical AI assistants and AI agents? The AI assistants will serve as aides to health professionals. AI agents will have more autonomy. They need to be accompanied by a robust reasoning framework, e.g., counterfactual (what-if) reasoning. The current top-down approach to developing AI agents is based on using LLMs for reasoning. However, LLMs exhibit a very uneven reasoning performance. Our medical superintelligence framework will take inspiration from neuroscience and include episodic and working memories to facilitate reasoning. The AI assistants in our superintelligence framework will be based on fine-tuned foundation models, targeted at various modalities, e.g., physiological signals, medical images, and medical text, that can be trained data-efficiently and are aligned with each other.  The initial goals of the framework are accurate disease detection, individual well-being, interpretability of AI predictions, and personalized medical decision-making. In this talk, we will explore our initial progress towards realizing this vision.

Lunch will be provided. Please note that getting to the seminar space currently requires that you climb a set of stairs. If an accommodation is needed, please contact PPH in advance at: princetonPPH@princeton.edu