Zoom Link: https://upenn.zoom.us/j/99074346805
Meeting ID: 990 7434 6805
Passcode: 519615

The artificial intelligence (AI) industry is currently focused on achieving general superintelligence in a top-down fashion by training very large (trillions of parameters) omniscient multimodal models using a large language model (LLM) as a base. This approach is insatiably thirsty for data during training, leading to unsustainable electricity/water costs and CO2 emissions. Even after incurring such huge costs, these models are known to hallucinate. This makes it difficult to employ them in domains where accuracy is important, e.g., medicine, law, business. We propose to take the opposite tack – build domain-specific 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 particular domain in a bottom-up fashion by looking at the problem from diverse angles. Could we build domain-specific superintelligence in the same bottom-up fashion through a society of AI assistants and AI agents? The AI assistants will serve as aides to experts in the field. AI agents will have more autonomy. Such a framework would need to be accompanied by robust reasoning grounded in verifiable facts. This means that abstraction should precede generalization. Currently, AI agents use LLMs for reasoning. However, LLMs exhibit a very uneven reasoning performance. Our framework will take inspiration from neuroscience and include episodic and working memories to facilitate reasoning. The AI assistants in the framework will be based on fine-tuned small multimodal models, targeted at various modalities, e.g., physiological signals, medical images, and medical text in the sphere of medicine, that can be trained data-efficiently and are aligned with each other.  In this talk, we will explore our initial progress towards realizing this vision.