Paul Blažek

Shane Waxler, Paul Blazek, Davis White et al.

Realizing personalized medicine at scale calls for methods that distill insights from longitudinal patient journeys, which can be viewed as a sequence of medical events. Foundation models pretrained on large-scale medical event data represent a promising direction for scaling real-world evidence generation and generalizing to diverse downstream tasks. Using Epic Cosmos, a dataset with medical events from de-identified longitudinal health records for 16.3 billion encounters over 300 million unique patient records from 310 health systems, we introduce the Curiosity models, a family of decoder-only transformer models pretrained on 118 million patients representing 115 billion discrete medical events (151 billion tokens). We present the largest scaling-law study of medical event data, establishing a methodology for pretraining and revealing power-law scaling relationships for compute, tokens, and model size. Consequently, we pretrained a series of compute-optimal models with up to 1 billion parameters. Conditioned on a patient's real-world history, Curiosity autoregressively predicts the next medical event to simulate patient health timelines. We studied 78 real-world tasks, including diagnosis prediction, disease prognosis, and healthcare operations. Remarkably for a foundation model with generic pretraining and simulation-based inference, Curiosity generally outperformed or matched task-specific supervised models on these tasks, without requiring task-specific fine-tuning or few-shot examples. Curiosity's predictive power consistently improves as the model and pretraining scale. Our results show that Curiosity, a generative medical event foundation model, can effectively capture complex clinical dynamics, providing an extensible and generalizable framework to support clinical decision-making, streamline healthcare operations, and improve patient outcomes.

Paul J. Blazek, Kesavan Venkatesh, Milo M. Lin

Human reasoning can distill principles from observed patterns and generalize them to explain and solve novel problems. The most powerful artificial intelligence systems lack explainability and symbolic reasoning ability, and have therefore not achieved supremacy in domains requiring human understanding, such as science or common sense reasoning. Here we introduce deep distilling, a machine learning method that learns patterns from data using explainable deep learning and then condenses it into concise, executable computer code. The code, which can contain loops, nested logical statements, and useful intermediate variables, is equivalent to the neural network but is generally orders of magnitude more compact and human-comprehensible. On a diverse set of problems involving arithmetic, computer vision, and optimization, we show that deep distilling generates concise code that generalizes out-of-distribution to solve problems orders-of-magnitude larger and more complex than the training data. For problems with a known ground-truth rule set, deep distilling discovers the rule set exactly with scalable guarantees. For problems that are ambiguous or computationally intractable, the distilled rules are similar to existing human-derived algorithms and perform at par or better. Our approach demonstrates that unassisted machine intelligence can build generalizable and intuitive rules explaining patterns in large datasets that would otherwise overwhelm human reasoning.

Alexander Felfernig, Christoph Zehentner, Paul Blazek

Constraint-based environments such as configuration systems, recommender systems, and scheduling systems support users in different decision making scenarios. These environments exploit a knowledge base for determining solutions of interest for the user. The development and maintenance of such knowledge bases is an extremely time-consuming and error-prone task. Users often specify constraints which do not reflect the real-world. For example, redundant constraints are specified which often increase both, the effort for calculating a solution and efforts related to knowledge base development and maintenance. In this paper we present a new algorithm (CoreDiag) which can be exploited for the determination of minimal cores (minimal non-redundant constraint sets). The algorithm is especially useful for distributed knowledge engineering scenarios where the degree of redundancy can become high. In order to show the applicability of our approach, we present an empirical study conducted with commercial configuration knowledge bases.

Paul J. Blazek, Milo M. Lin

How perception and reasoning arise from neuronal network activity is poorly understood. This is reflected in the fundamental limitations of connectionist artificial intelligence, typified by deep neural networks trained via gradient-based optimization. Despite success on many tasks, such networks remain unexplainable black boxes incapable of symbolic reasoning and concept generalization. Here we show that a simple set of biologically consistent organizing principles confer these capabilities to neuronal networks. To demonstrate, we implement these principles in a novel machine learning algorithm, based on concept construction instead of optimization, to design deep neural networks that reason with explainable neuron activity. On a range of tasks including NP-hard problems, their reasoning capabilities grant additional cognitive functions, like deliberating through self-analysis, tolerating adversarial attacks, and learning transferable rules from simple examples to solve problems of unencountered complexity. The networks also naturally display properties of biological nervous systems inherently absent in current deep neural networks, including sparsity, modularity, and both distributed and localized firing patterns. Because they do not sacrifice performance, compactness, or training time on standard learning tasks, these networks provide a new black-box-free approach to artificial intelligence. They likewise serve as a quantitative framework to understand the emergence of cognition from neuronal networks.