Technical Grand Challenges for Amazon Web Services
Solving the ontology of Project Science by creating a unified, machine-readable representation of a "project" as a dynamic socio-technical system.
We must ingest and fuse data from millions of sources: unstructured text from 10M+ academic PDFs (the *epistemological* layer), structured time-series data from project management APIs (the *technical-temporal* layer), communication streams (the *behavioral* layer), and eventually, biometric data (the *neurocognitive* layer). This requires a pipeline that can handle extreme diversity in schema, format, and velocity.
Why AWS is Essential: AWS Glue and Lake Formation are critical for cataloging this chaos. Amazon Kinesis is essential for real-time data ingestion.
The same concept (e.g., "risk" or "cognitive load") is described with different vocabularies across neuroscience, systems theory, and project management. We must build a semantic layer that can map these disparate representations onto a unified Project Science ontology.
Why AWS is Essential: The combination of Amazon Comprehend and Amazon SageMaker to train custom NLP models allows us to build the world's first comprehensive, machine-readable ontology of project dynamics.
Building a hybrid, neuro-symbolic AI engine that models the "physics of execution" by marrying symbolic reasoning with deep learning to achieve true causal inference.
We are building a graph with potentially trillions of nodes and edges representing sociotechnical systems. The challenge is performing complex, multi-hop traversal queries in real-time to infer causal pathways.
Why AWS is Essential: Amazon Neptune is the only managed graph database built for this scale. We propose a deep partnership with the Neptune team to pioneer new query optimization techniques for causal inference.
Standard LLMs are brilliant mimics but lack a true model of causality. We must train a new class of foundation models that learn the causal structure of human decision-making and can answer counterfactual questions.
Why AWS is Essential: This is a massive HPC problem. Amazon SageMaker's distributed training libraries and access to custom silicon like Trainium are essential for making this computationally feasible.
Creating a "virtual laboratory" to run experiments on a scale impossible in the real world, validating our AI-generated theories of human and organizational behavior.
We aim to simulate millions of concurrent, intelligent agents whose "brains" are powered by real-time inference calls to our causal models. Each agent will exhibit realistic cognitive biases, decision fatigue, and emotional responses.
Why AWS is Essential: This is precisely the problem AWS SimSpace Weaver was designed for. We propose a first-of-its-kind integration where agent behavior is driven by live calls to SageMaker inference endpoints.
The simulation will generate terabytes of output data per run. We need a robust system to capture, store, and analyze this data to validate our hypotheses and further refine our models.
Why AWS is Essential: We will stream outputs to S3 via Kinesis Data Firehose, then use Amazon Athena and QuickSight to query and visualize this massive dataset, enabling rapid scientific iteration.