In our fast-paced and increasingly complex world, organizations are facing two fundamental challenges related to data. On the one hand, they struggle to make sense and take full advantage of the vast amounts of data they collect in their day-to-day operations. Simultaneously, in sectors like defense or emerging technology research, data is often scarce, non-existent, or error-prone. In both cases, traditional analysis methods often fall short. As a result, business leaders and government officials alike struggle to capture the timely, actionable, and trustworthy insights they need to drive their business forward, make mission critical decisions, or craft effective policies.
Machine Learning (ML) is a constellation of algorithmic and data-driven methods aimed at solving complex problems. These artificial intelligence (AI) systems are fast and effective tools for uncovering the deep relationships hidden in large, ambiguous, or incomplete data sets. Emerging human-machine teaming solutions empower users to work symbiotically with AI/ML tools to take on difficult, dynamic, or non-traditional challenges. Combined with the mathematical rigor of formal methods, these tools connect dots, generate actionable insights, and provide trustworthy solutions in contexts too complicated for human brains to handle on their own.
At Galois, we offer end to end expertise—combining extensive formal methods experience with a mastery of cutting edge machine learning techniques. Our researchers take a multidisciplinary approach to tackling “big data” challenges, integrating expertise in topological analysis, human- machine teaming, measurement theory, multi-agent system design, cognitive science, and more. By integrating top-down model-based understanding with data-driven insights, we design and build novel ML algorithms, languages, and frameworks well-suited to noisy and ambiguous data.
The Result: Dynamic machine learning tools that empower our clients to leverage the full potential of their data sets and solve complex problems.