An Applied AI lab focused on creating specialized intelligence to solve real-world problems.
We specialize in developing cutting-edge computational techniques in areas like deep learning, reinforcement learning, and active learning to solve tough domain-driven challenges that require a deep understanding of context, constraints, and goals.
Our team blends AI expertise with deep domain knowledge to tackle challenges from every angle. By fusing cutting-edge AI with deep domain expertise, we’re able to frame and represent problems in ways that would otherwise be out of reach, enabling us to deliver transformative solutions.
We believe everything interesting happens at the intersection, which is why we are determined to work in an interdisciplinary fashion, applying bleeding-edge AI to domain-specific problems.
The origin of our name
FRACTAL LABS draws inspiration from the fascinating world of fractals in mathematics.
Fractals are grounded in algorithms and equations that iterate over themselves, generating seemingly endless, intricate patterns from simple rules. This interplay of simplicity and complexity captures the essence of fractals—a simple mathematical equation can unfold into a structure of infinite diversity. In much the same way, fractals are present throughout nature, where iterative processes—like the branching of trees, the veins in leaves, the adaptation of ecosystems, the growth of organisms and neural pathways—give rise to complex, self-replicating patterns that echo the structure of mathematical fractals.
At its core, machine learning mirrors the natural world, where simple, iterative processes give rise to stunningly complex patterns and behaviors. The elegance of machine learning is not the elegance of math or physics, it is the elegance of biology. Simple processes like stochastic gradient descent and backpropagation give rise to intricate structures and behaviors, much like the many biological processes that create the awe-inspiring complexity of nature. It is this parallel between the organic and the computational that reveals not only the power of machine learning but its potential to emulate the dynamic complexity found in the natural world.
Careers
We are determined to work in an interdisciplinary fashion to apply bleeding-edge Artificial Intelligence to domain specific problems.
