From intelligence sciene to innovative technology
Most AI startups and R&D labs are designed to commercialize technologies whose core invention already exists. As we found ourselves solving ever more complex problems, we found R&D simply wasn’t enough.
That’s why we founded our Research Institute, Cross Labs, to drive the fundamental research that sparks innovation. By mixing fundamental research with our development and consulting pipeline under one roof, we’ve created a vibrant, curious, and creative environment that drives innovation, reduces development time and delivers accelerated commercial introduction.
Led by Olaf Witkowski Ph.D, Cross Labs is an active hub for Intelligence Science, connecting university labs, tech companies, and research institutions with commercial consulting and application development. With this network, we bridge the gap between academic research and commercial application by bringing the entire process together. Our researchers work with our engineers to create and adapt cutting-edge technology to improve the quality of commercial processes, platforms, applications and products.
Through the Looking-Glass: Building an Interactive AI Experience
Cross Roads #34: "Chess Cheating Detection" with Prof. Ken Regan
A Focus on Intelligence
Cross Labs’ focus is on pushing fundamental research towards a thorough mathematical understanding of all intelligent processes observable both in nature and in artificial environments in order to spur innovation in machine intelligence.
Neuroscience of Intelligence
By understanding more about how the brain processes information, techniques from neuroscience, combined with physics of information, can help us translate human thinking from its biology into fundamental mathematical functions of intelligence.
Theory of Agency
Moving on from deep learning, designing new AI paradigms will require better mathematics for learning in itself. We start from understanding the emergence of goals in agents, eventually aiming at building a global framework for the problem of credit assignment.
We work towards the design of societies of AI agents that can truly parallelize their computation, learn collectively from each other, and invent new mechanisms that work on a collective scale, in order to work on a problem together.