Framework to rapidly generate insight by learning directly - from the experts.
A system to see the entire map of a given problem set, not just a snapshot.
A toolset using similarity to understand complex systems with limited data.
What makes AI unique is not machines; the learning is what matters. AI [done right] depends on being open to the world. Operationally, AI is continuous experimentation with data new and old—rebuilding operating principles and beliefs when the evidence stacks up. AI is a laboratory in the form of technology, rigorously governed by the principles of the scientific method.
What’s important about machine learning has little to do with machines. It’s the learning that matters. Expert-in-the-loop machine learning is a framework to engage and learn in real-time from experts on a specific time and place. Familiar digital channels, natural language.
Classical optimization frameworks and standard statistical approaches ignore the friction and dynamism of the real-world. Rarely is there an accurate map of how the supply-chain or health system functions, even as a static snapshot. We aim to do better by learning the map – directly from available data.
Similarity is a powerful concept that allows us to intelligently augment data and run synthetic clinical trials - metric learning is the computational approach to 'learning how to learn'
MACRO-EYES began with a commitment to high-risk, high-reward research. Chief Scientist Suvrit Sra, PhD is a theorist of machine learning. CEO Benjamin Fels brought experience leading efforts to build systems that had to learn to survive, assessing risk and opportunity within the fractions of a second, across global markets. Dr. Sra is the Esther and Harold E. Edgerton (1927) Career Development Associate Professor of Electrical Engineering and Computer Science at MIT and a core faculty member in the MIT Institute for Data, Systems, and Society.
What makes AI unique is not machines; the learning is what matters. AI [done right] depends on being open to the world. Operationally, AI is continuous experimentation with data new and old - rebuilding operating principles and beliefs when the evidence stacks up. AI is a laboratory in the form of technology, rigorously governed by the principles of the scientific method.
We launched MACRO-EYES with a culture of experimentation, rigorous testing, and speed-to-deployment. This culture is shaped by Suvrit’s intent to set deep mathematical understanding of machine learning against real-world unsolved problems, and Benjamin’s years on a trading desk. Financial markets are relentless (the rate of change is unceasing), dense with data, competition is fierce and global, and success and failure are measurable – down to the fractions of a second. Getting financial markets right requires perpetual R&D; this culture of experimentation, rigorous testing, and rapidly-to-prototype was inspiration for MACRO-EYES.
MACRO-EYES is a deeply technical organization. The significant proportion of our team is devoted to machine learning. This team is defined by its diversity and the commitment to research: scientists with doctoral degrees in computational biology, computer science, physics, radio astronomy, and statistical neuroscience.
The MACRO-EYES philosophy is to relentlessly tie product to research. The path to product at MACRO-EYES is marked by years of R&D. A problem will be peeled away from product, brought to the virtual lab, and if successful, ultimately folded back into product. The nature of our work and the environments in which we must deliver advanced capabilities means that our products are continuously challenged. Our technology must be capable of learning in any environment, in austere conditions and amid great uncertainty.
Why R&D? We have no choice.
Machine learning is the most important technology of our time, but it requires a next generation of tools to create the widespread impact that is possible. Machine learning must make the leap from being easily deployable in ideal settings to deployable in most settings. To solve the problems that matter to us at the scale that matters, the research must continue.