Make more informed decisions with fewer manual processes and less communication overhead. Based on human cognition research, Redpoll AI learns and stores its knowledge like people do, making it transparent and intuitive to both stakeholders and data scientists.
Learn the complex relationships within your unstructured data. Simulate or evaluate the likelihood of hypothetical events, predict new values given any number of conditions; quantify uncertainty -- all without needing to partition, transform, or fill in missing data.
No repeated cleaning, coding, or model validation. While the traditional approach to analytics is to model questions about data, Redpoll AI models the data itself, which streamlines the analytics cycle and eliminates the cost of asking new questions.
From exploration to production in minutes. End users and machines interact with the platform through the same API, which can be accessed through the web or dropped into your containerized environment.
A demo web client displaying a network of genomic similarity with respect to a disease phenotype. Your interface will differ to reflect your requirements.
G x E x Anything
Discover and characterize multi-locus, epistatic traits, and characterize the modifying effects of environment, management, and treatment. By mapping information flow through the data, the AI finds and characterizes complex structure in unstructured data.
From GWAS to Genome Editing
Predict or simulate phenotypes from genotypes, or simulate the genetics likely to produce desired phenotypes. All information the platform creates is reversible, which allows users to simulate, optimize, and compute probabilities bidirectionally.
Design experiments and data collection plans that are less costly and more effective. The AI can identify gaps in its knowledge and can request specific data to fill those gaps; and it can evaluate the information content of hypothetical data to help users avoid over-collecting, or collecting the wrong data.
We are engineers, scientists, psychologists, and AI researchers.
The AI industry is built around black box algorithms, like deep learning, which require incredible amounts of data, and which can fail in unexpected ways. These algorithms have found great success in domains for which data are plentiful and cheap, and for which the cost of failure is low -- domains like ad targeting, speech processing, and image classification. But in safety-intensive, scientific domains like biotech and public health, data are sparse and expensive, and the cost of failure is potentially lost lives. Using the same algorithms is unproductive, unsustainable, and potentially dangerous.
We believe the best way to solve these hard, impactful problems safely and sustainably is to start from the ground up. To develop new AI techniques based on human cognition. To build expressive, intuitive, and transparent AI that can be operated and understood by both data scientists and domain experts.
Baxter Eaves, PhD
Dr. Eaves has 10 years of experience building analytics tools and designing human cognitive systems that learn, teach, and selectively trust like people. He has applied these methods to problems in linguistics, image processing, biotech, and agriculture.
Patrick Shafto, PhD
Dr. Shafto is the Henry Rutgers Term Chair in Data Sciences at Rutgers University - Newark. He has led a number of projects for agencies like DARPA, DOD, and NSF; and his publications have appeared in top journals of machine and human learning.
Principal ML Engineer
Michael has 14 years of research and engineering experience. He has built production models for healthcare, agronomy, finance, and law; and has conducted research in the areas of high-energy physics, differential geometry, plasma physics, and high-performance computing.