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The neural engine for autonomous robots
We aren’t building AI to replace "knowledge work” that requires neither, but to bolster the working human intellect which has never failed to discover new paths to prosperity. For all their success, mainstream approaches to AI have fundamental limitations preventing them from solving critical classes of problems. We are building a new AI architecture from first principles to break from these limitations. The status quo has ossified around the supervised, brute-force pretraining paradigm. These systems do not, and cannot, handle the essential skills of continual learning, goal discovery, novel planning, and the creation of new knowledge. These limitations are not trivial. Our national prosperity depends directly on overcoming these limitations, because the essential skills underlie the skilled trades. From electricians to machinists to nurses, apprentice tradespeople in all of these fields learn “skilled improvisation” where every solution is novel, and even the goal must be discovered. Prosperity demands the precise capabilities where status quo AI is structurally weak. Worse, these limitations will not be overcome with more compute or training data. They are inescapable consequences of the underlying technologies of supervised learning, neural networks, transformers, backprop, and gradient descent. Progress requires independence from them. We are building a new AI architecture by reverse-engineering the system that got us this far: the human brain. By triangulating between neuroscience, cognitive science, and the evolutionary elaboration of brains, we have identified the brain’s algorithms to (a) continually learn a causal world model (b) select goals to both achieve and learn to achieve more (c) plan to reach novel goals, and (d) creatively produce new knowledge. Join us in building intelligence with ingenuity to bolster the skilled trades.
Well Principled presents itself as the neural engine for autonomous robots, aiming to redefine AI by building a new architecture inspired by the human brain. It emphasizes continual learning, goal discovery, planning, and knowledge creation to bolster skilled trades rather than replace human intellect.
The product is described as a neural engine for autonomous robots built on a new AI architecture derived from neuroscience and cognitive science. It focuses on continual learning of a causal world model, autonomous goal discovery and selection, planning to achieve novel goals, and creative production of new knowledge. The approach rejects traditional supervised learning paradigms and seeks independence from backpropagation, transformers, and other standard AI foundations.
Who it’s for: Manufacturing and skilled trades sectors (electricians, machinists, nurses, and other apprentice trades) requiring adaptable, continually-learning autonomous systems and novel problem solving.
Hiring/traction/funding mentions: none explicitly stated in the provided text
Ryan has galvanized teams with bold vision and disruptive, patented innovations since 2003. Ryan transformed distributed analytics across an academic lab, his first bootstrap startup and computational biology and cloud teams at Monsanto. He grew VC-backed CiBO from 7 to 70 engineers and data scientists. Ryan holds an MBA and BA prob/stats from Olin and WashU.
Chronically the first customer-facing engineer, Chris has delighted customers with deep-data applications from corn germplasm catalogs to global satellite data and environment simulations. Marshalling his broad expertise from cloud architecture to data visualization, Chris delivers outcomes customers love and leads solutions teams to productize them.
Joe has spent 15 years building foundational platforms, from middleware at AT&T, to startups across FPGA-accelerated high-frequency trading, accelerated ETL, machine learning based network security (acquired by Cisco) and hybrid AI/physical models of global-scale crop simulation. Joe holds a BS in Applied Math and a PhD in Computer Science from WashU.
Your body's app-based simulation that recommends nutrition and exercise to meet your goals.
BODYSIM expands from a metabolism-tracking tool to an end-to-end fitness forecasting system that uses a digital twin to integrate multisource data (DEXA, BIA, 3D scans, wearables, and biomarkers) and provides prescriptive meals, workouts, and forecasting to turn targets into measurable results.
Formerly “BODYSIM by Well Principled” · why startups rename →
Reshaping industrial quality with AI, hardware, and software
Human Intelligence for Robots
