An AISymmetric Website
We engineer the frontier of machine cognition — building systems that don't just compute, but understand, reason, and evolve.
Four converging frontiers driving our mission to redefine machine intelligence.
Automating the discovery of optimal neural network topologies through evolutionary and gradient-based methods. Our NAS framework achieves state-of-the-art efficiency.
Active ResearchBridging quantum computing with classical machine learning to unlock exponential speedups in optimization, sampling, and feature space exploration.
ExperimentalEngineering emergent reasoning capabilities in artificial systems through novel training paradigms, self-play curricula, and compositional generalization frameworks.
Breakthrough StageDesigning augmented intelligence interfaces that amplify human cognition through adaptive collaboration, real-time neural feedback, and intuitive AI co-pilots.
Applied ResearchCharting the inflection points in our pursuit of transformative AI.
Achieved a 340% improvement in inference speed through a novel sparse attention mechanism and dynamic computation graph pruning, reducing latency below 5ms on standard hardware.
Demonstrated a real-time neural interface prototype enabling bidirectional communication between biological neural networks and silicon architectures at millisecond resolution.
Successfully deployed the first hybrid quantum-classical ML model, leveraging quantum entanglement for feature correlation discovery in high-dimensional datasets exceeding 10,000 features.
Purpose-built infrastructure engineered for the scale and speed of frontier research.
Distributed computing fabric with next-gen accelerators optimized for parallel training workloads.
4,096 GPUsIn-house tensor processing units designed for specialized workloads including sparse operations and quantum simulation.
v5 CustomCurated multi-modal research datasets spanning text, vision, audio, and scientific corpora with real-time indexing.
50 PetabytesSub-millisecond inference pipeline with global edge deployment for latency-critical applications.
<1ms LatencyWorld-class researchers and engineers united by curiosity and purpose.
Former lead at DeepMind. 15 years in neural architecture research. Ph.D. in Computational Neuroscience from MIT.
Pioneered hybrid quantum-classical optimization at IBM Research. M.Sc. in Quantum Computing from ETH Zurich.
Published 40+ papers on emergent reasoning. Previously Stanford AI Lab. Ph.D. in Machine Learning from Oxford.
Built infrastructure at scale for OpenAI and Tesla Autopilot. Expert in distributed systems and ML operations.
Building in the open. Our most impactful tools, free for the research community.
High-performance PyTorch extensions for sparse attention, mixed-precision training, and custom CUDA kernels optimized for research workloads.
Quantum circuit simulation and hybrid quantum-classical ML framework. Provides seamless integration between quantum backends and standard ML pipelines.
Toolkit for building and evaluating synthetic cognition agents. Includes benchmark suites, training harnesses, and compositional reasoning evaluations.
We're looking for brilliant minds who want to push the boundaries of what's possible.