Crafting the future and driving innovation
Our Work
01
Research and Innovation Lab
The United Algorithmis Research and Innovation Lab is a proposed a 100,000-square-foot facility located within a 100-acre area of preserved forest. Currently the research lab is focused on AI, digital innovation, and business automaton, operating remotely with securely colocated compute.
02
Professional Services
United Algorithmics partners with enterprise clients to co-launch a joint venture or develop a tool for their internal use. The collaboration is highly strategic and tailored to align with the client's specific objectives.
03
Strategic Acquisitions
United Algorithmics employs a strategic acquisition model centered on the acquisition of stable, niche SaaS businesses that are often overlooked by other investors.
04
Venture Accelerator
United Algorithmics operates a venture accelerator model aimed at partnering with promising, niche SaaS startups that align with our expertise in innovation and automation.
05
In-House Product Incubator
United Algorithmics in-house startups are born from the insights and innovations generated by our Research and Innovation Lab. We identify emerging market needs and develop solutions that are both innovative and commercially viable.
06
Private Equity Fund
United Algorithmics manages a Private Equity Fund that provides capital and expertise to high-growth technology companies, including those from our Strategic Acquisitions, Venture Accelerator, and In-House Product Incubator.
The Algorithmic Edge: Driving Innovation in AI, Business, and Strategic Technologies
Research Blog
Understanding LLMs through Monosemanticity
The Scaling Monosemanticity paper by Anthropic demonstrates how sparse dictionary learning can extract interpretable features from large language models like Claude 3 Sonnet, allowing researchers to control and steer model behavior by manipulating specific features. This research enhances AI interpretability, offering new tools for model fine-tuning and safety by revealing how individual neuron activations correspond to human-interpretable concepts​
Train-of-thought modeling with RL
Reinforcement learning offers a compelling framework to guide LLMs in tackling multi-step reasoning tasks, encouraging models to make deliberate, goal-oriented decisions across several computation steps. By continuously refining the LLM’s process and guiding it through structured feedback, RL can enable more reliable and interpretable AI systems capable of solving complex problems, generating coherent long-form content, and engaging in meaningful multi-turn dialogues.
Probabilistic Ontology-Driven Multi-Agent Systems (pODMAS)
Our Probabilistic Ontology-Driven Multi-Agent System (pODMAS) uses Gaussian Mixture Models (GMMs) to represent entities, relationships, and interactions, evolving over time through online learning and bitemporal tracking. This blog explores the architecture, challenges, and innovations behind creating dynamic, adaptive systems that capture complex temporal and relational dynamics in various domains.
