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Unsupervised Learning, Redefining Intelligence
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DeepShift
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Making the AI of the future.
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RaSDAN
Random Spatial Deep Adversarial Network for Organic Stimulus-Based Memory Acquisition
A novel approach modeling the human hippocampal-entorhinal system to create a memory acquisition algorithm that can learn from organic stimuli. With vast performance improvements, this technology can be used to generate authentic and creative ideas from unforeseen stimuli intercorrelation.
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MAGIST
A data pipeline from Numbers --> Knowledge
A self-supervised, multi-agent approach to make an AI that can interpret sensor data, understand objects and features in the environment, and make intelligent conclusions.
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Project Zeta
Modular Biomimetic Robot Platform for AI Development
A fully modular quadruped robot platform used to collect data for AI development and to test AI algorithms. The robot is designed to be easily reconfigurable and adaptable to different environments and tasks. With compliant actuation, swappable sensor design, and extensible software design, this robots will combine other research projects at DeepShift Labs to a cumulative product.
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FAQ

  • What is our licensing policy?

    Most of our projects are fully open-source and licensed under the GNU GPL v3 license. However, select projects that require extensive funding or research may be kept closed source until the necessary documentation and project status is achieved.

  • What makes our algorithms different from other powerful LLM models?

    With the advent of multi-modal LLM, it may be difficult to see the difference between our algorithms and other powerful LLM models. However, our algorithms are designed to be more biologically accurate and are based on the latest research in neuroscience and cognitive science. This allows our algorithms to be more creative, more contextually aware, and more capable of generating unique, multi-sensory memories. Our algorithms are also designed to be more robust and more scalable, allowing them to be deployed on a wide range of real-world robotic systems.

  • How can you help our research?

    The answer to this question heavily depends on the nature of the visitor. If you are technically knowledgeable, contributions to the development, issues, and testing of our numerous algorithms would help boost the development of our research immensely. If you are an active researcher in the field, discussions and insight into field-specific nuances would help strengthen and diversify our research. Finally, if you have affiliations with other businesses who can financially contribute to the project, that can help hasten the development time and improve the quality of our robotic platforms.