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Intro

Ogma - Building AI using Neuroscience

Ogma is building new AI technology based on a multidisciplinary approach, combining the latest developments in machine learning and computational neuroscience. Systems matching the power of state-of-the-art Deep Learning algorithms can be built with the self-organizing structure, flexibility and efficiency found in the human brain.

Systems Neuroscience

We take our cues from the efficient processing of brain networks, where only small portions of the network are active at a time. Local learning and memory, combined with high levels of sparsity and online learning, allows for a far more efficient and scalable machine learning system than current Deep Learning systems.

Machine Learning

Our technology draws from state-of-the-art and historically successful machine learning algorithms and techniques, combined with lessons from cortical and neural information processing circuits. In addition, Ogma has developed a novel, brain-inspired neural network architecture which we believe will become the basis for new advances in AI.

Universal

We develop systems that are not tailored to one specific task, but are universal – a single architecture capable of reinforcement learning, modeling and classification of any data stream including video, audio, and text.

᚛ᚑᚌᚋᚐ᚜ is the word “Ogma” in the ancient Irish Ogham, which was carved on the edges of stone pillars. Legend has it that Ogham was invented by Ogma mac Elathan, or Ogmios, a demigod of learning and poetry.

Recent Posts

  • AOgmaNeo Lorcan Mini robot demo + CLOgmaNeo
  • Unsupervised Behavioral Learning (UBL) revisited
  • Real2Sim with AOgmaNeo
  • Tutorial: RL with OgmaNeo2
  • Learning to walk (faster) with OgmaNeo2

Recent Comments

  • Eric Laukien on A Tutorial on OgmaNeo2
  • Eduardo on A Tutorial on OgmaNeo2
  • Eric Laukien on Beating Atari Pong on a Raspberry Pi without Backpropagation
  • Joseph Yacura on Beating Atari Pong on a Raspberry Pi without Backpropagation
  • Eric Laukien on Beating Atari Pong on a Raspberry Pi without Backpropagation

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  • November 2016

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