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  • PROWLER.io’s
    AI Platform

    Our lead Engineer, Neil Ferguson, explains how the PROWLER.io platform turns research into reality.

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  • Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models

    We examine how natural gradients can be used in non-conjugate stochastic settings, together with hyperparameter learning.

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  • Variational Fourier Features for Gaussian Processes

    We bring together two powerful concepts: the variational approach to sparse approximation and the spectral representation of Gaussian processes.

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under the hood

The burgeoning AI industry is still focused on perception – recognising images, understanding speech, helping robots touch. But far greater benefits will come when AIs can make good, principled decisions based on those perceptions.

PROWLER.io’s Decision Making systems:
  • are autonomous
  • learn from experience
  • use Probabilistic models that are flexible enough to generalise to novel situations
  • adapt to changing environments by refining their strategies in data-driven ways
  • continually update their environmental models in light of data collected while operating.
  • use Reinforcement Learning (RL) algorithms that can help estimate, account for and even reduce uncertainty
  • can cope with the uncertainties of finite – even small – amounts of data
  • can favour decisions that loop back fresh information to models
  • use Game Theory to operate in multi-agent settings
  • can cooperate – and compete – by inferring what humans and other AIs are trying to do.

our core strategies

  • probabilistic modelling
  • reinforcement learning
  • multi-agent systems

how can our technology be used?

  • games
  • smart cities
  • robotic toys
  • autonomous vehicles
  • drones & robots