our blog posts

  • Mixture Density Networks in GPflow – A Tutorial

    Vincent shares a demonstration tutorial that uses GPFlow, a powerful, flexible framework for probabilistic modelling.

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  • Learning non-linear dynamical systems with GPSSM

    How to tame the mighty GPSSM! Stefanos proposes an approach that learns probabilistic dynamical models for understanding correlations and patterns in (non-)linear time-series.

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  • Large-scale Distributed Optimisation for Machine Learning and Beyond

    PROWLER.io are the first to suggest a fully distributed algorithm for solving symmetric diagonally dominant systems.

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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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  • Diff-DAC: Fully distributed deep reinforcement learning

    "Psst, pass it on." Sergio, Aleksi and Enrique teach our AI agents to spread the word.

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  • why Principled AI?

    Because by 2025, Principled AI will drive the world economy – by Vishal Chatrath

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  • Principled AI:

    Technical mission statement on Principled AI by PROWLER.io chairman Prof. Carl Edward Rasmussen

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  • AI tools for automated game testing

    Matthew, David and Haitham send our agents on a bug hunt to demonstrate how AI can help game developers save time and resources.

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  • Tuneable Artificial Intelligence

    Felix, Jordi and Haitham use a gaming example to introduce a powerful new tool for tuning AI

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  • Probabilistic
    models underpin principled AI

    Team leader James outlines Probabilistic Modelling's approach to principled decision making.

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  • A toolbox of principled learning paradigms

    Reinforcement Learning team leader Haitham explains the diversity of learning paradigms used by PROWLER.io

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  • Social AI: a principled decision making paradigm, by Enrique Muñoz de Cote

    Multi-agent Systems team leader Enrique outlines how PROWLER.io is using game theory to help AI agents work together.

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