Observe applies computer vision and machine learning to optimise aquaculture farms, so that farmers will not miss any opportunity to maximise stock value. Through interfacing with existing infrastructure already in place on a farm, Observe cuts feeding costs while getting fish out of the water faster and increasing farm production.

Hemang Rishi won the Young Engineer For Britain in 2012 before getting his Master’s degree in electrical engineering from Imperial College London. He worked and published in the computer vision field  for 2.5 years, at companies including Laing O’Rourke as well as the Biologically Inspired Computer Vision Group at Imperial.

Pieter Fabry has experience implementing reinforcement learning models on videos in real time. He has done research and publications in embedded systems, working as a nano-satellite researcher at the VLSI lab at the National University of Singapore, as well as building embedded systems for high speed financial option pricing during his Master’s degree at Imperial.

Optimal Labs applies bleeding-edge Deep Reinforcement Learning to create highly-intelligent autopilots for farms. The efficiency, reliability and nutritional output of our food system is a function of the decisions made by individual farmers – based on intuition. Our mission is to deploy AI-controlled farms to make those decisions optimally and globally – enabling a larger and longer-living population.

David Hunter is a recent member of the Machine Learning Lab at the University of Oxford – one of only a handful of labs in the world focused on Deep Reinforcement Learning. He previously ran the algorithmic trading quantitative strategies team at Deutsche Bank where his algorithms traded >5% of the European stock market volume.

João Abrantes left his PhD in Robotics at EPFL to start Optimal. He has research and teaching experience in Machine Learning as well as a masters in bioengineering. He was the Portuguese youth pole-vault champion and is from a family of Olympians. Human nutrition has always been a key part of his life.

Xihelm is using deep artificial intelligence software to revolutionise robotic harvesting. Hand picking is state of the art in greenhouses throughout the world; Xihelm’s mission is to build an AI that sees, understands and acts efficiently in the real world.

James Kent worked at Google in London and Silicon Valley for four years in modelling and analytics, and was granted six patents. He studied at London School of Economics, University of Oxford and Stanford University across Maths, Economics and Computer Science.