InstaDeep delivers AI-powered decision-making systems for the Enterprise. With expertise in both machine intelligence research and concrete business deployments, we provide a competitive advantage to our customers in an AI-first world.

Founded in 2014, InstaDeep is today 80 people strong. The company is headquartered out of London with additional offices in Paris, Tunis, Nairobi and Lagos. By combining the skills of our AI researchers, ML engineers, Hardware, Software and Visualisation experts, our in-house team harnesses the power of optimisation and deep reinforcement learning to create AI systems that can tackle the most challenging optimisation and automation challenges in real-life environments. As an industry-agnostic business, our expertise covers industries including financial services, energy, logistics, manufacturing, retail and aviation, amongst others.

We're on a mission to accelerate the transition to an AI-first world that benefits everyone, and in our pursuit to stay ahead of the curve on both research and delivery, we are proud to partner with organisations such as Google DeepMind, Intel and Nvidia.

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With a mission to help tackle client’s optimisation problems, one of InstaDeep’s use cases solves the Bin Packing problem. The solution consists of a set of boxes of different sizes that need to be packed efficiently in a container by minimising the wasted space and satisfying operational constraints, e.g. preventing items from overlapping, the need for physical support, weight distribution, etc. To succeed, the agent learned to solve the problem at a superhuman level without any prior human knowledge, and the results outperform and scale better than up-to-date optimisation solvers.

The Reinforcement Learning system was trained on an Intel multi-core system that helped to parallelise simulations and generate data for the agent to accelerate the learning process.

The solution generalises and is applicable to a range of NP-hard problems. Read more about it in the white paper.

Bin Packing AI vs Human

Bin Packing Blocks

Monte Carlo Tree Search Model

Bin Packing Blocks

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