InstaDeep is pleased to announce the formation of a new Quantum Machine Learning (QML) team within the company’s well-established research department. The announcement coincides with the publication of the “Autoregressive neural-network wavefunctions for ab initio quantum chemistry” research paper in the leading science journal, Nature Machine Intelligence. Publication in Nature Machine Intelligence The paper was… Read more »
InstaDeep is delighted to announce that a collaborative research project with a team from the University of Oxford on “Autoregressive neural-network wavefunctions for ab initio quantum chemistry” has been published in Nature Machine Intelligence magazine. The paper was authored by Dr Thomas Barrett of InstaDeep as well as Prof A. I. Lvovsky and Aleksei Malyshev… Read more »
LONDON (March 22, 2022) InstaDeep today announced it will use the NVIDIA Cambridge-1 supercomputer to accelerate the next wave of biology innovation. The company plans to train AI language models using genomics data, and these models will then be shared with the bioinformatics community. InstaDeep’s work on machine learning and bioinformatics has already led to… Read more »
Series B led by Alpha Intelligence Capital; BioNTech and Deutsche Bahn among Investors LONDON (JAN 25, 2022) – InstaDeep, a leader in advanced AI decision-making systems for solving real-world problems, announced today it has raised $100M and closed a Series B round led by Alpha Intelligence Capital together with CDIB. Investors in the round include… Read more »
InstaDeep is delighted to announce they are making a world-class NVIDIA A100 DGX supercomputing server and on-site support team available at The AI Hub located at the DOT innovation hub in central Tunis, for the exclusive benefit of the Tunisian Artificial Intelligence (AI) and Machine Learning (ML) ecosystem. This has been delivered in co-operation with… Read more »
With the continuous growth of the global economy and markets, resource imbalance has risen to be one of the central issues in real logistic scenarios. In particular, we focus our study on the marine transportation domain and show how Multi-Agent Reinforcement Learning can be used to learn cooperative policies that limit the severe disparity in… Read more »
(Part 2/4) In our introduction to meta-reinforcement learning, we presented the main concepts of meta-RL: Meta-Environments are associated with a distribution of distinct MDPs called tasks. The goal of Meta-RL is to learn to leverage prior experience to adapt quickly to new tasks. In Meta-RL, we learn an algorithm during a step called meta-training. At meta-testing, we apply this… Read more »
(Part 1/4) The recent developments in Reinforcement Learning (RL) have shown the incredible capacity of computers to outperform human performance in many environments such as Atari Games [1], Go, chess, shogi [2], Starcraft II [3]. This performance results from the development of Deep Learning and Reinforcement Learning methods like Deep Q-Networks (DQN) [4] and actor-critic methods [5, 6, 7, 8]. However, one essential advantage of… Read more »
InstaDeep is pleased to announce its Gold sponsorship of the forthcoming École Polytechnique Fédérale de Lausanne’s (EPFL) Applied Machine Learning Days (AMLD), taking place between 2-4 September 2021. The online event is the first time EPFL have hosted an event in Africa, and will include three days of talks, tutorials and workshops on Machine Learning… Read more »
This July, DeepChain lead, Nicolás Lopez Carranza, was invited to speak at this year’s IA Congreso (AI Congress) – the premier event for AI in Latin America. Representing InstaDeep, Nicolás presented on the topic of “AI-first Protein Design”, as part of a top line up of experienced AI professionals from the region and beyond. The… Read more »