We are delighted to be one of ten teams entering Stage 1 of SPRIND’s €125m Next Frontier AI Challenge.
We are building AI that learns directly from irregular, asynchronous and multiscale streams of real-world data.
Ten teams. Five countries. Up to €26.5M each in equity-free funding to build frontier AI labs for Europe. The Next Frontier AI Stage 1 teams are here. Physical AI, world models, continual learning and automated science. Each one a serious bet on the next paradigm.
Frontier forecasting with a much longer memory.
Meet ci-proto: #1 on fev-bench, with the ability to recall patterns from over 2 million observations ago.
Open weights. Available for commercial use. Ready for your data.
Try it now: https://t.co/iIyoHY0eeh
Both bring expertise directly relevant to our mission of building efficient foundation models that learn from irregular, asynchronous and multiscale time-series data.
Welcome to the team, Federico Nardi and Yi Heng Lim!
5/5
Two big additions to Continuous Intelligence!
We’re excited to welcome Federico Nardi and Yi Heng Lim to the team.
Some of the hardest challenges in time-series AI come down to two questions: how signals relate to one another, and how to learn efficiently across time.
1/5
His work laid the foundations for a new generation of parallelised recurrent models, including Apple’s ParaRNN, which scaled nonlinear recurrent language models to 7 billion parameters.
4/5
Great to join NVIDIA’s UK Ecosystem Celebration and hear about the future of AI in the UK. Thanks to NVIDIA for bringing everyone together!
Bonus points if you can spot our logo 👀
Six papers at NeurIPS 2026 from a team of four: that's 1.5 papers per person!
Among them is our inaugural Continuous Intelligence paper, Time Flies When You Parallelise.
Preprint coming soon. In the meantime, here is a brief intro to are all six papers!
Really enjoyed speaking at the SPRIN-D AI and Robotics conference about what we are building at @Continu_Intel !
Thank you to @IlirAliu_ for the great video!
Two months into Continuous Intelligence, we shared our progress building foundation models for time-series data at SPRIND’s conference in Berlin!
Exciting to see so much frontier AI innovation beyond LLMs across the UK and Europe. Thank you SPRIND for bringing us together!
Continuous Intelligence has been awarded up to €3 million through SPRIND’s Next Frontier AI Challenge.
We are one of ten teams selected from 500+ applications to enter Stage 1.
Read more
https://t.co/zExphbAUxL
We’re hiring in London
https://t.co/hV1psdJwkd
True sovereignty isn't copied. It's built. Here are the 10 teams we fund with €125M to create the next AI paradigm in Europe! Learn more about the teams and the @SPRIND Next Frontier AI Challenge 👇
1/
We are hiring our founding technical team in London:
• ML Research Engineer
• GPU and ML Systems Engineer
• ML Product and Evaluation Engineer
Explore the roles: https://t.co/w1aKcOIssr
We are delighted to be one of ten teams entering Stage 1 of SPRIND’s €125m Next Frontier AI Challenge.
We are building AI that learns directly from irregular, asynchronous and multiscale streams of real-world data.
Ten teams. Five countries. Up to €26.5M each in equity-free funding to build frontier AI labs for Europe. The Next Frontier AI Stage 1 teams are here. Physical AI, world models, continual learning and automated science. Each one a serious bet on the next paradigm.
The real world does not arrive as tokens.
It arrives as measurements, events and signals, observed asynchronously and across multiple scales.
Our models maintain an evolving representation of the world, updating whenever new observations arrive.
We’re hiring our founding technical team in London.
• ML Research Engineer
• GPU & ML Systems Engineer
• ML Product & Evaluation Engineer
Help us build models that learn directly from a continuously evolving world.
Explore the roles: https://t.co/w1aKcOIssr
The real world does not arrive as tokens.
It arrives as irregular, asynchronous and multiscale streams of measurements, events and signals.
Continuous Intelligence is building stream-native AI for real-world time-series data.