The European Union has announced the winners of a “Large AI Grand Challenge” it kicked off earlier this yr in a bid to speed up the pace of homegrown innovation by large-scale AI model makers.
4 startups will share €1 million in prize money and, perhaps more importantly, 8 million GPU hours to coach their models on a few the bloc’s high-performance computing (HPC) supercomputers over the subsequent 12 months. The Commission reckons it will enable them to shrink model training times “from years to weeks,” as its PR puts it.
The winning 4 startups are in alphabetical order: French fintech Lingua Custodia, which does financial document processing using natural language processing (NLP); Belgian startup Textgain, which also uses NLP for text processing but focuses on evaluation of unstructured data, similar to monitoring social media chatter for hate speech; Latvian startup Tilde, one other language specialist that’s focuses on Balto-Slavic languages, offering machine translation and AI-powered chatbots within the goal tongues; and Portugal’s Unbabel, which has historically blended machine translation with the expertise of native human speakers and applying AI for customer support and productivity use cases for enterprise customers.
The Commission said the AI Challenge received a complete of 94 proposals.
Unbabel likely has the very best profile of the 4 winners. The Y Combinator-backed translation business has been around for the perfect a part of a decade and raised near $100 million over its run, per Crunchbase.
Whether Unbabel needs an additional quarter million euros and even 2 million freebie GPU training hours is up for debate, but even veteran AI startups may feel every little bit helps, given the fast-paced developments in generative AI over the past 1.5 years or so.
At the top of the training period, the EU expects all of the winners to release their developed models under an open source license for noncommercial use or publish their research findings.
EU supercomputers to support AI startups
The EU unveiled a plan to expand startup access to the bloc’s supercomputing hardware in President Ursula von der Leyen’s state of the union address last fall, saying on the time that it wanted “ethical and responsible AI startups” to be first in line to tap computational support.
The European High Performance Computing Joint Undertaking (aka EuroHPC JU) — to offer the bloc’s supercomputer initiative its full name — currently has eight operational (nine procured) supercomputers, and two of which might be providing the allocation of 8 million GPU hours to the 4 winners: namely, Finland-based Lumi and Italy-based Leonardo (that are each pre-exascale HPC supercomputers).
A fifth startup, Spain-based Multiverse Computing, which is specializing in attempting to improve the energy efficiency and speed of huge language models using “quantum-inspired tensor networks,” just missed out on any prize money. But there’s a consolation: It should be allocated 800,000 computational hours on one other of the supercomputers, Spain’s (pre-exascale) MareNostrum 5.
This handful of European startups constructing large-scale AI models won’t be the primary to get a taste of what HPC hardware can do. French general purpose AI model maker Mistral was a participant in an early pilot phase of the supercomputing provision last summer, using Leonardo to “run just a few small experiments,” as co-founder and CEO Arthur Mensch told TechCrunch back in December — though he said it had not been used for model training at that time.
The EuroHPC JU has also historically provided some capability to industrial players. Nonetheless demand for the supercomputers typically far outstrips supply, so the AI startups are essentially getting bumped to the front of the queue.
EU policymakers have also recognized there’s a have to reconfigure and retool the HPC infrastructure for the generative AI age. Hence why, in January, the Commission announced a package of “AI innovation” measures that included proposals for upgrading the supercomputers and constructing out a support layer to enhance accessibility in order that AI startups can more easily tap the infrastructure.
