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LatticeFlow Gets $12M to Fix AI Errors and Expand to Sofia

LatticeFlow raises $12M to eliminate computer vision blind spots

LatticeFlow, which helps businesses improve their existing machine learning vision models, today announced a $12M Series A funding round.

ETH Zurich spin-off receives $2 8M for trustworthy Artificial Intelligence

These updates are republished press releases and communications from members of the Science|Business Network ETH Zurich spin-off receives $2.8M for trustworthy Artificial Intelligence Six months after its foundation, the ETH spin-​off LatticeFlow has received USD 2.8 million from two venture capital investors. The aim of this move, as the new company reveals in a press release, is to support its ambitious vision of ensuring reliable and trustworthy AI. Overcoming this strategic challenge will enable the safe use of AI technology, say the company’s founders. While there have been significant advances in the development of artificial intelligence in the past decade, many AI models are still overwhelmed when it comes to productive, real-​world use outside controlled research environments. This can result in errors and unwanted behaviours. Consider a driver assistance system that misreads a stop sign in difficult light conditions – when the sun is shining on the camera, for ex

ETH spin-off LatticeFlow raises $2 8M to help build trustworthy AI systems – TechCrunch

ETH spin-off LatticeFlow raises $2.8M to help build trustworthy AI systems LatticeFlow, an AI startup that was spun out of ETH Zurich in 2020, today announced that it has raised a $2.8 million seed funding round led by Swiss deep-tech fund btov and Global Founders Capital, which previously backed the likes of Revolut, Slack and Zalando. The general idea behind LatticeFlow is to build tools that help AI teams build and deploy AI models that are safe, reliable and trustworthy. The problem today, the team argues, is that models get very good at finding the right statistical patterns to hit a given benchmark. That makes them inflexible, though, since these models were optimized for accuracy in a lab setting, not for robustness in the real world.

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