Center for Data Innovation

Data innovation can make crop breeding more resilient. 👩‍🌾

Sebastian Schultheiss of Computomics explains how testing ML models across unseen locations and growing seasons helps breeders assess whether predictions hold up in real-world conditions.

Read the interview👇

5 Q's with Sebastian Schultheiss, CEO of Computomics The Center for Data Innovation recently spoke with Sebastian Schultheiss, CEO of Computomics, a Germany-based company that uses machine learning to help plant growers predict crop performance and make informed breeding decisions. Schultheiss explained how Computomics combines genetics, field trials,

Europe has made open-source AI central to its tech sovereignty push. But while it is unlikely to match the United States and China in closed frontier models, it has an opportunity to lead in open AI.

As Patrick Grady explains in his latest piece, the EU should double down by removing regulatory and procurement barriers and building a global open-model ecosystem.👇
https://vist.ly/5khaz

Opposition to data centers is growing, but many concerns mirror those Democrats have faced with wind and solar projects.

The lesson from clean energy wasn’t to stop building—it was to address legitimate concerns while allowing investment to proceed.

As Daniel Castro explains in his latest, Democrats should apply the same approach to data centers.👇
https://vist.ly/5kcbw

As China races to build and deploy AI, U.S. policymakers shouldn’t let AI anxiety push America toward more government control and restrictions on innovation.

In National Review, Daniel Castro argues that America can address real AI risks without sacrificing the economic freedom, competition, and technological ambition that have made it a global innovation leader.

Read the op-ed:
https://vist.ly/5jb5u

AI kill switches sound like a straightforward way to stop rogue AI systems. But mandating them could introduce new cybersecurity and reliability risks—and still fail to address the broader challenge of containing autonomous AI.

Emergency shutdown capabilities can be useful, but they are neither universally feasible nor a substitute for broader safeguards against autonomous AI.

David Kertai explains in his latest commentary👇
https://vist.ly/5i273

The House passed the Ratepayer Protection Act 417–3, sending the bill to the Senate.

But while the bill gets the core principle right—large electricity users should pay the grid costs they create—it shouldn’t single out data centers.

Michelle Lopes Maldonado explains why the standard should apply to all large electricity users.👇
https://vist.ly/5ita4

Anthropic researcher Jacob Coxon resigned this month, warning that people building advanced AI believe it could “kill us all” by the end of the decade. Other AI leaders have called for slowing frontier development.

But frightening scenarios are not evidence that catastrophe is likely. AI poses real, measurable risks that warrant investment in security, testing, monitoring, and accountability—and continued progress could give researchers better tools to manage those risks.

Daniel Castro explains in his latest👇
https://vist.ly/5ippq

The backlash to data centers focuses heavily on what communities might lose. Daniel Castro argues they should consider what they stand to gain: tax revenue, skilled-trade jobs, new infrastructure and lower costs for ratepayers.

Center for Data Innovation Center for Data Innovation

AI’s frontier is moving. The law should move with it.

Fixed compute thresholds are already aging poorly. Policymakers should define frontier AI by capabilities and real risk—not yesterday’s FLOP count.

AI’s Frontier Is Moving. Its Legal Definition Should Too Policymakers are increasingly concerned about “frontier models,” a term meant to identify a small group of highly-capable general-purpose models that could pose unusually serious risks, such as mass casualties or billion-dollar damage. Lawmakers are seeking to define the category so they can imp...

Data innovation can help R&D teams avoid costly dead ends.

In our 5Qs, Maria Paola de Salvo, CEO of EasyTelling, explains how AI can assess scientific evidence and flag approaches unlikely to work before organizations invest more resources.

🔗 Read the interview:

5 Q's with Maria Paola de Salvo, CEO of EasyTelling The Center for Data Innovation recently spoke with Maria Paola de Salvo, CEO of EasyTelling, a Brazil-based company developing an AI-powered platform that translates scientific research into practical applications. De Salvo explained how EasyTelling uses its proprietary Knowledge Translation methodo