🤯DeepMind Solves AI’s Data Problem

Google DeepMind’s solution for AI’s ‘peak data’ problem

Key Points 

• Google DeepMind's test-time compute technique breaks complex tasks into smaller steps, improving AI model accuracy and output.

• Researchers suggest that higher-quality outputs from test-time compute could serve as new training data for future models.

🥸Context of the news - The internet’s best data has already been used to train AI models, a process called pre-training that powered breakthroughs like ChatGPT. But now, progress is slowing. Experts call this moment “peak data,” meaning there’s not much new information left to work with. 

That’s a big deal since trillions of dollars in AI investments hinge on these models continuing to improve. Surprisingly, though, most experts aren’t too concerned. Here’s why.

👨🏻‍💻News - Google DeepMind researchers believe they’ve found a way to tackle AI’s “peak data” problem. Their solution, called test-time or inference-time compute, helps AI handle tough problems more effectively. Instead of trying to solve everything all at once, the model breaks the task into smaller steps, solving each one before moving to the next.  

This step-by-step reasoning approach leads to better results, especially for tasks with clear answers like math problems. But what makes it exciting is that these improved results could be used as new training data. DeepMind thinks this feedback loop could unlock new ways to push AI performance even further. They published a paper in August explaining how this could help AI get past the data wall.

🧐What does an expert think? Microsoft CEO Satya Nadella isn’t worried. On a podcast, he called test-time compute “another scaling law” and explained how it could feed better data back into AI models. “That’s a fantastic way to increase model capability,” he said. 

🤓In conclusion - We’ll see this idea put to the test in 2025. It’s not a guaranteed fix, but the AI world is optimistic that this approach could keep the progress going.

Microsoft shares roadmap to help U.S. stay ahead in the global AI race

Key Points 

• Microsoft suggests the U.S. invest in R&D, education, and workforce development to stay ahead in AI.

• Microsoft urges the U.S. to promote American AI globally, leveraging partnerships with international allies.

☕News - Microsoft President Brad Smith recently shared a roadmap for the U.S. to maintain its lead in the global AI race, especially against China. In a blog post, Smith emphasized the need for a national strategy focused on R&D funding, improving education and workforce development, and avoiding overbearing regulations that could slow American tech companies.

🤔Why is Microsoft getting involved in this? Microsoft’s involvement is tied to the growing competition with China, which is aggressively expanding its AI capabilities. In his post, Smith pointed out that China is not just advancing domestically but is also trying to influence other countries by offering subsidized tech like computer chips and helping build local AI infrastructure. Microsoft, as a major player in the AI space, is concerned that if China’s AI systems become the global standard, it could limit the future growth of American tech. Smith stressed the need for the U.S. to step up its efforts to keep its AI innovation at the forefront.

🕵🏻‍♂️What are some of Microsoft's suggestions? Smith believes the U.S. has significant advantages, including superior technology and a strong private sector. He also highlighted that American companies, including Microsoft, are investing in AI that is more trustworthy—focusing on cybersecurity, privacy, and responsible use. He believes that to win this race, the U.S. must act quickly and work closely with allies to promote American AI on a global scale. This strategy, Smith suggests, will help ensure the U.S. remains the leader in AI innovation.