Hub AI research

Artificial Intelligence is already part of everyday life. Since the ImageNet moment in 2012, when Deep Learning proved its great utility in combination with a lot of computing power and gigantic amounts of data, only about ten years have passed – yet there has been more progress than in half a century before.

Still, many questions remain: Will Deep Learning remain the gold standard of AI development? What are the performance limits of large AI systems? Do we need symbolic systems? What technology will lead to artificial general intelligence? Finally, what actually is intelligence, anyway?

We explore these topics and more in our Artificial Intelligence Research Content Hub.

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Wired reports that OpenAI is stepping back into robotics, with new hiring pointing toward work on humanoid machines.

According to job postings, the company is assembling a team focused on training robots through teleoperation and simulation. OpenAI is also seeking engineers specializing in sensing and prototyping. The listings describe the team’s mission as building "general-purpose robots" that could help push progress toward AGI.

It’s not confirmed that the effort targets humanoids, but signs point that way. New hires include Stanford researcher Chengshu Li, who worked on benchmarks for humanoid household robots. That makes it likely OpenAI’s robotics push could center on humanlike systems.

OpenAI shut down its robotics work in 2020, citing a lack of training data. But the company began posting robotics roles again in January, signaling a renewed focus on physical AI after a five-year pause.

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Google DeepMind has introduced a new language model called VaultGemma, designed with a focus on privacy. It is the largest open model to date trained from scratch with differential privacy, containing 1 billion parameters.

Normally, large language models can memorize parts of their training data, including sensitive information like names, addresses, or entire documents. Differential privacy avoids this by adding controlled random noise during training, making it statistically impossible to trace the model's outputs back to specific examples. In theory, even if VaultGemma were trained on confidential documents, those documents could not be reconstructed later.

According to Google, early tests confirm that the model does not reproduce training data. The tradeoff is performance: its output is roughly comparable to non-private LLMs released about five years ago.

The model weights are openly available on Hugging Face and Kaggle.

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Elon Musk's AI company xAI has laid off about 500 employees, including a third of its data annotation team. These workers had been training the company's chatbot Grok by sorting and explaining raw data. According to internal emails seen by Business Insider, xAI is eliminating most roles for so-called generalist tutors and plans to hire more specialists instead. Employees will continue to be paid through the end of their contracts, or no later than November 30, but lost access to company systems immediately. Just before the cuts, staff were required to take skills tests that were supposed to determine their future roles. On X, the company said it plans to expand its team of expert tutors in fields like science, medicine, and finance "tenfold." The idea is to give Grok deeper domain knowledge, a move similar to practices at other AI firms, which often outsource this work to external contractors. As a result, low-cost mass data annotation work is beginning to lose its role, gradually being replaced by the very AI systems it helped build.

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