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Most Americans want AI development to "slow down."

According to a Quinnipiac University poll released on September 30, 77% of US respondents want to reduce the pace or fully stop AI development until its safety has been verified. Only 5% favor faster progress.

Public trust in AI companies remains low. The survey found that 86% support independent safety standards, even if they hinder innovation; meanwhile, 74% express little or no trust in the leaders of AI companies. Researcher Chetan Jaiswal noted that these results suggest the public wants safeguards that do not depend simply on taking company leaders at their word.

CoreWeave announced Model Distillation, RL Rollouts, and a programmable training API on October 5 to close the loop between inference and post-training.

Traditionally, model improvement halts at deployment. These tools allow teams to capture production traffic failures as training data automatically, bypassing manual infrastructure setup.

In a cited example, customer Method used GPT-4o outputs to train a Llama 3.1 8B model for bank IVR systems. After iterating with fresh production data via the new workflow, the updated model won 57.5% of head-to-head evaluations against the previous production version. Another customer, Willow, combined supervised learning and reinforcement learning to improve text styling.

Note that these capabilities are largely in preview or newly launched states. Performance depends on user-specific data quality, and the cited metrics are vendor-reported without independent verification.

CoreWeave Senior Director Harsh Banwait reported that Nvidia's Vera CPU improved Sandboxes startup times by approximately 3x.

Sandboxes provides isolated environments for AI agents to execute tasks during reinforcement learning and inference. Banwait noted this addresses the bottleneck of waiting for environment initialization before agent task execution.

The data comes from CoreWeave's internal testing and has not been published with detailed benchmarks or independently verified.

Ghost announced its emergence from stealth today, completing an $11 million seed round led by Andreessen Horowitz.

Its first product, Core, is a personal computer designed specifically for running AI agents, priced at $3,499. The device includes an Nvidia RTX Pro 4000 SFF Blackwell GPU and comes pre-installed with models like Qwen and Gemma, aiming to let users process data and execute tasks locally without relying on cloud services.

The company states that all source code, logic, and model weights reside on the device, giving users full control. Pre-orders opened Monday, with shipping scheduled for the last week of October.

Having large numbers of phones jointly train one model, with raw data staying on each device and only the learned updates uploaded — this kind of approach is called federated learning. A long 2019 survey listed the problems this approach has yet to solve: unreliable devices, differing data across participants, constrained bandwidth, hard-to-prove privacy — and these are all entangled with one another.

Today, AI offerings in input methods, healthcare, and finance love to pitch with one line: data never leaves the device, so privacy is solved. Later research is still working through the items this survey listed one by one, and no single method solves them all. When you hear that pitch, press with the checklist: what about differing data across participants? How do you prove nothing was actually peeked at?

If you want to compare the merits or performance numbers of specific methods, don't use it to judge — it ran no experiments, only an inventory of problems. And if your scenario is learning without a central server, where devices connect directly to each other, the problems it lists don't match either.

Advances and Open Problems in Federated Learning (2019) | Next review 2027-09-20