If you have tips about the remaking of the federal government, you can contact Matteo Wong on Signal at @matteowong.52. A new phase of the president and the Department of Government Efficiency's attempts to downsize and remake the civil service is under way. The idea is simple: use generative AI to automate work that was previously done by people. The Trump administration is testing a new chatbot with 1,500 federal employees at the General Services Administration and may release it to the entire agency as soon as this Friday--meaning it could be used by more than 10,000 workers who are responsible for more than 100 billion in contracts and services. This article is based in part on conversations with several current and former GSA employees with knowledge of the technology, all of whom requested anonymity to speak about confidential information; it is also based on internal GSA documents that I reviewed, as well as the software's code base, which is visible on GitHub.

Elon Musk's so-called Department of Government Efficiency has deployed a proprietary chatbot called GSAi to 1,500 federal workers at the General Services Administration, WIRED has confirmed. The move to automate tasks previously done by humans comes as DOGE continues its purge of the federal workforce. GSAi is meant to support "general" tasks, similar to commercial tools like ChatGPT or Anthropic's Claude. It is tailored in a way that makes it safe for government use, a GSA worker tells WIRED. The DOGE team hopes to eventually use it to analyze contract and procurement data, WIRED previously reported.

At least dozens of workers for the Technology Transformation Services, housed within the General Services Administration, were fired Wednesday afternoon, sources tell WIRED. The sudden cuts seemingly targeted probationary and short-term staffers, including workers supplied by the Presidential Innovation Fellowship program, which brings skilled technologists from the private sector to work in government for a few years at a time. Around 50 of the 70 members of the US Digital Corps, an early-career two-year government fellowship, were terminated as well, sources say. Sources also tell WIRED that TTS management met with workers individually prior to the terminations, giving them one last chance to take the deferred resignation offered in the "Fork In the Road" email late last month. One TTS staffer called the meetings "coercive for sure."

In a Monday morning meeting, Thomas Shedd, the recently appointed Technology Transformation Services director and Elon Musk ally, told General Services Administration workers that the agency's new administrator is pursuing an "AI-first strategy," sources tell WIRED. Throughout the meeting, Shedd shared his vision for a GSA that operates like a "startup software company," automating different internal tasks and centralizing data from across the federal government. The Monday meeting, held in-person and on Google Meet, comes days after WIRED reported that many of Musk's associates have migrated to jobs at the highest levels of the GSA and the Office of Personnel Management (OPM). Prior to joining TTS, which is housed within the GSA, Shedd was a software engineer at Tesla, one of Musk's companies. The transition has caused mass confusion amongst GSA staffers who have been thrown into surprise one-on-one meetings, forced to present their code--often to young engineers who did not identify themselves--and left wondering what the future of the agency's tech task force will look like.

Semi-supervised domain adaptation methods leverage information from a source labelled domain with the goal of generalizing over a scarcely labelled target domain. While this setting already poses challenges due to potential distribution shifts between domains, an even more complex scenario arises when source and target data differs in modality representation (e.g. they are acquired by sensors with different characteristics). For instance, in remote sensing, images may be collected via various acquisition modes (e.g. optical or radar), different spectral characteristics (e.g. RGB or multi-spectral) and spatial resolutions. Such a setting is denoted as Semi-Supervised Heterogeneous Domain Adaptation (SSHDA) and it exhibits an even more severe distribution shift due to modality heterogeneity across domains.To cope with the challenging SSHDA setting, here we introduce SHeDD (Semi-supervised Heterogeneous Domain Adaptation via Disentanglement) an end-to-end neural framework tailored to learning a target domain classifier by leveraging both labelled and unlabelled data from heterogeneous data sources. SHeDD is designed to effectively disentangle domain-invariant representations, relevant for the downstream task, from domain-specific information, that can hinder the cross-modality transfer. Additionally, SHeDD adopts an augmentation-based consistency regularization mechanism that takes advantages of reliable pseudo-labels on the unlabelled target samples to further boost its generalization ability on the target domain. Empirical evaluations on two remote sensing benchmarks, encompassing heterogeneous data in terms of acquisition modes and spectral/spatial resolutions, demonstrate the quality of SHeDD compared to both baseline and state-of-the-art competing approaches. Our code is publicly available here: https://github.com/tanodino/SSHDA/

The Stout AgTech autonomous cultivator uses machine vision and AI to identify weeds. The smart implement then uses articulated "blades" to cut the undesired plants from their root systems. Today, farmers have a number of options for cultivating their crops. Herbicides and genetically modified crops are one cultivation method that has increasingly gone out of fashion due primarily to market pressure from consumers for organically grown crops. For organic-certified farms, the only option for cultivation is to use a mechanical means of removing weeds.