# Labor Published articles for Labor. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Reflecting on Digital Rights Wins and Losses in Sacramento DevFeed: [Reflecting on Digital Rights Wins and Losses in Sacramento]() Original publisher: [Read original article]() Author: Rindala Alajaji Published: 2026-11-05T23:33:19Z Content type: article Language: en Sources: [Deeplinks]() Topics: [Security & Privacy](), [sensitive data](), [Artificial Intelligence]() Tags: [2026](), [ai](), [ai-surveillance](), [algorithm](), [automated-decision-making](), [autonomy](), [california](), [collect](), [data](), [decision-making](), [eff](), [fire](), [labor](), [lawsuits](), [platforms](), [police](), [printers](), [privacy](), [privacy-concerns](), [protection]() ### AI overview California's 2026 session produced workplace protections and other digital-rights wins, alongside setbacks and unresolved privacy gaps, according to EFF's account. SB 947 limits employers' use of automated decisions in firing or disciplining workers and requires information about consequential decisions; AB 1883 bars workplace surveillance to predict emotions or collect neural data. Other enacted measures expand protections for lawful reproductive and gender-affirming care, support youth digital literacy, and allow damages suits against federal officials for constitutional violations. Lawmakers also declined to extend age-gating rules to browsers. Setbacks included a youth social-media ban (AB 1709), signing SB 690, which EFF says removes CIPA's private right of action, and vetoing a ban on sensitive-data sales. Bills to extend privacy safeguards to local governments, curb surveillance pricing and location-data practices, and regulate license-plate readers stalled or failed. The account says AB 2047's 3D-printer restrictions will not affect makers until an ASTM standard is published. EFF expects renewed debate over ALPRs and AI next session, and a state fusion-center audit is due,, ### Source excerpt California's 2026 legislative session has officially adjourned and it's time to reflect on the wins and losses that have shaped Californians' digital rights landscape this year. This was the second year of a two-year legislative session, which meant several bills resumed their legislative paths from 2025. EFF monitored nearly 100 bills in the state over the two-year session. These included proposed regulations on 3D printers, access to social media platforms, police, commercial, and workplace surveillance practices, AI, and other privacy concerns. While we saw some victories, alarming developments also raised serious concerns about the future of privacy protection in the state. Celebrating Wins Use of automated decision-making and surveillance in the workplace Governor Newsom signed vital protections on the use of automated decision-making in the workplace and workplace surveillance tools, specifically No Robo Bosses (SB 947) and AB 1883. Championed by our labor partners, SB 947 limits how employers can use automated decision-making to fire or discipline workers, including provisions that ensure workers get vital information about automated decisions that affect them, and that an algorithm is not the only authority for these consequential decisions. Alongside it, AB 1883 prohibits the use of workplace surveillance to predict emotions or collect neural data. This stops employers from using potentially biased, invasive, and exploitative forms of AI surveillance at the workplace, marking an important step toward creating workplaces that respect the privacy and autonomy of workers. We look forward to continuing to ensure that technology in California's workplaces is deployed fairly, thoughtfully, and with respect for all workers. Protecting against federal overreach EFF was also grateful to see Governor Newsom sign SB 747, which allows damages lawsuits against federal officials who violate the U.S. Constitution. Victims of constitutional violations deserve a complete re ## Perceptive Humanoid Motion from Simulation DevFeed: [Perceptive Humanoid Motion from Simulation]() Original publisher: [Read original article]() Author: Siyuan Hu, Abulikemu Abuduweili, Yue Meng, Shipeng Liu, Quentin Leboutet, Chen Chen, Matthias M\\"uller, Vladlen Koltun Published: 2026-11-05T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Humanoid Robots](), [Physical AI](), [Robot Manipulation]() Tags: [autonomy](), [compute](), [cs-ai](), [cs-cv](), [cs-lg](), [cs-ro](), [data](), [environments](), [halo](), [home](), [homes](), [horizon](), [humanoid](), [humanoid-robots](), [labor](), [language](), [low-level](), [mapping](), [pipeline](), [simulation](), [skills](), [solve](), [system](), [tables]() ### AI overview HALO addresses the shortage of paired vision, language, and joint-command training data by synthesizing vision-language-motion examples across 1,000 procedurally generated homes, scaling data production with compute rather than human labor. The system learns skills such as navigating to named objects, moving through doors, crouching or crawling under tables, and pushing boxes, then combines them for long-horizon tasks. Though trained without real homes, HALO reportedly transferred zero-shot to six unmapped real-world environments, with 91% success across 360 trials. ### Source excerpt arXiv:2611.06908v1 Announce Type: cross Abstract: Humanoid robots have the potential to automate a wide range of physical tasks. Progress toward generalist humanoid autonomy, however, remains constrained by a data bottleneck: mapping pixels and free-form language to low-level joint commands requires paired supervision, and no corpus of such data exists at sufficiently large scale. Some approaches rely on human teleoperation and wearable capture devices, which require at least an hour of human time to produce an hour of data. We present HALO, a humanoid autonomy system powered by a pipeline that synthesizes vision-language-motion data across a thousand procedurally generated homes. The corpus scales with compute rather than human labor. HALO acquires a repertoire of physical skills, including sitting on chairs, navigating to named objects, stepping sideways through half-open doors, crouching and crawling beneath tables, and pushing boxes. It orchestrates these skills to solve composite, long-horizon tasks. Without ever seeing a real home during training, HALO transfers zero-shot to the physical world, achieving a 91% success rate across 360 trials in six unmapped real-world environments. See https://apple-aiml-research.github.io/ml-halo/ for code and videos. ## Xbox Manager: AI Already Being Used In Xbox Game Development - And Throughout the Game Industry DevFeed: [Xbox Manager: AI Already Being Used In Xbox Game Development - And Throughout the Game Industry]() Original publisher: [Read original article]() Author: EditorDavid Published: 2026-11-01T04:34:00Z Content type: news Language: en Sources: [Slashdot]() Topics: [Machine learning](), [Unity](), [Xbox]() Tags: [activision](), [ai](), [ai-development](), [ai-use](), [capcom](), [developers](), [development](), [epic-games](), [fortnite](), [game-development](), [game-studios](), [generative](), [generative-ai](), [independent](), [industry](), [labor](), [labs](), [microsoft](), [sega](), [xbox]() ### AI overview Xbox strategy manager Brandon Stirrup claimed Xbox already uses AI and that studios denying its use are "pretending," but the excerpt does not establish how widely AI is used across the industry. It names major developers that have used or pledged to use AI, alongside independent studios that oppose generative AI, and notes that Xbox Labs' announcement implied--but did not confirm--AI use. Players and developers are divided: some accept AI, while others cite environmental costs, labor concerns or aesthetic preferences. Responses include tools that highlight Steam's AI disclosures and grassroots AI-free labels. The excerpt adds that some people accept AI for code generation while opposing other uses. ### Source excerpt Should generative AI be used for videogame development? Brandon Stirrup, a strategy manager at Xbox, claims Xbox is already using AI, reports Engadget. And he also says that those who say they aren't are merely "pretending." Stirrup deactivated his social media account shortly after (though his posts have been archived elsewhere), igniting conversations about what role generative AI actually has or should have in game development. In some ways, it would be unsurprising to learn that Xbox is using AI to the extent that Stirrup implies. Xbox recently announced the formation of Xbox Labs at least in part as a response to a rising trend of AI-coded game ports; the announcement heavily implied that the new team would use AI as well but didn't say it outright. Xbox owner Microsoft is also a major player in AI development... Developers including Capcom, Square Enix, Sega, Nexon and Activision have all either used AI in their games or pledged to do so. Fortnite developer Epic Games has been particularly up-front about its AI use, and CEO Tim Sweeney has aggressively promoted it on social media... On the other hand, there are large pockets of the game industry vocally opposed to generative AI. Notably, these tend to be smaller, independent studios without shareholders to impress by touting the cost-savings of AI. These studios include Tunic publisher Finji, Stardew Valley developer ConcernedApe and Clickolding developer Strange Scaffold, among many more. Due to the sheer number and variety of game studios, it's impossible to say just how much of the industry actually uses AI at this point, but claims that "everyone" is using it are clearly false. There's a similar divide among players about whether generative AI should be embraced or avoided at all costs. While some simply accept AI as part of games going forward, others have pledged to avoid games that use the technology outright due to its environmental costs, labor issues or pure aesthetic preference. That's led to the po ## Advancing Clinical Genomic Analysis with a Skill-Centred Agent DevFeed: [Advancing Clinical Genomic Analysis with a Skill-Centred Agent]() Original publisher: [Read original article]() Author: Qiaoyu Zheng, Weike Zhao, Yanjie Fan, Ye Cao, Mengqi Jiang, Xiao Zhou, Xiaoman Zhang, Pengcheng Qiu, Yongguo Yu, Yanfeng Wang, Ya Zhang, Chaoyi Wu, Weidi Xie Published: 2026-10-30T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Artificial Intelligence](), [decision-making](), [Agent Skills](), [human in the loop agent]() Tags: [agent](), [ai](), [ai-collaboration](), [analysis](), [benchmark](), [cases](), [collaboration](), [cs-ai](), [databases](), [decision-making](), [deepseek](), [dependency](), [disease](), [domain](), [experts](), [feedback](), [guidelines](), [human-ai-collaboration](), [integrate](), [labor](), [language](), [q-bio-gn]() ### AI overview ClinGeX combines 105 curated executable skills using task-specific retrieval and dependency-aware orchestration to support clinical genomic analysis. On 223 temporally held-out cases, it reached 71.7% macro-averaged task-level concordance with expert classifications, versus 45.7% for the strongest comparator. In a counterbalanced study with 45 professionals assessing 135 cases, ClinGeX assistance raised raw case-level concordance in variant analysis, rare-disease interpretation, and molecular oncology, and reduced reported interpretation time across scenarios. An exploratory, non-interventional one-month assessment involved 11 professionals reporting on 96 self-selected episodes; they reported frequent adoption of conclusions and time savings. These findings suggest potential to augment specialist interpretation, while the real-world evidence is limited by its exploratory design and self-selected cases. ### Source excerpt arXiv:2610.40556v1 Announce Type: new Abstract: Clinical genomic analysis requires experts to integrate evolving guidelines, databases, and computational evidence. As sequencing has become increasingly accessible, this complex and labor-intensive process has become a major bottleneck to the timely use of genomic information in clinical decision-making. To fill this gap, we developed ClinGeX, a large-language-model agent that composes 105 curated executable skills through task-specific retrieval and dependency-aware orchestration. On 223 temporally held-out benchmark cases, ClinGeX achieved 71.7\% macro-averaged task-level concordance with expert reference classifications, compared with 45.7\% for the strongest comparator. In a three-condition, counterbalanced human-AI collaboration study, 45 clinical genomic professionals assessed 135 clinically derived cases under routine (no AI assistance), DeepSeek-assisted and ClinGeX-assisted conditions. ClinGeX assistance increased raw case-level concordance across criterion-level variant analysis, genomic interpretation in rare disease and molecular oncology decision-making (51.1\%, 57.8\%, 64.4\% versus 15.6\%, 31.1\%, 20.0\% in comparator conditions) and reduced reported interpretation time in all scenarios. During an exploratory one-month real-world non-interventional assessment, 11 experienced professionals submitted feedback on 96 self-selected analysis episodes and reported frequent conclusion adoption and time savings. These findings support executable domain skills as a promising approach to augment specialist genomic interpretation. ## Structure, Not Sequence: Tracking and Validating Folding Mechanisms in ESMFold's Trunk with Sparse Autoencoders DevFeed: [Structure, Not Sequence: Tracking and Validating Folding Mechanisms in ESMFold's Trunk with Sparse Autoencoders]() Original publisher: [Read original article]() Author: Bohan Liu, Xiaohan Fan, Wenqian Ye, Aidong Zhang Published: 2026-10-28T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence](), [arXiv: Machine Learning]() Topics: [Neural Network](), [Artificial Intelligence](), [Transformer]() Tags: [3d](), [analysis](), [autoencoders](), [biology](), [blocks](), [concepts](), [cs-ai](), [cs-lg](), [folding](), [gradient](), [intermediate](), [labor](), [language](), [language-model](), [meaning](), [mechanism](), [model](), [modules](), [network](), [neural](), [q-bio-qm](), [sequence](), [structure]() ### AI overview The authors use sparse autoencoders to interpret ESMFold's folding trunk and report that its single representation carries inherited sequence semantics while its pair representation computes pairwise geometry. Their analysis locates distinct spatial computation across modules and recycles, traces how concepts compose across blocks with gradient-based attribution, and checks interpreted activations against folding outputs and known biochemistry. The learned concepts reportedly forecast ESMFold's fold quality. These findings are summarized in the supplied abstract; it does not provide detailed methods or quantitative results. ### Source excerpt arXiv:2610.36226v1 Announce Type: cross Abstract: Structure predictors like ESMFold fold proteins using neural network computation and have transformed structural biology. However, the driving mechanism from amino-acid (AA) sequence to 3D structure is insufficiently understood, and biological meaning remains locked in the dense network. Sparse autoencoders (SAEs) have shown promising results in obtaining interpretable concept features over an overcomplete basis of intermediate protein sequence-model representations. Yet folding models, which predict physical shape from AA sequence semantics, have no such account. We hypothesize that the features carrying the folding computation evolve in the 3D structural regression process. In this paper, we interpret the folding trunk in three steps. First, a pilot analysis on the representation distribution across modules and recycles locates distinct spatial computation in the trunk. Second, the dictionaries expose a division of labor: the single representation carries inherited semantics, and the pair representation computes the pairwise geometry. Third, we interpret the dictionary at scale with a reasoning language model, trace how the concepts compose across blocks by gradient-based attribution, and follow the interpreted code to the model's folding output, verifying where the computation aligns with, or departs from, known biochemistry. Together, the sparse dictionaries we train capture the relational geometry the trunk computes beyond the sequence model, and their concept activations forecast the model's own fold quality, turning the black-box folding trunk into a sparse, interpretable, and scientifically verifiable circuit. ## Proton To 'Re-Interview' Staff About AI To See Who Gets To Keep Their Job DevFeed: [Proton To 'Re-Interview' Staff About AI To See Who Gets To Keep Their Job]() Original publisher: [Read original article]() Author: Joseph Cox Published: 2026-10-26T20:16:02Z Content type: news Language: en Sources: [404 Media]() Topics: [developer-productivity](), [Users](), [email]() Tags: [404-media](), [ai](), [company](), [email](), [employment](), [interviewing](), [interviews](), [jobs](), [labor](), [monday](), [news](), [privacy](), [products](), [proton](), [staff]() ### AI overview Proton said it would re-interview staff across the company about how they use AI at work. An internal email obtained by 404 Media indicated the interviews could affect whether employees continue in their jobs. The excerpt does not describe the interview process or any decisions made. ### Source excerpt "These re-interviews are consequential," a leaked email seen by 404 Media reads. ## Robotics Analytics @ Target DevFeed: [Robotics Analytics @ Target]() Original publisher: [Read original article]() Author: Target Brands, Inc Published: 2015-04-09T05:00:00Z Content type: article Language: en Sources: [Target]() Topics: [Analytics Pipeline](), [data-engineering](), [FIRST](), [FIRST Robotics Competition]() Tags: [analytics](), [automated](), [automation](), [big-data](), [competition](), [complex](), [control](), [curiosity](), [data](), [dc](), [distribution](), [efficiency](), [electrical-engineering](), [engineering](), [equipment](), [first](), [interest](), [labor](), [machine](), [robotics](), [target]() ### AI overview Target's automated distribution centers produce machine data that could help improve operations, but engineers described a cumbersome process for collecting and interpreting it. Data was scattered across locations with duplicate file names, required server access, and had to be moved manually before spreadsheets and macros could parse and visualize it. The author introduced Splunk as a tool for bringing data from different sources into one place with built-in visualization. The excerpt does not describe the implementation or its results. ### Source excerpt One important part of a retailer is their supply chain network. In order to sell a product in our stores, or online at Target.com, we need to have a well run and maintained network to move all of those products from one place to another. It may not sound like a complex problem to ship products from a distribution center to a store, but as soon as you start to have multiple vendors with multiple stores (not to mention online orders that go directly to a guest's home) it becomes increasingly complex. Retailers get products from companies, referred to as vendors, and then distribute the products to stores through central locations, referred to as distribution centers (DCs). In order to improve throughput of our supply chain network we expand our distribution network by building new distribution centers or we increasing the efficiency of the current network. A great way to improve performance of a DC is to put robots and other automation equipment in it. High tech equipment like this requires high skilled labor to maintain and manage the equipment. These highly automated systems run on servers and other control equipment and they produce a lot of machine data that can lead to valuable insights. Robotics has always been an interest of mine, starting with my experience in high school with the FIRST Robotics Competition and continuing today as I volunteer with FIRST. When I first joined Target right out of school with a degree in Electrical Engineering my curiosity and interest in robotics led me to get to know some of the engineers who worked on the robotics systems within Target's DCs. I mentioned to them that I was working with a tool called Splunk that did a good job of aggregating data from different sources to a central location with a visualization platform built in. The engineers said they had a very cumbersome, inconsistent manual process which involved multiple spreadsheets and home grown macros to gather and visualize data. The data was spread out through multip