The Unesco standards define a new category of data, 'neural data', and suggest guidelines governing its protection. The Unesco standards define a new category of data, 'neural data', and suggest guidelines governing its protection. Unesco adopts global standards on'wild west' field of neurotechnology UN body's recommendations driven by AI advances and proliferation of consumer-oriented neurotech devices It is the latest move in a growing international effort to put guardrails around a burgeoning frontier - technologies that harness data from the brain and nervous system. Unesco has adopted a set of global standards on the ethics of neurotechnology, a field that has been described as "a bit of a wild west". "There is no control," said Unesco's chief of bioethics, Dafna Feinholz.

The filmmaker could not get Tiggy the alien to cooperate. He just needed the glistening brown creature to turn its head. But Tiggy, who was sitting in the passenger's seat of a cop car, kept disobeying. At first Tiggy rotated his gaze only slightly. Then he looked to the wrong side of the camera. Then his skin turned splotchy, like an overripe fruit. The filmmaker was not on a movie set, or Mars. He was sitting at his home computer in Los Angeles using a piece of AI software called FLUX Kontext to generate and regenerate images of the alien, waiting for a workable one to appear. He'd used a different AI tool, Midjourney, to generate the very first image of Tiggy (prompt: "fat blob alien with a tiny mouth and tiny lips"); one called ElevenLabs to create the timbre of Tiggy's voice (the filmmaker's voice overlaid with a synthetic one, then pitch-shifted way up); and yet another called Runway to describe the precise shot he wanted in this scene ("close up on the little alien as they ride in the passenger seat, shallow depth of field").

Recently, knowledge editing on large language models (LLMs) has received considerable attention. Compared to this, editing Large Vision-Language Models (LVLMs) faces extra challenges from diverse data modalities and complicated model components, and data for LVLMs editing are limited. The existing LVLM editing benchmark, which comprises three metrics (Reliability, Locality, and Generality), falls short in the quality of synthesized evaluation images and cannot assess whether models apply edited knowledge in relevant content. Therefore, we employ more reliable data collection methods to construct a new Large $\textbf{V}$ision-$\textbf{L}$anguage Model $\textbf{K}$nowledge $\textbf{E}$diting $\textbf{B}$enchmark, $\textbf{VLKEB}$, and extend the Portability metric for more comprehensive evaluation. Leveraging a multi-modal knowledge graph, our image data are bound with knowledge entities. This can be further used to extract entity-related knowledge, which constitutes the base of editing data. We conduct experiments of different editing methods on five LVLMs, and thoroughly analyze how do they impact the models. The results reveal strengths and deficiencies of these methods and hopefully provide insights for future research. The codes and dataset are available at: $\href{https://github.com/VLKEB/VLKEB}{\text{https://github.com/VLKEB/VLKEB}}$.

We present VESPA, an intentionally simple yet novel zero-shot system for layout, locale, and domain agnostic document extraction. In spite of the availability of large corpora of documents, the lack of labeled and validated datasets makes it a challenge to discriminatively train document extraction models for enterprises. We show that this problem can be addressed by simply transferring the information extraction (IE) task to a natural language Question-Answering (QA) task without engineering task-specific architectures. We demonstrate the effectiveness of our system by evaluating on a closed corpus of real-world retail and tax invoices with multiple complex layouts, domains, and geographies. The empirical evaluation shows that our system outperforms 4 prominent commercial invoice solutions that use discriminatively trained models with architectures specifically crafted for invoice extraction. We extracted 6 fields with zero upfront human annotation or training with an Avg. F1 of 87.50.

Devin Butts walked the tiled halls of the Pueblo Mall early one Friday morning in April, amazed at what he saw. The mall, the main shopping center for the city of Pueblo in southern Colorado, was larger than anything the 25-year-old was used to while living on the streets of quiet prairie towns in north central Texas. He wandered through T-shirt stores and schlocky gift shops, past American flag-adorned beer bongs and marijuana-emblazoned "Rocky Mountain High" shirts, not noticing how employees warily eyed his baggy jeans and the tattoos peeking out from the sleeves and collar of his Bob Marley T-shirt. Or maybe he'd learned from experience to ignore the looks. Butts inquired at shop after shop. Often he received an apologetic shake of the head. Sometimes he was told to fill out an application online, no easy feat for someone who didn't own a computer.