Once best known for developing the Taser, Axon has transformed into a 50 billion military and law enforcement tech giant.Mother Jones illustration; Michael Nigro/Pacific Press/Zuma; Arthur Ogleznev/Unsplash; Logan Weaver/Unsplash In April 2024, the American police tech firm Axon, which leads the market for police body cameras, released a tool it billed as "revolutionary": Draft One, an AI-powered software package that would turn body camera footage and audio into intelligible police reports. Once best known for developing the Taser, Axon has transformed into a 50 billion military and law enforcement tech giant, providing more than 5,000 police departments across the country with a suite of cloud-based products to manage evidence collection and storage. Draft One, the AI tool, connects with the company's body cameras and evidence storage service to write police reports with little human intervention. At least 21 departments have experimented with the software. The use of artificial intelligence in generating police reports has been particularly troubling, according to civil rights advocacy groups like the Electronic Frontier Foundation and ACLU, because of generative AI's propensity towards racial and gender bias, and its tendency to insert inaccuracies into texts--including wholesale inventions known by technologists as "hallucinations." "I can almost guarantee [AI] reports have been used in plea deals," a police captain wrote.

A bill headed to Utah's Senate floor would require police to include disclaimers in any report written with help from artificial intelligence. Introduced by Sen. Stephanie Pitcher, SB180 comes nearly a year after multiple police agencies across the country began testing software like Axon's Draft One, prompting concerns from critics and privacy advocates. Draft One was announced by Axon in April 2024, kicking off a major new phase for the company best known for manufacturing tasers and a popular line of body cameras used by law enforcement. Axon built Draft One using Microsoft's Azure OpenAI platform, and is designed to auto-generate police reports using only an officer's body cam audio records. Once processed, Draft One then crafts "a draft narrative quickly," reportedly cutting down on police officer's paperwork by as much as an hour per day.

Achieving a delicate balance between fostering trust in law en- forcement and protecting the rights of both officers and civilians continues to emerge as a pressing research and product challenge in the world today. In the pursuit of fairness and transparency, this study presents an innovative AI-driven system designed to generate police report drafts from complex, noisy, and multi-role dialogue data. Our approach intelligently extracts key elements of law enforcement interactions and includes them in the draft, producing structured narratives that are not only high in quality but also reinforce accountability and procedural clarity. This frame- work holds the potential to transform the reporting process, ensur- ing greater oversight, consistency, and fairness in future policing practices. A demonstration video of our system can be accessed at https://drive.google.com/file/d/1kBrsGGR8e3B5xPSblrchRGj-Y-kpCHNO/view?usp=sharing

We propose ReMiDi, a novel method for inferring neuronal microstructure as arbitrary 3D meshes using a differentiable diffusion Magnetic Resonance Imaging (dMRI) simulator. We first implemented in PyTorch a differentiable dMRI simulator that simulates the forward diffusion process using a finite-element method on an input 3D microstructure mesh. To achieve significantly faster simulations, we solve the differential equation semi-analytically using a matrix formalism approach. Given a reference dMRI signal $S_{ref}$, we use the differentiable simulator to iteratively update the input mesh such that it matches $S_{ref}$ using gradient-based learning. Since directly optimizing the 3D coordinates of the vertices is challenging, particularly due to ill-posedness of the inverse problem, we instead optimize a lower-dimensional latent space representation of the mesh. The mesh is first encoded into spectral coefficients, which are further encoded into a latent $\textbf{z}$ using an auto-encoder, and are then decoded back into the true mesh. We present an end-to-end differentiable pipeline that simulates signals that can be tuned to match a reference signal by iteratively updating the latent representation $\textbf{z}$. We demonstrate the ability to reconstruct microstructures of arbitrary shapes represented by finite-element meshes, with a focus on axonal geometries found in the brain white matter, including bending, fanning and beading fibers. Our source code will be made available online.

Officer Wendy Venegas spoke softly in Spanish to the 14-year-old standing on the side of a narrow residential road in East Palo Alto. The girl's face was puffy from crying as she quietly explained what had happened. The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. The girl said her father had caught her and her boyfriend "doing stuff" that morning, and her dad had either struck or pushed the boy, Venegas later explained.

Several police departments nationwide are debuting artificial intelligence that writes officers' incident reports for them, and although the software could cause issues in court, an expert says, the technology could be a boon for law enforcement. Oklahoma City's police department was among the first to experiment with Draft One, an AI-powered software that analyzes police body-worn camera audio and radio transmissions to write police reports that can later be used to justify criminal charges and as evidence in court. Since The Associated Press detailed the software and its use by the department in a late August article, the department told Fox News Digital that it has put the program on hold. "The use of the AI report writing has been put on hold, so we will pass on speaking about it at this time," Capt. Valerie Littlejohn wrote via email.

This article was originally featured on MIT Press Reader. This article is adapted from Marcus Carter and Ben Egliston's book "Fantasies of Virtual Reality." The political and cultural theorist Paul Virilio famously wrote that we live in a state of permanent (or "pure") war. By this, he meant that there is an increasing "perversion" of any clear-cut distinction between civilian and military institutions and, by extension, civilian and military life. According to Virilio, after the Second World War, Western economies and societies were permanently reorganized to support military power.

Neuronal damage, in the form of both brain and spinal cord injuries, is one of the major causes of disability and death in young adults worldwide. One way to assess the direct damage occurring after a mechanical insult is the simulation of the neuronal cells functional deficits following the mechanical event. In this study, we use a coupled mechanical electrophysiological model with several free parameters that are required to be calibrated against experimental results. The calibration is carried out by means of an evolutionary algorithm (differential evolution, DE) that needs to evaluate each configuration of parameters on six different damage cases, each of them taking several minutes to compute. To minimise the simulation time of the parameter tuning for the DE, the stretch of one unique fixed-diameter axon with a simplified triggering process is used to speed up the calculations. The model is then leveraged for the parameter optimization of the more realistic bundle of independent axons, an impractical configuration to run on a single processor computer. To this end, we have developed a parallel implementation based on OpenMP that runs on a multi-processor taking advantage of all the available computational power. The parallel DE algorithm obtains good results, outperforming the best effort achieved by published manual calibration, in a fraction of the time. While not being able to fully capture the experimental results, the resulting nerve model provides a complex averaging framework for nerve damage simulation able to simulate gradual axonal functional alteration in a bundle.

Police departments are often some of the tech industry's earliest adopters of new products like drones, facial recognition, predictive software, and now–artificial intelligence. After already embracing AI audio transcription programs, some departments are now testing a new, more comprehensive tool--software that leverages technology similar to ChatGPT to auto-generate police reports. According to an August 26 report from Associated Press, many officers are already "enthused" by the generative AI tool that claims to shave 30-45 minutes from routine officework. Initially announced in April by Axon, Draft One is billed as the "latest giant leap toward [the] moonshot goal to reduce gun-related deaths between police and the public." The company--best known for Tasers and law enforcement's most popular lines of body cams--claims its initial trials cut an hour of paperwork per day for users.

Open-source simulation tools play a crucial role for neuromorphic application engineers and hardware architects to investigate performance bottlenecks and explore design optimizations before committing to silicon. Reconfigurable Architecture for Neuromorphic Computing (RANC) is one such tool that offers ability to execute pre-trained Spiking Neural Network (SNN) models within a unified ecosystem through both software-based simulation and FPGA-based emulation. RANC has been utilized by the community with its flexible and highly parameterized design to study implementation bottlenecks, tune architectural parameters or modify neuron behavior based on application insights and study the trade space on hardware performance and network accuracy. In designing architectures for use in neuromorphic computing, there are an incredibly large number of configuration parameters such as number and precision of weights per neuron, neuron and axon counts per core, network topology, and neuron behavior. To accelerate such studies and provide users with a streamlined productive design space exploration, in this paper we introduce the GPU-based implementation of RANC. We summarize our parallelization approach and quantify the speedup gains achieved with GPU-based tick-accurate simulations across various use cases. We demonstrate up to 780 times speedup compared to serial version of the RANC simulator based on a 512 neuromorphic core MNIST inference application. We believe that the RANC ecosystem now provides a much more feasible avenue in the research of exploring different optimizations for accelerating SNNs and performing richer studies by enabling rapid convergence to optimized neuromorphic architectures.