When you purchase through links in our articles, we may earn a small commission. Here's how to add USB-C ports to an older Windows PC, from cheap USB-A adapters to more powerful PCIe expansion cards for desktops. Over the past few years, USB-C has become the go-to connection for IT devices. Smartphones, external SSDs, cameras, and docking stations now rely almost exclusively on this standard. However, many older Windows PCs still have no USB-C ports, or just one.

InquiryIQ, a previously unreported prototype, tested a model from xAI, maker of Grok, to surface associates, social accounts, and other information about people identified through Clearview. In 2020, face-recognition firm Clearview AI became infamous for scraping more than 3 billion photos from the internet to turn faces into names for police and security professionals. Now the company is experimenting with trawling the web again, this time using AI to potentially help law enforcement fill in the person behind the name--who they are, who they know, where they live, and what they've left behind online. Clearview has quietly built and tested what it describes as an experimental AI "analyst assistant," WIRED has learned. Called InquiryIQ, the unreleased tool is designed to take details an investigator unearths from a Clearview search and then automatically fan out across the web--opening webpages, analyzing images, and assembling what it finds into a profile containing the possible employers, aliases, associates, and physical characteristics of a person police are investigating. InquiryIQ's interface states that supplying age, gender, and race can help the system make "smarter decisions" as it searches, according to code for the tool analyzed by WIRED.

Installing a large language model on your personal computer gives you a handy digital assistant that won't compromise your data privacy. It's no exaggeration to say the large language models that power AI bots like ChatGPT and Gemini are changing the world. Many of us are now relying on them for coding, writing, summarizing, and searching the web. Whatever directions AI goes in over the next few years, LLMs will be part of that future. If you use ChatGPT, Claude, Perplexity, or any of the other AI platforms, then you're using an LLM.

When you purchase through links in our articles, we may earn a small commission. Witcher 3 on a phone is cool. A cool phone modding project has me seeing visions of doom and gloom for the future of the PC market. Somebody built a desktop "PC" out of a gaming phone. The modder basically disassembled a powerful Android phone, strapped on an enclosure and cooler setup, and connected it to an external monitor.

Hosted by Ben Byford, The Machine Ethics Podcast brings together interviews with academics, authors, business leaders, designers and engineers on the subject of autonomous algorithms, artificial intelligence, machine learning, and technology's impact on society. This month we speak with Demetrios for the second time about: what ML and MLops are, narrow machine learning being still relevant, vibe coding, working with agents, talking to your computer, unknown productivity gains of LLMs, the chat interface as a bad interface for all knowledge, AIs that know when they're wrong, the lack of ground truth, and more Demetrios founded the largest community dealing with producitonizing AI and ML models. The MLOps Community is now where tens of thousands of practitioners come to learn from one another. In his free time he can be found building stone stackings in the woods with his daughters. This podcast was created and is run by Ben Byford and collaborators.

Knowing which training examples drive outputs is fundamental to auditing, correcting, and understanding language models, yet for modern LLMs this remains expensive, approximate, and largely post-hoc. Standard language models generate tokens through a dense network pathway, causing training data's influence to be distributed across parameters rather than organized along explicit, traceable components. We introduce a prototype language model architecture, Prototypes for Interpretable Sequence Modeling (PRISM), that forms each prediction via a sparse, non-negative mixture of learned prototypes, trained with clustering objectives that anchor each prototype to coherent neighborhoods of training examples. Across architectures from 130M to 1.6B parameters trained on up to 50B tokens, prototype language models either surpass or remain within 2.5 percentage points on average downstream accuracy of matched dense baselines. We show that sparse prototype structure localizes curvature in the loss landscape, yielding a more tractable Hessian and enabling training data attribution that is ~500x faster than post hoc baselines when consuming equivalent memory. Calibrating linear prototype controllers can improve downstream accuracy by roughly 3 points while tracing those corrections back to training neighborhoods, and targeted prototype suppression can remove model behaviors without finetuning or measurable loss in generation quality.

Figure 5: Word clouds of text prompts for the text-only generation (T2I) task (left) and the multimodal generation task (right). Figure 5 visually summarizes the prominent semantic elements in the benchmark prompts for text-only492 (T2I) and multimodal generation tasks. The differentiation of the word clouds reflects task-specific493 features of MMGen-Bench, emphasizing spatial and descriptive details in T2I tasks, while multimodal494 tasks more frequently involve social and interactive scenarios.495 Aspect Objects Relations Attributes Counting Overall Spearman ω 0.469 0.909 0.601 0.839 0.699 As depicted in Figure 6, the distribution of aspect types differs notably between the text-only497 generation (T2I) and multi-modal generation tasks. In the T2I setting, "Objects" dominate with498 38.3%, while "Attributes" and "Relations" also constitute substantial proportions (33.9% and 25.4%,499 respectively).

A.1 Behavioral Task A male nonhuman primate (NHP, Macaca mulatta), Monkey N (age 7 at the beginning of the dataset, age 11 at the end), was trained to perform a trial-based, two degree-of-freedom (DOF) dexterous finger movement task, shown in Figure 1. During all sessions, Monkey N sat in a primate chair (Crist Instruments, Hagerstown, MA) in a shielded chamber, with his arms fixed at his sides and flexed 90 degrees at the elbow, resting on a table. The left hand was positioned securely in a manipulandum, which used bend sensors (FS-L-0073-103-ST, Spectra Symbol, Salt Lake City, UT) to measure the flexion of two finger groups, index (IDX) and middle-ring-small (MRS). At the beginning of each experimental session (and as needed throughout a session), these flexion sensors were calibrated such that a reading of 1 indicated full flexion of a finger group and a reading of 0 indicated full extension. These readings were used to update the positions of the corresponding finger groups of a virtual hand presented on a screen in front of Monkey N. Bend sensor values were sampled at 1000 Hz. Updates to the virtual hand were limited to the refresh rate of the monitor (120 Hz). The task itself involved trial-based target acquisitions. At the beginning of each trial, two color-coded spherical targets, one for each DOF, were placed on the screen, covering 15% of the full arc of motion (see Figure 1A). Monkey N then acquired the targets by moving his fingers to the correct positions and holding his position for 750 ms.

We introduce REAL, a benchmark and framework for multi-turn agent evaluations on deterministic simulations of real-world websites. REAL comprises high-fidelity, publicly hosted, deterministic replicas of 11 widely-used websites across domains such as e-commerce, travel, communication, and professional networking. We also release a benchmark consisting of 112 practical tasks that mirror everyday complex user interactions requiring both accurate information retrieval and state-changing actions. All interactions occur within this fully controlled setting, eliminating safety risks and enabling robust, reproducible evaluation of agent capability and reliability. REAL environments are highly configurable, offer complete action/observation space control, and allow researchers to inspect state-changes at any step to define reward signals for training. Our novel evaluation framework combines programmatic checks of website state for action-based tasks with rubric-guided LLM-based judgments for information retrieval, and our harness supports both open-source and proprietary agentic systems. Our empirical results show that frontier language models achieve at most a 41%success rate on REAL, highlighting critical gaps in current autonomous capabilities. REAL enables easy integration of new tasks, reproducible evaluation, and scalable data generation for post-training web agents. The websites, framework, and leaderboard are available at https://realevals.xyzand https://github.com/agi-inc/REAL.

Skill Incremental Learning (SIL) is the process by which an embodied agent expands and refines its skill set over time by leveraging experience gained through interaction with its environment or by the integration of additional data. SIL facilitates efficient acquisition of hierarchical policies grounded in reusable skills for downstream tasks. However, as the skill repertoire evolves, it can disrupt compatibility with existing skill-based policies, limiting their reusability and generalization. In this work, we propose SIL-C, a novel framework that ensures skill-policy compatibility, allowing improvements in incrementally learned skills to enhance the performance of downstream policies without requiring policy re-training or structural adaptation. SIL-C employs a bilateral lazy learning-based mapping technique to dynamically align the subtask space referenced by policies with the skill space decoded into agent behaviors. This enables each subtask, derived from the policy's decomposition of a complex task, to be executed by selecting an appropriate skill based on trajectory distribution similarity. We evaluate SIL-C across diverse SIL scenarios and demonstrate that it maintains compatibility between evolving skills and downstream policies while ensuring efficiency throughout the learning process.