Workforce transformation across diverse industries has driven an increased demand for specialized natural language processing capabilities. Nevertheless, tasks derived from work-related contexts inherently reflect real-world complexities, characterized by long-tailed distributions, extreme multi-label target spaces, and scarce data availability. The rise of generalist embedding models prompts the question of their performance in the work domain, especially as progress in the field has focused mainly on individual tasks. To this end, we introduce WorkBench, the first unified evaluation suite spanning six work-related tasks formulated explicitly as ranking problems, establishing a common ground for multi-task progress. Based on this benchmark, we find significant positive cross-task transfer, and use this insight to compose task-specific bipartite graphs from real-world data, synthetically enriched through grounding. This leads to Unified Work Em-beddings (UWE), a task-agnostic bi-encoder that exploits our training-data structure with a many-to-many InfoNCE objective, and leverages token-level embeddings with task-agnostic soft late interaction. UWE demonstrates zero-shot ranking performance on unseen target spaces in the work domain, enables low-latency inference by caching the task target space embeddings, and shows significant gains in macro-averaged MAP and RP@10 over generalist embedding models.

We present Flotta, a Federated Learning framework designed to train machine learning models on sensitive data distributed across a multi-party consortium conducting research in contexts requiring high levels of security, such as the biomedical field. Flotta is a Python package, inspired in several aspects by Apache Spark, which provides both flexibility and security and allows conducting research using solely machines internal to the consortium. In this paper, we describe the main components of the framework together with a practical use case to illustrate the framework's capabilities and highlight its security, flexibility and user-friendliness.

We introduce WorkBench: a benchmark dataset for evaluating agents' ability to execute tasks in a workplace setting. WorkBench contains a sandbox environment with five databases, 26 tools, and 690 tasks. These tasks represent common business activities, such as sending emails and scheduling meetings. The tasks in WorkBench are challenging as they require planning, tool selection, and often multiple actions. If a task has been successfully executed, one (or more) of the database values may change. The correct outcome for each task is unique and unambiguous, which allows for robust, automated evaluation. We call this key contribution outcome-centric evaluation. We evaluate five existing ReAct agents on WorkBench, finding they successfully complete as few as 3% of tasks (Llama2-70B), and just 43% for the best-performing (GPT-4). We further find that agents' errors can result in the wrong action being taken, such as an email being sent to the wrong person. WorkBench reveals weaknesses in agents' ability to undertake common business activities, raising questions about their use in high-stakes workplace settings. WorkBench is publicly available as a free resource at https://github.com/olly-styles/WorkBench.

Take a gander at these Home Depot Black Friday deals from the comfort of your living room without having to line up at the gates to do battle with the masses in the store. Winter means, for more of us than not, spending a lot more time at home than we'd otherwise choose to spend. Vacations are a fine remedy, sure, but who has money to travel all season long? Make home a pleasant place to be, inside and outside, with these deals on equipment for your garage, backyard, and home, and you won't want to leave. WIRED tests products year-round and handpicked these deals based on the actual discounts, not just the discounts retailers claim to offer. Products that are sold out or no longer discounted as of publishing will be crossed out . We'll update this guide through November.

Take a gander at these early Home Depot Black Friday deals and get a lead on the masses who'll be ready to crowd the digital gates before the Thanksgiving turkey even gets cold. Winter means, for more of us than not, spending a lot more time at home than we'd otherwise choose to spend. Vacations are a fine remedy, sure, but who has money to travel all season long? Make home a pleasant place to be, inside and outside, with these deals on equipment for your garage, backyard, and home, and you won't want to leave. WIRED tests products year-round and handpicked these deals based on the actual discounts, not just the discounts retailers claim to offer.

Winter means, for more of us than not, spending a lot more time at home than we'd otherwise choose to spend. Vacations are a fine remedy, sure, but who has money to travel all season long? Make home a pleasant place to be, inside and outside, with these deals on equipment for your garage, backyard, and home, and you won't want to leave. WIRED tests products year-round and handpicked these deals based on the actual discounts, not just the discounts retailers claim to offer. Products that are sold out or no longer discounted as of publishing will be crossed out .

We introduce RLang, a domain-specific language (DSL) for communicating domain knowledge to an RL agent. Unlike existing RL DSLs that ground to \textit{single} elements of a decision-making formalism (e.g., the reward function or policy), RLang can specify information about every element of a Markov decision process. We define precise syntax and grounding semantics for RLang, and provide a parser that grounds RLang programs to an algorithm-agnostic \textit{partial} world model and policy that can be exploited by an RL agent. We provide a series of example RLang programs demonstrating how different RL methods can exploit the resulting knowledge, encompassing model-free and model-based tabular algorithms, policy gradient and value-based methods, hierarchical approaches, and deep methods.

The rapid development of e-commerce has brought new challenges to warehouse operations. Order picking plays a crucial role among all these operations, which directly affects the overall order fulfillment efficiency (Lamballais et al., 2017; Shen et al., 2020). The Robotic Mobile Fulfillment System (RMFS) is invented to improve order picking efficiency and reduce labour costs by exploiting rack-moving mobile robots (Boysen et al., 2017). The cooperation between the robots and movable racks eliminates pickers' unproductive movement in the picker-to-parts system (Battini et al., 2017). Compared with traditional manual warehouses, the picking performance of RMFS is far superior, which is reported to achieve over 600 order-lines per hour per workstation (Wulfraat, 2012; Banker, 2016). Nevertheless, order picking in RMFS needs further efficiency improvement due to the growing demand and increasingly tight delivery schedules brought by the prosperity of e-commerce (Batt & Gallino, 2019; Azadeh et al., 2017; Zhuang et al., 2021).

TigerGraph, maker of a graph analytics platform for data scientists, during its Graph & AI Summit event today introduced its TigerGraph ML (Machine Learning) Workbench, a new-gen toolkit that ostensibly will enable analysts to improve ML model accuracy significantly and shorten development cycles. Workbench does this while using familiar tools, workflows, and libraries in a single environment that plugs directly into existing data pipelines and ML infrastructure, TigerGraph VP Victor Lee told VentureBeat. The ML Workbench is a Jupyter-based Python development framework that enables data scientists to build deep-learning AI models using connected data directly from the business. Graph-enabled ML has proven to have more accurate predictive power and take far less run time than the conventional ML approach. Conventional machine learning algorithms are based on the learning of systems by training sets to develop a trained model.

Semantic technology trends are expanding well beyond an interesting, more advanced search engine. Besides providing scientists with a more functional search engine, semantic technology is now being used to improve artificial intelligence and machine learning. Semantic technology uses a variety of tools and methods designed to add "meaning" to a computer's understanding of data. When asked a question, rather than simply searching for keywords, semantic technologies will explore a wide variety of resources for topics, concepts, and relationships. In the financial and science industries, companies have begun to semantically "enrich" content, processing complex data from a variety of sources.