The value of AI agents, systems that can carry out tasks for humans, is evident, with opportunities for productivity gains, especially for businesses. However, the performance of large language models (LLMs) can hinder the effective deployment of agents. Salesforce's AI Research seeks to address that issue. Also: 60% of AI agents work in IT departments - here's what they do every day On Thursday, Salesforce launched its inaugural Salesforce AI Research in Review report, highlighting the tech company's innovations, including new foundational developments and research papers from the past quarter. Salesforce hopes these pieces will help support the development of trustworthy and capable AI agents that can perform well in business environments.

The survey identified the top five retail industry challenges: industry competition, inflation and high costs, rising customer acquisition costs, changing consumer behavior, and cost of returns. Also: Will synthetic data derail generative AI's momentum or be the breakthrough we need? The top five retail opportunities: leverage AI, implement unified commerce, increase e-commerce sales, improve customer service, and increase store associate productivity. The survey found that physical store purchase volume decreases as shopping spreads across digital channels.

Sortition is a political system in which decisions are made by panels of randomly selected citizens. The process for selecting a sortition panel is traditionally thought of as uniform sampling without replacement, which has strong fairness properties. In practice, however, sampling without replacement is not possible since only a fraction of agents is willing to participate in a panel when invited, and different demographic groups participate at different rates. In order to still produce panels whose composition resembles that of the population, we develop a sampling algorithm that restores close-to-equal representation probabilities for all agents while satisfying meaningful demographic quotas. As part of its input, our algorithm requires probabilities indicating how likely each volunteer in the pool was to participate.

AI agents are revolutionizing the workplace by performing actions autonomously for workers and, in turn, streamlining operations. Microsoft has sprinkled agents throughout its offerings to help workers across different industries, and the newest addition includes sales. On Wednesday, Microsoft unveiled Sales Agent, which can help sales teams close more deals faster by researching leads, setting up meetings, reaching out to customers, and even closing sales for low-impact leads, according to Microsoft's blog post. The agent uses the company's CRM, company data, and Microsoft 365 data to inform its responses. Microsoft also launched Sales Chat which, as the name implies, is an AI-chat interface where the sales team can access all the information they need from CRM data, pitch decks, meetings, emails, the web, and more.

In this section we provide a detailed proof for the correctness and completeness of the ICD algorithm. For easier referencing we describe ICD in Algorithm 1, and describe the ICD-Sep conditions. A set Z is a subset of ICD-Sep(A, B) given r {0,..., |O| 2}, if and only if 1. |Z| = r, 2. Z Z, there exists a PDS-path Π Let G be a PAG n-representing DAG D(O, S, L). Denote A, B a pair of nodes from O that are connected in G and disconnected in D, and such that A is not an ancestor of B in D. If A B | [Z It was previously shown that a minimal separating set for A and B, where A is not an ancestor of B, is a subset of D-Sep(A, B) (Spirtes et al., 2000, page 134 and Theorem 6.2; Spirtes et al., 1999). By definition, a node Z is in D-Sep(A, B) if and only if in the MAG there is a path between A and Z such that every node, except for the end points, is: 1. a collider and 2. an ancestor of A or B. Denote such path DS-Path (an inducing path for L, S).

In many important machine learning applications, the source distribution used to estimate a probabilistic classifier differs from the target distribution on which the classifier will be used to make predictions. Due to its asymptotic properties, sample-reweighted loss minimization is a commonly employed technique to deal with this difference. However, given finite amounts of labeled source data, this technique suffers from significant estimation errors in settings with large sample selection bias. We develop a framework for robustly learning a probabilistic classifier that adapts to different sample selection biases using a minimax estimation formulation. Our approach requires only accurate estimates of statistics under the source distribution and is otherwise as robust as possible to unknown properties of the conditional label distribution, except when explicit generalization assumptions are incorporated.

Customer Relationship Management (CRM) systems are vital for modern enterprises, providing a foundation for managing customer interactions and data. Integrating AI agents into CRM systems can automate routine processes and enhance personalized service. However, deploying and evaluating these agents is challenging due to the lack of realistic benchmarks that reflect the complexity of real-world CRM tasks. To address this issue, we introduce CRMArena, a novel benchmark designed to evaluate AI agents on realistic tasks grounded in professional work environments. Following guidance from CRM experts and industry best practices, we designed CRMArena with nine customer service tasks distributed across three personas: service agent, analyst, and manager. The benchmark includes 16 commonly used industrial objects (e.g., account, order, knowledge article, case) with high interconnectivity, along with latent variables (e.g., complaint habits, policy violations) to simulate realistic data distributions. Experimental results reveal that state-of-the-art LLM agents succeed in less than 40% of the tasks with ReAct prompting, and less than 55% even with function-calling abilities. Our findings highlight the need for enhanced agent capabilities in function-calling and rule-following to be deployed in real-world work environments. CRMArena is an open challenge to the community: systems that can reliably complete tasks showcase direct business value in a popular work environment.

For small business owners, time is one of the most valuable – and limited – resources. Running a business often means juggling multiple tasks, from customer relations to finances, marketing to production, and about 101 other things. Fortunately, recent advancements in artificial intelligence (AI) are revolutionizing time management, allowing small business owners to automate tasks, streamline workflows, and reclaim valuable time. Here's a closer look at how AI is transforming the way small business owners and solopreneuers manage their time. One of the biggest time-sucks when running a business is completing routine, repetitive tasks.

Starting a business while working a full-time job is challenging. Both your time and your resources are limited, which means you need to manage them properly in order to stay organized and productive. Over the past few years, artificial intelligence (AI) tools have emerged as powerful resources that can make this entrepreneurial balancing act more manageable, allowing side hustlers to manage their ventures efficiently alongside their day jobs. Here's a look at just some of the ways AI can help you maximize productivity and simplify tasks so that you can manage a side hustle more efficiently. Implementing AI tools can be particularly useful for repetitive tasks.

We consider the problem of learning the causal MAG of a system from observational data in the presence of latent variables and selection bias. Constraint-based methods are one of the main approaches for solving this problem, but the existing methods are either computationally impractical when dealing with large graphs or lacking completeness guarantees. We propose a novel computationally efficient recursive constraint-based method that is sound and complete. The key idea of our approach is that at each iteration a specific type of variable is identified and removed. This allows us to learn the structure efficiently and recursively, as this technique reduces both the number of required conditional independence (CI) tests and the size of the conditioning sets.