Abstract: Systems and methods are provided for customizing flow paths to better handle queries and questions directed to a Large Language Model (LLM) or another automated search model. According to one implementation, a method includes a step of receiving a question from a user device. The method further includes a step of determining a level of complexity of the question. Also, the method includes tailoring a divide-and-conquer plan when the complexity of the question is determined to be higher than a lowest level of complexity. The method then includes a step of executing the divide-and-conquer plan to produce an answer to the question.

Abstract: Techniques for using transfer learning to address label data shortage in seniority modeling for an online service are disclosed herein. In some embodiments, a computer-implemented method comprises training an initialized neural network using training examples comprising profile data and labels for the profile data, where each label comprises a standardized position title, and the training of the initialized neural network forms a pre-trained neural network. Next, the computer system may train the pre-trained neural network using training examples comprising profile data and labels for the profile data, where the labels comprise a position seniority, and the training of the pre-trained neural network forms a fine-tuned neural network. The computer system may then compute the position seniority for a user based on profile data of the user using the fine-tuned neural network, and use the position seniority of the user in an application of an online service.

Abstract: Systems and methods for malicious beaconing detection include extracting one or more beaconing sequences from log data associated with a network; performing feature extraction for the one or more extracted beaconing sequences; and implementing one or more Machine Learning (ML) models for classifying each of the one or more beaconing sequences as any of clean, malicious, suspicious, and unknown. The one or more ML models can be associated with an ensemble model, where a final classification of a beaconing sequence can be based on results of each of the one or more ML models.

Abstract: Systems and methods for inline Uniform Resource Locator (URL) categorization include training a lightweight machine learning model to score content associated with unknown Uniform Resource Locators (URLs) to determine a category of the plurality of categories for each of the unknown URLs; deploying the trained lightweight machine learning model to a node in a cloud-based system for use in production; and utilizing the trained lightweight machine learning model to monitor traffic inline to categorize unknown URLs.

Abstract: Systems and methods for detecting and fixing collisions in Artificial intelligence agents include, responsive to obtaining a plurality of tuples in a Retrieval-Augmented Generation (RAG) system with each tuple including a first value and a second value, generating a plurality of different first values from a corresponding first value where the plurality of different first values are similar to the corresponding first value; determining top-k, k is an integer greater than or equal to one, matches for the plurality of different first values to the second values in the RAG system; determining a confusion matrix based on the top-k matches; and utilizing the confusion matrix to debug the RAG system.

Abstract: Multimodal Data Loss Protection (DLP) includes receiving an input comprising data in any of a plurality of formats; processing the input to determine whether or not the data includes sensitive data; and responsive to the input including sensitive data, performing steps of: processing the input to classify the input into a category of a plurality of categories; and providing an indication of the category of the plurality of categories. Advantageously, the trained multimodal system can detect categories of data being accessed, transferred, etc., without the requirement of up-front dictionaries from corporate Information Technology (IT).

Abstract: Inline Multimodal Data Loss Protection (DLP) includes training one or more machine learning models for classifying input data into categories of a plurality of categories; performing one or more modifications to the one or more machine learning models, wherein the one or more modifications reduce latency associated with the one or more machine learning models; receiving an input comprising data in any of a plurality of formats; processing the input to classify the input into a category of a plurality of categories; and providing an indication of the category of the plurality of categories. Advantageously, by performing the various modifications to the one or more models, the systems can accurately classify data inline with minimal latency.

Abstract: Systems and methods for next generation artificial intelligence agents include operating an Artificial Intelligence (AI) agent system that includes an agent core connected to memory, one or more tools, and a planner; receiving a request from a user; utilizing the planner to break the request down into a plurality of sub-parts that are each individually simpler than the request; and generating an answer to the request using the plurality of sub-parts with the memory and the one or more tools.

Abstract: Techniques for using transfer learning to address label data shortage in seniority modeling for an online service are disclosed herein. In some embodiments, a computer-implemented method comprises training an initialized neural network using training examples comprising profile data and labels for the profile data, where each label comprises a standardized position title, and the training of the initialized neural network forms a pre-trained neural network. Next, the computer system may train the pre-trained neural network using training examples comprising profile data and labels for the profile data, where the labels comprise a position seniority, and the training of the pre-trained neural network forms a fine-tuned neural network. The computer system may then compute the position seniority for a user based on profile data of the user using the fine-tuned neural network, and use the position seniority of the user in an application of an online service.

Abstract: In an example embodiment, a framework to infer a user's value for a particular attribute based upon a multi-task machine learning process with uncertainty weighting that incorporates signals from multiple contexts is provided. In an example embodiment, the framework aims to measure a level of a user attribute under a certain context. Rather than attempting to devise a universal, one-size-fits-all value for the attribute, the framework acknowledges that the user's value for that attribute can vary depending on context and factors in the context under which the user's attribute levels are measured. Multiple contexts are defined depending on different situations where users and entities such as companies and organizations need to evaluate user attribute levels. Signals for attribute levels are then collected for each context. Machine learning models are utilized to estimate attribute values for different contexts. Multi-task deep learning is used to level attributes from different contexts.

Abstract: Techniques for ranking skills using an ensemble machine learning approach are described. The outputs of two heterogenous, machine-learned models are combined to rank a set of skills that may be possessed by an end-user of an online service. Some subset of the highest-ranking skills is then presented to the end-user with a recommendation that the skills be added to the end-user's profile. The ensemble learning technique involves a concept referred to as “boosting”, in which a weaker performing model is enhanced (e.g., “boosted”) by a stronger performing model, when ranking the set of skills. Accordingly, by using a combination of models, better results are achieved than might be with either one of the individual models alone. Furthermore, the approach is scalable in ways that cannot be achieved with heuristic-based approaches.

Abstract: Techniques for ranking skills using an ensemble machine learning approach are described. The outputs of two heterogenous, machine-learned models are combined to rank a set of skills that may be possessed by an end-user of an online service. Some subset of the highest-ranking skills is then presented to the end-user with a recommendation that the skills be added to the end-user's profile. The ensemble learning technique involves a concept referred to as “boosting”, in which a weaker performing model is enhanced (e.g., “boosted”) by a stronger performing model, when ranking the set of skills. Accordingly, by using a combination of models, better results are achieved than might be with either one of the individual models alone. Furthermore, the approach is scalable in ways that cannot be achieved with heuristic-based approaches.

Abstract: In an example embodiment, a framework to infer a user's value for a particular attribute based upon a multi-task machine learning process with uncertainty weighting that incorporates signals from multiple contexts is provided. In an example embodiment, the framework aims to measure a level of a user attribute under a certain context. Rather than attempting to devise a universal, one-size-fits-all value for the attribute, the framework acknowledges that the user's value for that attribute can vary depending on context and factors in the context under which the user's attribute levels are measured. Multiple contexts are defined depending on different situations where users and entities such as companies and organizations need to evaluate user attribute levels. Signals for attribute levels are then collected for each context. Machine learning models are utilized to estimate attribute values for different contexts. Multi-task deep learning is used to level attributes from different contexts.

Abstract: This disclosure is directed to determining various economic graph indices and, in particular, to systems and methods that leverage a graph analytic engine and framework to determine values assigned to graph nodes extracted from one or more member profiles, and visualizing said values to correlate skills, geographies, and industries. The disclosed embodiments include a client-server architecture where a social networking server has access to a social graph of its social networking members. The social networking server includes various modules and engines that import the member profiles and then extracts certain defined attributes from the member profiles, such as employer (e.g., current employer and/or past employers), identified skills, educational institutions attended, and other such defined attributes. Using these attributes as nodes, the social networking server constructs a graph using various graph processing techniques.

Abstract: This disclosure is directed to determining various economic graph indices and, in particular, to systems and methods that leverage a graph analytic engine and framework to determine values assigned to graph nodes extracted from one or more member profiles, and visualizing said values to correlate skills, geographies, and industries. The disclosed embodiments include a client-server architecture where a social networking server has access to a social graph of its social networking members. The social networking server includes various modules and engines that import the member profiles and then extracts certain defined attributes from the member profiles, such as employer (e.g., current employer and/or past employers), identified skills, educational institutions attended, and other such defined attributes. Using these attributes as nodes, the social networking server constructs a graph using various graph processing techniques.

Abstract: Estimation of workforce skill gaps using social network services are described herein. An unfilled job is represented by a job posting on a social network service. A skill is predicted as being required for the unfilled job by determining that each member of a set of members has an electronic profile on the social network service listing the skill as possessed by the member. A quantity of unfilled jobs on the social network service requiring the predicted skill is calculated. A quantity of selected job-seeking members of the social network service is calculated, each selected job-seeking member having an electronic profile on the social network service listing the predicted skill as possessed by the selected job-seeking member. A workforce skill gap for the predicted skill is estimated by subtracting the calculated quantity of job-seeking members from the calculated quantity of unfilled jobs.

Abstract: Techniques for standardizing and deduplicating unpaid job postings obtained from third-party systems are described. An unpaid job posting is obtained by a social networking service from a third-party system. The title and description of the unpaid job posting are standardized and combined into a standardized unpaid job posting. A deduplication process is performed to prevent the standardized unpaid job posting from replacing a paid job posting within the social networking service, and to prevent the standardized unpaid job posting from replacing a more authoritative, unpaid job posting within the social networking service.

Abstract: A computer-based method, and computer system, for matching candidates with job openings. The technology more particularly relates to methods of providing a candidate with a score for a particular job opening, where the score is derived from a comparison of features in the candidate's resume with job features in a description of the job opening, as well as use of external data gathered from other sources and based on information contained in the candidate's resume and/or in the description of the job opening. Particular features are weighted to take account of their significance in matching candidates to job openings in a statistical survey of such matching. The technology further provides for notifying employers that one or more high scoring candidates have been identified.

Abstract: A computer-based method, and computer system, for matching candidates with job openings. The technology more particularly relates to methods of providing a candidate with a score for a particular job opening, where the score is derived from a comparison of features in the candidate's resume with job features in a description of the job opening, as well as use of external data gathered from other sources and based on information contained in the candidate's resume and/or in the description of the job opening. Particular features are weighted to take account of their significance in matching candidates to job openings in a statistical survey of such matching. The technology further provides for notifying employers that one or more high scoring candidates have been identified.