Abstract: This disclosure introduces a novel method and system for using a large language model (LLM) to create a convenient interface for a complex database. The system includes a custom prompt generator that creates custom prompts from natural language queries. The custom prompts are used to control how the LLM interacts with a database look-up tool. The database look-up tool provides queries to the database in a format understandable by the database and receives responses from the database. This system is useful for obtaining information that is not in a natural language, and thus, is poorly suited for being processed as an embedding by the LLM. Information obtained from the database is included in an answer produced by the LLM.

Abstract: This disclosure describes a causal query system that determines causal outcomes for agriculture-based causal queries using one or more deep causal machine-learning models, including deep multimodal causal machine-learning models. For example, the causal query system generates one or more deep causal machine-learning models to determine targeted causal outcomes based on combinations of treatments and covariates. Additionally, in many instances, these deep causal machine-learning models also allow for various types of data input, such as overhead images and unstructured data.

Abstract: This disclosure describes utilizing a causal query system to determine causal outcomes for domain-specific causal queries using a framework that includes causal graphs for targeted domains, a large generative model (LGM), and other models or systems. In various implementations, the causal query system provides a framework that includes generating domain-specific causal graphs, encoding or mapping the causal graphs with local data values, and using the encoded causal graphs to determine causal outcomes to causal queries. In some implementations, the causal query system uses the LGM and data resources (e.g., external sources) to populate missing values of an embedded causal graph before using the causal graph to determine causal outcomes.

Abstract: A Generative Artificial Intelligence (Gen. AI) based chatbot apparatus implements classical AI models and Generative AI models to answer user queries with results retrieved from relational databases and unstructured knowledge bases. When a user query is received, it is determined if the intent of the user query can be determined with a confidence greater than a configured confidence limit. If yes, a structured query mapped to the intent is employed to answer the user query. If the intent determination confidence is less than the configured confidence limit then Gen. AI-based techniques using a plurality of LLMs are used to respond to the query. A Gen. AI switch is also implemented to switch between the plurality of LLMs to answer user queries with greater accuracy.

Abstract: The techniques disclosed herein enable an autonomous agent to interpret an input dataset and orchestrate a suite of software modules to perform a computational task on a representation of a chemical material. The input dataset includes a prompt defining a computational task to be performed on a chemical material. Moreover, the input dataset includes data defining a chemical included in the chemical material, molecular descriptors describing the chemical and/or the chemical material, and an external variable. The agent analyzes the benefits and drawbacks of each model within the context of the computational task to determine a technique for performing the computational task. Accordingly, the agent formulates a chain of calls invoking the functionality of data processing tools and models to perform the computational task responsive to the prompt.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, an apparatus may establish a protocol data unit (PDU) session on a bearer. The apparatus may receive a packet in the PDU session. The apparatus may forward the packet based at least in part on whether traffic descriptor (TD) information in the packet matches TD information obtained from traffic policy information. Numerous other aspects are described.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating customized recommendations for environmentally-conscious cloud computing frameworks for replacing computing resources of existing datacenters. One of the methods involves receiving, through a user interface presented on a display of a computing device, data regarding a user's existing datacenter deployment and the user's preferences for the new cloud computing framework, generating one or more recommendations for environmentally-conscious cloud computing frameworks based on the received data, and presenting such recommendations through the user interface for the user's review and consideration.

Abstract: This disclosure provides a machine learning technique to predict a protein characteristic. A first training set is created that includes, for multiple proteins, a target feature, protein sequences, and other information about the proteins. A first machine learning model is trained and then used to identify which of the features are relevant as determined by feature importance or causal relationships to the target feature. A second training set is created with only the relevant features. Embeddings generated from the protein sequences are also added to the second training set. The second training set is used to train a second machine learning model. The first and second machine learning models may be any type of regressors. Once trained, the second machine learning model is used to predict a value for the target feature for an uncharacterized protein. The model of this disclosure provides 91% accuracy in predicting an ideal digestibility score.

Abstract: This disclosure provides a data-driven and scalable method to discover cause-and-effect relationships in data from natural systems that include sparse data sets. This technique can learn a causal graph from heterogenous data sources by combining embeddings from real data and embeddings from simulated data generated by process-based models. The causal graph is used for what-if analysis in out-of-distribution settings. One application is understanding the factors that affect soil carbon. A causal model created by these techniques can be used to discover cause-and-effect relationships that affect soil carbon. This model has applications such as forecasting soil carbon for a future time point to help inform farm practices. Farm practices, like tilling, may be modified in response to predictions provided by the model.

Abstract: A deep learning system is used to predict crop characteristics from inputs that include crop variety features, environmental features, and field management features. The deep learning system includes domain-specific modules for each category of features. Some of the domain-specific modules are implemented as convolutional neural networks (CNN) while others are implemented as fully-connected neural networks. Interactions between different domains are captured with cross attention between respective embeddings. Embeddings from the multiple domain-specific modules are concatenated to create a deep neural network (DNN). The prediction generated by the DNN is a characteristic of the crop such as yield, height, or disease resistance. The DNN can be used to select a crop variety for planting in a field. For a crop that is planted, the DNN may be used to select a field management technique.

Abstract: The techniques disclosed herein enable systems to optimize generation and dispatch of renewable energies using data-driven models. In many contexts, a renewable energy system is collocated with a local consumer such as a datacenter, a smart building, and so forth. The objective of the renewable energy system is to meet local power needs while participating in various energy markets of differing trading frequencies. To optimally manage the renewable energy system, a data-driven model is configured to analyze current conditions and generate policies to control renewable energy system operations. For instance, the model can retrieve current market prices, generation capacity, costs associated with generating energy, and so forth. Based on the collected information, the model can generate a policy that maximizes revenue obtained by the renewable energy system while meeting local demand. Through many iterations, the model can determine a realistically optimal policy for managing the renewable energy system.

Abstract: The systems and methods provide assistance in employee interactions. The systems and methods automatically schedule meetings between a manager and a report in response to changes in human resource data. The systems and methods track the meetings between the manager and the report during a time period and obtain meeting minutes for the meetings. The systems and methods analyze the meeting minutes and automatically generate feedback based on the analysis of the meeting minutes. The feedback is provided to the report or the manager.

Abstract: Systems and methods for evaluating attributes in supply chain management is disclosed. The system may receive data from a set of data sources corresponding to a supply chain associated with at least a product, pre-process the data based on integration of the data from each of the set of data sources, generate supply chain data based on the integrated data, analyze, via an orchestration engine, the supply chain data to assess an impact of the supply chain data on the supply chain, predict, via the orchestration engine, a state associated with a purchase event of the product in the supply chain, and generate a resolution flow to be executed in the supply chain for managing the predicted state associated with the purchase event of the product.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating customized recommendations for environmentally-conscious cloud computing frameworks for replacing computing resources of existing datacenters. One of the methods involves receiving, through a user interface presented on a display of a computing device, data regarding a user's existing datacenter deployment and the user's preferences for the new cloud computing framework, generating one or more recommendations for environmentally-conscious cloud computing frameworks based on the received data, and presenting such recommendations through the user interface for the user's review and consideration.

Abstract: A computing system is provided comprising a processor and a memory storing instructions executable by the processor. The instructions are executable to, during a run-time phase, receive run-time input data that includes time series data indicating a state of a graph network at each of a series of time steps. The graph network includes a plurality of nodes, and at least one edge connecting pairs of the nodes. The run-time input data is input into a trained graph neural network to thereby cause the graph neural network to output a predicted state of the graph network at one or more future time steps.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating customized recommendations for environmentally-conscious cloud computing frameworks for replacing computing resources of existing datacenters. One of the methods involves receiving, through a user interface presented on a display of a computing device, data regarding a user's existing datacenter deployment and the user's preferences for the new cloud computing framework, generating one or more recommendations for environmentally-conscious cloud computing frameworks based on the received data, and presenting such recommendations through the user interface for the user's review and consideration.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating customized recommendations for environmentally-conscious cloud computing frameworks for replacing computing resources of existing datacenters. One of the methods involves receiving, through a user interface presented on a display of a computing device, data regarding a user's existing datacenter deployment and the user's preferences for the new cloud computing framework, generating one or more recommendations for environmentally-conscious cloud computing frameworks based on the received data, and presenting such recommendations through the user interface for the user's review and consideration.

Abstract: Techniques of implementing partition level operations with concurrent activities are disclosed. A first operation can be performed on a first partition of a table of data. The first partition can be one of a plurality of partitions of the table, where each partition has a plurality of rows. A first partition level lock can be applied to the first partition for a period in which the first operation is being performed on the first partition, thereby preventing any operation other than the first operation from being performed on the first partition during the period the first partition level lock is being applied to the first partition. A second operation can be performed on a second partition of the table at a point in time during which the first operation is being performed on the first partition.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating customized recommendations for environmentally-conscious cloud computing frameworks for replacing computing resources of existing datacenters. One of the methods involves receiving, through a user interface presented on a display of a computing device, data regarding a user's existing datacenter deployment and the user's preferences for the new cloud computing framework, generating one or more recommendations for environmentally-conscious cloud computing frameworks based on the received data, and presenting such recommendations through the user interface for the user's review and consideration.

Abstract: Implementations of the present disclosure include receiving, by the cloud migration platform, user input indicating a type of case for cloud migration planning, the type including one of an abstract case and a detailed case, in response to receiving the type of case, selectively initiating, by the cloud migration platform, a discovery process for generating discovery data representative of infrastructure assets and application assets of the enterprise network, processing the discovery data through a sub-set of engines of a set of engines of the cloud migration platform, the sub-set of engines providing output including cloud platform selection data, application disposition data, target architecture data, bill of materials (BOM) data, and application remediation data, and generating, by a cloud migration planning engine of the cloud migration platform and based on the output, a cloud migration plan including an application sequence plan for migrating applications to one or more cloud platforms.