Abstract: Implementations relate to a method implemented by one or more processors, the method including: receiving natural language (NL) based input associated with a client device; generating, using a large language model (LLM) and based on processing the NL based input, LLM output; determining, based on the LLM output, a sequence of LLM responses, the sequence of LLM responses including at least one intermediate LLM response and a final LLM response. In some implementations, the method may further include causing the final LLM response to be rendered at the client device. In additional or alternative implementations, the method may further include storing, as an instance of training data for fine-tuning the LLM or an additional LLM, the NL based input along with the final LLM response.

Abstract: At least utilizing a custom corpus of documents to condition a large language model (LLM) when generating a response to a user query. In some implementations, a user query associated with a client device is received. An API query for an external application is generated by an LLM based on the user query. The external application has access to a custom corpus of documents comprising a plurality of documents. The external application is queried using the API query. Data representative of one or more documents in the custom corpus of documents is received from the external application in response to the API query. The LLM generates a response to the query that is conditioned on the data representing one or more of the documents in the custom corpus of documents received from the external application. The response to the user query is caused to be rendered on the client device.

Abstract: Implementations relate to helping a large language model generate factual responses to prompts that request factual content is disclosed. The large language model may receive a prompt context, a plurality of encoded context passages as input. The large language model is trained to determine whether or not to utilize the encoded context passages in generating the response. Implementations also relate to different methods of fine-tuning the responses generated by the large language model through query refinements, response re-writes, and evaluation of factual accuracy.

Abstract: Systems and methods for performing inference for word or wordpiece tokenization are disclosed using a left-to-right longest-match-first greedy process. In some examples, the vocabulary may be organized into a trie structure in which each node includes a precomputed token or token_ID and a fail link, so that the tokenizer can parse the trie in a single pass to generate a list of only those tokens or token_IDs that correspond to the longest matching vocabulary entries in the sample string, without the need for backtracking. In some examples, the vocabulary may be organized into a trie in which each node has a fail link, and any node that would share token(s) or token_ID(s) of a preceding node is instead given a prev_match link that points back to a chain of nodes with those token(s) or token_ID(s).

Abstract: Systems and methods for performing inference for word or wordpiece tokenization are disclosed using a left-to-right longest-match-first greedy process. In some examples, the vocabulary may be organized into a trie structure in which each node includes a precomputed token or token ID and a fail link, so that the tokenizer can parse the trie in a single pass to generate a list of only those tokens or token IDs that correspond to the longest matching vocabulary entries in the sample string, without the need for backtracking. In some examples, the vocabulary may be organized into a trie in which each node has a fail link, and any node that would share token(s) or token_ID(s) of a preceding node is instead given a prev_match link that points back to a chain of nodes with those token(s) or token_ID(s).

Abstract: Generally discussed herein are devices, systems, and methods for scheduling tasks to be completed by resources. A method can include identifying features of the task, the features including a time-dependent feature and a time-independent feature, the time-dependent feature indicating a time the task is more likely to be successfully completed by the resource, converting the features to feature values based on a predefined mapping of features to feature values in a first memory device, determining, by a gradient boost tree model and based on a first current time and the feature values, a likelihood the resource will successfully complete the task, and scheduling the task to be performed by the resource based on the determined likelihood.

Abstract: Systems and methods for performing inference for word or wordpiece tokenization are disclosed using a left-to-right longest-match-first greedy process. In some examples, the vocabulary may be organized into a trie structure in which each node includes a precomputed token or token ID and a fail link, so that the tokenizer can parse the trie in a single pass to generate a list of only those tokens or token IDs that correspond to the longest matching vocabulary entries in the sample string, without the need for backtracking. In some examples, the vocabulary may be organized into a trie in which each node has a fail link, and any node that would share token(s) or token_ID(s) of a preceding node is instead given a prev_match link that points back to a chain of nodes with those token(s) or token_ID(s).

Abstract: A recyclable pile foundation is provided. The recyclable pile foundation includes several inner cylinders, several outer cylinders and several reciprocating components which are circumferentially distributed between the inner cylinders and the outer cylinders. Each reciprocating component includes several steel collars, a push-pull rod, a hold component and at least one motion component. The motion components are distributed along the push-pull rod. Each motion component includes at least one triangular connection plate, several connection rods, an inner wedge block, an outer wedge block, a motion block and a pointed rod. When the push-pull rod is pushed along its own axis to the pushed position, the pointed rod protrudes from the outer cylinders to increases the friction between the surrounding soil and the recyclable pile foundation. When the push-poll rod is pulled along its own axis to the pulled position, the pointed rods retract back into the outer cylinders.

Abstract: A recyclable pile foundation is provided. The recyclable pile foundation includes several inner cylinders, several outer cylinders and several reciprocating components which are circumferentially distributed between the inner cylinders and the outer cylinders. Each reciprocating component includes several steel collars, a push-pull rod, a hold component and at least one motion component. The motion components are distributed along the push-pull rod. Each motion component includes at least one triangular connection plate, several connection rods, an inner wedge block, an outer wedge block, a motion block and a pointed rod. When the push-pull rod is pushed along its own axis to the pushed position, the pointed rod protrudes from the outer cylinders to increases the friction between the surrounding soil and the recyclable pile foundation. When the push-poll rod is pulled along its own axis to the pulled position, the pointed rods retract back into the outer cylinders.

Abstract: A workflow engine tool is disclosed that enables scientists and engineers to programmatically author workflows (e.g., a directed acyclic graph, “DAG”) with nearly no overhead, using a simpler script that needs almost no modifications for portability among multiple different workflow engines. This permits users to focus on the business logic of the project, avoiding the distracting tedious overhead related to workflow management (such as uploading modules, drawing edges, setting parameters, and other tasks). The workflow engine tool provides an abstraction layer on top of workflow engines, introducing a binding function that converts a programming language function (e.g., a normal python function) into a workflow module definition. The workflow engine tool infers module instances and induces edge dependencies automatically by inferring from a programming language script to build a DAG.

Abstract: A workflow engine tool is disclosed that enables scientists and engineers to programmatically author workflows (e.g., a directed acyclic graph, “DAG”) with nearly no overhead, using a simpler script that needs almost no modifications for portability among multiple different workflow engines. This permits users to focus on the business logic of the project, avoiding the distracting tedious overhead related to workflow management (such as uploading modules, drawing edges, setting parameters, and other tasks). The workflow engine tool provides an abstraction layer on top of workflow engines, introducing a binding function that converts a programming language function (e.g., a normal python function) into a workflow module definition. The workflow engine tool infers module instances and induces edge dependencies automatically by inferring from a programming language script to build a DAG.

Abstract: Systems and methods are disclosed for intelligent scheduling of calls to sales leads, leveraging machine learning (ML) to optimize expected results. One exemplary method includes determining, using a connectivity prediction model, call connectivity rate predictions; determining timeslot resources; allocating, based at least on the call connectivity rate predictions and timeslot resources, leads to timeslots in a first time period; determining, within a timeslot and using a lead scoring model, lead prioritization among leads within the timeslot; configuring, based at least on the lead prioritization, the telephone unit with lead information for placing a phone call; and applying a contextual bandit (ML) process to update the connectivity prediction model, the lead scoring model, or both. During subsequent time periods, the updated connectivity prediction and lead scoring models are used, thereby improving expected results over time.

Abstract: A workflow engine tool is disclosed that enables scientists and engineers to programmatically author workflows (e.g., a directed acyclic graph, “DAG”) with nearly no overhead, using a simpler script that needs almost no modifications for portability among multiple different workflow engines. This permits users to focus on the business logic of the project, avoiding the distracting tedious overhead related to workflow management (such as uploading modules, drawing edges, setting parameters, and other tasks). The workflow engine tool provides an abstraction layer on top of workflow engines, introducing a binding function that converts a programming language function (e.g., a normal python function) into a workflow module definition. The workflow engine tool infers module instances and induces edge dependencies automatically by inferring from a programming language script to build a DAG.

Abstract: Systems and methods are disclosed for intelligent scheduling of calls to sales leads, leveraging machine learning (ML) to optimize expected results. One exemplary method includes determining, using a connectivity prediction model, call connectivity rate predictions; determining timeslot resources; allocating, based at least on the call connectivity rate predictions and timeslot resources, leads to timeslots in a first time period; determining, within a timeslot and using a lead scoring model, lead prioritization among leads within the timeslot; configuring, based at least on the lead prioritization, the telephone unit with lead information for placing a phone call; and applying a contextual bandit (ML) process to update the connectivity prediction model, the lead scoring model, or both. During subsequent time periods, the updated connectivity prediction and lead scoring models are used, thereby improving expected results over time.

Abstract: A workflow engine tool is disclosed that enables scientists and engineers to programmatically author workflows (e.g., a directed acyclic graph, “DAG”) with nearly no overhead, using a simpler script that needs almost no modifications for portability among multiple different workflow engines. This permits users to focus on the business logic of the project, avoiding the distracting tedious overhead related to workflow management (such as uploading modules, drawing edges, setting parameters, and other tasks). The workflow engine tool provides an abstraction layer on top of workflow engines, introducing a binding function that converts a programming language function (e.g., a normal python function) into a workflow module definition. The workflow engine tool infers module instances and induces edge dependencies automatically by inferring from a programming language script to build a DAG.

Abstract: Generally discussed herein are devices, systems, and methods for scheduling tasks to be completed by resources. A method can include identifying features of the task, the features including a time-dependent feature and a time-independent feature, the time-dependent feature indicating a time the task is more likely to be successfully completed by the resource, converting the features to feature values based on a predefined mapping of features to feature values in a first memory device, determining, by a gradient boost tree model and based on a first current time and the feature values, a likelihood the resource will successfully complete the task, and scheduling the task to be performed by the resource based on the determined likelihood.

Abstract: Generally discussed herein are devices, systems, and methods for scheduling tasks to be completed by resources. A method can include identifying features of the task, the features including a time-dependent feature and a time-independent feature, the time-dependent feature indicating a time the task is more likely to be successfully completed by the resource, converting the features to feature values based on a predefined mapping of features to feature values in a first memory device, determining, by a gradient boost tree model and based on a first current time and the feature values, a likelihood the resource will successfully complete the task, and scheduling the task to be performed by the resource based on the determined likelihood.

Abstract: Generally discussed herein are devices, systems, and methods for task routing. A method can include receiving, from a resource, a request for a task, in response to receiving the request, determining whether to retrieve a new task of new tasks stored in a first queue or a backlog task of backlog tasks stored in a second queue based on a combined amount of backlog tasks and new tasks relative to a capacity of the resource or the resources, retrieving the new task or the backlog task from the determined first queue or second queue, respectively, based on the determination, and providing the retrieved task to the resource.

Abstract: Generally discussed herein are devices, systems, and methods for task routing. A method can include receiving, from a resource, a request for a task, in response to receiving the request, determining whether to retrieve a new task of new tasks stored in a first queue or a backlog task of backlog tasks stored in a second queue based on a combined amount of backlog tasks and new tasks relative to a capacity of the resource or the resources, retrieving the new task or the backlog task from the determined first queue or second queue, respectively, based on the determination, and providing the retrieved task to the resource.

Abstract: A processing unit can successively operate layers of a multilayer computational graph (MCG) according to a forward computational order to determine a topic value associated with a document based at least in part on content values associated with the document. The processing unit can successively determine, according to a reverse computational order, layer-specific deviation values associated with the layers based at least in part on the topic value, the content values, and a characteristic value associated with the document. The processing unit can determine a model adjustment value based at least in part on the layer-specific deviation values. The processing unit can modify at least one parameter associated with the MCG based at least in part on the model adjustment value. The MCG can be operated to provide a result characteristic value associated with test content values of a test document.