Abstract: A system for processing and presenting a conversation includes a sensor, a processor, and a presenter. The sensor is configured to capture an audio-form conversation. The processor is configured to automatically transform the audio-form conversation into a transformed conversation. The transformed conversation includes a synchronized text, wherein the synchronized text is synchronized with the audio-form conversation. The presenter is configured to present the transformed conversation including the synchronized text and the audio-form conversation. The presenter is further configured to present the transformed conversation to be navigable, searchable, assignable, editable, and shareable.

Abstract: Apparatus and methods to decipher and analyze hidden/figurative meanings in communications using contextual analysis are provided. The apparatus and methods may include generating an initial list of slang words and phrases and their corresponding hidden/figurative meanings, searching the Internet for new slang words and phrases, determining the corresponding hidden/figurative meanings of the new slang words and phrases, and contextually analyzing a set of communications for hidden/figurative meaning. The apparatus and methods may include generating an alert if a relevant hidden/figurative meaning is found in the set of communications.

Abstract: Techniques described herein relate to facilitating end-to-end multilingual communications with automated assistants. In various implementations, speech recognition output may be generated based on voice input in a first language. A first language intent may be identified based on the speech recognition output and fulfilled in order to generate a first natural language output candidate in the first language. At least part of the speech recognition output may be translated to a second language to generate an at least partial translation, which may then be used to identify a second language intent that is fulfilled to generate a second natural language output candidate in the second language. Scores may be determined for the first and second natural language output candidates, and based on the scores, a natural language output may be selected for presentation.

Abstract: Systems and methods for generating phonetic spelling variations of a given word based on locale-specific pronunciations. A phoneme-letter density model may be configured to identify a phoneme sequence corresponding to an input word, and to identify all character sequences that may correspond to an input phoneme sequence and their respective probabilities. The phoneme-phoneme error model may be configured to identify locale-specific alternative phoneme sequences that may correspond to a given phoneme sequence, and their respective probabilities. Using these two models, a processing system may be configured to generate, for a given input word, a list of alternative character sequences that may correspond to the input word based on locale-specific pronunciations, and/or a probability distribution representing how likely each alternative character sequence is to correspond to the input word.

Abstract: A method, system, and computer program product for testing a container orchestration system are disclosed. The method includes replicating objects of a production cluster by extracting an object definition from an object and transforming the object definition to create a replicated object definition with an equivalent syntactic form. The replicated object definition requires fewer resources than the object definition. The method also includes applying the replicated objects of the production cluster to a simplified test cluster that replicates a configuration of the production cluster in a scaled down form. Additionally, the method includes testing, with the simplified test cluster, an upgraded version of the container orchestration system.

Abstract: [Problem] To provide a system capable of correcting an error in speech recognition by easier work than in the prior art. [Solution] A conversation analysis system comprising: a speech analysis unit 3 for analyzing contents contained in a conversation; a topics grasping unit 5 for grasping topics of the conversation; a topics-related correction terms storage unit 7 for, with respect to the respective topics, storing pronunciations to be speech-recognized or speech-recognized terms, and candidates for correction terms related to the pronunciations to be speech-recognized or the speech-recognized terms; and a conversation correction unit 9 for correcting the conversation analyzed by the speech analysis unit 3.

Abstract: Generate, for each of the words of a common vocabulary of first and second text corpora, a first word embedding vector in the first text corpus and a second word embedding vector in the second text corpus. Generate, for each word in a random sample of non-landmark words, an artificially shifted word embedding vector by modifying the first word embedding vector for that word. Train a machine learning classifier to predict whether an artificial shift has been injected for a given word, based on the artificially shifted word embedding vector and the second word embedding vector for the given word. Predict semantic shifts for at least a plurality of the words of the common vocabulary by providing the first word embedding vectors and the second word embedding vectors for at least the plurality of the words of the common vocabulary as input to the trained machine learning classifier.

Abstract: Aspects of the present disclosure include a system comprising a machine-readable storage medium storing at least one program and computer-implemented methods for detecting a language of a text string. Consistent with some embodiments, the method may include applying multiple language identification models to a text string. Each language identification model provides a predicted language of the text string and a confidence score associated with the predicted language. The method may further include weighting each associated confidence score based on historical performance of the corresponding language identification model in predicting languages of other text strings. The method may further include selecting a predicted language of the text string from among the multiple predicted languages provided by the multiple language identification models based on a result of the weighting of the confidence score associated with the particular predicted language.

Abstract: A created sentence evaluating device 1 using a neural network unit 10 of an encoder/decoder model in which an encoder unit 100 inputs a sentence in a first language, and a decoder unit 101 sequentially outputs word candidates for a sentence in a second language corresponding to the sentence in the first language and likelihood of the word candidates includes: an encoder input unit 13 configured to input a created sentence created in the second language to the encoder unit 100 sequentially for each word; and an evaluation unit 17 configured to evaluate words of the created sentence on the basis of word candidates in the second language and the likelihood of the word candidates output by the decoder unit 101 on the basis of an input from the encoder input unit 13.

Abstract: A method for creating a template for content including receiving at least one audience parameter representing at least one preference of a target audience when receiving the content, receiving at least one context parameter representing a context of the content, determining a content storyline based on the at least one audience parameter and the at least one context parameter, and generating a content template based on the content storyline.

Abstract: Provided are computer implemented systems and methods for providing improved language translation, including translation of input text to an output translated text element. This may include providing for rule-based language translation. This may further include a plurality of machine translation models adapted to different translation domains. A machine translation selector is described that selects an in-domain machine translator from the plurality of machine translation models based on an input text element. The selected in-domain machine translator may generate a first translated text element from the input text element. A post-editor module may generate a second translated text element by predicting a post-edit to the first translated text element. The first translated text element and the second translated text element are evaluated by a quality evaluation model that determines a first metric associated with the first translated text element and a second metric associated with the second text element.

Abstract: Various tools are disclosed for providing assistive or augmentative means to enhance the fluency and accuracy of persons having speech disabilities. These technologies may automatically ascertain and dynamically improve the accuracy with which automatic speech recognition (ASR) systems recognize utterances of persons having impaired speech conditions. In an embodiment, digitized audio information about a speaker’s utterance is processed to determine a set of candidate words matching the utterance. From these candidate words, a set of concepts is determined using a finite state machine model. A pictogram representing each concept is identified and presented to the speaker so that the speaker may select the pictogram corresponding to the best match of his or her intended meaning associated with the utterance. An action corresponding to speaker’s selection then may be performed. For example, displaying or synthesizing speech from textual information describing the selected concept.

Abstract: Interleaved conversation concept flow enhancement can include detecting a topic of an on-going conversation over an electronic communication system. In response to detecting a topic newly introduced into the on-going conversation, a topic-divergence metric (TDM) with respect to the original topic and the newly introduced topic can be determined. A topic-divergence response action can be initiated in response to the newly introduced topic diverging from the original topic by more than a predetermined threshold based on the TDM.

Abstract: In response to a user selecting a key on a keyboard in a first manner, a first alphanumeric character is displayed on a display device. In response to the user selecting the key on the keyboard in a second manner, a virtual key of a diacritic is displayed on the display device. In response to the user selecting the virtual key of the diacritic on the display device, the diacritic is displayed at a location of a second alphanumeric character on the display device.

Abstract: A system for testing the technical applicability of intellectual property rights having a computational linguistics module, which is designed to receive chains of technical graphemes and/or digitized symbolic images of intellectual property right specifications as input information, and to mechanically transfer the input information into an information model, an ontology memory, which is designed to store taxonomies, which are linked via inference rules, of conceptual classified object types, a semiotics module, which is coupled to the computational linguistics module and the ontology memory and which is designed to instantiate semiotic signs included in the information model, which is generated by the computational linguistics module, using conceptual classified object types stored in the ontology memory, and to create a contextualized information model, a construction module, which is coupled to the semiotics module and which is designed to generate a CAD model from the contextualized information model crea

Abstract: System and methods are provided for performing table navigation operations on a display, wherein the table comprises rows and columns and at least one cell that contains more information than presented on the display. Disclosed embodiments include receiving a scroll signal for scrolling the table, wherein the scroll signal results from a motion of a user on the display; in response to a vertical component in the motion, causing the table to scroll vertically; in response to a horizontal component in the motion, causing the table to scroll horizontally; receiving an enhancing scroll signal resulting from an enhancing motion for revealing hidden information within the at least one cell on the table; and in response to the enhancing scroll signal, revealing at least a portion of the hidden information within the at least one cell.

Abstract: Disclosed are a method and system for verifying a resulting behavior of a graph query language. A lexical parser and a preset lexical rule are added to a BDD test framework, to construct an improved BDD test framework; a first Gherkin text in a graph query language standard is migrated to be a second Gherkin text in a graph query language; a first parsing result of the second Gherkin text is acquired from the improved BDD test framework; a first return result from a graph database is acquired, a comparison result between the first parsing result and the first return result is acquired, wherein the graph query language meets the graph query language standard if the first parsing result and the first return result are the same. The present disclosure solves the problem that the BDD test framework cannot recognize complex Gherkin texts, thereby facilitating development of graph databases.

Abstract: Internet of Things (IoT) system and method of interfacing arbitrary non-network connected devices to wireless computer networks. The invention provides a configurable wireless communications module, in either fixed or removable formats, with wireless (e.g. WiFi) network connectivity. The invention uses at least one internal processor, which is configured to operate as a sandbox or virtual machine manner to isolate the code used to operate the arbitrary non-network connected device from the code used to operate the communications module.

Abstract: Techniques are described herein for subquery removal given two set operation-based subqueries in a query, where one subquery contains the result of the other. The described optimization technique of subquery removal is enabled by join and set operation-based containment of the set operation-based subqueries where semantic equivalence can be established for a given pair of set operation-based subqueries when some table(s)—with associated join condition(s), correlation condition(s), and/or filter predicate(s)—in one subquery are not considered. Subquery removal reduces multiple access to the same table and multiple evaluations of the same join conditions required to evaluate the query. When a subquery is removed from a disjunction, this may lead to other optimizations such as subquery unnesting, e.g., when the original query configuration would not permit query unnesting and the rewritten query (with one or more removed subqueries) permits unnesting.

Abstract: Systems and methods are described for training an artificial intelligence model to infer a log sourcetype of a log. For example, logs may have different log sourcetypes, and logs having the same log sourcetypes may have different messagetypes. The artificial intelligence model may be a machine learning model, and can be trained using training data that includes logs with known log sourcetypes. Each log can be tokenized, filtered, converted into a vector, and applied to a machine learning model as an input to perform the training. The machine learning model may output an inferred log sourcetype, which can be compared with the known log sourcetype to update model parameters to improve the machine learning model accuracy. The trained machine learning model may be trained to infer a log sourcetype of a log regardless of the messagetype of the log.

Abstract: A method includes obtaining n pairs of translation sentences of a source language and a target language, where each of the n pairs of translation sentences includes a source language sentence and a target language sentence that are translations of each other, extracting a source language segment from each source language sentence in the n pairs of translation sentences using an extraction rule of the source language, extracting a target language segment from each target language sentence in the n pairs of translation sentences, and generating an extraction rule of the target language based on n target language segments extracted from n target language sentences.

Abstract: Method, apparatus and computer program product for querying a group-based communication platform are described herein. For example, the apparatus comprises at least one processor and at least one non-transitory memory including program code. The at least one non-transitory memory and the program code are configured to, with the at least one processor, cause the apparatus to at least store a plurality of electronic messages to a message corpus; receive a search query associated with the message corpus, wherein the search query comprises reaction metadata criteria; and apply the search query to the message corpus to generate a reaction search results set.

Abstract: Methods, systems and computer readable media are provided for automatically creating a semantic model of a relational database for processing natural language queries. A computing device automatically extracts relational database metadata. The computing device prompts a user to enter textual labels for columns of the extracted metadata. The computing device automatically generates a schema annotation file based upon the relational database metadata and the textual labels for the columns. A natural language query is processed for the relational database using the schema annotation file.

Abstract: “Semi-supervised” machine learning relies on less human input than a supervised algorithm to train a machine learning algorithm to perform entity recognition (NER). Starting with a known entity value or known pattern value for a specific entity type, phrases in a training data corpus are identified that include the known entity value. Context-value patterns are generated to match selected phrases that include the known entity value. One or more context-value patterns may be validated based on human input. The validated patterns identify additional entity values. A subset of the additional entity values may also be validated based on human input. Occurrences of validated entity values may be labeled in the training corpus. Sample phrases from the labeled training dataset may be extracted to form a reduced-size training set for a supervised machine learning model which may be further used in production to label data for any named entity recognition application.

Abstract: Disclosed herein is an artificial intelligence server which receives a user dictionary, calculates rankings of a plurality of keywords included in the user dictionary, generates a cloud user dictionary according to the rankings of the plurality of keywords, and trains a natural language processing model.

Abstract: A computer-implemented method according to one embodiment includes receiving a single instance of training data, simplifying the single instance of training data to create a single instance of simplified training data, generating a plurality of training data variants, based on the single instance of simplified training data, and training a machine learning model, utilizing the plurality of training data variants.

Abstract: The present disclosure discloses a method and apparatus for processing a dataset. The method includes: obtaining a first text set meeting a preset similarity matching condition with a target text from multiple text blocks provided by a target user; obtaining a second text set from the first text set, in which each text in the second text set does not belong to a same text block as the target text; generating a negative sample set of the target text based on content of a candidate text block to which each text in the second text set belongs; generating a positive sample set of the target text based on content of a target text block to which the target text belongs; and generating a dataset of the target user based on the negative sample set and the positive sample set, and training a matching model based on the dataset.

Abstract: Systems and methods for enhanced classification of sequences of objects based on clique similarity and metadata associated with the sequences are presented. Sequences are received. Events are detected based on analyzing k-skip-n-grams included in the sequences. For each event of the detected plurality of events, a graph is generated. The graph for a particular event includes z-cliques that correspond to portions of the k-skip-n-grams that are included in the sequences that are associated with the particular event. A first sequence, which is separate from the other sequences, is received. The first sequence includes a first plurality of k-skip-n-grams. A trained classifier is employed to classify the first sequence as being associated with a first event of the detected events. Classifying the first sequence is based on a comparison between the first plurality of k-skip-n-grams and the z-cliques of the graph that is generated for the first event.

Abstract: There is a need for more effective and efficient document summarization. This need can be addressed by, for example, techniques for contextual summarization using semantic intelligence. In one example, a method includes identifying a plurality of senses associated with a plurality of words in a document; for each word-sense pair, determining a word-sense probability score; determining, based at least in part on each word-sense probability score for a word-sense pair, one or more context buckets for the document and one or more sense buckets for the document; determining, based at least in part on the one or more context buckets for the document and the one or more sense buckets for the document, the contextual summarization of the document; and performing one or more document processing actions based at least in part on the contextual summarization.

Abstract: A translation processing method executed by a computer, the translation processing method includes calculating a first translation probability from each of first phonemes included in a first document described in a first language into each of second phonemes included in a second document, whose contents substantially equivalent to those of the first document, described in a second language and a second translation probability from each of the second phonemes into each of the first phonemes; extracting a phoneme pair in which the first translation probability and the second translation probability are equal to or higher than a threshold value; and generating translation phrases in the first document and the second document based on the extracted phoneme pair.

Abstract: An apparatus has a network interface circuit to receive a source sentence from a network connected client device. A processor is connected to the network interface circuit. A memory is connected to the processor. The memory stores parameters of a multilingual neural review system and instructions executed by the processor to operate the multilingual neural review system trained on a corpus of source sentences, draft target sentences and corrected target sentences. The multilingual neural review system produces a corrected target sentence from a draft target sentence representing a proposed translation of the source sentence. The draft target sentence and the corrected target sentence are supplied to the network connected client device. Approval for the corrected target sentence is received from the network connected client device. Parameters of the multilingual neural review system are updated based upon the approval for the corrected target sentence.

Abstract: A system of identifying and tracking occupants of a building is provided. The system includes multiple detector devices that each receive an identification signal from an identification device and multiple image capture devices that each output an image signal depicting a respective monitored location of the building. The system further includes a processing circuit that receives the identification signal from one of the detector devices, associates the detector device with a detector location, retrieves a user attribute, and receives the image signal from one of the image capture devices depicting a monitored location corresponding to the detector location. The processing circuit further generates an annotated video signal including the user attribute overlaid on the video signal, and displays the annotated video signal on a display device.

Abstract: Implementations described herein relate to methods, systems, and computer-readable media to localize dynamic content. In some implementations, a computer-implemented method includes receiving visual content associated with a game from a first client locale, the visual content including text being represented at the first client locale by a first language, converting the text to generate translated text in at least a second language associated with a second client locale, and storing the translated text in a database in association with the visual content.

Abstract: In an example information search method, if a first terminal performs a first search based on a first keyword, and does not obtain a result, the first terminal generates a request for performing a second search, and sends, to a network device, the request for performing the second search. The network device sends a second request to a second terminal. The second terminal performs the second search based on the first keyword, obtains a second search result, and sends the second search result to the first terminal. Through this technique, it can be effectively ensured that a user finds a search result corresponding to the first keyword, and that user privacy is also ensured.

Abstract: A goal management system receives input of a qualitative first goal related to a body of a user (step S111), identifies a quantitative second goal related to the body of the user from the first goal thus received (step S112 to step S117), and presents the second goal thus identified (step S118). The first goal is converted into the quantitative goal for at least one of a plurality of feature amounts related to the body, thereby identifying the second goal including at least one goal obtained by such conversion. The first goal is converted into the quantitative goal for each feature amount corresponding to a meaning obtained by linguistic analysis. When there are a plurality of meanings obtained by linguistic analysis of the first goal, the first goal is converted into the quantitative goal for each feature amount on the basis of a range of each feature amount per meaning.

Abstract: A method includes: a first electronic device establishes a call connection to a second electronic device and then displays a call interface; after receiving a first operation of a first user, in response to the first operation, the first electronic device switches from displaying the call interface to displaying a translation interface; the first electronic device receives a first speech of the first user in a first language and sends the first speech to the second electronic device; in response to the first speech, the translation interface sequentially displays at least a first text and a second text, where the first text is obtained by recognizing the first speech, and the second text is obtained by translating the first speech into a target language; and when the translation interface displays the second text, the first electronic device sends a machine speech in the target language to the second electronic device.

Abstract: An electronic device and method for controlling the electronic device are provided. The electronic device includes a first surface in a first direction, a second surface in a second direction opposite to the first direction, and a third surface enclosing at least a portion of a space formed between the first surface and the second surface. The electronic device also includes an audio module changing an audio signal input to the electronic device into an electronic signal, and a processor being operationally connected to the display and the audio module. The processor is configured to detect the audio signal input via the audio module from a first user via the first surface, detect a touch signal dragged from the third surface in the direction of the second surface, and display in the second surface a translation corresponding to the audio signal.

Abstract: Systems and methods for parsing natural language sentences using an artificial neural network (ANN) are described. Embodiments of the described systems and methods may generate a plurality of word representation matrices for an input sentence, wherein each of the word representation matrices is based on an input matrix of word vectors, a query vector, a matrix of key vectors, and a matrix of value vectors, and wherein a number of the word representation matrices is based on a number of syntactic categories, compress each of the plurality of word representation matrices to produce a plurality of compressed word representation matrices, concatenate the plurality of compressed word representation matrices to produce an output matrix of word vectors, and identify at least one word from the input sentence corresponding to a syntactic category based on the output matrix of word vectors.

Abstract: Provided is a technique for generating a tagging model for attaching a tag in consideration of a phrase based on dependency between words. A tagging model generation apparatus includes a learning section 2 which generates, by using inputted learning data, a tagging model including probability-related information serving as information related to the probability that each tag is associated with each word-related information, and joint probability-related information serving as information related to a joint probability which serves as the probability of appearance of each tag in which appearance frequencies of a plurality of consecutive tags associated with pieces of word-related information of a plurality of consecutive words in each text are taken into consideration, and a storage section 3 which stores the generated tagging model.

Abstract: A set of candidate intent vectors is generated from an input intent vector. A validation of the set of candidate intent vectors is performed that selects as valid intent vectors any of the set of candidate intent vectors that are semantically similar to the input intent vector.

Abstract: A method, apparatus, and system including performing a partitioning function on a first table of a join query, determining a size of the first table based on the partitioning function, and distributing the first table to a set of nodes of a computing cluster based on the size of the first table.

Abstract: The present disclosure provides a wireless earphone device and method for using the wireless earphone device. The wireless earphone device comprises a processing unit, a switch unit, a wireless transceiving unit, a language input unit, and a speaker unit. The switch unit is electrically connected to the processing unit. The wireless transceiving unit is electrically connected to the processing unit, transmitting wirelessly with the earphone. The language input unit is electrically connected to the processing unit. The speaker unit is electrically connected to the processing unit. While the earphone charging case is activated and switched to a translation mode by the switch unit, a first voice signal is inputted into the processing unit through the earphone, the processing unit processes the first voice signal into a second voice signal, and the second voice signal is emitted by the speaker unit.

Abstract: A method for processing information, includes: performing word segmentation on first corpus data to obtain second corpus data containing at least one word; processing, based on a first pretrained model, the at least one word contained in the second corpus data, to obtain a first eigenvector sequence corresponding to the second corpus data; processing, based on a second pretrained model, characters contained in the first corpus data, to obtain a second eigenvector sequence corresponding to the first corpus data; and obtaining a target eigenvector based on the first eigenvector sequence and the second eigenvector sequence.

Abstract: Methods for gradient accumulation with free momentum are performed by systems and devices during neural network model training. An accumulator that includes a processor circuit and a memory element generates free momentum between passes of a neural network model training process. The processor circuit receives a difference weight (gradient) and generates a first input by applying a weighting parameter thereto. The processor circuit obtains a prior weight from the memory element and generates a second input by applying another weighting parameter thereto. The processor circuit generates a filtered input with momentum by filtering the first and second input. The memory element generates a stored next pass weight by accumulating the filtered input with the prior weight. A computing resource then processes the next pass of the neural network model training using the stored next pass weight. The methods, systems, and devices are applicable to pipelined model parallelism training processes.

Abstract: An electronic apparatus is provided. The electronic apparatus includes a memory configured to store an entity identification model, and a processor configured to control the electronic apparatus to: identify a plurality of entities included in an input sentence input based on the entity identification model, acquire a search result corresponding to the plurality of entities, and correct and provide the input sentence based on the search result based on the plurality of entities not corresponding to the search result, wherein the entity identification model may be acquired by learning through an artificial intelligence algorithm to extract a plurality of sample entities included in each of a plurality of sample dialogues. At least a part of a method for identifying an entity from a dialogue may use an artificial intelligence model learned according to at least one of machine learning, a neural network, deep learning algorithm, or the like.

Abstract: A method may include determining whether a first item description matches at least a second item description stored in a database. If the first item description matches the second item description, an ontology of the database may be updated by storing, in the database, the first item description including a first association between the first item description and a first headword associated with the second item description. Alternatively, if the first item description does not match any item descriptions in the database, a second headword for the first item description may be determined based on user inputs. Moreover, the ontology of the database may be updated by storing, in the database, the first item description including a second association between the first item description and the second headword. Related systems and articles of manufacture, including computer program products, are also provided.

Abstract: In one embodiment, a method receives a search query including terms formed in a natural language expression. The terms are included in entities that are categorized in types of entities. The method analyzes the entities for the terms to generate a set of branch queries that include different combinations of entities for the terms. The set of branch queries are ranked based on the types of entities associated with entities of the respective branch queries and a branch query is selected based on the ranking for the branch query. The method uses the entities of the branch query to perform a search of a structured database of structured objects.

Abstract: A sensory eyewear system for a mixed reality device can facilitate user's interactions with the other people or with the environment. As one example, the sensory eyewear system can recognize and interpret a sign language, and present the translated information to a user of the mixed reality device. The wearable system can also recognize text in the user's environment, modify the text (e.g., by changing the content or display characteristics of the text), and render the modified text to occlude the original text.

Abstract: A method includes: a first electronic device establishes a call connection to a second electronic device and then displays a call interface; after receiving a first operation of a first user, in response to the first operation, the first electronic device switches from displaying the call interface to displaying a translation interface; the first electronic device receives a first speech of the first user in a first language and sends the first speech to the second electronic device; in response to the first speech, the translation interface sequentially displays at least a first text and a second text, where the first text is obtained by recognizing the first speech, and the second text is obtained by translating the first speech into a target language; and when the translation interface displays the second text, the first electronic device sends a machine speech in the target language to the second electronic device.

Abstract: Provided are a computer program product, system, and method for classifying text to determine a goal type used to select machine learning algorithm outcomes. Natural language processing of text is performed to determine features in the text and their relationships. A classifier classifies the text based on the relationships and features to determine a goal type. The determined features and relationships from the text are inputted into a plurality of different machine learning algorithms to generate outcomes. For each of the machine learning algorithms, a determination is made of performance measurements resulting from the machine learning algorithms generating the outcomes. A determination is made of at least one machine learning algorithm having performance measurements that are highly correlated to the determined goal type. An outcome is determined from at least one of the outcomes.