In implementations of systems for estimating terminal event likelihood, a computing device implements a termination system to receive observed data describing values of a treatment metric and indications of a terminal event. Values of the treatment metric are grouped into groups using a mixture model that represents the treatment metric as a mixture of distributions. Parameters of a distribution are estimated for each of the groups and mixing proportions are also estimated for each of the groups. In response to receiving a user input requesting an estimate of a likelihood of the terminal event for a particular value of the treatment metric, the termination system generates an indication of the estimate of the likelihood of the terminal event for the particular value based on a distribution density at the particular value for each of the groups and a probability of including the particular value in each of the groups.

A method and system of discovering materials for use in carbon dioxide separation includes extracting references to chemical molecules from online sources. The extracted references are encoded into chemical formulas. Molecular properties are calculated from the encoded chemical formulas. Features are extracted from the chemical formulas. Molecular properties of predicted molecular structures are predicted through a machine learning engine. The predicted molecular properties are based on the calculated molecular properties and extracted features. Target properties for predicted molecular structures are defined. Synthesized molecular structures are generated. The synthesized molecular structures include predicted molecular properties satisfying the defined target properties.

A method and system of discovering materials for use in carbon dioxide separation includes extracting references to chemical molecules from online sources. The extracted references are encoded into chemical formulas. Molecular properties are calculated from the encoded chemical formulas. Features are extracted from the chemical formulas. Molecular properties of predicted molecular structures are predicted through a machine learning engine. The predicted molecular properties are based on the calculated molecular properties and extracted features. Target properties for predicted molecular structures are defined. Synthesized molecular structures are generated. The synthesized molecular structures include predicted molecular properties satisfying the defined target properties.

A computer system includes a memory configured to store instructions, and one or more processors configured to execute the instructions to cause the computer system to perform a method for event detection. The method includes obtaining a user profile and a persona category associated with the user profile corresponding to a user; receiving first data associated with the user and second data associated with one or more environmental or situational factors; detecting an event based on the first data or the second data; and querying a database in response to the detected event to determine one or more recommended actions for the user based on the user profile and the persona category of the user.

A computer system includes a memory configured to store instructions, and one or more processors configured to execute the instructions to cause the computer system to perform a method for event detection. The method includes obtaining a user profile and a persona category associated with the user profile corresponding to a user; receiving first data associated with the user and second data associated with one or more environmental or situational factors; detecting an event based on the first data or the second data; and querying a database in response to the detected event to determine one or more recommended actions for the user based on the user profile and the persona category of the user.

A management system operates in conjunction with entities to provide component recommendations for objects. The management system trains a machine learning model used to generate the component recommendations. The machine learning model is trained based on historical component entries describing components previously provided and identifiers of the components. The management system generates training data by classifying the historical component entries into predetermined component classifications. After the machine learning model is trained, the management system generates a customized recommendation of components for an object based on likelihoods of selection of the predetermined component classifications.

A management system operates in conjunction with entities to provide component recommendations for objects. The management system trains a machine learning model used to generate the component recommendations. The machine learning model is trained based on historical component entries describing components previously provided and identifiers of the components. The management system generates training data by classifying the historical component entries into predetermined component classifications. After the machine learning model is trained, the management system generates a customized recommendation of components for an object based on likelihoods of selection of the predetermined component classifications.

The techniques herein include using an input context to determine a suggested action and/or cluster. Explanations may also be determined and returned along with the suggested action. The explanations may include (i) one or more most similar cases to the suggested case (e.g., the case associated with the suggested action) and, optionally, a conviction score for each nearby cases; (ii) action probabilities, (iii) excluding cases and distances, (iv) archetype and/or counterfactual cases for the suggested action; (v) feature residuals; (vi) regional model complexity; (vii) fractional dimensionality; (viii) prediction conviction; (ix) feature prediction contribution; and/or other measures such as the ones discussed herein, including certainty. The explanation data may be used to determine whether to perform a suggested action.

The techniques herein include using an input context to determine a suggested action and/or cluster. Explanations may also be determined and returned along with the suggested action. The explanations may include (i) one or more most similar cases to the suggested case (e.g., the case associated with the suggested action) and, optionally, a conviction score for each nearby cases; (ii) action probabilities, (iii) excluding cases and distances, (iv) archetype and/or counterfactual cases for the suggested action; (v) feature residuals; (vi) regional model complexity; (vii) fractional dimensionality; (viii) prediction conviction; (ix) feature prediction contribution; and/or other measures such as the ones discussed herein, including certainty. The explanation data may be used to determine whether to perform a suggested action.

A method for stabilizing an image based on artificial intelligence includes acquiring tremor detection data with respect to the image, the tremor detection data acquired from two or more sensors; outputting stabilization data for compensating for an image shaking, the stabilization data outputted using an artificial neural network (ANN) model trained to output the stabilization data based on the tremor detection data; and compensating for the image shaking using the stabilization data. A camera module includes a lens; an image sensor to output an image captured through the lens; two or more sensors to output tremor detection data with respect to the image; a controller to output stabilization data based on the tremor detection data using an ANN model; and a stabilization unit to compensate for an image shaking using the stabilization data. The ANN model is trained to output the stabilization data based on the tremor detection data.