Systems and methods for using artificial intelligence to predict recipes for a food product

A non-transitory computer-readable medium comprising program instructions that, when executed by at least one processor, cause a computing platform to:

• receive, from a first client device via a first communication path comprising at least one data network, an indication of configuration parameters for predicting candidate recipes for a food product, wherein the configuration parameters include at least (i) a set of ingredients to include in the candidate recipes, (ii) at least one target variable for the candidate recipes that quantifies an extent to which the candidate recipes achieve a target goal associated with the food product, (iii) a set of constraints for the candidate recipes that impose limits on amounts of ingredients included in the candidate recipes, and (iv) a set of assessment variables that are to be evaluated during testing of the candidate recipes, wherein the set of assessment variables includes variables corresponding to sensory attributes for which feedback is to be gathered from taste testers of test samples produced from the candidate recipes;
• based on a first subset of the configuration parameters and a first set of training data, carry out a multi-task machine learning process to train a predictor model that is configured to output, for a candidate recipe that is passed as input to the predictive model, (i) a predicted value of the at least one target variable and (ii) predicted values of a given subset of the assessment variables, wherein the first set of training data comprises a first set of training data records that each includes (a) a combination of amounts for at least a subset of the set of ingredients, (b) a value for the at least one target variable, and (c) values for the given subset of the assessment variables, and wherein multi-task machine learning process functions to identify and exploit correlations between the at least one target variable and the given subset of assessment variables;
• utilize a generator model and the predictor model to select, from a multi-dimensional space of possible recipes having a number of dimensions that corresponds to a number of ingredients in the set of ingredients, a first group of candidate recipes for the food product in which each respective candidate recipe comprises a respective combination of amounts for the set of ingredients by:
• utilizing the generator model to carry out a first iteration of a single-task machine learning process to train a first instance of an underlying predictive model based on a second subset of the configuration parameters and a second set of training data, wherein the second set of training data comprises a second set of training data records that each includes (a) a combination of amounts for at least a subset of the set of ingredients and (b) a value for the at least one target variable, and wherein the first instance of the underlying predictive model functions to output a first version of (i) predicted values of the at least one target variable for the multi-dimensional space of possible recipes and (ii) uncertainty estimates for the predicted values;
• utilizing the generator model to carry out a first iteration of an optimization process to select a first candidate recipe to include in the first group of candidate recipes from the multi-dimensional space of possible recipes based on (i) a balancing between the predicted values and the uncertainty estimates output by the first instance of the underlying predictive model and (ii) the set of the constraints, wherein the first candidate recipe comprises a first combination of amounts for the set of ingredients;
• utilizing the predictor model to determine a first predicted value of the at least one target variable for the first candidate recipe;
• updating the second set of training data by adding, to the second set of training data records, a first new training data record that includes (a) the first combination of amounts for the set of ingredients and (b) the first predicted value of the at least one target variable for the first candidate recipe;
• utilizing the generator model to carry out a second iteration of the single-task machine learning process to train a second instance of the underlying predictive model based on the second subset of the configuration parameters and the updated second set of training data that includes the first new training data record, wherein the second instance of the underlying predictive model functions to output a second version of (i) predicted values of the at least one target variable for the multi-dimensional space of possible recipes and (ii) uncertainty estimates for the predicted values;
• and
• utilizing the generator model to carry out a second iteration of the optimization process to select a second candidate recipe to include in the first group of candidate recipes from the multi-dimensional space of possible recipes based on (i) a balancing between the predicted values and the uncertainty estimates output by the second instance of the underlying predictive model and (ii) the set of the constraints, wherein the second candidate recipe comprises a second combination of amounts for the set of ingredients;
• and
• transmit, to a second client device via a second communication path comprising at least one data network, one or more messages that collectively cause the second client device to display the first group of candidate recipes for the food product.

A computer-implemented method comprising:

• receiving, from a first client device via a first communication path comprising at least one data network, an indication of configuration parameters for predicting candidate recipes for a food product, wherein the configuration parameters include at least (i) a set of ingredients to include in the candidate recipes, (ii) at least one target variable for the candidate recipes that quantifies an extent to which the candidate recipes achieve a target goal associated with the food product, (iii) a set of constraints for the candidate recipes that impose limits on amounts of ingredients included in the candidate recipes, and (iv) a set of assessment variables that are to be evaluated during testing of the candidate recipes, wherein the set of assessment variables includes variables corresponding to sensory attributes for which feedback is to be gathered from taste testers of test samples produced from the candidate recipes;
• based on a first subset of the configuration parameters and a first set of training data, carrying out a multi-task machine learning process to train a predictor model that is configured to output, for a candidate recipe that is passed as input to the predictive model, (i) a predicted value of the at least one target variable and (ii) predicted values of a given subset of the assessment variables, wherein the first set of training data comprises a first set of training data records that each includes (a) a combination of amounts for at least a subset of the set of ingredients, (b) a value for the at least one target variable, and (c) values for the given subset of the assessment variables, and wherein multi-task machine learning process functions to identify and exploit correlations between the at least one target variable and the given subset of assessment variables;
• utilizing a generator model and the predictor model to select, from a multi-dimensional space of possible recipes having a number of dimensions that corresponds to a number of ingredients in the set of ingredients, a first group of candidate recipes for the food product in which each respective candidate recipe comprises a respective combination of amounts for the set of ingredients by:
• utilizing the generator model to carry out a first iteration of a single-task machine learning process to train a first instance of an underlying predictive model based on a second subset of the configuration parameters and a second set of training data, wherein the second set of training data comprises a second set of training data records that each includes (a) a combination of amounts for at least a subset of the set of ingredients and (b) a value for the at least one target variable, and wherein the first instance of the underlying predictive model functions to output a first version of (i) predicted values of the at least one target variable for the multi-dimensional space of possible recipes and (ii) uncertainty estimates for the predicted values;
• utilizing the generator model to carry out a first iteration of an optimization process to select a first candidate recipe to include in the first group of candidate recipes from the multi-dimensional space of possible recipes based on (i) a balancing between the predicted values and the uncertainty estimates output by the first instance of the underlying predictive model and (ii) the set of the constraints, wherein the first candidate recipe comprises a first combination of amounts for the set of ingredients;
• utilizing the predictor model to determine a first predicted value of the at least one target variable for the first candidate recipe;
• updating the second set of training data by adding, to the second set of training data records, a first new training data record that includes (a) the first combination of amounts for the set of ingredients and (b) the first predicted value of the at least one target variable for the first candidate recipe;
• utilizing the generator model to carry out a second iteration of the single-task machine learning process to train a second instance of the underlying predictive model based on the second subset of the configuration parameters and the updated second set of training data that includes the first new training data record, wherein the second instance of the underlying predictive model functions to output a second version of (i) predicted values of the at least one target variable for the multi-dimensional space of possible recipes and (ii) uncertainty estimates for the predicted values;
• and
• utilizing the generator model to carry out a second iteration of the optimization process to select a second candidate recipe to include in the first group of candidate recipes from the multi-dimensional space of possible recipes based on (i) a balancing between the predicted values and the uncertainty estimates output by the second instance of the underlying predictive model and (ii) the set of the constraints, wherein the second candidate recipe comprises a second combination of amounts for the set of ingredients;
• and
• transmitting, to a second client device via a second communication path comprising at least one data network, one or more messages that collectively cause the second client device to display the first group of candidate recipes for the food product.