Citrine Informatics, Inc.

X Development LLC

Tidalx Ai Inc.

Tidaix Ai Inc.

Julia Black Ling

Laura Valentine Chrobak

Barnaby John James

Peter Kimball

Erin Melissa Tan Antono

Edward Soo Kim

Jason Stuart Koeller

Brendan David Folie

Alexander Willem Anton Van Grootel

Maxwell Lipford Hutchinson

Ryan Michael Latture

Sean Phillip Paradiso

Gregory Joseph Mulholland

James Samuel Peerless

Yoolhee Kim

Bryce William Meredig

Selami Emre Sevgen

A device receives an ingredient list having a sequence of ingredients ordered by relative amount, and generates formulation vectors by sampling the ingredients list. The device inputs the plurality of formulation vectors into a machine-learned model, the machine-learned model generating an encoded version of each of the plurality of formulation vectors using an encoder, and then outputting a plurality of reconstructed formulation vectors as derived using a decoder. The device identifies reconstructed formulation vectors that have an order that matches the sequence, defines a latent space using the encoded version of the matching reconstructed formulation vectors. The device iteratively samples the latent space until a threshold number of samples are derived that match an ordering constraint that corresponds to the sequence, performs a statistical aggregation of the samples, and outputs an indication of an absolute amount of each ingredient in the ingredients list.

Two-stage sampling for accelerated deformulation generation

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for monocular underwater camera biomass estimation. In some implementations, an exemplary method includes obtaining a plurality of images of fish captured by a monocular underwater camera; providing the plurality of images that were captured by the monocular underwater camera to a first model trained to detect one or more fish within the plurality of images; generating one or more values for each detected fish as a set of values; generating a biomass distribution of the fish based on the set of values; and determining an action based on the biomass distribution.

Monocular underwater camera biomass estimation

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that generate from a first pair and a second pair of images of livestock that are within an enclosure and that are taken at different times using a stereoscopic camera, at least two distance distributions of the aquatic livestock within the enclosure. The distance distributions can be used to determine a measure associated with an optical property of the water within the enclosure. A signal associated with the measure can be provided.

Turbidity determination using computer vision

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for monocular underwater camera biomass estimation. In some implementations, an exemplary method includes obtaining an image of a fish captured by a monocular underwater camera; providing the image of the fish to a depth perception model; obtaining output of the depth perception model indicating a depth-enhanced image of the fish data in the image; determining a biomass value estimate of the fish based on the output; and determining an action based on one or more biomass values estimates including the biomass value estimate of the fish.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for underwater camera biomass prediction aggregation. In some implementations, an exemplary method includes obtaining images of fish captured by an underwater camera; providing data of the images to a trained model; obtaining output of the trained model indicating the likelihoods that the biomass of fish are within multiple ranges; combining likelihoods of the output based on one or more ranges common to likelihoods of two or more fish to generate a biomass distribution; and determining an action based on the biomass distribution.

Underwater camera biomass prediction aggregation

A device generates a capability map. The device receives one or more design spaces from a materials supplier, the one or more design spaces including candidate components and capabilities of tools available to the materials supplier. The device inputs a design space of the one or more design spaces into a machine learning model, the training data including a plurality of components including input materials and/or chemicals, and, for respective combinations of the plurality of components, a plurality of respective performance properties. The device receives as output from the model a capability map of the materials supplier storing possible combinations of performance properties and a respective difficulty of developing a composition with that combination of performance properties. The device outputs a user interface for display to a user indicating data of the capability map.

Product design and materials development integration using a machine learning generated capability map

A system and a method are disclosed that, in an embodiment, receive first input from a user of a candidate formulation recipe, and second input from the user of target properties and target property constraints. The system inputs the first input into a machine learning model, the model having been trained using historical training data, each element of the historical training data corresponding to a known formulation having a known feature representation, each known formulation having associated properties and statistical representations of each feature of the known formulation that form the known feature representation. The system receives as output from the model a predicted property of a candidate formulation derived using the first input and the likelihood that the candidate formulation satisfies the target property constraints using the second input. The system generates for display to the user a predicted likelihood that the predicted property satisfies the second input.

Using machine learning to explore formulations recipes with new ingredients

A system and a method are disclosed for predicting design space quality for materials development and manufacture. In an embodiment, a processor receives input of a material property and a design space. The processor identifies a best data point. For each respective candidate material of the design space, the processor receives, as output from a model, a respective property value. The processor determines respective property values that exceed the property value of the best data point adds them to a subset of candidate materials. The processor determines a PFIC score for candidates in the subset. The processor generates a plurality of curves, each reflecting a respective probability distribution of property values. The processor determines a CMLI score based on the plurality of respective curves. The processor determines that the design space is high quality based on the PFIC and CMLI scores, and outputs a recommendation to proceed.

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Redwood City, CA, United States of America

Julia Black Ling