Abstract: A computer implemented system for interpreting data using machine learning, including one or more processors; one or more memories; and one or more computer executable instructions embedded on the one or more memories, wherein the computer executable instructions are configured to execute a unified encoder comprising a neural network encoding data into one or more feature vectors, wherein the encoder is trained using machine learning to generate the one or more feature vectors useful for performing a plurality of different tasks each comprising different interpretations of the data. A plurality of decoders are connected to the unified encoder, each of the decoders comprising a neural network interpreting the one or more feature vectors so as to decode one or more of the feature vectors to output one of the interpretations.

Abstract: A device and a method for producing a high-purity industrial silicon, wherein the device is a refining ladle provided with a steel plate layer, a heat insulation layer, a heat preservation layer, a protective layer and a working layer from outside to inside. The refining ladle is provided with two conical oxygen inlet channels and two conical argon inlet channels at the bottom. The method comprises: preparing a molten silicon with temperature?2200° C.; pouring the molten silicon into a refining ladle after introducing oxygen and argon thereto and keeping the gas pressure at 1 standard atmospheric pressure, and keeping the molten silicon at 1700-1900° C.; adding a slag-forming agent thereto, and introducing oxygen and argon from the refining ladle bottom, obtaining a slag molten silicon; cooling the slag molten silicon to ambient temperature, solidifying and separating, obtaining a high-purity industrial silicon.