Abstract: An artificial intelligence device is provided. The artificial intelligence device according to an embodiment of the present disclosure includes: an input unit configured to receive a speech input; and a processor configured to operate in an interaction mode if a second wakeup word for setting an operation mode is recognized after a first wakeup word for calling the artificial intelligence device is recognized, and process one or more commands received after the second wakeup word according to the operation mode indicated by the second wakeup word.

Abstract: Embodiments provide an artificial intelligence apparatus for recognizing an utterance voice of a user. The artificial intelligence apparatus includes: a communication unit configured to communicate with at least one external artificial intelligence apparatus which obtains first sound data including the utterance voice of the user to generate a first speech recognition result from the first sound data; a microphone configured to obtain second sound data including the utterance voice; and a processor configured to receive first speech recognition results from each of the at least one external artificial intelligence apparatus, generate a second speech recognition result from the second sound data, generate a final speech recognition result for the utterance voice by using the first speech recognition results and the second speech recognition result, and perform a control corresponding to the final speech recognition result.

Abstract: A semiconductor package includes a first semiconductor chip, a second semiconductor chip on the first semiconductor chip, a first semiconductor structure and a second semiconductor structure that are on the first semiconductor chip and spaced apart from each other across the second semiconductor chip, and a resin-containing member between the second semiconductor chip and the first semiconductor structure and between the second semiconductor chip and the second semiconductor structure. The semiconductor package may be fabricated at a wafer level.

Abstract: An embodiment of the present invention provides, comprising: a communication unit configured to communicate with a plurality of external AI apparatuses; and a processor configured to receive sound signals of the user from the plurality of external AI apparatuses, calculate a distance and a variation of the distance from each of the plurality of external AI apparatuses to the user based on the received sound signals, determine a current path of the user based on the calculated distance and the calculated variation of the distance, and determine a future path of the user based on the current path.

Abstract: An application installation file for multiple operating systems (OSs), a method of installing an application for multiple OSs, and an electronic apparatus for installing an application installation file for multiple OSs are provided. The application installation file includes a first container including application meta data corresponding to the plurality of heterogeneous operating systems, a second container including application installation files corresponding to the plurality of heterogeneous operating systems, a third container including asset information for each of the application installation files, and a fourth container including certification information for the file structure.

Abstract: A semiconductor package includes a first semiconductor chip. A second semiconductor chip is below the first semiconductor chip. A third semiconductor chip is below the second semiconductor chip. The second semiconductor chip includes a first surface in direct contact with the first semiconductor chip, and a second surface facing the third semiconductor chip. A first redistribution pattern is on the second surface of the second semiconductor chip and is electrically connected to the third semiconductor chip. The third semiconductor chip includes a third surface facing the second semiconductor chip. A conductive pad is on the third surface.

Abstract: An artificial intelligence server is disclosed. The artificial intelligence server includes an input unit to which input data is inputted, and a processor, when a first output value outputted by an artificial intelligence model with respect to first input data is correct and a second output value outputted by the artificial intelligence model with respect to second input data is incorrect, configured to use the first input data and the second input data to obtain a first domain causing an incorrect answer, and train the artificial intelligence model to be domain-adapted for the first domain.

Abstract: A semiconductor package includes a package substrate, a lower semiconductor device arranged on the package substrate and including first through electrodes, first lower connection bumps arranged between the package substrate and the lower semiconductor device and electrically connecting the package substrate to the first through electrodes, a connecting substrate arranged on the package substrate and including second through electrodes, second lower connection bumps arranged between the package substrate and the connecting substrate and electrically connecting the package substrate to the second through electrodes, and an upper semiconductor device arranged on the lower semiconductor device and electrically connected to the first through electrodes and the second through electrodes.

Abstract: An artificial intelligence (AI) device may acquire user information, use information of a home appliance and weather information, acquire whether use of the home appliance is recommended and a recommended use time of the home appliance from the user information, the use information of the home appliance and the weather information, using an appliance use recommendation model, and output a recommendation comment indicating information on use of the home appliance based on the recommended use time.

Abstract: Disclosed herein an artificial intelligence apparatus for recognizing speech with multiple languages including a microphone, and a processor configured to obtain, via the microphone, speech data including speech of a user with multiple languages, calculate a word recognition reliability of each word in the obtained speech data using an acoustic model of a main language, calculate a word recognition reliability of each word in the obtained speech data using an acoustic model of at least one sub language, select a language having a highest word recognition reliability for each word, convert the speech data into text in consideration of a word recognition result corresponding to the selected language for each word, and generate a speech recognition result corresponding to the speech data using the converted text.

Abstract: An artificial intelligence device mounted on a wine refrigerator including one or more divided spaces includes an input unit, a processor, and an output unit. The input unit is configured to recognize a wine label of each space and recognize an image for determining opening or non-opening of a wine. The processor is configured to acquire wine information by using an artificial intelligence model that receives image data acquired from the input unit as an input value, create a wine list table of each space by using the acquired information, and group wines having the same storage condition into at least one group according to the wine list table, and perform a control such that a temperature of each space is set based on the storage condition of the group. The output unit is configured to output a signal received from the processor.

Abstract: A package-on-package type package includes a lower semiconductor package and an upper semiconductor package. The lower semiconductor package includes a first semiconductor device including a through electrode, a second semiconductor device disposed on the first semiconductor device and including a second through electrode electrically connected to the first through electrode, a first molding member covering a sidewall of at least one of the first semiconductor device and the second semiconductor device, a second molding member covering a sidewall of the first molding member, and an upper redistribution layer disposed on the second semiconductor device and electrically connected to the second through electrode.

Abstract: A chip-stacked semiconductor package includes a first chip including a first detection pad and a second detection pad; a second chip provided on the first chip, the second chip including a third detection pad facing the first detection pad and a fourth detection pad facing the second detection pad; and a first medium provided between the first detection pad and the third detection pad to connect the first detection pad to the third detection pad through the first medium, and a second medium, different from the first medium, provided between the second detection pad and the fourth detection pad to connect the second detection pad to the fourth detection pad through the second medium.

Abstract: A laundry treatment device includes a washing module configured to perform operation related to washing, a camera configured to capture an image of a tag attached to a laundry, and a processor configured to acquire laundry information of a plurality of laundries, to convert the acquired laundry information into encoding data, and to acquire values of laundry control variables corresponding to the converted encoding data based on a laundry course learning model learned using a plurality of reference data through a deep learning algorithm.

Abstract: Disclosed is a laundry scheduling apparatus. The apparatus includes a communication unit, an output unit, and a processor configured to pair with at least one washing machine via the communication unit, obtain laundry preference parameters of a user generated by learning based on at least one of a deep learning algorithm or a machine learning algorithm, using at least one of a laundry log of the user or laundry satisfaction information of the user as input data, generate laundry scheduling information by using washing machine information about the paired at least one washing machine, the laundry preference parameters, and laundry item information obtained via at least one of a user input unit, an interface unit, or a sensor, and cause the output unit to output the laundry scheduling information.

Abstract: A laundry treatment device includes a wireless communication unit, at least one sensor, and a processor configured to apply a learning model learned through a supervised learning algorithm to sensing information including a sensing value collected from the at least one sensor and a measurement time of the sensing value, to acquire microorganism information including a type of microorganism and a proliferation rate of the microorganism, to acquire laundry guide information based on the acquired microorganism information, and to transmit the acquired laundry guide information to a terminal through the wireless communication unit.

Abstract: Disclosed herein is a washing machine including a first data acquirer configured to collect data related to a laundry pattern of a user, a second data acquirer configured to collect data related to context information, and a processor configured to provide the laundry pattern of the user and the context information to a reinforcement learning model as an environment and to train the reinforcement learning model using feedback of the user on a recommended laundry course when the reinforcement learning model recommends the laundry course.

Abstract: A laundry scheduling device according to an embodiment of the present invention includes an input interface that receives speech including a plurality of words respectively representing a plurality of laundry items from a use, and a processor that acquires one or more features represented by each of the plurality of words by inputting speech data corresponding to the received speech to a learning model and performing word embedding, and generates a laundry schedule of the plurality of laundry items based on the one or more features represented by each of the plurality of words.

Abstract: A semiconductor package includes a first semiconductor chip. A second semiconductor chip is below the first semiconductor chip. A third semiconductor chip is below the second semiconductor chip. The second semiconductor chip includes a first surface in direct contact with the first semiconductor chip, and a second surface facing the third semiconductor chip. A first redistribution pattern is on the second surface of the second semiconductor chip and is electrically connected to the third semiconductor chip. The third semiconductor chip includes a third surface facing the second semiconductor chip. A conductive pad is on the third surface.

Abstract: A semiconductor package may include a base layer, and a redistribution layer on the base layer. The semiconductor package may include a first pattern, a second pattern, and a passivation layer covering the first and second patterns. The semiconductor package may include a semiconductor chip on the base layer, a first connection terminal between the base layer and the semiconductor chip and coupled to one of chip pads of the semiconductor chip, and a mold layer between the base layer and the semiconductor chip. The first connection terminal may extend into the passivation layer and may be coupled to the first pattern. The second pattern may be electrically insulated from the semiconductor chip.