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Companion Robot Discussion Forums
Loona PetBot by KEYi Tech
KEYi TECH’s 10,000 Loona: Towards Artificial General Intelligence
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<blockquote data-quote="Chris" data-source="post: 339" data-attributes="member: 1"><p>Bard conversation continued.</p><p></p><p>Chris: Would it be possible to develop a way for a scaled down version of you to store and access data on the Loona PetBot offline without changing the existing hardware?</p><p></p><p>Bard: It is possible to develop a way for a scaled down version of me to store and access data on the Loona PetBot offline without changing the existing hardware. One way to do this would be to use a technique called federated learning. Federated learning is a machine learning technique where multiple devices can train a model together without sharing their data. This can be done by having each device train a local model on its own data. The local models can then be aggregated to create a global model.</p><p></p><p>In the case of the Loona PetBot, each PetBot could train a local model on its own data. The local models could then be aggregated to create a global model that could be used to make predictions on all of the PetBots. This would allow the PetBots to learn from each other without having to share their data.</p><p></p><p>Another way to store and access data on the Loona PetBot offline would be to use a microSD card. A microSD card is a small, portable storage device that can be inserted into the PetBot. The microSD card could be used to store the model data and any other data that the PetBot needs to access offline.</p><p></p><p>Using a microSD card would allow the PetBot to store more data than it could if it was only using the internal storage. This would allow the PetBot to train more complex models and to access more data offline.</p><p></p><p>Both federated learning and using a microSD card are possible ways to store and access data on the Loona PetBot offline without changing the existing hardware. The best approach would depend on the specific needs of the application.</p></blockquote><p></p>
[QUOTE="Chris, post: 339, member: 1"] Bard conversation continued. Chris: Would it be possible to develop a way for a scaled down version of you to store and access data on the Loona PetBot offline without changing the existing hardware? Bard: It is possible to develop a way for a scaled down version of me to store and access data on the Loona PetBot offline without changing the existing hardware. One way to do this would be to use a technique called federated learning. Federated learning is a machine learning technique where multiple devices can train a model together without sharing their data. This can be done by having each device train a local model on its own data. The local models can then be aggregated to create a global model. In the case of the Loona PetBot, each PetBot could train a local model on its own data. The local models could then be aggregated to create a global model that could be used to make predictions on all of the PetBots. This would allow the PetBots to learn from each other without having to share their data. Another way to store and access data on the Loona PetBot offline would be to use a microSD card. A microSD card is a small, portable storage device that can be inserted into the PetBot. The microSD card could be used to store the model data and any other data that the PetBot needs to access offline. Using a microSD card would allow the PetBot to store more data than it could if it was only using the internal storage. This would allow the PetBot to train more complex models and to access more data offline. Both federated learning and using a microSD card are possible ways to store and access data on the Loona PetBot offline without changing the existing hardware. The best approach would depend on the specific needs of the application. [/QUOTE]
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Loona PetBot by KEYi Tech
KEYi TECH’s 10,000 Loona: Towards Artificial General Intelligence
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