{"id":1432,"date":"2022-07-06T04:39:17","date_gmt":"2022-07-06T04:39:17","guid":{"rendered":"http:\/\/brianchami.com\/?page_id=1432"},"modified":"2022-09-12T02:53:14","modified_gmt":"2022-09-12T02:53:14","slug":"trees-bots","status":"publish","type":"page","link":"https:\/\/brianchami.com\/index.php\/trees-bots\/","title":{"rendered":"Trees-Bots"},"content":{"rendered":"\t\t
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Trees\u2026 and Bots: Conversational AI with Trees?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Sample application:\u00a0<\/p>

Conversational survey that asks users about urban green spaces within cities in terms of potential benefits. This approach is using a large language model, in this case, it’s davinici-text from OpenAI, along with GCP’s\u00a0text to speech and speech to text API’s. This approach is significantly better than using a virtual assistant. Especially since people are interacting with the model, the model has the ability to train on previous answers and suggest some answers. Running this is also less computationally intensive due to the cloud based API’s, rather than a system installed on the raspberry pi that process some elements and communicates back and forth with the cloud.\u00a0<\/span><\/p>

[To be added, hopefully soonish: the model running on an RPi, where the RPi is running on a tree connected to tree sensors.]<\/span><\/p>

The other approach is using a virtual assistant, e.g.\u00a0I used an Alexa SDK and installed on a raspberry pi. The system works in a very similar way to interacting with Alexa, Siri, google assistant. This approach is different, since\u00a0I hard coded intents and answers about the project to train it to respond in the way we want.<\/span><\/p>

[demo about this to be added, hopefully soonish…]<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Alexa.. For Trees: Would Conversational AI and bots make talking to tree more natural? <\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Natural assets\u2019 benefits include social values, inherently associated with better physical and mental health. Active interactions between people and natural assets has been constrained to individuals texting messages back and forth with HelloLampPost [HelloUBC] servers through the campus\u2019s inanimate objects, such as parking meters and lamp posts. Our approach introduces a pseudo-natural approach where introducing conversational AI to trees may allow people to feel more welcome and included within green spaces. People would have the ability to ask trees questions, and the trees would vocally respond back about the project itself, their own distress levels, or any random topics.<\/p>

Conversational AI is a virtual assistant, connected to a cloud computing server, that responds with commands and\/or phrases upon specific engagements from users. Examples include Google\u2019s Home Assistant, where a user prompts the device to wake up using a very specific phrase, i.e. keyword. When the device enters the listening phase, the user mentions a phrase or a command, prompting the device to send to the cloud server to search for answers pertaining to the phrase, or issue pre-programmed commands to control network-connected devices. Searchable prompts include questions that require the device to provide answers for, whether pre-programmed answers or answers based on internet searches.\u00a0<\/p>

On the RPi, we ported an (Amazon) Alexa-based system, using the Alexa-SDK, utilizing Alexa\u2019s keyword spotting, speech\/language recognition, text-to-speech, and custom programmed intents\/ answers, e.g. prompting Alexa to respond about the UBC-Rogers\u2019 project with very specific answers we upload.\u00a0<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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MCUs Are More Powerful and Cheaper Now.<\/span><\/p>

Porting a conversational AI on a microcontroller ( ESP32 fitted with a Low Power Wide Area (LPWA) Wireless module) for communications through a cellular network.<\/span>\u00a0<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Example interaction:<\/b><\/p>