Ian Villanueva@sprites20847

The system generates emotions from images, primarily intended for game NPCs so that they would react emotionally to the game environment. The sentiment analysis model is an LSTM-based Dense Neural Network that are fed Word2Vec embeddings. The model was trained using generated data from cohere.ai using the prompt: "I felt <emotion> when I saw <img2text>" System Flow: img2txt -> cohere.ai generator -> text2emote -> LSTM+DenseNN The images are passed to a CLIP Interrogator (BLIP + CLIP (ViT-32-B)) to generate text descriptions. Such text descriptions are elaborated by cohere.ai generator to generate emotional responses using the prompts: "When I saw <img2txt output>, I felt emotions such as"

(Work in progress) Please go to the GitHub page. The presentation link is troll since I can't make a for such a web app for such a short time. Soon I would implement agent creation and the actual Yi model. TY! In the realm of artificial intelligence, our project stands as a beacon of innovation, set to redefine machine capabilities. We aspire to create an AI system that not only automates tasks but comprehends the nuances of human thought. By integrating cloud computing and edge devices, unifying language models, and incorporating autonomous learning, we envision a future where AI is an intuitive companion. Cloud computing offers computational prowess, while edge devices provide local agility. Unifying language models with macros, interpreters, and the Retrieval-Augmented Generation (RAG) approach enhances linguistic capabilities. Our AI evolves autonomously, generating programs and refining itself. The YI model, with a colossal context size, enables nuanced responses. Join us on GitHub to witness AI's future.

Code: https://github.com/sprites20/Anthroid-AI Using together.ai to host the LLM serverless, Vectara for querying the documents, LLamaIndex for text embeddings (or LLM), and unstructured.io cleaning and translating the HTML or maybe even formatting the prompts. Uses Google Search API or Azure Bing Search service API to query the search engine, returns links, and sends to Vectara for indexing and querying, (perhaps more options in the future). For now, it uses only Vectara but will implement the LLamaindex soon. Shall also use the Meta's Graph API to gather posts, not only from search engines but social media apps like Facebook for the latest news about a topic using its query engine and more content not only from websites but from actual people in real-time. (WIP) It is also capable of choosing, retrieving code, and running code. With a built-in Python interpreter; the exec() function, that can also run other languages via bindings like jnius (Java), Cython/CPython (C/C++), C# DLLs, and whatever binding in the Python library there is. Even OpenGL for 3D rendering for true multimodality, OpenCV for RTSP streaming, image processing, and computer vision, matplotlib for mathematical visualizations, or even a custom web browser like Chrome and Edge that can also run JS evaluate to execute JS code for websites. A cross-platform native app, that in the future should be able to run on most operating systems, not only on PC but also for mobile phones like Android and Ios.