Introducing

Jarvis2 uses standard feed-forward neural network models with the TensorFlow framework for tasks such as classification, regression, or feature extraction.

JarvisAI uses both the Deep Q-Learning Network (DQN) and Proximal Policy Optimization (PPO) for decision-making tasks based on environment observation.

Jarvis2 AI can fetch and parse web content using the BeautifulSoup library.

Jarvis2 AI Implements KMeans clustering for unsupervised learning tasks.

Jarvis2 AI can query a knowledge base stored in a Neo4j graph database.

Jarvis2 AI Incorporates a chatbot feature for conversation and a GPT-3 model for generating text. Jarvis2 AI is designed in such a manner that even your own custom AI chat models could be used if preferred.

Jarvis2 AI can convert spoken language into written form using the SpeechRecognition library.

Jarvis2 AI has the ability to process and analyze images using OpenCV and a pre-trained model.

Jarvis2 AI uses Support Vector Machines (SVM) for predictive tasks.

Jarvis2 AI can control smart home devices via the Home Assistant API.

Jarvis2 AI can schedule tasks according to the user’s instructions.

These are only a few features of the Jarvis2 AI Personal Assistant Framework. Jarvis2 AI was built to be expansive and modular. It is built as a template for other users to customize and extend upon. Jarvis2 AI acts as a blank canvas in it's untrained state and allows the user to decide it's purpose, direction, training data, limitations, and overall use case.