Core Idea IRIS AI Caregiver AIs are designed to provide empathetic support and guidance to caregivers, patients, and families navigating palliative end of life care. By using cutting-edge AI, computer vision, and natural language processing, IRIS AI creates a personalized, compassionate, and intuitive support system. *Key Objectives* 1. *Emotional Support*: Offer emotional comfort and reassurance to caregivers, patients, and families. 2. Personalized Guidance Provide tailored guidance and recommendations for caregiving, symptom management, and end-of-life care. 3. *Care Coordination*: Facilitate seamless communication and coordination among caregivers, healthcare professionals, and family members. 4. Respite and Relief: Offer respite and relief to caregivers, reducing burnout and improving overall well-being. Innovative Approach IRIS AI Caregiver AIs combine: 1. *Edge AI*: Enables real-time processing and analysis of sensitive data. 2. Computer Vision: Analyzes human movements, facial expressions for better help and illness progression tracking using CXR & CT image analysis 3. Natural Language Processing: Understands and responds to emotional cues, concerns, and questions. 4. Bidirectional Intelligence Hierarchy: Facilitates continuous learning, adaptation, and improvement. By integrating these technologies, IRIS AI Caregiver AIs can simulate a Hospice support ecosystem, empowering caregivers, patients, and families to navigate the complexities of palliative care with compassion, dignity, and hope.

You have outlined the process quite comprehensively: 1. Utilize the EnCodec model to encode audio files into vector representations, saved as text files. 2. Process these text embeddings using the "emojiintrospector" tool to generate emoji sequences that represent the audio. 3. Validate the emoji outputs across test audio samples to ensure that the harmonic relationships are maintained. Key points: - EnCodec encodes audio to discrete embeddings, output as text. - The "emojiintrospector" tool maps these text embeddings to emojis. - Generated audio samples with 3 harmonics are encoded. - Analyze the emoji outputs to identify common patterns representing harmonic frequencies. - This demonstrates that the pipeline retains the harmonic structure in the emoji mapping. - The resulting emoji sequences can be used for visualization or further analysis.

In response to the demand for customizable AI assistants, we propose a solution leveraging the Clarifai platform. This involves developing adaptable workflows and models to overcome limitations of rigid approaches. Key technical elements: 1. **Workflow Generation:** A dynamic engine constructs workflows based on intuitive emoji sequences, enabling complex mathematical logic execution. 2. **Metadata Ingestion:** Our solution processes diverse enterprise data, enhancing AI's contextual understanding beyond images. 3. **Prompt Engineering:** Specialized models for domains and tasks using techniques like prompting and fine-tuning. 4. **Orchestration:** An end-to-end framework manages workflow generation, data ingestion, model training, and execution. Implementation: - Clarifai clients, APIs, and gRPC for scalability. - Kubernetes-based microservices for workflow steps. - CI/CD via GitHub Actions, facilitating versioning and testing. - Monitoring with Grafana, Prometheus, and Sentry. - Caching strategies for performance optimization. - Airflow for workflow pipelines. Context: - Users: Data scientists, ML engineers, SWEs. - Industries: Finance, insurance, healthcare. - Use cases: Personalized recommendations, customer service automation. Next Steps: - Develop MVP workflow generator. - Ingest sample metadata for data processing. - Curate prompts and fine-tune models. - Scale tests on larger datasets. - Establish CI/CD pipeline. - Implement monitoring and instrumentation. - Iterate for continued enhancements. Our solution aims to revolutionize AI assistance for enterprises, driven by Clarifai's platform and innovative technical approaches, offering flexibility and efficiency in workflow development and execution.

This app lets the user automatically scrape websites by letting Stablecode generate JavaScript code to parse a part of the HTML and convert it to a CSV. Here is how our app works: The user enters a URL and the app returns the page's HTML. The user can then tell the AI where the data is located, and Stablecode will then write javascript file to parse the HTML and convert the relevant part into a csv. We are taking inputs from the user, that input will be HTML link that user wants to scrape and parse, we have the option given to user to select an element and write a prompt accordingly to scrap and parse that element from the given HTML link. Stablecode will generate JavaScript code to parse part of HTML and convert it into CSV format. This will help users to scrape websites and get JavaScript code for the selected element as well as the Downloadable CSV format code for the selected element.

🗓️ This will be a 7-day virtual hackathon from 13-20 January 💻 Access AI21 Labs' state-of-the-art language models to build innovative applications 🏆 Prizes and awards of up to $9000 API credits + $3500 cash 💡 Meet and learn from AI21 Labs and Lablab experts 👥 Join the community and find your team ✔️ All levels are welcome 📖 AI tutorials and mentors to help you ⭐ Receive a certificate of completion and Wordtune premium for submission 🐱‍💻 Sign up now! It’s free!

🗓️ This 7-day virtual hackathon from January 27 to February 3 will be your time to shine! 💻 Construct AI apps with the latest LLM-powered technology from Cohere and try the newly released Multilingual Text Understanding model ✔️ There's no prerequisite level. Just starting out with AI? Or perhaps you're a veteran Data Scientist? A Designer? A Business Developer? We want you and your domain expertise! 🐱‍💻 Sign up now for free and let's get going!

🛠️ Build your MVP in 7 days 🤝 Find your team on the platform 📈 Hours of educational context 🦾 Generative AI tools to build with 🏅 Further support for winners