Clarifai AI technology page Top Builders

Explore the top contributors showcasing the highest number of Clarifai AI technology page app submissions within our community.

Clarifai

Clarifai's groundbreaking technology aims to bring The World’s AI(™) to developers and data scientists around the globe. One of the first deep learning platforms since its establishment in 2013 by Matthew Zeiler, Ph.D, a distinguished expert in the realm of machine learning, the company has consistently set high benchmarks in the AI industry.

Prior to founding Clarifai, Dr. Zeiler took the top five places in image classification at the 2013 ImageNet Challenge. By 2019, Clarifai had made such a significant impact that it was recognized as a leader in Forrester’s New Wave Computer Vision Platforms report, making it the sole startup to attain a differentiated rating. In 2022 Clarifai launched a new free service – Clarifai Community – designed to empower everyone to create, share and use The World's AI. That same year Clarifai further solidified its reputation when IDC MarketScape acknowledged it as a leader in Computer Vision AI Platforms. In 2023, Gartner named Clarifai a niche player in the Magic Quadrant for Cloud AI Developer Services for the third year in a row.

Catering to the entire AI developmental life cycle, today Clarifai provides an AI platform for unstructured image, video, text, and audio data. The platform supports data exploration, data labeling, model training, evaluation and inference, and deployment. Clarifai enables users to implement AI technology into their products via API, Mobile SDK, and/or on-premise solutions.

General

Platform: https://www.clarifai.com/
Release date: 2013
Founder: Matthew Zeiler, Ph.D.
Type: AI-powered software platform
Achievements: Winner of the top five places in image classification at the ImageNet 2013 competition, Recognized as a leader in Forrester’s New Wave Computer Vision Platforms in 2019, Computer Vision AI Platforms by IDC MarketScape in 2022, and Gartner CAIDS ‘21-’23.

Flexible Deployment

Clarifai is known for its flexible deployment options, allowing users to choose from an array of platforms, be it any cloud such as AWS, Azure, or Google Cloud, or on bare-metal with options for air-gapped setups and on-edge deployments.

AI Lake

Clarifai's AI Lake is designed to enhance AI adoption and its reusability across enterprises. It has features such as:

Storing and analyzing vast amounts of data objects.
Auto-indexing your data using AI.
Tracking the lineage and versioning of all AI assets.

Centralized and Secure AI

The platform offers centralized and secure AI functionalities that employ foundation models to simplify the machine learning model development and deployment process. It provides an efficient structure to organize, share, and reuse all AI resources.

Data Labeling with Scribe

Data labeling has never been more streamlined:

Automated data labeling upon data ingestion.
Scalability to cater to labeling projects of any magnitude.
Intuitive labeling and reviewer user interfaces to optimize the quality and quantity of annotations.

Open, Reusable Building Blocks

Clarifai’s fully-integrated AI platform brings forth a wide variety of machine learning products and services that seamlessly collaborate to produce AI on an impressive scale:

AI Lake: A central repository for all AI assets.
Spacetime: Enables vector and input searching.
Enlight: Fine-tune any open-source or third-party model with ease.
**Mesh: **Connect models to create more complex logic.
Armada: Instantaneously deploy your models for production.
Modules: Effortlessly deploy Streamlit-powered UIs in your apps or on top of Clarifai's Portal.

Start Building with Clarifai

Clarifai provides an extensive range of libraries and resources to assist developers in embarking on their AI journey. Dive into a vibrant ecosystem and see what others are creating using Clarifai’s cutting-edge technology. For a deeper understanding and tutorials, explore the dedicated sections on Clarifai's official website.

Documentation for developers

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Clarifai AI technology page Hackathon projects

Discover innovative solutions crafted with Clarifai AI technology page, developed by our community members during our engaging hackathons.

Disease identify and mapping

Disease identify and mapping are artificial intelligence use camera to capture and give report output from disease symptoms it's like our doctor watching our health as private healthcare, and give us good response to our disease. It's taking much time to our doctor for take information from patients, it's more useful make this AI trainable for our doctor and the fast respon and massif data collecting make our doctor job's less busy and they can take attention to our health development time by time Starts from now. But in this prototype I'm use a small case with Small data but not in real time condition.

Bloom

Individuals often face complex life decisions, such as choosing a career path, committing to a relationship, or making significant purchases, without fully understanding how these decisions align with their core values. This misalignment can lead to dissatisfaction, stress, and a lack of fulfillment. We address the problem by providing a tool called Bloom that allows users to explore different life scenarios and assess how well each scenario aligns with their personal values. By using synthetic data and value alignment metrics, the simulator offers insights into potential outcomes, helping users make more informed, value-consistent decisions that enhance their overall well-being and life satisfaction.

CodeBlast Dream Catcher

The CodeBlast Dream Catcher proposes an innovative approach to searching multidimensional space for knowledge based on the following eight principles: 1. An "all possible combinations space" exists in a multidimensional space where knowledge is discovered, not created. 2. This multidimensional space is best searched with LLMs using goals, as goals carry the recipes for accomplishing them. 3. There exists a multidimensional "all possible" Codestral goal space consisting of interconnected goals resembling a graph. 4. This multidimensional goals space can best be searched by remapping it to the 2D Infinite Canvas proposed in the LabLab.ai Build Your Business Startup Hackathon's "Navigating the Infinite Plane". 5. The infinite canvas can be created using a 50256 base number system derived from the GPT-2 tokenization labels. 6. To avoid the costly computational expense of base number conversion, hidden and unhidden states are created in the 2D infinite plane. 7. These hidden and unhidden states correspond to the conscious and unconscious mind, proposing that the human brain uses a similar mechanism to avoid the heavy cost of base number conversion. 8. Thus, searching for knowledge becomes a simple mapping problem in 2D and 1D space in both hidden and unhidden states. Business Value: The CodeBlast Dream Catcher approach offers significant business value through the following benefits: Efficient Knowledge Discovery Resource Optimization Enhanced Decision-Making Scalability Flexibility Strategic Advantage The CodeBlast Dream Catcher approach redefines knowledge discovery by leveraging LLMs and innovative mapping techniques to efficiently explore multidimensional spaces. By optimizing resources, enhancing decision-making, and offering scalability and flexibility, it provides a strategic advantage, making it a valuable tool for businesses aiming to lead in advanced AI knowledge discovery.

TaijiFlow

TaijiFlow is an app designed to increase human healthspan by offering personalized exercise plans based on traditional Chinese medicine principles. The global population of people over 60 years old is expected to nearly double by 2050. In the U.S., there is a 12-year gap between life expectancy and healthy life expectancy, meaning people are living longer but not necessarily healthier in their later years. Current healthcare systems primarily treat symptoms rather than proactively improving health. TaijiFlow addresses this issue by recommending exercises and postures from traditional Chinese medicine practices such as tai chi and medical qigong. These exercises have been shown to act as natural anti-aging medicine and can be more effective than some conventional physical exercises. The app includes example video clips tailored to specific medical conditions or vital functions.

Fronto

Fronto is a revolutionary AI-powered website builder designed to simplify the website creation process for individuals and businesses. With innovative features like "ScreenShot to code" and "Image Link to code," Fronto eliminates the need for manual coding and design, allowing users to create professional websites quickly and easily. Leveraging advanced AI assistants like GPT-4 and GPT-4 Vision and another models, Fronto automates the entire process, from design inspiration to functional code generation. Trulens provides feedback and evaluates the performance of our OpenAI assistants, ensuring the effectiveness and reliability of Fronto's AI-powered features. Whether you're a novice or an experienced developer, Fronto empowers you to bring your website ideas to life effortlessly.

Adapt-a-RAG

Introduction Adapt-a-RAG is an innovative application that leverages the power of retrieval augmented generation to provide accurate and relevant answers to user queries. By adapting itself to each query, Adapt-a-RAG ensures that the generated responses are tailored to the specific needs of the user. The application utilizes various data sources, including documents, GitHub repositories, and websites, to gather information and generate synthetic data. This synthetic data is then used to optimize the prompts of the Adapt-a-RAG application, enabling it to provide more accurate and contextually relevant answers. How It Works Adapt-a-RAG works by following these key steps: Data Collection: The application collects data from various sources, including documents, GitHub repositories, and websites. It utilizes different reader classes such as CSVReader, DocxReader, PDFReader, ChromaReader, and SimpleWebPageReader to extract information from these sources. Synthetic Data Generation: Adapt-a-RAG generates synthetic data using the collected data. It employs techniques such as data augmentation and synthesis to create additional training examples that can help improve the performance of the application. Prompt Optimization: The synthetic data is used to optimize the prompts of the Adapt-a-RAG application. By fine-tuning the prompts based on the generated data, the application can generate more accurate and relevant responses to user queries. Recompilation: Adapt-a-RAG recompiles itself every run based on the optimized prompts and the specific user query. This dynamic recompilation allows the application to adapt and provide tailored responses to each query. Question Answering: Once recompiled, Adapt-a-RAG takes the user query and retrieves relevant information from the collected data sources. It then generates a response using the optimized prompts and the retrieved information, providing accurate and contextually relevant answers to the user.