Fast.ai is a research institute that aims to democratize access to artificial intelligence by making deep learning more accessible to individuals with diverse backgrounds. Founded by Jeremy Howard and Dr. Rachel Thomas, the company's goal is to create cutting-edge technology that is available to anyone interested in learning and integrating AI into their work processes. Fast.ai provides a range of solutions, including educational programs, open-source libraries, and a supportive community that fosters inclusivity and diversity in the AI industry.
Their innovative approach to AI is characterized by a commitment to transparency, ethics, and equality, and has allowed them to break down barriers for people who were previously unable to access deep learning technology. Using Fast.ai, anyone interested in AI can easily learn and apply cutting-edge models, fostering a new generation of diverse and inclusive AI professionals, and making AI accessible to everyone.
TLDR
Fast.ai is a research institute that provides accessible and innovative solutions to democratize deep learning access to people from a diverse range of backgrounds. Founded by Jeremy Howard and Dr. Rachel Thomas, Fast.ai offers educational programs, an open-source library, and a supportive community. Fast.ai's approach to AI prioritizes inclusivity, ethics, and transparency, making it easier for anyone to learn and apply cutting-edge models.
The company's initiative has led to the creation of a new generation of diverse and inclusive AI professionals, making AI accessible to everyone.
Company Overview
Fast.ai is a research institute dedicated to making deep learning more accessible to individuals with diverse backgrounds. In an age where cutting-edge technological advancements require top-notch technical expertise, Fast.ai aims to democratize access to artificial intelligence (AI) by bringing it closer to people who have limited proficiency in the field.
The institute recognizes that the world needs everyone involved with AI, regardless of their background. It strives to make deep learning accessible to individuals using programming languages, operating systems, datasets, and backgrounds that previous institutions regarded as uncool or obscure. Founded by Jeremy Howard and Dr. Rachel Thomas, Fast.ai is set to open doors for people with limited resources and enable them to integrate deep learning into their daily work processes.
Jeremy Howard, a founding researcher at Fast.ai, has years of experience in the field of AI. He was the founding CEO of Enlitic, the first company to apply deep learning to medicine, and has invested in, mentored, and advised many startups.
He was also the president and chief scientist of Kaggle, a data science platform where he topped international machine learning competitions two years in a row. Besides his accomplishments, Jeremy is an honorary professor at the University of Queensland and has made many media appearances writing for The Guardian, USA Today, and The Washington Post.
Dr. Rachel Thomas, co-founder of Fast.ai, is a professor of practice at Queensland University of Technology. She was the founding director of the USF Center for Applied Data Ethics and was selected by Forbes as one of 20 Incredible Women in AI. Dr. Thomas has a math PhD from Duke and was an early engineer at Uber.
Her writing has been read by over a million people, has been translated into multiple languages, and has made the front page of Hacker News multiple times.
Fast.ai's goal is to make deep learning accessible to everyone irrespective of their background, ensuring that everyone can benefit from the advancements in the field of AI. Therefore, their advocacy is to make it easy for people to utilize deep learning for any task, such as providing expertise to individuals who aim to work in the field or teaching them the necessary skills to start working with deep learning technology. Fast.ai plans to continue to push the boundaries in the field, create new technologies that are accessible to everyone, and make their innovative technologies more impactful.
Features
Fast.ai Education Program
Accessible Learning
Fast.ai Education Program is dedicated to providing accessible learning opportunities for individuals with diverse backgrounds. It seeks to democratize access to deep learning technology by bringing it closer to people who have limited proficiency in the field. The program recognizes that everyone has something to contribute to the field and works to make deep learning accessible to individuals using programming languages, operating systems, datasets, and backgrounds that were previously regarded as obscure.
Flexible Learning Formats
Fast.ai Education Program offers flexible learning formats that can cater to the needs of different individuals. The program provides online tutorials, interactive courses, study groups and a discussion forum, making it possible for people to learn deep learning regardless of their location, time zone or schedule. Moreover, the program's courses are open-source, meaning users can revise and adapt them for their needs, (for example, to teach deep learning at the high school, college or industrial levels).
Real-World Application
The core of the Fast.ai Education Program is the application of deep learning to real-world problems. The program offers case studies from various fields such as healthcare, manufacturing, retail, and transportation.
These case studies provide a context in which deep learning technology can be used practically, allowing students to understand not only the theory but the practical applications of deep learning. Through real-world application, students can understand what deep learning can achieve and how it can solve existing problems.
Fast.ai's Deep Learning Library
High-Level Components
Fast.ai's deep learning library provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains. The library offers a Layered API for Deep Learning, which abstracts away most of the mathematical details of deep learning, allowing users to focus on quick experimentation with cutting-edge techniques. In essence, the library makes it easy for practitioners to utilize deep learning technology for any task, serving as a gateway for those who aim to work in the field or need to integrate deep learning into their business processes.
Low-Level Components
In addition to providing high-level components, Fast.ai's deep learning library offers low-level components that researchers can use to build new approaches. Low-level components provide the flexibility needed to experiment and develop novel deep learning architectures, and together with the high-level components, provide practitioners with a comprehensive set of tools to work with.
User-Friendly Training Process
Fast.ai's deep learning library provides a user-friendly training process that allows practitioners to achieve state-of-the-art results with fewer labeled examples and faster. The library includes a training API that simplifies the process of training deep neural networks, as well as a new approach to transfer learning, called "Discriminative Fine-Tuning," that has enabled the fast training of highly accurate models. With user-friendly training processes, the deep learning library makes it possible for practitioners to achieve accurate results faster and without requiring extensive expertise.
Fast.ai's Open-Source Community
Active and Engaged Community
Fast.ai's open-source community is active and engaged, offering support and mentoring to individuals who are interested in deep learning technology. The community comprises of individuals with diverse backgrounds, making it possible for everyone to connect and share knowledge.
Members are encouraged to ask and answer questions, share their experiences and successes, and provide feedback to help improve the resources provided by the community. Additionally, the community provides an opportunity for users to collaborate on projects and to build networks that can lead to opportunities.
Contributions to AI Research
Fast.ai's open-source community has made significant contributions to AI research by developing tools and techniques that simplify the use of deep learning technology. The community has published several research papers that have pushed the boundaries of existing deep learning techniques, and a diverse range of use cases and applications. Additionally, the community has generated datasets for research purposes, and contributed to the development of the state-of-the-art models in various fields including computer vision, natural language processing, and speech recognition.
Global Reach
Fast.ai's open-source community has a global reach, with members from different countries and of different cultures. This global reach has enabled the community to create resources that cater to individuals from diverse backgrounds, thus making deep learning technology more accessible to everyone. The community also provides a platform where people can collaborate and share knowledge without borders, creating a sense of unity and inclusivity.
Moreover, the open-source nature of the community's resources allows people from different parts of the world to revise and adapt them for their needs.
Fast.ai's Commitment to Equality and Ethics in AI
Efforts on Diversity & Inclusion
Fast.ai is committed to equality and ethics in AI. As such, the company has programs and initiatives to foster diversity and inclusion in the tech industry.
The company recognizes that access to opportunity and resources is critical to the development of the next generation of AI professionals. The company actively works to ensure that racial and gender biases do not influence the development, application, or use of AI technology.
Moreover, the company partners with organizations and communities that promote diversity and inclusion in tech.
Focus on Data Ethics
Fast.ai is also committed to ethical considerations in data usage, which includes emphasizing aspects of data ethics within its curriculum. The company recognizes the impact that data usage has on society, including the potential for it to reinforce biases in AI.
As such, Fast.ai is committed to providing training and tools to guide ethical data usage and to break down biases in data. The company seeks to cultivate an academic environment where students can critically evaluate the ethical implications of AI and participate in discussions around the topics.
Transparency of AI algorithms
Fast.ai is committed to transparency in AI. Its approach to developing AI algorithms is open-source, meaning that individuals can see the code and how the algorithm works. This commitment to transparency ensures that others can evaluate the efficacy and fairness of the algorithms to develop and apply.
Furthermore, Fast.ai's deep learning library is designed to give practitioners access to the latest research, providing tools that they can develop and refine, thus empowering users to build AI systems that are transparent and ethical.
FAQ
What is Fast.ai?
Fast.ai is a research institute that specializes in democratizing access to artificial intelligence by making deep learning accessible to individuals with diverse backgrounds, programming languages, operating systems, datasets, and experiences. The institute was founded by Jeremy Howard and Dr. Rachel Thomas, and their goal is to create new technologies that are accessible to everyone.
Who are the founders of Fast.ai?
The founders of Fast.ai are Jeremy Howard, a founding researcher at Fast.ai, who has extensive experience in AI and has invested in, mentored, and advised many startups; and Dr. Rachel Thomas, a professor of practice at Queensland University of Technology, who has a math PhD from Duke and was an early engineer at Uber. Their combined expertise and experience in the field of AI have been instrumental in the success of Fast.ai.
What is the mission of Fast.ai?
The mission of Fast.ai is to make deep learning accessible to everyone. They aim to achieve this by creating technologies that are accessible to anyone seeking to integrate AI into their daily work processes or careers, irrespective of their background, programming languages, or operating systems.
What is Deep Learning?
Deep learning is a subset of machine learning that uses artificial neural networks to capture and model complex data. It involves training a neural network with large amounts of data to recognize and classify patterns and infer relationships between input data and output data. Deep learning is particularly suited to handle complex, unstructured data such as images, audio, and natural language processing.
What is the goal of Fast.ai's tool/product/service?
The goal of Fast.ai's tool/product/service is to democratize access to deep learning technologies by providing everyone with the tools they need to integrate AI into their daily work processes. Fast.ai's tool/product/service aims to make it easier for people to utilize deep learning technologies irrespective of their background or experience, by providing high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains and low-level components that can be mixed and matched to build new approaches.
