AI Research Foundations – An AI Research Fundamentals Program Jointly Launched by Google and UCL

What is AI Research Foundations？

AI Research Foundations is a free online course jointly launched by Google DeepMind and University College London (UCL). The program helps learners deeply understand Transformer models and gain hands-on experience in building and fine-tuning modern language models. Guided by Oriol Vinyals, Vice President of Research at Google DeepMind, the course covers topics such as language model fundamentals, data processing, neural network design, and Transformer architecture. It is designed for learners who wish to enhance their AI research and development skills. The course is available on the Google Skills platform.

Main Content of AI Research Foundations

• Google DeepMind: 01 Build Your Own Mini Language Model
  Learn the fundamentals of language models and the machine learning development process. Compare the strengths and weaknesses of traditional n-gram models and advanced Transformer models.

• Google DeepMind: Train a Mini Language Model (Challenge Experiment)
  A hands-on challenge to develop basic tools and data preparation frameworks to train a robust, character-based language model.

• Google DeepMind: 02 Represent Your Language Data
  Learn how to prepare text data for language modeling, focusing on tools and techniques for preprocessing, constructing, and representing text data.

• Google DeepMind: 03 Design and Train Neural Networks
  Focus on the machine learning training process. Learn to identify and mitigate training issues such as overfitting and underfitting. Includes practical coding exercises.

• Google DeepMind: 04 Explore the Transformer Architecture
  Dive into the mechanics of Transformer architecture. Understand how Transformers process prompts for context-aware next-word prediction, and visualize attention weights through hands-on exercises.

Key Features of AI Research Foundations

• Education and Learning – Offers university-level content to help learners grasp both foundational and advanced AI concepts.

• Practical Skill Development – Hands-on projects allow learners to build and fine-tune models, enhancing practical capabilities.

• Understanding Transformer Models – Deepens comprehension of the Transformer architecture, the backbone of modern large language models.

• Responsible AI Research – Teaches ethical considerations and methods to avoid bias in AI research.

• Data Processing – Covers techniques for preparing and processing text data suitable for model input.

• Neural Network Design – Explains how to design and train neural networks, and how to diagnose and solve common training problems.

Course Link

Official Course Page: https://www.skills.google/collections/deepmind

Application Scenarios of AI Research Foundations

• Academic Research – Provides university students, graduate researchers, and scholars with a deeper understanding of AI and machine learning principles for use in research projects.

• Career Development – Helps professionals upskill for roles in data science, machine learning, and natural language processing.

• Education Sector – Enables educators to design and improve AI- and ML-related curricula.

• Technical Development – Assists software developers and engineers in learning how to build efficient AI models for intelligent systems and applications.

• Corporate Training – Companies can use the course to train employees, strengthen AI literacy, and promote innovation in products and technology.