Google’s AI Course for Beginners (in 10 minutes)!

Introduction

Artificial intelligence (AI) has carved its niche in the modern world, influencing various sectors from healthcare to finance. If you're not technically inclined but eager to grasp the basics of AI, you're in the right place. In this article, we will distill core concepts from Google’s AI course into a digestible format, taking about 10 minutes to cover essential terms and ideas.

Understanding Artificial Intelligence

First and foremost, let's clarify what artificial intelligence is: AI is a broad field of study, akin to physics. Within AI, we find machine learning, which is a subfield similar to how thermodynamics relates to physics. Diving deeper, deep learning surfaces as a subset of machine learning.

Levels of AI Knowledge

1. Machine Learning:

   • This program utilizes input data to create a model that can make predictions on unseen data. For example, if trained on Nike’s sales data, a model could predict how a new Adidas shoe might perform in the market. Machine learning can be categorized into:
     • Supervised Learning: Involves labeled data, where the model learns to predict outputs based on historical input-output pairs. For example, predicting tip amounts based on a restaurant bill.
     • Unsupervised Learning: Uses unlabeled data to identify patterns or groupings within the data, such as employee income versus tenure.

2. Deep Learning:

   • This type of machine learning uses artificial neural networks inspired by the human brain. A key aspect of deep learning is semi-supervised learning, where a model is trained on a small amount of labeled data and a larger pool of unlabeled data, such as using 5% of labeled transactions from a bank to identify fraudulent transactions in the remaining 95%.

3. Discriminative vs. Generative Models:

   • Discriminative Models: These learn to differentiate between data points and classify them (labeling items as ‘spam’ or ‘not spam’).
   • Generative Models: These focus on understanding patterns within the training data and can generate new data points. For instance, given images of cats and dogs without labels, a generative model can produce new images resembling the training data.

Types of Generative AI Models

Here's a brief overview of different generative AI models:

• Text-to-Text Models: Examples include ChatGPT and Google Bard.
• Text-to-Image Models: Such as Midjourney, DALL-E, and Stable Diffusion, allowing for image creation and modification.
• Text-to-Video Models: These can create and edit video content, e.g., Google's ImageGen and Make-a-Video.
• Text-to-3D Models: Used for game asset creation, an example being OpenAI’s ShapeNet.
• Text-to-Task Models: Designed for specific tasks, like summarizing emails in Gmail.

Large Language Models (LLMs)

Large language models are a subset of deep learning and are distinct from generative AI models, though they overlap. LLMs undergo a two-part training process:

• Pre-training: Using large datasets to handle various language tasks.
• Fine-tuning: Adjusting the model with specific datasets to cater to particular industry needs (e.g., healthcare, finance).

For example, a hospital may utilize an existing large language model and fine-tune it using its medical data to enhance diagnostics.

Conclusion

The fundamental concepts of AI may be complex, but they pave the way for understanding and utilizing various AI tools effectively. If you're interested in diving deeper, the entire Google AI course is free and consists of five modules, allowing learners to earn badges upon completion.

Keywords

• Artificial Intelligence
• Machine Learning
• Deep Learning
• Supervised Learning
• Unsupervised Learning
• Generative Models
• Discriminative Models
• Large Language Models (LLMs)

FAQ

What is artificial intelligence?
Artificial intelligence is a broad field of study that involves creating systems capable of performing tasks that normally require human intelligence.

What is the difference between machine learning and deep learning?
Machine learning is a subset of AI that uses data to train models for predictions, while deep learning is a subset of machine learning that uses neural networks for complex pattern recognition.

What are supervised and unsupervised learning models?
Supervised learning models use labeled data to learn and make predictions, while unsupervised learning models operate on unlabeled data to identify patterns and groupings.

What is the purpose of large language models (LLMs)?
LLMs are designed to process and understand human language, performing tasks like text classification, summarization, and generating coherent text based on input.

What are generative models?
Generative models learn patterns from training data and can produce new data points, such as generating text or images based on learned information.