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

Google's 4-Hour AI course for beginners is condensed into essential concepts that enhance understanding of artificial intelligence. Initially skeptical, the practical insights gained from the course improved proficiency with tools like ChatGPT and Google Bard while clarifying misconceptions about AI, machine learning, and large language models. The focus on foundational knowledge equips learners to navigate the complexities of these technologies effectively.

Artificial Intelligence (AI) is a comprehensive field of study, akin to physics. Within AI, machine learning serves as a subfield, similar to how thermodynamics relates to physics. Delving deeper, deep learning emerges as a subset of machine learning and can be categorized into discriminative models and generative models. Large language models (LLMs), which include technologies like ChatGPT and Google Bard, are positioned at the intersection of generative modeling and deep learning.

Machine learning is a program that trains models using input data to make predictions on unseen data. For instance, training a model with Nike sales can help predict Adidas shoe sales based on their own historical data. The two main types of machine learning are supervised and unsupervised learning; the former uses labeled data while the latter relies on unlabeled information. In supervised scenarios, like predicting tips from restaurant bills, labels indicate whether an order was picked up or delivered. Unsupervised models analyze raw datasets for natural groupings without predefined categories, such as assessing employee income relative to tenure.

Deep learning is a specialized form of machine learning that utilizes artificial neural networks, which are modeled after the human brain. These networks consist of layers of nodes and neurons; more layers enhance model capability. A key feature is semi-supervised learning, where models train on a small set of labeled data alongside a larger pool of unlabeled data—like banks identifying fraud by labeling only 5% of transactions while applying learned concepts to predict outcomes for the remaining 95%. Additionally, deep learning encompasses two main types: discriminative models focus on classifying existing labels (e.g., distinguishing between cats and dogs), whereas generative models can create new instances based on learned patterns.

Generative AI differs from discriminative models by learning patterns in training data to create new outputs based on input prompts. For instance, when given a prompt related to dogs, it identifies characteristics like ears and tails without needing labeled examples. The key distinction of generative AI is that its output can be natural language text, images, or audio rather than classifications or probabilities. Various types of generative models exist: text-to-text (like ChatGPT), text-to-image (such as Midjourney and DALL-E), video generation tools (Google's Imagen Video), 3D model creators for game assets (OpenAI’s Shape E), and task-specific models designed for particular functions.

Large Language Models (LLMs) are a subset of deep learning, distinguished by their pre-training on vast datasets followed by fine-tuning for specific applications. This process is akin to training a dog; while the initial commands represent general language skills, specialized roles like police or guide dogs require additional targeted training. In practice, LLMs can be adapted using industry-specific data to enhance performance in sectors such as healthcare and finance. For example, hospitals may refine a generic model with proprietary medical data to improve diagnostic accuracy.