Google’s AI Course for Beginners in 10 Minutes

Google’s AI Course for Beginners in 10 Minutes

If you’re curious about artificial intelligence (AI) but lack a technical background, you’re in the right place. Today, we’re condensing Google’s four-hour AI course for beginners into just ten minutes. Initially, I was skeptical that a brief overview would be useful, but I found the course concepts significantly enhanced my understanding of tools like ChatGPT and Google Bard, clearing up many misconceptions I had about AI, machine learning, and large language models.

Let’s start with the basics: What is artificial intelligence? Surprisingly, AI is an entire field of study, similar to physics, with machine learning being a subfield of AI—much like thermodynamics is to physics. Delving deeper, we have deep learning, which is a subset of machine learning. Within deep learning, there are two main categories: discriminative models and generative models. Large language models (LLMs), such as ChatGPT and Google Bard, fall under deep learning.

Now that we understand the landscape, let’s explore the key takeaways at each level. Machine learning involves creating programs that use input data to train models, which can then make predictions on unseen data. For example, if you train a model with Nike sales data, it can predict how well a new Adidas shoe might sell.

There are two primary types of machine learning models: supervised and unsupervised. Supervised models use labeled data, while unsupervised models use unlabeled data. For instance, in a supervised learning example, we can predict tips at a restaurant based on historical data of bill amounts and whether the order was picked up or delivered. Conversely, unsupervised learning looks for natural groupings in data, such as analyzing employee tenure against income to determine patterns.

Next, let’s talk about deep learning, which utilizes artificial neural networks inspired by the human brain. These networks consist of layers of nodes, and more layers typically mean more powerful models. Deep learning also allows for semi-supervised learning, where a model is trained on a small amount of labeled data and a larger amount of unlabeled data. For instance, a bank might label 5% of its transactions as fraudulent, using that data to learn and predict for the remaining 95%.

Deep learning can be classified into discriminative and generative models. Discriminative models classify data points based on learned relationships, while generative models learn patterns from training data and generate new outputs based on that input. For example, a generative model might create a new image of a dog based on learned features from a dataset.

Generative AI encompasses various models, including text-to-text models like ChatGPT and Google Bard, as well as text-to-image models like DALL-E and Stable Diffusion. There are also text-to-video and text-to-3D models designed for specific applications.

Large language models (LLMs) are a specific type of deep learning model that is pre-trained on large datasets and then fine-tuned for particular tasks. Think of it like training a dog: the initial training covers basic commands, while specialized training tailors the dog for specific roles, such as a service or police dog.

In real-world applications, organizations can take advantage of pre-trained LLMs and fine-tune them with their own data, such as a hospital improving diagnostic accuracy by adapting a general model with its medical data. This collaboration allows smaller institutions to leverage the advanced capabilities developed by larger tech companies.

If you decide to explore the full course, it’s completely free and includes five modules, each offering a badge upon completion. For those taking notes, you can easily navigate the video by copying the URL at specific timestamps.

That’s a quick overview of Google’s AI course for beginners! If you’re interested in mastering prompting, check out the next video. Until then, have a great day!