If you don't have a technical background but you still want to learn the basics of artificial intelligence stick around because we were distilling Google's 4-Hour AI course for beginners into just 10 minutes.
I was initially very skeptical because I thought the course would be too conceptual we're all about practical tips on this channel and knowing Google the course might just disappear after 1 hour but I found the underlying Concepts actually made me better at using tools like Chachi BT and Google bard and cleared up a bunch of misconceptions I didn't know I had about AI machine learning and large language models.
So starting with the broadest possible question: what is artificial intelligence?
It turns out and I'm so embarrassed to admit I didn't know this AI is an entire field of study like physics and machine learning is a subfield of AI much like how thermodynamics is a subfield of physics. Going down another level, deep learning is a subset of machine learning, and deep learning models can be further broken down into something called discriminative models and generative models. Large language models (LLMs) also fall under deep learning, and right at the intersection between generative and LLMs is the technology that powers the applications we're all familiar with: ChatGPT and Google Bard.
Now that we have an understanding of the overall landscape and you see how the different disciplines sit in relation to each other, let's go over the key takeaways you should know for each level.
In a nutshell, machine learning is a program that uses input data to train a model. That trained model can then make predictions based on data it has never seen before.
For example, if you train a model based on Nike sales data, you can then use that model to predict how well a new shoe from Adidas would sell based on Adidas sales data.
Two of the most common types of machine learning models are supervised and unsupervised learning models.
The key difference between the two is supervised models use labeled data and unsupervised models use unlabeled data.
In this supervised example, we have historical data points that plot the total bill amount at a restaurant against the tip amount.
Here, the data is labeled:
Blue Dot = The order was picked up
Yellow Dot = The order was delivered
Using a supervised learning model, we can now predict how much tip we can expect for the next order given the bill amount and whether it's picked up or delivered.
For unsupervised learning models, we look at the raw data and see if it naturally falls into groups.
In this example, we plotted the employee tenure at a company against their income. We see this group of employees have a relatively high income-to-years-worked ratio versus this group.
We can also see all these are unlabeled data. If they were labeled, we would see male, female, years worked, company function, etc.
We can now ask this unsupervised learning model to solve a problem like: If a new employee joins, are they on the fast track or not?
If they appear on the left, then yes. If they appear on the right, then no.
Pro tip: Another big difference between the two models is that after a supervised learning model makes a prediction, it will compare that prediction to the training data used to train that model. If there's a difference, it tries to close that gap. Unsupervised learning models do not do this.
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Now that we have a basic grasp of machine learning, it's a good time to talk about deep learning.
Deep learning is just a type of machine learning that uses something called artificial neural networks.
Don't worry! All you have to know for now is that artificial neural networks are inspired by the human brain and look something like this:
Layers of nodes and neurons, and the more layers there are, the more powerful the model.
Because we have these neural networks, we can now do something called semi-supervised learning.
A deep learning model is trained on a small amount of labeled data and a large amount of unlabeled data.
For example, a bank might use deep learning models to detect fraud.
The bank spends a bit of time to tag or label 5% of transactions as either fraudulent or not fraudulent, and they leave the remaining 95% of transactions unlabeled because they don't have the time or resources to label every transaction.
The magic happens when the deep learning model uses the 5% of labeled data to learn the basic concepts of the task:
"Okay, these transactions are good, and these are bad."
"Okay, apply those learnings to the remaining 95% of unlabeled data."
Using this new aggregate dataset, the model makes predictions for future transactions.
That's pretty cool! And we're not done yet.
Deep learning can be divided into two types: discriminative and generative models.
Discriminative models learn from the relationship between labels of data points and only have the ability to classify those data points.
Example: Fraud / Not Fraud.
If you have a bunch of pictures or data points, and you purposefully label some of them as cats and some of them as dogs, a discriminative model will learn from the label "cat" or "dog." If you submit a picture of a dog, it will predict the label for that new data point: "dog."
We finally get to generative AI!
Unlike discriminative models, generative models learn about the patterns in the training data. Then, after they receive some input (for example, a text prompt from us), they generate something new based on the patterns they just learned.
Going back to the animal example:
The pictures or data points are not labeled as "cat" or "dog," so a generative model will look for patterns:
"Oh, these data points all have two ears, four legs, a tail, like dog food, and bark."
When asked to generate something called a "dog," the generative model generates a completely new image based on the patterns it just learned.
There's a super simple way to determine if something is generative AI or not:
If the output is a number, a classification (Spam / Not Spam), or a probability → It is NOT generative AI.
If the output is natural language text, speech, an image, or audio → It is generative AI.
Moving on to different generative AI model types:
Most of us are familiar with text-to-text models like ChatGPT and Google Bard. Other common model types include:
Text-to-image models like MidJourney, DALL·E, and Stable Diffusion.
Text-to-video models like Google's Imagen Video, Cog Video, and "Make-A-Video."
Text-to-3D models used for game assets, such as OpenAI’s "Shape-E."
Text-to-task models used for performing specific tasks, like "Gmail, summarize my unread emails."
Moving over to large language models (LLMs):
Don't forget that LLMs are also a subset of deep learning. Although there is some overlap, LLMs and generative AI are not the same thing.
An important distinction:
LLMs are generally pre-trained with a very large set of data and then fine-tuned for specific purposes.
Example:
Imagine you have a pet dog. It can be pre-trained with basic commands like "sit, come, down, stay." But if that same dog becomes a police dog, a guide dog, or a hunting dog, it needs specialized training.
Similarly, LLMs are pre-trained to solve common language problems (text classification, question answering, document summarization, text generation). Then, they're fine-tuned with specific data for industries like retail, finance, and healthcare.