What is the difference between AI vs. ML vs. Deep Learning?

Transcript

What is the difference between AI versus ML, or machine learning versus deep learning? So we’ve already covered AI broadly. Now you may have heard other terms like machine learning or ML or deep learning, or neural networks being thrown around and wondered what are the differences.

Let’s first zoom in on machine learning, or ML.

ML is a subset of AI that focuses on using computational techniques to learn from data and make predictions.

Essentially, it can recognize patterns that distinguish it between dogs and cats based on the features that are fed to it. This type of machine learning requires more structured data, often labeled by humans. So, cat is a cat, dog is a dog, but a human has to label that data first.

So you’ve seen this in action when your email filters out spam or when you receive product recommendations on your favorite shopping site. A data scientist generally has to label the data for the ML to learn appropriately.

Next, let’s talk about deep learning, which is basically a more advanced form of machine learning.

Deep learning involves multiple layers of neural networks that work a bit like the human brain. So imagine it like this. When you see an unfamiliar animal, you might notice its fluffy tail, reddish fur, and decide it’s a mammal.

Deep learning works similarly using layers of neural networks to analyze and learn from data at different stages and from different perspectives.

This allows deep learning to process structured and unstructured data and learn with minimal human intervention.

So, deep learning is what powers things like facial recognition and autonomous vehicles. A subset of deep learning that’s gaining a lot of attention right now is generative AI. This is AI that doesn’t just analyze data, but actually creates new content, text, images, audio, and even video.

A specific form of AI that’s important to us as legal professionals is the large language model, or LLM. LLMs are specifically designed for natural language processing, and they’re behind tools like ChatGPT.

These models generate human-like text, making them useful for drafting documents, summarizing case law, and even answering client inquiries.

So to recap, AI today falls under the category of narrow intelligence.

It’s a powerful but specialized form of AI.

Machine learning helps it learn from data, and deep learning gives it the capacity to analyze data in a more sophisticated way, even generating new content.

So understanding these distinctions will help you navigate the growing landscape of AI tools in the legal profession.

Thanks for tuning in, and don’t forget to like and subscribe for more insights on how technology is reshaping the legal landscape.