Clarifying AI: ML, Deep Learning, Foundation Models
Key Points
- Artificial intelligence (AI) is the broad field that aims to simulate human intelligence in machines, encompassing many sub‑disciplines such as machine learning and deep learning.
- Machine learning (ML) is a subset of AI that develops algorithms enabling computers to learn from data and make decisions without explicit programming, and it includes supervised, unsupervised, and reinforcement learning approaches.
- Deep learning is a further subset of ML that uses multi‑layered artificial neural networks to automatically extract complex features from large, unstructured datasets like images and natural language.
- Traditional ML techniques (e.g., linear regression, decision trees, support vector machines, clustering) remain valuable and are often more appropriate than deep learning for simpler or smaller‑scale problems.
- Emerging concepts such as foundation models, generative AI, and large language models build on these underlying AI, ML, and deep‑learning foundations to create versatile, high‑capacity systems.
Sections
- Clarifying AI Terminology - The passage demystifies the relationships among AI, machine learning, deep learning, foundation models, generative AI, and large language models, explaining each concept to eliminate common confusion.
- Foundation Models Within Deep Learning - The speaker explains that foundation models are large, pre‑trained deep‑learning neural networks that act as versatile bases for various tasks—such as translation, content generation, and image recognition—enabling faster, more scalable AI applications compared to training models from scratch.
- Generative AI vs. Foundation Models - The speaker clarifies that generative AI models create new content by leveraging the knowledge base of foundation models, concluding the AI buzzword overview.
Full Transcript
# Clarifying AI: ML, Deep Learning, Foundation Models **Source:** [https://www.youtube.com/watch?v=Beh13Cd_QbY](https://www.youtube.com/watch?v=Beh13Cd_QbY) **Duration:** 00:07:22 ## Summary - Artificial intelligence (AI) is the broad field that aims to simulate human intelligence in machines, encompassing many sub‑disciplines such as machine learning and deep learning. - Machine learning (ML) is a subset of AI that develops algorithms enabling computers to learn from data and make decisions without explicit programming, and it includes supervised, unsupervised, and reinforcement learning approaches. - Deep learning is a further subset of ML that uses multi‑layered artificial neural networks to automatically extract complex features from large, unstructured datasets like images and natural language. - Traditional ML techniques (e.g., linear regression, decision trees, support vector machines, clustering) remain valuable and are often more appropriate than deep learning for simpler or smaller‑scale problems. - Emerging concepts such as foundation models, generative AI, and large language models build on these underlying AI, ML, and deep‑learning foundations to create versatile, high‑capacity systems. ## Sections - [00:00:00](https://www.youtube.com/watch?v=Beh13Cd_QbY&t=0s) **Clarifying AI Terminology** - The passage demystifies the relationships among AI, machine learning, deep learning, foundation models, generative AI, and large language models, explaining each concept to eliminate common confusion. - [00:03:19](https://www.youtube.com/watch?v=Beh13Cd_QbY&t=199s) **Foundation Models Within Deep Learning** - The speaker explains that foundation models are large, pre‑trained deep‑learning neural networks that act as versatile bases for various tasks—such as translation, content generation, and image recognition—enabling faster, more scalable AI applications compared to training models from scratch. - [00:06:35](https://www.youtube.com/watch?v=Beh13Cd_QbY&t=395s) **Generative AI vs. Foundation Models** - The speaker clarifies that generative AI models create new content by leveraging the knowledge base of foundation models, concluding the AI buzzword overview. ## Full Transcript
You've probably seen all sorts of items flying around recently, and it can get a little confusing as to how they all relate to one another.
Machine learning, deep learning, foundation models. And you've probably seen other terms like generative AI and large language models.
So let's bring an end to the confusion and put these terms in their place.
There's one thing they all have in common.
They are all terms related to the field of artificial intelligence or A.I.
Now A.I. refers to the simulation of human intelligence in machines enabling them to perform tasks that typically require human thinking.
Now and its various forms and paradigms has been around for decades.
Perhaps you've heard of the chat bot called Eliza, that was developed in the mid 1960s, and that could mimic human like conversation, to an extent.
Now a subfield of A.I. is called machine learning, so this sits within the field of AI. Now what's machine learning?
Well, it focuses on developing algorithms that allow computers to learn from and make decisions based upon data,
rather than being explicitly programed to perform a specific task.
These algorithms use statistical techniques to learn patterns in data and make predictions or decisions without human intervention.
But like A.I. ML or machine learning is a very broad term. It encompasses a range of techniques and approaches
from traditional statistical methods through to complex neural networks. Now, some of the core categories within ML we can think of are firstly supervised learning,
where models are trained on labeled data. There's also unsupervised learning,
and that's where the models find patterns in data without predefined labels. And there's also reinforcement learning.
And that's where models learn by interacting with an environment and receiving feedback.
Okay, so where does deep learning come in? Well, deep learning is a subset of machine learning.
Goes right there. Now, that specifically focuses on artificial neural networks with multiple layers,
and we can think of them looking bit like this. So these are nodes and all of our connections.
Now, those layers where we get the deep part from. And while traditional ML techniques might be efficient for linear separations or simpler patterns,
deep learning excels at handling vast amounts of unstructured data like images or natural language
and discovering intricate structures within them.
Now, I do want to point out that not all machine learning is deep learning. Traditional machine learning methods still play a pivotal role in many applications.
So we've got techniques like linear regression, that's a popular technique, or decision trees, or support vector machines, or clustering algorithms.
These are all other types of machine learning, and they've been widely used for a long time. In some scenarios, look, deep learning might be overkill
or it just isn't the most suitable approach. Okay, so machine learning, deep learning,
what else ah, yeah, foundation models. Okay, so where do foundation models fit into this?
Well, the term foundation model was popularized in 2021 by researchers at the Stanford Institute
and it fits primarily within the realm of deep learning. So I'm going to put foundation models right here.
Now, these models are large scale neural networks trained on vast amounts of data, and they serve as a base or a foundation for a multitude of applications.
So instead of training a model from scratch for each specific task, you can take a Pre-trained foundation model and fine tune it for a particular application,
which saves a bunch of time and resources. Now, foundation models have been trained on diverse datasets,
capturing a broad range of knowledge and can be adapted to tasks ranging from language translation to content generation to image recognition.
So in the grand scheme of things foundation models, they sit within the deep learning category but represent a shift towards more generalized,
adaptable and scalable AI solutions.
So look, I think this is hopefully looking a bit clearer now. But there are some other A.I. related terms. I think it's worth also explaining.
And one of those is large language models or LLMs Now,
these are a specific type of foundation model, so I've put them in this box here,
and they are centered around processing and generating humanlike text. So let's break it down, LLM.
The first L that's large, and that refers to the scale of the model. LLMs possess a vast number of parameters, often in the billions or even more.
And this enormity is part of what gives LLMs their nuanced understanding and capability.
Second, L that language that designed to understand and interact using human languages,
as they are trained on massive data sets. LLMs can grasp grammar, context, idioms and even cultural references.
And the last letter and that's for model at the core that computational models
a series of algorithms and parameters working together to process input and produce output.
LLMs can handle a broad spectrum of language tasks like answering questions, translating or even creative writing.
Now, if LLMs one example of foundation models, what are some others? Well, there's a bunch we can think of.
One of those is being vision models that can see in and in quotes, interpret and generate images. There are scientific models.
Give that an S and scientific models, for example, are used in biology where there are models for predicting how proteins fold into 3D shapes,
and there are audio models as well for generating human sounding speech or composing the next fake Drake hit song.
And finally, one last term that's gaining traction. We've all heard about it. It's generative AI.
Now this term pertains to models and algorithms specifically crafted to generate new content.
Essentially, while foundation models provide the underlying structure and understanding,
generative AI is about harnessing that knowledge to produce something that is new.
It's the creative expression that emerges from the vast knowledge base of these foundation models.
And with that, I think we've fully filled out a AI buzzword bingo scorecard.
And look, we have detailed videos on all of these topics. So check those out to learn more.