Machine Learning vs. Deep Learning vs. Foundation Models

Summary

This script clarifies the relationship between AI terms like machine learning, deep learning, foundation models, and generative AI. It explains that AI simulates human intelligence, machine learning involves algorithms learning from data, deep learning uses multi-layer neural networks, foundation models are pre-trained neural networks for various applications, and large language models (LLMs) process and generate human-like text. Generative AI focuses on creating new content using these models.

Takeaways

• 🤖 Artificial Intelligence (AI) refers to the simulation of human intelligence in machines, enabling them to perform tasks typically requiring human thinking.
• 📚 Machine Learning (ML) is a subfield of AI that focuses on developing algorithms for computers to learn from data and make decisions without explicit programming.
• 🔍 Machine Learning encompasses a range of techniques including supervised learning, unsupervised learning, and reinforcement learning.
• 🧠 Deep Learning is a subset of ML that specifically focuses on artificial neural networks with multiple layers, excelling at handling unstructured data like images or natural language.
• 🚫 Not all machine learning is deep learning; traditional ML methods like linear regression and decision trees still play pivotal roles in many applications.
• 🏗️ Foundation models, popularized in 2021, are large-scale neural networks trained on vast data, serving as a base for various applications, allowing for pre-trained models to be fine-tuned.
• 🌐 Foundation models represent a shift towards more generalized, adaptable, and scalable AI solutions, trained on diverse datasets and adaptable to tasks like language translation and image recognition.
• 📚 Large Language Models (LLMs) are a specific type of foundation model designed to process and generate human-like text, with capabilities in understanding grammar, context, and cultural references.
• 👀 Vision models, scientific models, and audio models are examples of other foundation models, each specialized in interpreting and generating content in their respective domains.
• 🎨 Generative AI pertains to models and algorithms crafted to generate new content, harnessing the knowledge of foundation models to produce creative expressions.

Q & A

What is the common factor among terms like machine learning, deep learning, foundation models, generative AI, and large language models?

They all relate to the field of artificial intelligence (AI).

What is artificial intelligence (AI)?

AI refers to the simulation of human intelligence in machines, enabling them to perform tasks that typically require human thinking.

What is machine learning and how does it fit within AI?

Machine learning is a subfield of AI focused on developing algorithms that allow computers to learn from and make decisions based on data, rather than being explicitly programmed for specific tasks.

What are the core categories within machine learning?

The core categories are supervised learning, unsupervised learning, and reinforcement learning.

How is deep learning different from traditional machine learning?

Deep learning is a subset of machine learning that focuses on artificial neural networks with multiple layers, which excel at handling vast amounts of unstructured data and discovering intricate structures within them.

What are foundation models and where do they fit in?

Foundation models are large-scale neural networks trained on vast amounts of data. They serve as a base for multiple applications and primarily fit within the realm of deep learning.

What are large language models (LLMs) and how do they relate to foundation models?

LLMs are a type of foundation model centered around processing and generating human-like text. They are large in scale, designed to understand and interact using human languages, and consist of a series of algorithms and parameters.

What are some examples of tasks that large language models (LLMs) can handle?

LLMs can handle tasks like answering questions, translating languages, and creative writing.

What is generative AI and how does it differ from foundation models?

Generative AI pertains to models and algorithms specifically crafted to generate new content. While foundation models provide the underlying structure and understanding, generative AI focuses on producing new and creative expressions based on that knowledge.

What are some other types of foundation models apart from large language models?

Other types include vision models for image interpretation and generation, scientific models for predicting protein folding in biology, and audio models for generating human-sounding speech or music.

Outlines

00:00

🤖 Understanding AI: From Eliza to Modern Paradigms

The first paragraph introduces the broad field of artificial intelligence (AI), mentioning its history and evolution from early examples like the chatbot Eliza from the 1960s. It explains that AI involves the simulation of human intelligence in machines to perform tasks typically requiring human thinking. The paragraph also delves into machine learning (ML), a subfield of AI that focuses on developing algorithms that enable computers to learn from and make decisions based on data without explicit programming. ML includes various techniques such as supervised learning, unsupervised learning, and reinforcement learning. Additionally, deep learning, a subset of ML, uses artificial neural networks with multiple layers to handle vast amounts of unstructured data like images or natural language.

05:02

🧠 Foundation Models and Their Applications

The second paragraph introduces foundation models, a term popularized by researchers at the Stanford Institute in 2021. These large-scale neural networks are trained on vast amounts of data and serve as a base for numerous applications, eliminating the need to train a model from scratch for each specific task. Foundation models can be fine-tuned for various tasks, such as language translation, content generation, and image recognition. The paragraph highlights the importance of foundation models in deep learning and their role in creating generalized, adaptable, and scalable AI solutions.

💬 Large Language Models (LLMs) Explained

The third paragraph focuses on large language models (LLMs), a specific type of foundation model designed to process and generate human-like text. LLMs are characterized by their large scale, often possessing billions of parameters, which enables them to understand and generate nuanced text. These models are trained on massive datasets, allowing them to grasp grammar, context, idioms, and cultural references. LLMs are capable of handling various language tasks such as answering questions, translating text, and creative writing. The paragraph also mentions other types of foundation models, such as vision models for image interpretation and generation, scientific models for predicting protein structures, and audio models for generating human-like speech.

🎨 Generative AI: Creativity from Knowledge

The fourth paragraph introduces generative AI, which focuses on models and algorithms crafted to generate new content. It explains that while foundation models provide the underlying structure and understanding, generative AI leverages this knowledge to produce new and creative outputs. Examples include generating text, images, or even music. The paragraph emphasizes that generative AI represents the creative expression emerging from the vast knowledge base of foundation models, concluding with a note that detailed videos on these topics are available for further learning.

Keywords

💡Artificial Intelligence (A.I.)

Artificial Intelligence, or A.I., refers to the simulation of human intelligence in machines, enabling them to perform tasks that typically require human thinking. It is the overarching field that encompasses various techniques and approaches for machines to mimic human cognitive functions. In the video, A.I. is the central theme, with all other concepts being subsets or related to it.

💡Machine Learning (ML)

Machine Learning is a subfield of A.I. that focuses on developing algorithms that allow computers to learn from and make decisions based on data, rather than being explicitly programmed. It uses statistical techniques to identify patterns in data and make predictions or decisions autonomously. In the script, ML is positioned as a subset of A.I., highlighting its role in enabling machines to learn and adapt.

💡Deep Learning

Deep Learning is a subset of Machine Learning that specifically focuses on artificial neural networks with multiple layers. These networks are capable of handling vast amounts of unstructured data like images or natural language, discovering intricate structures within them. The script emphasizes that while deep learning is a powerful tool, not all machine learning tasks require it, indicating its specialized application.

💡Foundation Models

Foundation Models, popularized in 2021, are large-scale neural networks trained on vast amounts of data. They serve as a base for various applications, allowing for pre-trained models to be fine-tuned for specific tasks. This concept is highlighted in the video as a shift towards more generalized, adaptable, and scalable AI solutions, demonstrating a strategic approach to AI development.

💡Supervised Learning

Supervised Learning is a core category within Machine Learning where models are trained on labeled data. It is a method where the input data is paired with correct outputs, allowing the model to learn the mapping function from inputs to outputs. The script mentions this as one of the techniques within ML, illustrating its importance in training models to make predictions based on provided examples.

💡Unsupervised Learning

Unsupervised Learning is another category within Machine Learning where models find patterns in data without predefined labels. Unlike supervised learning, unsupervised learning does not require labeled data, making it suitable for discovering hidden patterns or structures in the data. The video script briefly touches on this concept, showing its role in exploratory data analysis.

💡Reinforcement Learning

Reinforcement Learning is a type of Machine Learning where models learn by interacting with an environment and receiving feedback. It is a dynamic approach where the model learns to make decisions that maximize some notion of cumulative reward. The script positions this as a distinct category within ML, emphasizing its application in environments where continuous learning and adaptation are crucial.

💡Large Language Models (LLMs)

Large Language Models, or LLMs, are a specific type of foundation model that focuses on processing and generating human-like text. They possess a vast number of parameters, often in the billions, which enables them to understand and interact using human languages. The video script highlights LLMs as a subset of foundation models, emphasizing their capability to handle a broad spectrum of language tasks.

💡Generative AI

Generative AI refers to models and algorithms specifically crafted to generate new content. It harnesses the knowledge base of foundation models to produce something new, representing the creative expression emerging from these models. The script introduces generative AI as a concept that builds on the capabilities of foundation models, focusing on the creation of novel content.

💡Neural Networks

Neural Networks are a fundamental component of Deep Learning, modeled after the human brain to recognize patterns. They consist of nodes and connections, with the 'deep' in deep learning referring to the multiple layers of these networks. The video script uses the analogy of neural networks to explain how deep learning models can process complex data and discover intricate structures.

💡Data

Data is central to both Machine Learning and Deep Learning, serving as the input that models learn from. The script discusses how machine learning algorithms learn patterns in data and make predictions, while deep learning excels at handling unstructured data like images or natural language. Data is the raw material that enables AI models to learn, adapt, and make decisions.

Highlights

Artificial intelligence (AI) simulates human intelligence in machines, enabling them to perform tasks that typically require human thinking.

Machine learning (ML) is a subfield of AI that focuses on developing algorithms allowing computers to learn from and make decisions based on data.

ML encompasses a range of techniques, from traditional statistical methods to complex neural networks.

Core categories of ML include supervised learning, unsupervised learning, and reinforcement learning.

Deep learning, a subset of ML, specifically focuses on artificial neural networks with multiple layers.

Deep learning excels at handling vast amounts of unstructured data, such as images or natural language.

Foundation models, popularized in 2021 by Stanford researchers, are large-scale neural networks trained on vast amounts of data.

Foundation models serve as a base for various applications, allowing for fine-tuning rather than training from scratch.

Large language models (LLMs) are a specific type of foundation model focused on processing and generating human-like text.

LLMs possess a vast number of parameters, often in the billions, contributing to their nuanced understanding and capability.

LLMs can grasp grammar, context, idioms, and cultural references due to their training on massive datasets.

Generative AI refers to models and algorithms specifically crafted to generate new content.

Generative AI harnesses the knowledge of foundation models to produce creative expressions.

Examples of foundation models include vision models for image interpretation, scientific models for predicting protein folding, and audio models for generating human-like speech.

Deep learning is not always the most suitable approach; traditional machine learning methods still play a pivotal role in many applications.

Transcripts

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.