The Mystery of 'Latent Space' in Machine Learning Explained!

Hey there, Dylan Curious here, delving into the intriguing world of machine learning and, more precisely, the mysterious ‘Latent Space’. Now, if you’ve been immersing yourself into artificial intelligence, you’ve possibly encountered this term but might be grappling with its concept due to its unintuitive nature, especially relating to how our brain comprehends dimensions. The concept is crucial to understanding AI models, including ones like ChatGPT, and today, we’re going to unravel its secrets together.

Imagine a library, so vast that organizing it is an unattainable task. Here, ‘latent space’ acts as a magical map, not directing you to specific books but grouping them based on themes and content - representing data in a higher-dimensional space, such as digital information or word tokens. When we talk about AI models like autoencoders or GANs (Generative Adversarial Networks), they’re essentially trying to learn this map, providing a compressed yet incredibly insightful piece of information that describes a much larger data set. A crucial point is that this latent space, while compact, is all we often need because it essentially captures the essence or meaning of the comprehensive data.

But now let’s twist our minds a bit around multi-dimensional space. As our entire evolution and understanding revolve around a three-dimensional world, we find it tricky to comprehend a multi-dimensional one, which is integral in AI. It is vital to note that, mathematically, objects in multi-dimensional spaces can be adjacent or diagonal to one another in ways that are computationally viable but not naturally comprehensible to us.

Now, let’s dive into 'Data Representation'. While dealing with data, especially in the context of AI, we refer to different data attributes or patterns as ‘features’ or dimensions, and each additional one offers a new perspective or context to our data - like describing a house through various attributes like price, size, and location. Imagine trying to describe an image; even a simple 100x100 pixel colored image can have up to 30,000 dimensions, considering height, width, and RGB color channels. When an AI model learns from millions of such images, it perceives patterns across an astoundingly multi-dimensional space!

However, with the growing dimensions, we encounter the ‘Curse of Dimensionality’. As the dimensions increase, data becomes sparse, and learning algorithms require considerably more data to be useful. AI has developed techniques, such as the T-SNE dimension reduction algorithm, to manage and reduce the dimensionality, compressing it down while maintaining relevant information and enabling the creation of a usable map from overwhelmingly large data, like the entire internet!

Through AI models like LLMa and ChatGPT, we can ask questions, and these tools, having learned from vast libraries of information, navigate through multi-dimensional latent spaces to provide answers, exploring mathematical relationships and patterns that are fundamentally abstract to us. As I always say, the magic of AI lies in its patterns and dimensions. And while we’re still in infancy in fully understanding these complex relationships and patterns in artificial intelligence, every exploration, like today’s journey into latent space, brings us one step closer.

Thanks for joining me, Dylan Curious, in another AI exploration, and don’t forget to like, share, and subscribe for more AI mysteries unraveled! If you want to dive deeper into AI, machine learning, latent space, and multi-dimensional mysteries, make sure to hit that notification bell to stay updated on our next adventures into the digital realm!

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00:00 - The Mystery of ‘Latent Space’ in Machine Learning Explained!
00:47 - Let’s Start With An Analogy
02:13 - Everything You Thought You Knew About Distance Is Wrong
04:39 - Data Representation: Features Are Dimensions
07:01 - Curse of Dimensionality
07:48 - T-SNE Dimension Reduction Algorithm
11:41 - Latent Space in AI: What Everyone’s Missing!

WATCH THE FULL VIDEO ⤵

#latent space #machinelearning #artificialintelligence #aiexplained #dylancurious #generativeadversarialnetworks #autoencoders #highdimensionalspace #datascience #tsnealgorithm #dimensionalityreduction #aibreakthroughs #chatgpt #machinelearningbasics #aiapplications #AI #artificialintelligence #tech #tingtingin #AnastasiInTech #MattVidPro #mreflow #godago #AllAboutAI #BrieKirbyson #NicholasRenotte #aiexplained #OlivioSarikas #AdrianTwarog #aiadvantage #obscuriousmind #max-imize #DavidShapiroAutomator

[salmanmalik]

this is by far the best explanation of latent space with such fx used here

[RobTheQuant]

good stuff. but I'd reduce the amount of special FX and voice changes...

[meriemkh]

thank you so much it was so well explained I was struggling with the concept for long enough

[jverart]

Since LLMs came out I've been constantly thinking about stuff like this. I think in the near future we will be exploring AI inside complex, infinite and dynamic architectures through several means. This is wonderful

[ghelmstetter-AI]

Great explanation of latent space and really cool images, thank you. One point of clarification, a 100x100 RGB image has only 3 dimensions (width, height, and color channel). There are 3 color channels (R, G, B) so it has 100x100x3 = 30, 000 data points. (At 8 bits per channel, that's 240, 000 bits.)

[MrSurferDoug]

Creativity Latent Space
Infinite Text, Images, Video, …

The vast repository of every creation made by humanity or could be made by humanity

[MrSurferDoug]

"THE UNIVERSE (which others call the Library)…” Thus Jorge Luis Borges began his 1941 story
“The Library of Babel, ”
about the mythical library that contains all books, in all languages, …
the detailed history of the future, …
This library (which others call the universe) enshrines all the information.
Yet no knowledge can be discovered there, precisely because all knowledge is there,  shelved side by side with all falsehood. …
In the mirrored galleries, on the countless shelves,  can be found everything and nothing.
There can be no more perfect case of information glut.“
The Information: A History, a Theory, a Flood by James Gleick

[sinfinite]

Don’t listen to the people about reducing fx and voice changes if u don’t want to, feel free to be unique as long as you can get the information across effectively!

[iloveit]

This really is not a difficult concept at all once you understand the lexicon. Once I know the basic definition of the term the concepts are easy to grasp. You need to relate their lexicon to words you understand…like latent space was not a difficult concept just words that we would normally associate with a different idea, however once I have adjusted the language it’s just easy!

[Wayneburg]

Thanks for the Latent Space lesson. Your simplified explanation helped me get a rudimentary understanding of the topic.

Okay so you did a tour of your bookshelf, I think you need to do a tour of your t-shirts lol

"I've hidden the subscribe button somewhere on this page. Find it and click it before anyone else!"

[MrSurferDoug]

With little effort today, hardly more than a flick of the wrist, an average person can Summon the Library of Everything.” - Kevin Kelly, The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future (2016)

With little effort today, with only a prompt, an average person can Create the Library of Everything (What is possible in Latent Space) – Doug Hohulin

[Me__Myself__and__I]

Omg. This is the best explanation I've heard about how MY brain works. This is easy to understand and normal IMHO. Special dimensions isn't quite right. It's more like an RDBMS full of information bits where they are indexed by a nearly infinite set of indicies. Where it is easy to relate seemingly unrelated things together. But its also at multiple hierarchial levels. So individual pieces of info are related but also automatic sets of conglomerated information are summarized into more dense nuggets (on up multiple levels) and all of those are indexed and related too. Its almost like the relations between the data are more important than the data itself. Which would be the exact opposite of people who win trivia contests or have a didactic memory. Interesting. Though transformers have the unfair advantage of this extreme relational structure also with didactic perfect recall too. They get both which is totally unfair.

[Gee]

Most people can grasp 3d space plus time (or when) as the 4th dimension but after that it gets a bit more complicated. I used to think of it as another 3 dimension inside any point, like a word in a book, a letter in a word, a pixel in a letter, a molecule in a pixel etc but that's just being more accurate with the 3d coordinates. These days I think of extra dimensions as extra attributes. So you have a vector from a:x, y, z to b:x, y, z, on a bus, with a sandwich, listening to Beethoven's Piano Concerto No. 5 in E-flat major, Op. 73 Adagio Un Poco Mosso. Those are my extra dimensions, they add something to the vector without affecting its properties.

P.S. Try the music too.

[Ben_D.]

You have quite a collection of shirts. heh.

[doodlefisher]

can you chill out with the SFX it's incredibly distracting lol

[MrSurferDoug]

“It is an interesting question to consider how much of the human experience can be reconstructed from internet data alone.” - Lex Fridman
“This software object is currently the most complex humanity has produced. encompasses the entire history of human civilization, technological advancements, and the vast amount of data on the internet. The GPT model is essentially a compression of all textual output humanity has produced.” -- Sam Altman: OpenAI CEO on GPT-4, ChatGPT, and the Future of AI | Lex Fridman Podcast

[MMABeijing]

U r just talking about vector/embeddings stores.

[MMABeijing]

You are talking about embedding space without talking about embeddings? Do I judge too fast, or do you not understand what you are talking about?