We often talk about AI, like it's just one huge
thing, right? One big brain. But there's actually
a massive difference, a really functional difference
between, say, an AI that's read everything and
can only talk about a solution versus one that
can actually do the task for you. Right. It's
pure theory versus action. It's like the shift
from asking a large language model, an LLM, how
to book a cheap flight to telling a language
action model. a lamb, hey, book the cheapest
flight, email me the receipt. Beat. That's the
jump from talking to actually doing. Welcome
to the deep dive. Yeah. And look, if you're starting
to feel a bit lost in all the acronyms flying
around, you're definitely not alone. Our mission
today really is to cut through that noise, give
you a kind of shortcut to understanding the different
specializations. We need the AI toolbox. Exactly.
Think about it, right? You wouldn't use a hammer
to turn a screw. Doesn't matter how great the
hammer is. We need to know the right tool for
the job. So today we're diving into eight key
AI model types. We'll look at what they do, their
main uses, and yeah, the important watch outs
for each. Okay. Let's start with the foundation.
Probably the one most people know. The LM. Okay,
let's unpack this. The LLM, the Large Language
Model. This is... like the definition model.
It's the most famous one out there. Think of
it like that super smart friend who's basically
read every book, every website, you know, Chet,
GPT, Gemini, Claude, those ones. Yeah, and they're
large because the amount of text they train on
is just astronomical. They absorb all these patterns
of language, but how they work, it's actually
pretty simple at its core. They're mostly just
next -word guessers. They don't actually know
truth. Right. They break down sentences into
tokens, sort of like pieces of words. Exactly.
Pieces of words or even punctuation. And then
they just do the math. calculating the most likely
next token, the next piece, based on, well, billions
of examples they've seen. If you start with,
I need to put this on there. I guess a shelf
or table or whatever fits statistically. Yeah.
And this guessing ability makes them really good
for tech stuff. Writing content, sure. Blog posts,
emails, summarizing huge articles down to like
three bullet points. Oh, yeah. Summarizing is
huge and helping programmers, right? Explaining
tricky code, debugging. Yeah. Even translation
or customer service chat bots. But that's also
where prompting gets tricky. Yeah. And honestly,
I still wrestle with getting problems just right
myself sometimes. It's not always easy. You really
see the difference between just asking vaguely.
versus being really specific? Totally. Like,
a bad prompt is just, write an email to work
from home, that's it. Vague. Super vague. A good
prompt gives the goal, maybe the specific days
you want off, the reasons why, like, needing
deep work focus, and even the tone. Make it polite,
professional, but ask firmly. You've got to guide
the guesser. And this leads to the big watch
out, right? The whole hallucination problem.
Because they only guess what looks right statistically.
They can just confidently make stuff up. Fake
studies, incorrect historical facts, citations
that lead nowhere. pure invention, but sounds
plausible. So if an LLM is fundamentally a great
guesser, what's the single most critical thing
a user has to remember about its output? You
absolutely must check important facts. The AI
only guesses what looks right. It doesn't verify.
Good. OK, so LLMs are great text guessers, but
they can be slow and Well, inventive. Yeah. What
if we need pictures and we need them fast? Ah,
speed. That takes us to the LCM, right? Exactly.
The latent consistency model, LCM. Think of it
as the visual cousin to the LLM, but its whole
reason for being is speed in making images. Much
faster than older models. Yeah. If you've ever
waited like 30 seconds or more for an AI image,
that old model like stable diffusion was working
slowly. taking maybe 50, even 100 steps to refine
the image from noise like a really carol paint
party adding tiny details. Right. The LCM, though,
is more like a master painter who's done this
a million times. It learns these underlying patterns
of the latent consistency and figures out how
to jump huge steps. Goes from step one, maybe
straight, to step 25. Whoa. So it can generate
really decent images in just like two to eight
steps total. Two to eight. Compared to 50. That's
a huge difference. Massive difference. And that
speed opens up doors for anything real time.
Think AR, VR apps. The scene needs to update
instantly when you turn your head, right? Right,
yeah. No lag. Or making pictures right there
on your phone without needing to send data to
the cloud and wait. fast design tools, improving
video call quality in real time. OK, so speed's
the big win. What's the catch? Well, the trade
-off for skipping all those steps is sometimes
the images might be a bit less detailed, maybe
a little too smooth compared to the slower methods.
But for many uses, that near -instant result
is what matters most. So why is that master painter
approach, that speed, so crucial for something
like a VR headset? Because everything has to
update instantly as you move. Lag breaks the
whole illusion of immersion. Makes sense. Okay,
so we have LLMs for talking, LCMs for fast painting,
but they're both creating things. What if we
need the AI to actually, you know, do a task?
A multi -step task. Ah, now we get to the LAM.
The LAM. Language Action Model. This is that
critical shift you mentioned from talking to
doing. Exactly. If the LLM is the smart friend
who just talks, the LAM is like the project manager.
It takes action. How does it do that? Is it just
a better LLM? Not quite. It's more like a system.
It starts with an LLM. Yeah, that's the brain
for understanding your request. But then it adds
memory like a notebook, a planner to map out
the steps. And the really crucial part, the ability
to use tools. This means it can connect to other
things, your email, a web browser, calendars,
using APIs. Ah, APIs. That's the key difference
then, the connection to other services. That's
the game changer. So an LLM tells you how to
book a flight. A LAM connects to, say, Expedia's
API, searches flights based on your criteria,
finds the cheapest one, books it, and then asks
you to confirm. Wow. OK. That sounds like the
future of automation, really. And it is. Think,
AI agents, these things can handle complex sequences.
Like, you could tell it. Find 10 potential customers
in the software industry in California, draft
a personalized intro email based on our new product
launch, and show me the drafts. A lot more sophisticated
than just asking a question. Definitely. Advanced
customer support, too, actually processing a
refund, not just answering questions about the
policy or just digital assistance that can actually
turn on your lights or set alarms because they
can connect to those systems. So what core function
lets a lamb move beyond just conversation and
actually access, say, your calendar or email?
It's that ability to connect directly to other
tools and services using APIs. Those are the
hands and feet. Got it. But building one giant
lamb that can do everything sounds incredibly
complex and frankly expensive to run. It absolutely
is. Which leads us perfectly to the next model
type, which is all about efficiency. The MOE.
MOE, mixture of experts. Yeah, mixture of experts.
Now, this isn't one single giant model. It's
actually a whole system. Think of lots of smaller...
specialized models, the experts. And they're
all managed by another AI called the router.
OK, so instead of one generalist, it's like having
a team of specialists. Exactly, like asking a
specific group of pros. The router looks at your
request, your prompt, and figures out, OK, this
is about coding, and sends it only to the coding
expert. Or this needs history knowledge, so it
activates the history expert. Usually just one
or two experts get activated for any given task.
Right, so you're not running the whole giant
brain all the time. Precisely. And here's the
really cool part. the efficiency and size benefit.
You can build a massive model overall, let's
say, a trillion parameters worth of knowledge.
But because the router only turns on maybe 5
% or 10 % of those experts for any single query,
you're only paying the computational cost of
running a much smaller model, maybe just 100
billion parameters in that example. Wait, hold
on. So you get the knowledge of a potentially
enormous model. but the running cost of something
much smaller. That's the magic. Wow. I mean,
that just fundamentally changes the economics
of building top -tier AI, doesn't it? That scaling
efficiency is incredible. It's huge. That's why
this architecture, MoE, is believed to be used
in the really big, powerful models like Mixed
Roll and probably GPT -4, too. It's also great
for companies wanting to personalize. They can
add their own private experts, train just on
their internal documents or specific industry
regulations. So you could ask it to explain I
don't know, inflation using general knowledge.
Yeah. And write Python code to calculate CPI
using a coding expert, all in one go. Exactly.
Dual knowledge handled efficiently. OK, the efficiency
is amazing. But beyond cost, what's the core
risk if that router AI messes up its job in a
Moe system? If the router picks the wrong expert,
the whole answer will likely be poor quality
or just plain wrong. Bad robbing means bad results.
Makes sense. Garbage in, garbage out. Or rather,
wrong expert in, garbage out. Pretty much. And
training them is complex, plus you need enough
memory VRAM to hold all those experts, even if
most are idle. OK, so Moe's give us efficient
expertise. But all these models so far, they've
been dealing with text or maybe creating images.
based on text, what if the AI needs to actually
see the world? Ah, giving the AI eyes. That must
be the VLM vision language model. You got it.
V -L -M. It can see and talk. It understands
both pictures and text at the same time. LLMs,
fundamentally, were blind. VLMs have eyes. How
does that work internally? Is it merging two
different kinds of models? Kind of, yeah. It
uses an image encoder. Think of that as the eyes,
which looks at a picture and translates it into
a sort of numerical description, like orange
cat sitting on a green chair. OK. A number string
that represents the image. Right. And then that
numerical string gets fed into a language model,
the mouth. which can then understand it and talk
about it, answer questions about it. And that
opens up some really intuitive uses. The classic
example is taking a picture of your fridge and
asking, OK, what can I make for dinner with this
stuff? That's the famous one. But it goes way
beyond that. Think about visual search. Take
a picture of some cool shoes you see someone
wearing and ask, where can I buy these exact
ones? Ooh, dangerous for my wallet. Huh. Tell
me about it. Or analyzing video content. and
really powerfully, tools for the visually impaired.
Imagine it describing the world around them,
reading signs, maybe even recognizing faces or
expressions. That's incredible, a truly helpful
application. But I guess the same watch out applies
here as with LLMs. Can VLMs hallucinate about
images? Absolutely. They can misinterpret an
image, describe an object that isn't there, or
get an action wrong. And of course, privacy becomes
a bigger concern when you're uploading images,
especially of people or inside your home. Right.
So besides the fridge example, what's a really
powerful, maybe less obvious application of a
VLM? I think describing the world in real time
for visually impaired people is one of the most
profound uses. Yeah, definitely. OK, VLMs are
powerful. They see and talk. But they sound like
they need a lot of computing power, just like
the big LLMs. What if you need AI on a smaller
device, like your phone, and privacy is paramount?
Great question. That's where the SLM comes in
the small language model. While everyone's chasing
bigger and bigger, these SLMs, like Microsoft's
Fi3, are quietly becoming super important for
on -device AI. They're designed specifically
to run efficiently on phones, laptops, maybe
even smart appliances. Small. So fewer parameters,
I guess. How small are we talking? Just maybe
fewer, maybe just one to three billion parameters
compared to hundreds or even over a trillion
for the big LLMs. Okay, one to three billion
versus a trillion. How do they stay capable then?
How aren't they just dumb? The secret sauce is
the training data. Instead of just scraping the
entire messy internet like LLMs often do. Right.
Researchers focus on quality over quantity. They
use what they call textbook quality data, highly
curated information, specific examples of reasoning,
logic problems. It's like feeding it a very well
-structured education instead of just letting
it browse the web randomly. Textbook quality.
Yeah. That's interesting. So why is the training
data for an SLM described that way? Because they
prioritize high -quality curated knowledge and
reasoning examples over just sheer volume from
the internet. Quality over quantity. But doesn't
that curated approach risk making them less robust?
Like, they only know what's in the textbook.
They can't handle the messy, unpredictable real
world as well. Are they trading robustness for
that efficiency? That is the trade -off, yeah.
They will know less about really news or uncommon
topics. Their conversational memory might be
shorter too, but the payoff is huge. True on
-device AI. Meaning it runs right there on your
phone. Exactly. Your phone's assistant can work
instantly without needing a cloud connection.
That means better privacy. Your data doesn't
have to leave your device. Think about instant
code completion in your programming editor or
voice commands in your car that just work, even
if you have no signal. Smart microwaves, maybe.
OK, so SLMs are great for efficient, private,
on -device tasks. But they're still fundamentally
about generating text or understanding commands,
like LLMs. What if the main goal isn't generation
at all, but really deep understanding of language
meaning? For deep understanding, we need to look
back at a slightly older but foundational model
type, the MLM Masked Language Model. Masked Language
Model. OK, how is that different from an LLM
guessing the next word? So LLMs play the predict
the next word game. MLMs, like Google's famous
BERT model, play fill in the blank. Fill in the
blank? Yeah. During training, they take a sentence
and just hide or mask about 15 % of the words.
Then they force the model to guess the missing
words. Crucially, to do that well, the model
has to look at the words before the blank and
the words after the blank. Ah, so it needs context
from both directions. bidirectional context.
Exactly. That forces it to develop a much deeper
understanding of the sentence's actual meaning
and structure, not just predicting the most likely
next word in sequence. OK, so where is that deep
understanding most useful if it's not writing
poems? Right. They're analysis models, not creative
writers. Their big strength is understanding
meaning, which is critical for search engines.
When you search something like, best camera is
A to Z, the MLM helps the engine understand A
to Z means a complete guide, not literally the
letter. Uh, understanding the intent behind the
search. Precisely. They're also great for sentiment
analysis, quickly sorting thousands of customer
reviews into positive, negative, neutral, and
for something called Named Entity Recognition,
or N -E -R, that's pulling out specific pieces
of info from text, like finding all the company
names or dollar amounts mentioned in a long report.
So since MLMs aren't really creative writers,
what's their crucial function for something like
a search engine? They understand the deep context
and the actual meaning behind your search query,
not just the keywords. Got it. Pure contextual
depth. OK, one more model to go. We've covered
text, images, actions, efficiency, seeing, small
size, deep understanding, what's left. Precision
in vision, specifically identifying exact boundaries.
We need the SAM, the Segment Anything model.
Segment Anything. SAM. Okay, this sounds specialized.
Highly specialized. It's a computer vision model,
but its focus isn't on telling you what an object
is. Its job is to draw an incredibly precise
outline around everything it sees in a picture.
It segments objects right down to the individual
pixel. Down to the pixel. Wow, how does it learn
to do that for... Well, anything. It was trained
on a massive data set, something like 11 million
images, containing over a billion segmentation
masks, those precise outlines. So it learned
the general concept of what is an object and
what is its boundary, even if it doesn't know
the object's name. You usually activate it just
by clicking on an object or drawing a rough box
around it. So it knows This boundary belongs
to object one, but not necessarily that object
one is a dog or a car. Exactly. It just finds
the edges. Okay. Where do you use that kind of
pixel perfect? outlining. It's actually a foundational
tool for lots of applications. Think photo editing,
that remove background feature. Sam is likely
powering the precise selection. Or critically,
medical imaging. Imagine a surgeon analyzing
an MRI. Sam can draw the exact outline around
a tumor or an organ, which allows for really
accurate measurements. Right. Precision is key
there. Absolutely. And it's vital for robotics
and self -driving cars too. They need to know
the exact edges of objects in the real world
to nap and interact safely. So why would a surgeon
looking at an MRI need SAM, the segmenter, maybe
more than a VLM, the describer? SAM provides
those precise pixel level outlines needed to
accurately measure biological structures, not
just a general description. Measurement needs
precision. Makes perfect sense. Is there a watch
out for SAM? The main thing is just remembering
what it is. It's brilliant at finding outlines,
but it won't tell you what the object is. It
often needs to be paired with other models, maybe
a VLM, to get both the precise outline and the
identification. Okay, wow. That's eight different
types. Quite the toolbox. It really is. So let's
try and pull this all together. What's the big
idea here for you, the listener? I think the
core takeaway is just realizing that AI isn't
one thing, it's this specialized toolbox. And
now hopefully knowing the model name and the
acronym actually tells you something concrete
about its core function. You know which tool
to think about for which job. Yeah, let's do
a quick recap. If you need writing help, content
generation, chatbots, you're thinking LLM. Right,
needs super fast images, especially for real
-time stuff like AR or on your phone, that's
the LCM. Want to automate a complex task? Have
the AI actually do things by connecting to other
tools. That's the LAM, the action model. Need
maximum efficiency and the ability to build huge
models cost -effectively using specialist parts.
That's the MoE architecture. Need an AI that
can see and understand images and talk about
them. That's the VLM. Want AI that runs directly
on your device, prioritizing privacy and efficiency.
over knowing absolutely everything. That's the
SLM, the small one. Need deep understanding of
language meaning for search or analysis, not
generation. That's the MLM. And finally, need
to draw those perfect pixel -level outlines around
objects in images, like for background removal
or medical scans. That's the SAM. Exactly. So
now you have this framework, right? We really
hope you can use this knowledge next time you
hear about a new AI tool. Don't just ask, is
it AI? Ask yourself, okay. Based on what it does,
is this primarily analyzing things, creating
things, or acting on things? Which tool from
the toolbox does it sound like? Yeah, that's
a great way to approach it. And the pace things
are moving, these tools are starting to combine
in really interesting ways. We've covered models
that talk, models that act, models that see.
So here's a thought to leave you with. If the
next big step... combines that incredible efficiency
of the Moe's specialized experts with the Lamb's
ability to connect to tools and automate real
-world tasks, well, that creates the potential
for truly autonomous, hyper -specialized AI agents.
So thinking about that future, what's the single
most complex task, maybe in your personal life,
maybe in your business, that you would finally
trust an autonomous AI agent to handle completely?
Something to chew on. Definitely something to
think about. We'll see you next time on the Deep
Dive.