Imagine you are reading a long passage, maybe
something complex, history or science. Your brain
doesn't just process word one, then word two
and completely forget how the beginning connects
to the end. Exactly right. Your mind is, you
know, constantly mapping relationships. When
you read something like the black cat set on
the mat, you instantly know black describes cat.
Right. That natural human thing deciding which
parts are important to other parts. That's attention.
And that that basic idea. It seems so simple,
but that's the critical insight. The thing that
launched all the AI we use now, ChatGPT, Gemini.
It really is. And look, the pace of new AI names,
new tools, it can feel completely overwhelming
if you're trying to keep up. Yeah, it's a lot.
So our goal today is pretty simple. Let's get
some foundational clarity. Think of this deep
dive like understanding the scientific blueprints
for these huge systems. OK, so we've looked at
the source material, and it really boils down
to four big shifts that made modern AI possible.
First, building the core engine. That's attention.
Then the scale needed for new powers, which is
few -shot learning. Getting bigger. How we made
them helpful and safe alignment. Crucial step.
And then how they actually connect and interact
with the real world. That's RIG and agents. So
let's unpack this. All right, let's start at
the beginning. The cornerstone paper from 2017,
the one that introduced the transformer architecture.
Before this, AI had this, well, fundamental memory
problem. Oh, huge structural problem. Yeah. Older
AI models, things like recurrent neural networks
or RNNs, they read text sequentially, like reading
a scroll, word one, then word two. OK. By the
time they got to maybe the 50th word or the 100th,
the computational memory, it just faded. They
forgot the start of the sentence. Meaning they
couldn't realistically summarize a long document
or translate a complex paragraph accurately because
they lost the context. Exactly, they lost the
context. The transformer totally changed this.
It allowed the model to look at all the words
in a sentence basically simultaneously. All at
once. All at once. Which allows for massive parallel
processing. It can instantly connect the beginning
of a really long sentence with the end. and the
key mechanism is called self -attention. Okay,
explain self -attention again. You had a good
analogy for this. Right. Think about being at
a noisy party. Okay. You're talking to your friend,
but there are dozens of other conversations swirling
around. Your brain uses self -tension to filter
out all that noise and focus just on your friend's
voice. The AI does basically the same thing.
For every single word it processes, it gives
every other word an important score. It builds
this like instantaneous map of relationships.
So instead of just step by step, it's building
a whole web of connections for everything it
sees. That sounds incredibly powerful. And it
is, I mean, it's the foundation for pretty much
every large language model today. It is. But
you mentioned there's a big technical bottleneck
baked into that architecture. Yeah, there is.
It's the quadratic resource limit. It sounds
technical, but the idea is because the model
has to calculate the relationship between every
word and every single other word. If you double
the length of the text you feed it, The computation
cost doesn't just double. It squares. It grows
exponentially faster. Right. That term quadratic
growth sounds academic. But when I paste a really
long article into a chat bot and it slows way
down, or maybe it just says too long, that's
the quadratic limit hitting me. Yes. That's exactly
it. It limits how much text the models can handle
at once, creating that context window. OK. So
the transformer could handle these complex connections.
That ability led directly to the next major shift.
just scaling things up. In 2020, researchers
showed that simply making these transformer models
really, really big thing GPT -3 unlocked this
completely new skill. It's called few shot learning.
Few shot learning. This feels like the moment
AI stopped being just this niche engineering
thing and started becoming usable for, well,
almost anyone. Precisely. That's a great way
to put it. before this huge scaling push. If
you wanted an AI to do a new task, like summarizing
customer feedback in a specific way, you needed
a team of engineers, probably months of GPU compute
time, training it with thousands, maybe tens
of thousands of examples. But few shot changed
that. Why did just making the model bigger suddenly
enable this? Well, when the models got massive,
they developed this thing called in -context
learning. They weren't just predicting the next
word anymore. They'd seen so many patterns in
the training data that they actually learned
to follow specific instructions given in plain
English. It completely shifted the paradigm from
training a new model, which is an engineer's
job, to simply writing a good prompt, which almost
anyone can do. You just needed one or two good
examples in the prompt itself. Show the model.
Product, widget, price, and $10 once. And it
suddenly knows how to pull the price out of 1
,000 other descriptions. That democratization,
that's really profound. It is. It really is.
But these early giant models, they were still
pretty flawed. They were smart. Yeah. But also
incredibly stubborn sometimes. Excellent at predicting
the next statistically likely word. but they
didn't always grasp human intention. They could
hallucinate very confidently or give answers
that were just wildly inappropriate or unhelpful.
Yeah, I still wrestle with prompt drift myself
sometimes trying to get the output just right.
Even with the latest models, it's a real thing.
So this need for helpfulness brought us to the
next key idea, alignment. Specifically, reinforcement
learning from human feedback or RLHF. RLHF. This
is basically the secret sauce that taught the
AI to be a helpful assistant, not just a text
generator. They train the AI based on what text
humans actually preferred. How does that work?
Like, in practice? It's basically a three -step
process. First, you have human contractors actually
write out high -quality, good answers to prompts.
That's called supervised fine -tuning. Gives
the model a baseline. OK, step one. Step two,
they train a separate, smaller model. Its only
job is to predict which of two answers humans
would prefer. This is the reward model. Like
a judge scoring the answers based on human taste.
Exactly, like a high score for helpfulness. Then
the final step is the reinforcement learning
part. They let the main AI generate answers,
the reward model scores them instantly, and the
AI adjusts its own parameters to try and maximize
that reward score. It's like training a dog with
treats basically, reinforcing the good behavior.
And the big insight there was that a smally model
that was aligned and struck GPT was actually
preferred by users much more than the giant but
unaligned GPT -3. Usefulness beats sheer size.
Yes. Usefulness suddenly became the key metric.
Alignment was critical. So if alignment's so
crucial and RLHF was the way, why are we seeing
newer, maybe simpler methods starting to replace
it now? Well, RLHF is quite complex and, frankly,
very expensive to implement. that's driving research
into simpler, cheaper alignment methods like
DPO. Okay, so we have this aligned AI brain.
Now let's talk about connecting it to the real
world. First up is RAG, retrieval augmented generation.
This is essentially giving the AI an outside
brain that can access current information. Right,
because we built this giant LLM brain, but its
knowledge is frozen at the time it was trained.
Why couldn't we just retrain it more often to
keep it updated? Because retraining one of these
massive models costs potentially tens of millions
of dollars and can take weeks or months. It's
just not feasible to do it constantly. So its
knowledge gets stale fast. Margay solves this.
It works by first finding relevant external info,
maybe real -time news, maybe private company
documents. Then it adds that specific text directly
into the prompt it sends to the AI. OK. And crucially,
it forces the AI to generate its answer based
only on that source text provided in the prompt.
So if I ask my bank's chatbot about, say, my
specific mortgage rate, which is private info.
Argue would search the bank's secure document
database, find the paragraph with your rate,
paste only that paragraph into the prompt for
the LLM, and instruct it, answer the customer
using only this text. Got it. So the main LLM
never actually gets trained on or learns my private
data. It just uses it for that one answer. Exactly.
That protects privacy and it also massively reduces
hallucinations because the AI is grounded in
a specific source document. Makes sense. What's
the main risk then when you're relying on a ROJ
system? Well, the final answer quality depends
entirely on that first step, the retrieval or
search step. If the search pulls up bad or irrelevant
info, the AI's answer will be bad too. Garbage
in, garbage out. Okay. That brings us to the
next step. Agents. This feels like a really big
shift, moving from the AI being a passive chat
bot waiting for me to type something to being
an active tool that can actually go out and do
things to achieve a goal. That's exactly it.
Agents are about planning, using tools like running
a web search, executing code, calling an external
API like weather or stocks, and then, importantly,
observing the results and correcting mistakes.
So what's the structure? How does an agent work?
It's pretty simple conceptually. You have a brain,
which is usually the LLM doing the high level
thinking and planning. You have perception, which
is the agency and the results of the tools it
uses, and action, which is actually using those
tools. And it works in this loop. Think, act,
act, see the result, think again based on the
result. Over and over. until the goal is met.
So instead of me asking like three separate questions,
what's the weather in Hanoi? What's the weather
in Ho Chi Minh City? OK, based on that, what
should I pack? Right. I could just give the agent
one complex goal, like compare the weather forecast
for Hanoi and HCMC for the next three days and
suggest what clothes I should pack for a business
trip. Exactly that. You give it the complex goal.
Analyze the last three financial reports for
Company X, check recent market sentiment about
them on Twitter, and draft me a summary email
recommending whether I should buy or sell the
stock. The agent figures out the steps and uses
its tools sequentially. Whoa! Okay, imagine scaling
that agent structure up to manage, say, a billion
dynamic calendar scheduling requests a day across
a huge company. That changes everything about
how work gets done almost instantly. That's the
potential, absolutely. It's the immediate future
of productivity enhancement. But these systems
are still pretty new and can be tricky to manage
reliably. Right, they are complex. So what's
the biggest, like, operational headache or risk
when people try to deploy agents in the real
world today? They can still get stuck sometimes.
They might get into self -repeating loops, like
endlessly searching for a file that doesn't exist
or calling a broken tool over and over. Reliability
is still a challenge. So, okay, we've built this
powerful, aligned, goal -seeking AI. Awesome.
But for a while, it remained way too huge and
expensive for most individuals or smaller companies
to actually run themselves. Right. Locked up
in big tech clouds mostly. Exactly. So the next
three concepts we need to touch on really solve
this accessibility and cost problem. made it
more democratic. This is what some people call
the efficiency triad. LoRa, MoE, and quantization,
basically making giant AI cheaper, faster, and
much easier to deploy. Let's start with LoRa
low -rank adaptation. OK, LoRa, think of the
massive bass AI model as like a giant expertly
pre -trained symphony orchestra. Okay, orchestra.
Now, if you wanted that orchestra to learn a
completely new style, say experimental jazz,
the old way full fine -tuning was like retraining
every single musician on every single instrument.
Hugely expensive. Massive amounts of data, time,
storage. Prohibitively expensive, yeah. And you'd
end up with a whole new giant orchestra file.
Laura completely sidesteps this. Laura says,
keep 99 % of the original orchestra musicians
frozen. Don't touch them. Just add a few small
new specialized pieces. Think like adding a dedicated
jazz conductor and maybe a specific drummer.
Then you only train those tiny new adapter layers
to learn the jazz style. Ah, so you end up with
just the original massive model plus this tiny
little instruction file that tells it how to
play jazz when needed. Precisely. It allows you
to fine -tune a huge model, often using just
a single consumer GPU, and the resulting adapter
file might only be, say, 100 megabytes instead
of hundreds of gigabytes. That's why the open
-source community exploded with custom models,
right? Yeah. Specialization became cheap and
portable. Totally. Okay, next up, Moe, mixture
of experts, popularized by models like Google's
Switch Transformer and Mixeroll. This tackles
the speed problem of running these enormous models.
Right. How can a model with maybe a trillion
parameters run fast? It's a really clever architectural
trick. Imagine the AI model is now a massive
hospital staffed with like a thousand different
medical specialists. Okay, hospital analogy.
In the old dense model architecture, every time
any patient came in, even with just a common
cold, All 1 ,000 specialist doctors had to consult
on the case. A huge waste of expert time. Right.
Makes sense. The MOE approach is different. Yeah.
With MOE, there's a quick router at the front
desk. When a patient or a query comes in talking
about, say, programming, the router sends them
only to the programming expert wing of the hospital.
Only that relevant small set of specialists gets
activated. Ah. So. Companies can build these
models with trillions of parameters, making them
incredibly knowledgeable across many domains.
Yes. But for any single question, they only actually
run a small fraction of those parameters. Maybe
just a relevant expert. That's exactly it. So
we went from building one massive general practitioner
brain that had to read every textbook for every
patient to building a huge team of specialists,
but only calling in the one needed for the job.
That's how they stay fast, despite the enormous
total size. Clever. OK, and the third part of
the efficiency triad. Quantization. Quantization,
the memory hack. This is basically saving memory
by using less precise numbers, like rounding.
Pretty much. It's a pure engineering optimization.
AI model weights, the parameters, are often stored
as very precise numbers, like 16 -bit floating
point numbers. Quantization is like saying, OK,
instead of storing pi as 3 .14159265, let's just
store it as 3 .14. It's good enough for the calculation.
Often, yes. For many models, reducing the precision
maybe down to 8 -bit integers, Intellidate, or
even 4 -bit cuts the memory requirement roughly
in half, or even more, without a major drop in
performance quality. And this trick, this is
what allows huge models like Metos Llama 3 to
potentially run not just on giant server farms,
but maybe on a high -end gaming PC, or eventually
even your smartphone. That's the goal. It bridges
the gap between AI being purely a cloud or corporate
asset and becoming a truly personal, locally
runnable tool. Huge implications. Okay, so we
have the engine, it's efficient, it's connected.
What's the last piece? The last really critical
concept addresses the final hurdle for agents
to really take off and work together seamlessly.
The need for a common language or standard. Right,
the model context protocol or MCP. Yeah, MCP.
This aims to solve what developers call the N
by M problem. Yeah. Imagine you have a hundred
different AI models or agents. Okay. And you
have maybe a thousand different digital tools
you want them to use. Notion, Slack, Google Calendar,
Salesforce, whatever. Right now you'd have to
write custom code, like specific glue, to connect
every single model to every single tool. That's
a hundred times a thousand, hundred thousand
custom connections. A completely unsustainable
integration nightmare. Exactly. MCP wants to
be like the universal USB standard for AI tools.
unplugged to rule them all. Kind of. Tool developers
just implement one standard MCP server interface
for their tool. Then any AI agent that understands
the MCP standard can instantly plug in and use
that tool. No custom code needed. That seems
absolutely critical if we want agents to eventually
manage our whole digital life smoothly. It's
fundamental for realizing the true potential
of interconnected AI agents. So let's just quickly
recap the journey we took through these foundational
concepts from the source material. It's quite
a story. We started with the core engine, the
breakthrough idea of attention and the transformer
architecture. Yep, built the engine. Then we
scaled that engine way up with models like GPT
-3, unlocking few -shot learning, the ability
to instruct AI with prompts, not just program
it with data. Right. Then we realized raw power
wasn't enough. We needed alignment. We taught
the AI to be helpful and safe using human preferences
through techniques like RLHF. Made it useful.
And then we connected that aligned brain to the
live dynamic world, using RG to give it access
to real -time data safely, and empowering it
to actually act on goals using the agent framework.
And finally, the efficiency revolution, making
all this incredible power accessible, affordable,
and fast enough for widespread use through clever
engineering like LoRa, MoE, and quantization.
Made it practical. Yeah. The field moves incredibly
fast, feels like it sometimes, but when you break
it down like this, these core building blocks,
they're actually quite understandable. You really
are. And what's truly fascinating now... building
on that last point about MCP, the common language,
is that the next big frontier might not just
be about smarter algorithms or bigger models.
It's shifting towards better governance and better
standards. Right. That MCP idea raises a huge
question for the future, doesn't it? It really
does. As these agents using protocols like MCP
gain the ability to plug into and potentially
manage everything, your email, your bank accounts,
your work calendar, your smart home, how are
we as a society going to solve the immense security
challenges, the liability questions, the need
for industry consensus to make this safe and
reliable for mass adoption. That's the big one
to think about. That's really the challenge for
all of us, for you listening, to consider as
you watch this space evolve incredibly rapidly
over the next few years. It's going to be quite
a ride. Thanks for diving deep into the sources
with us today.