Welcome back to the Deep Dive. So if you're like
most people right now, AI isn't. I mean, it's
not some far off concept anymore. It's right
here. It's central to everything. A bit chaotic,
right? You've got students using it. Managers
are definitely assessing it. Oh, absolutely.
Every professional is trying to figure out is
this a tool or is this a thread? Exactly. And
the speed of these systems, their competency.
It's forcing us all to look at the fundamental
work we do, whether that's learning a new skill
or, you know, writing a big report. What is getting
lost when things just become too efficient? And
that is the mission for today. We're going to
do a deep dive into this idea of black box learning.
We're leaning on some analysis from Gary Ackerman,
who was reading Brian Christian's book, The Alignment
Problem. Our goal is to really define what this
black box is, especially with modern AI, and
then figure out why its efficiency can actually
undermine the entire point of learning. OK. So
let's dig in. The term black box, I feel like
we all think we know what that means. Right.
You put something in, you get something out.
Exactly. You can see the input, a prompt, some
data, and you see the output, an essay, a recommendation,
whatever. The middle part. The middle part is
mystery. The logic, the actual operations that
turned A into B, they're hidden. But the sources
we're looking at, they add a really interesting
layer to this, a distinction. They do. They talk
about two different kinds of opacity, really,
when it comes to machine learning, two knowledge
gaps. OK, so what's the first one? Well, the
first is kind of the classic version. The logic
is unknown to the user, but the designers, they
know it. Right, like a trade secret. A proprietary
algorithm? Precisely. The knowledge exists. It's
just locked away. But then there's the second
layer. And this is the one that's really central
to modern AI. It is. This is where it gets, frankly,
a bit unsettling. In a lot of these complex deep
learning systems, the logic isn't just unknown
to the user. Often, even the people who deploy
the system don't fully understand the internal
logic. Wait, really? Even the deployers? Even
the deployers, they know the correlation. Input
X gives you output Y with incredible accuracy,
but they can't actually trace the step -by -step
causal path inside the box. That's a huge distinction.
This isn't just a secret. It's... It's genuine
opacity. And that brings us right to causality
versus correlation. Because if that internal
part is a total mystery, you can still get amazing
correlations. You can predict what will happen
with stunning accuracy. But you cannot understand
causality. You don't know how that input was
caused to become that output. And that difference
is just, it's everything. It's vital. Especially
when the stakes are high. Okay, let's use an
example here before we get to education. Say
a bank uses an AI tool for loan applications,
and it's 99 % accurate. Great correlation. Fantastic
correlation. But then it starts flagging applicants
from a certain neighborhood, a specific zip code.
And because it's a black box. The bank can't
say why. They have no rationale for why those
inputs led to that output. They just know it
happens. They can't check for bias. They can't
explain it to a regulator. And they can't fix
it. You can't fix it. How do you debug something
when you can't see the code? You can't point
to the specific weight or decision that caused
the problem. So the data looks perfect, but the
knowledge is a dead end. Exactly. The knowledge
is capped. Causality is what lets you fix things,
extend things, make them better. Without it,
you're just stuck. That is the perfect transition.
So if that's the risk in finance or medicine,
let's bring this black box problem into, well,
into learning. Right. And the source material
argues that no matter the subject, The real work
of learning is just answering questions. Even
if they look like other things. Yeah, even if
they're disguised as essays or, you know, proofs
or business plans, the process is always converting
an input the assignment into a graded output.
And here's the trap. Machine learning tools are
unbelievably good at this. They're answer machines.
They are built to maximize the correlation between
a prompt and what a good answer should look like.
Which is, let's be honest, incredibly tempting
for a student. Oh, it's a seductive power. You're
faced with this huge complex assignment, and
the AI black box just hands you the perfect output
almost instantly. So here's the pushback I imagine.
If the AI output is genuinely good, like 95 %
of what the teacher wanted, why should an efficiency
win? Why should a student struggle for five hours
on something an AI does in five seconds? That
is the critical question. Yeah. And it's because
the student's job isn't just to produce the output.
The problem is that when the student uses the
black box, they get the data of learning the
finished essay, but they completely bypass building
the internal logic that was supposed to produce
it. So they get the perfect cake, but they didn't
measure the flour or crack an egg. They didn't
even read the recipe. Not a single step. They
found a correlation between the prompt and the
answer, but they learned nothing about the causal
mechanism and that mechanism. That is what we
call learning. So let's talk about the student's
actual job description then. Yeah. According
to the sources, a student's real job is to take
that input, the question, and use their own logic.
Their own developing internal operations. Yeah.
To generate the output. It's an internal manufacturing
process. You're building mental scaffolding.
You're laying the wiring in your own brain. So
for a math problem, it's not the final number.
It's building the sequence of steps that cause
that number. Exactly. Or for a history essay,
it's constructing the argument. the causal chain
of evidence that leads to your conclusion. And
the black box just lets you skip all of that
heavy lifting. The AI path is fast, efficient.
And the learning path is the total opposite.
It's slow, it's difficult, it can be really frustrating.
But that inefficiency is the entire point. But
the pressure is real. The student gets the grade
now by using the box, so besides just... you
know, not learning, what's the actual consequence?
There are two big ones. First, like we said,
you haven't built the internal model, so you
can't apply that knowledge to a new problem later.
And second, it messes with the production of
data that the instructor needs to see. Ah, the
breadcrumbs. The breadcrumbs, exactly. A student's
messy draft, the things they cross out, their
step -by -step work. That visible trail is the
only way an instructor knows that the student's
own brain is developing. So if you just hand
in the perfect cake with no flour on your hands.
Right, there's no proof of work. And the instructor
loses their ability to diagnose where you're
struggling or what you've mastered. That output
becomes incredibly hard to accept as proof of
real learning. So it comes down to this. Causing
the logic and the operations that become your
output. That is hard work. It requires struggle.
But that struggle is the job of being a student.
If you bypass that, you bypass the learning itself.
That struggle is the only way to go from knowing
what to really knowing how and why. Fantastic.
So let's quickly consolidate this. In this deep
dive, we define the black box input, output,
but the causal logic inside is a mystery. And
sometimes a mystery even to its creators. Right.
And we saw that AI is incredibly efficient at
producing answers, but that efficiency is a trap.
It directly undermines the student's real job.
Which is to build and strengthen their own internal
logic, their own understanding. And this goes
way beyond the classroom. This is the core takeaway
for you, for the learner. Absolutely. Understanding
how knowledge is made, the causality, the mechanism.
That's the only way you can ever truly advance
it or fix its flaws or adapt it to something
new in your career. If you only rely on correlation,
you're accepting a ceiling on your own potential.
And that is the essential insight we want to
leave you with. If the goal of real learning
isn't just predicting the right answer correlation,
but understanding the why, the causation, then
here's one final thought. How can you apply this
black box thinking to your own work? Where are
you maybe accepting a correlation without demanding
to understand the cause? Where are you using
an answer or relying on a system's output without
knowing the logic that made it? Because if you
don't know the logic, You can't innovate on it.
You can't correct it. So demand the rationale.
Demand the causality. That's the work of a true
expert. We'll see you next time on the Deep Dive.